From 5fcf7a9194690483f0cb6210e3f93aa17693842a Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sat, 23 May 2026 11:26:54 -0700 Subject: [PATCH 01/23] initial development of seed eval gen --- commands/autoctx/expand-dataset.toml | 5 + evals/core-cujs/dataset.json | 11 +- skills/autoctx-dataset-expansion/SKILL.md | 50 ++ skills/autoctx-dataset-generation/SKILL.md | 238 +++++++- .../dataset/eval_seed_gen/__init__.py | 0 .../eval_seed_gen/dataset_generator.py | 558 ++++++++++++++++++ .../dataset/eval_seed_gen/genai_client.py | 72 +++ .../dataset/eval_seed_gen/generator_base.py | 36 ++ .../dataset/eval_seed_gen/generators.py | 395 +++++++++++++ .../dataset/eval_seed_gen/schema.py | 323 ++++++++++ .../dataset/eval_seed_gen/state_manager.py | 431 ++++++++++++++ .../cloud/db_context_enrichment/main.py | 16 + 12 files changed, 2105 insertions(+), 30 deletions(-) create mode 100644 commands/autoctx/expand-dataset.toml create mode 100644 skills/autoctx-dataset-expansion/SKILL.md create mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/__init__.py create mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py create mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py create mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py create mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py create mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py create mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py diff --git a/commands/autoctx/expand-dataset.toml b/commands/autoctx/expand-dataset.toml new file mode 100644 index 00000000..8486b73d --- /dev/null +++ b/commands/autoctx/expand-dataset.toml @@ -0,0 +1,5 @@ +description = "Expands seed evaluation datasets of Natural Language Questions (NLQ) and SQL pairs for evaluation." +prompt = """ +Your task is to expand a seed evaluation dataset of Natural Language Questions and their corresponding SQL queries. +Please use the `activate_skill` tool to activate the `skill-autoctx-dataset-expansion` skill, and strictly follow its workflow. +""" diff --git a/evals/core-cujs/dataset.json b/evals/core-cujs/dataset.json index d883f260..969dd24e 100644 --- a/evals/core-cujs/dataset.json +++ b/evals/core-cujs/dataset.json @@ -9,7 +9,7 @@ "max_turns": 15 }, { - "id": "autoctx:generate-dataset", + "id": "autoctx:expand-dataset", "starting_prompt": "generate a evaluation dataset for my alloydb database financial named golden.json, based on my seed dataset to be provided.", "conversation_plan": "Ask the agent to generate a evaluation dataset for my alloydb database financial named golden.json. The 'seed' includes the following two golden NL-SQL pair:\n\nHow many accounts who choose issuance after transaction are staying in East Bohemia region? A3 contains the data of region; 'POPLATEK PO OBRATU' represents for 'issuance after transaction'.\nSELECT COUNT(DISTINCT \"T1\".\"account_id\") FROM \"account\" AS \"T1\" INNER JOIN \"district\" AS \"T2\" ON \"T1\".\"district_id\" = \"T2\".\"district_id\" WHERE \"T2\".\"A3\" = 'east Bohemia' AND \"T1\".\"frequency\" = 'POPLATEK PO OBRATU'\n\nHow many accounts who have region in Prague are eligible for loans? A3 contains the data of region\nSELECT COUNT(\"T1\".\"account_id\") FROM \"account\" AS \"T1\" INNER JOIN \"loan\" AS \"T2\" ON \"T1\".\"account_id\" = \"T2\".\"account_id\" INNER JOIN \"district\" AS \"T3\" ON \"T1\".\"district_id\" = \"T3\".\"district_id\" WHERE \"T3\".\"A3\" = 'Prague'. If prompted for validating the query, reply yes to the agent. Once the initial golden dataset is generated, ask the agent to expand it with 2 additional examples in the same file. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], @@ -17,6 +17,15 @@ "work_dir": "workspace_post_init/", "max_turns": 15 }, + { + "id": "autoctx:generate-dataset", + "starting_prompt": "generate a seed evaluation dataset for my alloydb database financial named golden.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 medium-level SQL/Question pairs for my alloydb database financial named golden.json. I prefer the generated SQL to have filter conditions on text or jsonb columns", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, { "id": "autoctx:bootstrap", "starting_prompt": "create initial context set for my alloydb database", diff --git a/skills/autoctx-dataset-expansion/SKILL.md b/skills/autoctx-dataset-expansion/SKILL.md new file mode 100644 index 00000000..12a563b9 --- /dev/null +++ b/skills/autoctx-dataset-expansion/SKILL.md @@ -0,0 +1,50 @@ +--- +name: skill-autoctx-dataset-expansion +description: "Expand seed evaluation datasets of Natural Language Questions (NLQ) and SQL pairs for evaluation." +--- + +You are an agent that helps a user expand evaluation datasets of Natural Language Questions (NLQ) and their corresponding SQL queries. Your main goal is to create evaluation datasets by converting user-provided seeds into a standard JSON format and then optionally expanding them with high-quality, diverse, and validated NL-SQL pairs. + +## Workflow + +1. **Verification**: Check for `tools.yaml` (located in `autoctx/` for Autoctx workflows) to identify available database configurations. Prompt the user to select the target database for dataset generation. If `tools.yaml` is missing, invoke the `skill-autoctx-init` skill to establish a connection first. + +2. **Initiate Interaction**: Greet the user and ask for a "seed." The "seed" is the starting point for the dataset. It can be: + * **A file path**: The user can provide a path to a file containing a small set of existing NL-SQL pairs. + * **A raw NL-SQL pair**: The user can provide a single natural language query and its corresponding SQL query directly in the CLI. This is useful for debugging a specific failing case. + +3. **Acquire Database Schema**: Use the `-list-schemas` MCP tool to fetch the schema of the relevant database. + +5. **Initial Save**: You must use the `generate_dataset` MCP tool to save the dataset. Do not directly write the data into the file. You must provide the exact `output_file_path`. Pass the constructed dataset as a JSON string (`dataset_entries_json`). + +6. **Prompt for Validation**: Ask the user if they want to validate the `golden_sql` in the saved dataset file. This is a recommended step. + +7. **Validate SQL (if requested)**: If the user agrees, read the dataset file, iterate through it, and use the `-execute-sql` MCP tool for each entry. Report any failures. Overwrite the file with any corrections if the user approves them. + +8. **Prompt for Expansion**: Ask the user if they want to expand the dataset with more variations. + +9. **Expand Dataset (if requested)**: If the user says yes: + a. Read the current dataset file. + b. **Generate Variations**: Generate new, diverse NL-SQL pairs. Be creative and think about how to vary the existing questions. Here are some examples: + * **Change Filters**: Modify `WHERE` clauses with different values (e.g., if the original query is for 'USA', create a new one for 'Canada'). + * **Use Synonyms**: Rephrase the natural language question with synonyms (e.g., 'total revenue' vs. 'sum of sales'). + * **Change Aggregations**: If the original query uses `COUNT`, try `AVG`, `SUM`, or `MAX` and adjust the NLQ accordingly. + * **Add/Remove Conditions**: Add new `AND`/`OR` conditions to the `WHERE` clause to create more complex queries. + * **Vary Sorting**: Change the `ORDER BY` clause to sort by different columns or use `ASC`/`DESC` differently. + c. Validate all newly generated SQL queries with `execute_sql`. + d. Present validated variations for user review (accept, edit, reject). + e. Append the user-approved variations to the dataset file. + +10. **Finalize**: Inform the user that the process is complete and confirm the final location of the dataset file. + +The standard evaluation format is a JSON object: +```json +[ + { + "id": "eval_001", + "database": "", + "nlq": "What is the total revenue for the top 5 products by seller?", + "golden_sql": "SELECT \"product_id\", sum(\"net_revenue\") FROM \"sales\" GROUP BY \"product_id\" ORDER BY sum(\"net_revenue\") DESC LIMIT 5;" + } +] +``` diff --git a/skills/autoctx-dataset-generation/SKILL.md b/skills/autoctx-dataset-generation/SKILL.md index 34724809..6c1a365e 100644 --- a/skills/autoctx-dataset-generation/SKILL.md +++ b/skills/autoctx-dataset-generation/SKILL.md @@ -1,50 +1,230 @@ --- name: skill-autoctx-dataset-generation -description: "Generate and expand datasets of Natural Language Questions (NLQ) and SQL pairs for evaluation." +description: "Generate a seed dataset of Natural Language Questions (NLQ) and SQL pairs for evaluation." --- -You are an agent that helps a user generate and expand evaluation datasets of Natural Language Questions (NLQ) and their corresponding SQL queries. Your main goal is to create evaluation datasets by converting user-provided seeds into a standard JSON format and then optionally expanding them with high-quality, diverse, and validated NL-SQL pairs. +You are an agent that helps a user generate evaluation datasets of Natural Language Questions (NLQ) and their corresponding SQL queries. Your main goal is to create evaluation datasets by converting user-provided seeds into a standard JSON format and then optionally expanding them with high-quality, diverse, and validated NL-SQL pairs. Your workflow enforces a strict multi-step workflow with crash-resilient state recovery. You are acting as a strict instruction follower that performs setup and validation step-by-step to guarantee a completely autonomous loop in later stages. + +## CRITICAL: Anti-Patterns to Avoid + +To ensure execution stability, the agent MUST strictly avoid the following anti-patterns. Violating any of these rules will result in corrupted datasets or broken loop execution. + +* **The "Hallucinated Progress" Trap:** Never assume or guess that a tool execution succeeded or that the target size was reached. +* **The "Conversation Hijack" Trap:** Do NOT attempt to manually step through the loop in the chat window if an MCP tool call fails. When you are asked to call an MCP tool, you MUST call the MCP tool. You are FORBIDDEN from guessing, simulating, or approximating the tool results. Do NOT try to manually guess the SQL/Question pairs. +* **The "No-Op / Comment Leak" Trap:** You are strictly forbidden from inserting pseudo-code comments, markdown explanations, or phrases like `# No-op to explain intent` inside any JSON payload, tool argument, or telemetry log. Tool arguments must strictly match the expected parameters and contain zero conversational or structural noise. + ## Workflow -1. **Verification**: Check for `tools.yaml` (located in `autoctx/` for Autoctx workflows) to identify available database configurations. Prompt the user to select the target database for dataset generation. If `tools.yaml` is missing, invoke the `skill-autoctx-init` skill to establish a connection first. +### Step 1. Setup and Verification + +You MUST NEVER attempt to combine steps 1.1 and 1.2 into a single step or skip any steps. You MUST follow the exact order of the steps and complete all required actions in each step before proceeding to the next step. + +#### Step 1.1: Workspace Setup and Validation + +Complete the following checklist before proceeding to step 1.2: +* Check for `tools.yaml` (located in `autoctx/` for Autoctx workflows) to identify available database configurations. Prompt the user to select the target database for dataset generation. If `tools.yaml` is missing, invoke the `skill-autoctx-init` skill to establish a connection first. +* Infer the database name `target_database_name` from the user and the `tools.yaml` file. If unsure, ask the user to confirm. +* Infer the database dialect `database_dialect` from the user and the `tools.yaml` file. If unsure, ask the user to confirm. +* Check for the current workspace directory by running `pwd`. +* You must provide the exact `output_file_path`. +* Set the `evalset_working_dir` to the absolute path of the folder `.dataset_cache`, which is a hidden directory in the current workspace directory. + +#### Step 1.2 Gather Input Parameters & Authorize Headless Execution + +Check to see whether the config file `/config.json` already exists. If it already exists, inform the user and proceed to step 2. If the config file does not exist, either infer the following input parameters from the user if they are provided, or ask the user to provide them if not already provided before proceeding: + +* `target_complexity`: the generated SQL complexity (`easy`, `medium`, `hard`). Default: `medium` +* `generation_constraints`: the guideline for generating SQL. Default: `None` +* `target_dataset_size`: a positive integer representing the number of valid SQL/Question pairs to generate. Default: `10` + +Once all parameters are staged, you MUST explicitly ask the user to confirm the parameters so that they can be saved in the config file. Always save the parameters to `/config.json` in the following JSON format immediately before proceeding to step 2, so that they can be retrieved for state recovery if the execution gets interrupted: -2. **Initiate Interaction**: Greet the user and ask for a "seed." The "seed" is the starting point for the dataset. It can be: - * **A file path**: The user can provide a path to a file containing a small set of existing NL-SQL pairs. - * **A raw NL-SQL pair**: The user can provide a single natural language query and its corresponding SQL query directly in the CLI. This is useful for debugging a specific failing case. +```json +{ + "dialect": "[database_dialect]", + "complexity": "[target_complexity]", + "constraints": "[generation_constraints]", + "database_name": "[target_database_name]", + "size": "[target_dataset_size]", + "output_file_path": "[output_file_path]" +} -3. **Acquire Database Schema**: Use the `-list-schemas` MCP tool to fetch the schema of the relevant database. +``` -5. **Initial Save**: You must use the `generate_dataset` MCP tool to save the dataset. Do not directly write the data into the file. You must provide the exact `output_file_path`. Pass the constructed dataset as a JSON string (`dataset_entries_json`). +### Step 2. Generate Database Profile -6. **Prompt for Validation**: Ask the user if they want to validate the `golden_sql` in the saved dataset file. This is a recommended step. +You must perform all actions in this step in a completely headless manner without prompting the user for intervention. -7. **Validate SQL (if requested)**: If the user agrees, read the dataset file, iterate through it, and use the `-execute-sql` MCP tool for each entry. Report any failures. Overwrite the file with any corrections if the user approves them. +#### Step 2.1: Verify Existence of Database Profile -8. **Prompt for Expansion**: Ask the user if they want to expand the dataset with more variations. +**IMPORTANT**: Check to see whether the file `/db_profile.txt` exists. If it exists, skip directly to Step 3 to generate SQL/Question pairs. Otherwise, proceed to Step 2.2. -9. **Expand Dataset (if requested)**: If the user says yes: - a. Read the current dataset file. - b. **Generate Variations**: Generate new, diverse NL-SQL pairs. Be creative and think about how to vary the existing questions. Here are some examples: - * **Change Filters**: Modify `WHERE` clauses with different values (e.g., if the original query is for 'USA', create a new one for 'Canada'). - * **Use Synonyms**: Rephrase the natural language question with synonyms (e.g., 'total revenue' vs. 'sum of sales'). - * **Change Aggregations**: If the original query uses `COUNT`, try `AVG`, `SUM`, or `MAX` and adjust the NLQ accordingly. - * **Add/Remove Conditions**: Add new `AND`/`OR` conditions to the `WHERE` clause to create more complex queries. - * **Vary Sorting**: Change the `ORDER BY` clause to sort by different columns or use `ASC`/`DESC` differently. - c. Validate all newly generated SQL queries with `execute_sql`. - d. Present validated variations for user review (accept, edit, reject). - e. Append the user-approved variations to the dataset file. +#### Step 2.2: List Tables -10. **Finalize**: Inform the user that the process is complete and confirm the final location of the dataset file. +Check whether the database schema profile is available. If not available, use the `-list-schemas` MCP tool to fetch the schema profile. Save results to `/_dbp/tables.json` in the following JSON format: -The standard evaluation format is a JSON object: ```json [ - { - "id": "eval_001", - "database": "", - "nlq": "What is the total revenue for the top 5 products by seller?", - "golden_sql": "SELECT \"product_id\", sum(\"net_revenue\") FROM \"sales\" GROUP BY \"product_id\" ORDER BY sum(\"net_revenue\") DESC LIMIT 5;" + { + "schema_name": "[schema_name]", + "object_details": { + "object_name": "[table_name]", + "columns": [ + { + "column_name": "[column_name]", + "data_type": "[data_type]" + } + ], + "constraints": [ + { + "constraint_name": "[constraint_name]", + "constraint_type": "[constraint_type]", + "constraint_definition": "[constraint_definition]" + } + ] } + } ] + ``` + +#### Step 2.3: Generate Database Profiling Plan + +Call the `generate_seed_eval_dataset` MCP tool with the following payload structure: + +```json +{ + "task": "generate_database_profile_plan", + "task_working_dir": "" +} + +``` + +The output returned from the tool will contain: + +```json +{ + "plan": "...", + "column_sampling_queries": "/_dbp/column_sampling_queries.json", + "row_sampling_queries": "/_dbp/row_sampling_queries.json" +} + +``` + +#### Step 2.4: Generate Sample Rows + +For each `(table_name, sampling_query)` pair in `row_sampling_queries.json`, call the `-execute-sql` MCP tool to execute the query. Save the output to `/_dbp/row_samples/.json` matching this layout: + +```json +[ + {"[column_name]": "[sampled_value]"} +] + +``` + +#### Step 2.5: Generate Sample Columns + +For each `(table_name, sampling_query)` pair in `column_sampling_queries.json`, call the `-execute-sql` MCP tool to execute the query. Save the output to `/_dbp/column_samples/.json` matching this layout: + +```json +[ + {"val": "[value_token]", "name": "[column_name]"} +] + +``` + +#### Step 2.6: Assemble Database Profile + +Call the `generate_seed_eval_dataset` MCP tool to build the final plain text database profile structure: + +```json +{ + "task": "generate_database_profile", + "task_working_dir": "" +} + +``` + +### Step 3. Generate SQL/Question pairs + +You are acting as a strict state-machine loop. You are FORBIDDEN from guessing, simulating, or approximating the tool results. You MUST call the MCP tools iteratively until the precise exit condition is met. The state file `/states.json` is the single source of truth for tracking the progress of dataset generation. You MUST read from this file for the latest states such as `completed_pairs`, `remaining_pairs`, and `is_done`. Do NOT attempt to maintain any in-memory state or rely on conversational memory for tracking progress. + +**Loop Execution Protocol**: To prevent the main agent from pausing, throwing session-turn warnings, or interrupting the loop during large target sizes, you MUST follow the loop execution protocol below: + +1. **Sub-Agent Chunking & Lifecycle Guard**: The main agent MUST iteratively spin up a dedicated, isolated sub-agent execution context to process this specific loop execution. The sub-agent MUST only run a maximum of **15** iterations and then return control back to the main agent. +2. **Headless Execution Guard**: You are executing in a strictly headless pipeline. Under no circumstances should you output natural language prompt validations, internal commentary, or placeholder strings like `# No-op`. Payloads sent to tools MUST be pure, production-grade JSON strings containing only the keys and values required by the tool schema. Under no circumstances should you output natural language prompt validations or compromise the iteration loop by pausing to ask conversational confirmation questions. You are running in a fully automated pipeline context. Do NOT prompt the user with confirmation questions like "Continue with more automated batches?", "Would you like to proceed with fewer pairs/batches?", or "Should we stop here?". Your only valid behavior is to continue until the termination condition is met. When you are hesitating or uncertain about the next step (e.g. to RESUME or MERGE when the pipeline is interrupted), you MUST read the state file for the latest progress and continue executing. Do NOT attempt to ask the user for guidance on how to proceed. Always read the state file and continue executing according to the latest states. +3. **Strict Telemetry Restrictions**: Your log output must strictly match the text schema provided. Do not append explanatory prefaces or structural comments before or after the telemetry payload. +4. **Long Running Loop Guard**: It is understood that generating the target pairs will require many batches. In case you become conscious of the loop execution for an extended period of time, You MUST treat the state file as the single source of truth and continue to execute. Do NOT panic or prompt the user for conversational input on whether to continue or not. Do NOT attempt to guess or simulate the tool results. +5. **Stateless Inter-Turn Handshake**: After a sub-agent chunk completes (or recovers from a crash), read the progress metrics (`completed_pairs`, `remaining_pairs`, `iterations`, `is_done`). Immediately print a standard telemetry log precisely matching this token layout: +`[TELEMETRY] Batch Complete. Completed: . Iterations: . Remaining: . Target achieved: .` + +#### Loop Execution Single Iteration: + +Call the `generate_seed_eval_dataset` MCP tool with the following payload structure to generate raw candidate SQL expressions: + +```json +{ + "task": "generate_sql", + "task_working_dir": "" +} + +``` + +The output structure returned will be parsed as follows: + +```json +{ + "sqls": [ + { + "qid": "[unique_sql_identifier]", + "sql": "[generated_candidate_sql]" + } + ] +} + +``` + +Validate the generated batch using these rules: + +1. For each SQL query returned, call the `-execute-sql` MCP tool to validate query syntax and engine compatibility. +2. A SQL query is considered valid if it runs without returning syntax errors. + +Once the entire batch from the current iteration has been processed and filtered, compile the `qid` of all valid SQL items into a single array. + +Generate Questions for the valid SQLs by invoking the `generate_seed_eval_dataset` MCP tool with the following payload configuration: + +```json +{ + "task": "generate_nlq", + "task_working_dir": "", + "golden_sql_qids": [ + "[validated_qid_string]" + ] +} + +``` + +Verify the generated Questions by invoking the `generate_seed_eval_dataset` MCP tool: + +```json +{ + "task": "review_nlq", + "task_working_dir": "" +} + +``` + +If the questions verification returns a non-zero value for `rejected_pairs`, immediately reconcile them by calling the refinement task: + +```json +{ + "task": "refine_nlq", + "task_working_dir": "" +} + +``` + +##### Loop Termination Criteria: + +At the end of each iteration, you must read the state file `/states.json` to check the updated states. The loop will only terminate when `is_done` is marked as **true** in the state file, which indicates that either `remaining_pairs` is **0** or `iterations` has reached or exceeded **500**. If `is_done` is **false**, you MUST continue the loop and generate more SQL/Question pairs until the termination criteria is met. Do not invent a shortcut. Do not assume the next result. \ No newline at end of file diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/__init__.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py new file mode 100644 index 00000000..42351249 --- /dev/null +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py @@ -0,0 +1,558 @@ +import asyncio +import json +import os +import collections +from typing import Any +from pathlib import Path + +import warnings +warnings.filterwarnings("ignore") + +from .genai_client import GenAiClient +from .state_manager import CommittedPairCollection, PendingPairCollection, RejectedPairCollection +from .state_manager import DatasetGenConfig, DatasetGenDbProfile +from .state_manager import DatasetGenStates, DatasetGenEventLogger +from .state_manager import SqlCollection + +from .schema import generate_ddls_from_json_schema, SchemaDDL, TableProfile, SchemaProfile, TableSelector +from .generators import SQLCandidateGenerator, QuestionGenerator, QuestionReviewer, QuestionRefiner + + +assert os.getenv('GEMINI_API_KEY'), "Missing GEMINI_API_KEY environment variable." + + +def _prepare_sql_for_execution_accuracy_check(sql: str) -> str: + if sql.strip().endswith(";"): + sql = sql.strip()[:-1] + sql = sql.replace("\n", " ") + sql = sql.strip() + sql = f'SELECT 1 FROM ({sql}) AS validate_target LIMIT 1;' + return sql + + +class SeedEvalDatasetGenerator: + + def __init__(self): + self._genai_client = GenAiClient(timeout_secs=45) + + async def generate_sql( + self, task_working_dir: str) -> list[dict]: + """Reads database schema profile and use it to generate SQL candidates. + + Args: + task_working_dir: The directory containing the schema profile text file. + + Returns: + SQL candidates in the format of [{"database": database_name, "golden_sql": sql, "tags": [str]}] + """ + config = DatasetGenConfig(task_working_dir) + dialect = config.dialect + complexity = config.complexity + database_name = config.database_name + + sql_candidate_generator = SQLCandidateGenerator(genai_client=self._genai_client) + sql_collection = SqlCollection(task_working_dir) + + schema_profile = DatasetGenDbProfile(task_working_dir) + elogger = DatasetGenEventLogger(task_working_dir) + payload = { + "schema_profile_path": schema_profile.path, + "dialect": dialect, + "complexity": complexity, + "database_name": database_name, + "parallelism": config.parallelism + } + + elogger.log_event("generate_sql_triggered", payload, {}) + + max_parallelism = config.parallelism + try: + if sql_collection.is_empty(): + candidate_sqls = await sql_candidate_generator.generate_candidates( + schema_profile=schema_profile.get_profile(), + dialect=dialect, + complexity=complexity, + count=max_parallelism + ) + candidate_sqls = [ + { + "qid": f"sql_{index}", + "golden_sql": sql_entry.text, + "database": database_name, + "tags": [ + f"gen_id: {sql_entry.gen_id}", + f"complexity: {complexity}" + ] + } + for index, sql_entry in enumerate(candidate_sqls) + ] + sql_collection.add_sqls(candidate_sqls) + else: + candidate_sqls = sql_collection.get_sqls(None) + + response = { + "sqls": [{ + "qid": sql_entry["qid"], + "sql": _prepare_sql_for_execution_accuracy_check(sql_entry["golden_sql"]), + } + for sql_entry in candidate_sqls] + } + + elogger.log_event("generate_sql", payload, { + "golden_sql_sample": candidate_sqls[0] if candidate_sqls else None, + "golden_sql_count": len(candidate_sqls), + "golden_sqls_path": sql_collection.path, + }) + return response + except Exception as e: + elogger.log_event("generate_sql_error", payload, { + "error": str(e) + }) + return {"sqls": []} + + async def generate_nlq(self, golden_sql_qids: str | list[dict], task_working_dir: str) -> dict[str, Any]: + """Save the generated SQL/Question pairs to the provided generated pairs path. + + Args: + golden_sql_qids: the qids of the golden SQL queries for which to generate questions + task_working_dir: the directory to store the generated pairs + + Returns: + A dictionary object which holds the results + """ + + if isinstance(golden_sql_qids, str): + golden_sql_qids = json.loads(golden_sql_qids) + + if not isinstance(golden_sql_qids, list): + raise ValueError(f"Unknown golden_sql_qids type: {type(golden_sql_qids)}") + + output_file_path = DatasetGenConfig(task_working_dir).output_file_path + + committed_pair_collection = CommittedPairCollection(output_file_path) + pending_pair_collection = PendingPairCollection(task_working_dir) + elogger = DatasetGenEventLogger(task_working_dir) + schema_profile = DatasetGenDbProfile(task_working_dir) + + seen = committed_pair_collection.get_sql_set() + sql_collection = SqlCollection(task_working_dir) + + golden_sqls = sql_collection.get_sqls(lambda entry: entry["qid"] in golden_sql_qids) + rejected_sqls = sql_collection.get_sqls(lambda entry: entry["qid"] not in golden_sql_qids) + golden_sqls = [sql_entry for sql_entry in golden_sqls if sql_entry.get("golden_sql") not in seen] + + payload = { + "rejected_sql_sample": rejected_sqls[0] if rejected_sqls else None, + "rejected_sql_count": len(rejected_sqls), + "golden_sql_sample": golden_sqls[0] if golden_sqls else None, + "golden_sql_count": len(golden_sqls), + "schema_profile_path": schema_profile.path, + "generated_pairs_path": pending_pair_collection.path, + } + elogger.log_event("generate_nlq_triggered", payload, { + "generated_nlq_path": pending_pair_collection.path, + "golden_sqls_path": sql_collection.path, + }) + + pairs = [] + if not golden_sqls: + status = { + "generated_nlq_count": 0 + } + else: + question_generator = QuestionGenerator(genai_client=self._genai_client) + + first_sql = golden_sqls[0] + assert "golden_sql" in first_sql, "golden_sqls must be a list of dict with 'golden_sql' key or a JSON string of such list." + assert "database" in first_sql, "golden_sqls must be a list of dict with 'database' key or a JSON string of such list." + + mini_schema_profile = schema_profile.get_mini_profile() + tasks = [ + question_generator.translate_sql_nl( + schema_profile=mini_schema_profile, + sql=sql_entry["golden_sql"]) + for sql_entry in golden_sqls] + results = await asyncio.gather(*tasks) + elogger.log_event("generate_nlq_genai_done", payload, { + "generated_nlq_path": pending_pair_collection.path, + "golden_sqls_path": sql_collection.path, + }) + for nlq_entry, sql_entry in zip(results, golden_sqls): + if not nlq_entry: + continue + database_name = sql_entry["database"] + tags = sql_entry.get("tags", []) + current_question = nlq_entry.text + pair = { + 'database': database_name, + 'nlq': current_question, + 'golden_sql': sql_entry["golden_sql"], + "tags": tags + [ + f"sql2nl_gen_id: {nlq_entry.gen_id}", + ], + "status": "nlq_review_pending" + } + pairs.append(pair) + + pending_pair_collection.add_pending_pairs(pairs) + + # clean up the interim golden SQLs since they are no longer needed after generating the NLQs + sql_collection.delete() + + status = { + "generated_nlq_count": len(pairs) + } + elogger.log_event("generate_nlq", payload, { + "generated_nlq_sample": pairs[0]["nlq"] if pairs else None, + "generated_nlq_count": status["generated_nlq_count"], + }) + return status + + + async def review_nlq(self, task_working_dir: str) -> dict[str, Any]: + """Review the generated NLQs and update the tags in the generated pairs file. + + Args: + task_working_dir: the directory to store the generated pairs + + Returns: + A dictionary object which holds the results + """ + output_file_path = DatasetGenConfig(task_working_dir).output_file_path + + pending_pair_collection = PendingPairCollection(task_working_dir) + rejected_pair_collection = RejectedPairCollection(task_working_dir) + committed_pair_collection = CommittedPairCollection(output_file_path) + + states = DatasetGenStates(task_working_dir) + elogger = DatasetGenEventLogger(task_working_dir) + + states.increment_iterations() + + elogger.log_event("review_nlq_triggered", { + "generated_pairs_path": pending_pair_collection.path, + }, { + "verified_nlq_path": committed_pair_collection.path, + "rejected_nlq_path": rejected_pair_collection.path + }) + + pending_pairs = pending_pair_collection.get_pairs(lambda pair: pair.get("status") == "nlq_review_pending") + + verified_pairs = [] + rejected_pairs = [] + if pending_pairs: + reviewer = QuestionReviewer(genai_client=self._genai_client) + tasks = [ + reviewer.review_question( + sql=pair["golden_sql"], + question=pair["nlq"]) + for pair in pending_pairs + ] + review_reports = await asyncio.gather(*tasks) + rejected_pairs = [] + for report, pair in zip(review_reports, pending_pairs): + if report: + if report.is_golden: + pair["tags"].append(f"nlq_review_report: {report.summary}") + pair["tags"].append(f"timestamp: {states.timestamp()}") + pair["tags"].append(f"elapsed_hr: {states.elapsed_time_hr()}") + verified_pairs.append(pair) + else: + pair["nlq_review"] = report.model_dump_json() + rejected_pairs.append(pair) + else: + pair["status"] = "nlq_review_rejected" + pair["nlq_review"] = "" + rejected_pairs.append(pair) + + + committed_pair_collection.add_verified_pairs(verified_pairs) + rejected_pair_collection.add_rejected_pairs(rejected_pairs) + + states.update_states_on_pairs_committed(committed_pair_collection, rejected_pair_collection) + + # clean up the pending pairs file since all pending pairs have been processed (either verified or rejected) + pending_pair_collection.delete() + + self._sync_resources(task_working_dir=task_working_dir) + + status = states.to_dict() + elogger.log_event("review_nlq", { + "verified_pair_sample": verified_pairs[0]["nlq"] if verified_pairs else None, + "verified_pair_count": len(verified_pairs), + "desired_output_pairs": states.desired_output_pairs, + "verified_pairs_path": committed_pair_collection.path, + "rejected_pairs_path": rejected_pair_collection.path, + }, status) + + return status + + + async def refine_nlq(self, task_working_dir: str) -> dict[str, Any]: + """Refine the generated Question + + Args: + task_working_dir: the directory to store the generated pairs + + Returns: + A dictionary object which holds the results + """ + output_file_path = DatasetGenConfig(task_working_dir).output_file_path + committed_pair_collection = CommittedPairCollection(output_file_path) + rejected_pair_collection = RejectedPairCollection(task_working_dir) + elogger = DatasetGenEventLogger(task_working_dir) + states = DatasetGenStates(task_working_dir) + + elogger.log_event("refine_nlq_triggered", { + "rejected_pairs_sample": rejected_pair_collection.first(), + "rejected_pairs_count": len(rejected_pair_collection), + "verified_pairs_path": committed_pair_collection.path, + "rejected_pairs_path": rejected_pair_collection.path, + }, states.to_dict()) + + if not rejected_pair_collection.is_empty(): + pending_pairs = rejected_pair_collection.pop_pairs(3) + new_rejected_pairs = [] + new_verified_pairs = [] + + refiner = QuestionRefiner(genai_client=self._genai_client) + tasks = [ + refiner.refine_question( + sql=pair["golden_sql"], + question=pair["nlq"], + question_report=pair["nlq_review"]) + for pair in pending_pairs] + results = await asyncio.gather(*tasks) + + for (review_report, refined_nlq), pair in zip(results, pending_pairs): + if review_report: + if review_report.is_golden: + pair["nlq"] = refined_nlq + pair["tags"].append(f"nlq_review_report: {review_report.summary}") + pair["tags"].append(f"notes: refined_nlq") + pair["tags"].append(f"timestamp: {states.timestamp()}") + pair["tags"].append(f"elapsed_hr: {states.elapsed_time_hr()}") + new_verified_pairs.append(pair) + else: + pair["status"] = "nlq_review_rejected" + pair["nlq_review"] = review_report.model_dump_json() if review_report else pair.get("nlq_review", "") + new_rejected_pairs.append(pair) + else: + pair["status"] = "nlq_review_rejected" + pair["nlq_review"] = pair.get("nlq_review", "") + new_rejected_pairs.append(pair) + + committed_pair_collection.add_verified_pairs(new_verified_pairs) + rejected_pair_collection.add_rejected_pairs(new_rejected_pairs) + + states.update_states_on_pairs_committed(committed_pair_collection, rejected_pair_collection) + + self._sync_resources(task_working_dir=task_working_dir) + + elogger.log_event("refine_nlq", { + "verified_pair_sample": new_verified_pairs[0]["nlq"] if new_verified_pairs else None, + "verified_pair_count": len(new_verified_pairs), + "desired_output_pairs": states.desired_output_pairs, + "committed_pairs_path": committed_pair_collection.path, + "rejected_pairs_path": rejected_pair_collection.path + }, states.to_dict()) + + return states.to_dict() + + + async def generate_database_profile(self, task_working_dir: str) -> dict[str, Any]: + """Reads the contents from the input directory and assemble the database schema profile. + The input directory will be removed after the database schema profile is generated and save to the output path. + + Args: + task_working_dir: the directory to store the generated pairs + + Returns: + None + """ + + input_dir = os.path.join(task_working_dir, "_dbp") + output_path = os.path.join(task_working_dir, "db_profile.txt") + + config = DatasetGenConfig(task_working_dir) + constraints = config.constraints + + list_schema_result_path = os.path.join(input_dir, 'tables.json') + assert os.path.exists(list_schema_result_path), f"File not found: {list_schema_result_path}" + list_schema_result_text = Path(list_schema_result_path).read_text(encoding='utf-8') + schema_ddls: list[SchemaDDL] = generate_ddls_from_json_schema(list_schema_result_text) + + output_dir: str = Path(output_path).parent.as_posix() + + table_profiles = [] + table_names = [] + + payload = { + "input_dir": input_dir, + "output_path": output_path, + "constraints": constraints, + } + elogger = DatasetGenEventLogger(output_dir) + + try: + for schema_ddl in schema_ddls: + table_name = schema_ddl.get_table_name() + ddl = schema_ddl.ddl + columns = schema_ddl.columns + full_table_name = schema_ddl.full_table_name + + table_names.append(table_name) + + sample_values_per_column = collections.defaultdict(list) + + column_samples_path = os.path.join(input_dir, "column_samples", f"{full_table_name}.json") + if os.path.exists(column_samples_path): + column_samples = json.loads(Path(column_samples_path).read_text(encoding='utf-8')) + for row in column_samples: + column_name, column_value = row['name'], row['val'] + sample_values_per_column[column_name].append(column_value) + + sample_rows_path = os.path.join(input_dir, "row_samples", f"{full_table_name}.json") + sample_rows = [] + if os.path.exists(sample_rows_path): + sample_rows = json.loads(Path(sample_rows_path).read_text(encoding='utf-8')) + + table_profile = TableProfile( + table_name=table_name, + ddl=ddl, + columns=[c.name for c in columns], + sample_data=sample_rows, + sample_values_per_column=sample_values_per_column, + ) + table_profiles.append(table_profile) + + final_context = SchemaProfile( + table_profiles=table_profiles, + general_context=constraints, + ).to_string() + Path(output_path).parent.mkdir(parents=True, exist_ok=True) + Path(output_path).write_text(final_context, encoding='utf-8') + response = { + "schema_profile_path": output_path + } + + elogger.log_event("generate_database_profile", payload, response) + + return response + except RuntimeError as e: + elogger.log_event("generate_database_profile", payload, { + "error": str(e) + }) + return f"Error profiling database: {e}" + + + def _sync_resources(self, task_working_dir: str) -> dict[str, Any]: + """Reads the content from the generated pairs path and merge them to the output path. + + Args: + task_working_dir: The directory containing the generated pairs. + + Returns: + A dictionary object which holds the results + """ + states = DatasetGenStates(task_working_dir) + response = { "is_done": states.is_done(), "status": states.to_dict() } + + # remove check point files + if states.is_done(): # clean up + states.delete() + RejectedPairCollection(task_working_dir).delete() + PendingPairCollection(task_working_dir).delete() + DatasetGenConfig(task_working_dir).delete() + DatasetGenDbProfile(task_working_dir).delete() + + elogger = DatasetGenEventLogger(task_working_dir) + elogger.log_event("sync_resources", { }, response) + + return response + + + async def generate_database_profile_plan(self, task_working_dir: str) -> dict[str, Any]: + """Read the full table schema and select the desired tables. + + Args: + full_table_schema_path: The absolute path to the full table schema. + output_dir: The absolute path to the output directory. + output_summary_path: The absolute path to the output summary. + + Returns: + A dictionary object which holds the results + """ + full_table_schema_path = os.path.join(task_working_dir, "_dbp/tables.json") + output_dir = os.path.join(task_working_dir, "_dbp") + + table_schema: list[SchemaDDL] = generate_ddls_from_json_schema(Path(full_table_schema_path).read_text(encoding='utf-8')) + table_selector = TableSelector(genai_client=self._genai_client) + selected_tables: list[str] = await table_selector.select_tables(tables=table_schema, top_k=8) + + schema_data_list = json.loads(Path(full_table_schema_path).read_text(encoding='utf-8')) + schema_data_list = [item for item in schema_data_list if item['schema_name'] + '.' + item['object_details']['object_name'] in selected_tables] + Path(full_table_schema_path).write_text(json.dumps(schema_data_list, indent=2), encoding='utf-8') + + table_schema = [item for item in table_schema if item.full_table_name in selected_tables] + + column_sampling_queries = [] + row_sampling_queries = [] + + for table in table_schema: + columns = table.columns + qlist_by_cat = collections.defaultdict(list) + qlist = [] + for column in columns: + if column.is_primary_key or column.is_foreign_key: + continue + query = f"SELECT DISTINCT {column.name} as val, '{column.name}' as name FROM {table.full_table_name} LIMIT 5"; + qlist_by_cat[column.data_type].append(query) + qlist.append(query) + if len(qlist) > 10: + qlist = qlist[:10] + for dtype in qlist_by_cat: + q_by_cat = qlist_by_cat[dtype][0] + if q_by_cat not in qlist: + qlist.append(q_by_cat) + union_query = " UNION ALL ".join([f"({q})" for q in qlist]) + if union_query: + column_sampling_queries.append({ + 'table_name': table.full_table_name, + 'sampling_query': union_query + }) + row_sampling_queries.append({ + 'table_name': table.full_table_name, + 'sampling_query': f"SELECT * FROM {table.full_table_name} LIMIT 5" + }); + + column_sampling_query_path = os.path.join(output_dir, 'column_sampling_queries.json') + row_sampling_query_path = os.path.join(output_dir, 'row_sampling_queries.json') + Path(output_dir).mkdir(parents=True, exist_ok=True) + Path(column_sampling_query_path).write_text(json.dumps(column_sampling_queries, indent=2), encoding='utf-8') + Path(row_sampling_query_path).write_text(json.dumps(row_sampling_queries, indent=2), encoding='utf-8') + + return { + 'plan': f'Profile the database for the following tables: {selected_tables}', + 'column_sampling_query_path': column_sampling_query_path, + 'row_sampling_query_path': row_sampling_query_path, + } + + async def run_task(self, payload: dict[str, Any]) -> dict[str, Any]: + if 'task' not in payload: + raise KeyError(f"'task' must be in the payload {payload}") + task = payload.pop('task') + if task == 'generate_sql': + return await self.generate_sql(**payload) + elif task == 'generate_nlq': + return await self.generate_nlq(**payload) + elif task == 'review_nlq': + return await self.review_nlq(**payload) + elif task == 'refine_nlq': + return await self.refine_nlq(**payload) + elif task == 'generate_database_profile': + return await self.generate_database_profile(**payload) + elif task == 'generate_database_profile_plan': + return await self.generate_database_profile_plan(**payload) + else: + raise ValueError(f"Unknown task: '{task}' from payload {payload}") diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py new file mode 100644 index 00000000..7b9f76eb --- /dev/null +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py @@ -0,0 +1,72 @@ +"""GenAI client for NL2SQL Eval Dataset Generator.""" + +import os +import asyncio + +from google import genai +from google.genai import types +from google.genai.errors import APIError +import pydantic + +BaseModel = pydantic.BaseModel + +RETRYABLE_STATUS_CODES = [408, 429, 500, 502, 503, 504] +RETRYABLE_ATTEMPTS = 3 # can be set to 5 but we want to reduce it for faster failure in case of issues + +class GenAiClient: + """GenAI client for NL2SQL Eval Dataset Generator.""" + + def __init__(self, timeout_secs: int = 60): + # Ensure API key is set in environment, or set it here for local testing + api_key = os.environ.get("GEMINI_API_KEY", "") + self.timeout_secs = timeout_secs + assert api_key, "GEMINI_API_KEY is not set in environment variables." + # http_options=types.HttpOptions( + # retry_options=types.HttpRetryOptions( + # initial_delay=1.0, + # attempts=RETRYABLE_ATTEMPTS, + # http_status_codes=RETRYABLE_STATUS_CODES, + # ), + # timeout=120 * 1000, + # ) + self._client = genai.Client( + api_key=api_key, + http_options=types.HttpOptions( + retry_options=types.HttpRetryOptions( + attempts=1 # 1 attempt means 0 retries + ), + timeout=timeout_secs * 1000, + ) + ) + + @staticmethod + def is_retryable_genai_error(exception): + """Return True if it's a rate limit (429) or server error (503).""" + if isinstance(exception, APIError): + return exception.code in RETRYABLE_STATUS_CODES + return False + + async def generate_content_async( + self, + *, + prompt: str, + model: str, + response_schema: types.SchemaUnion | None = None, + response_mime_type: str = "application/json", + temperature: float = 0.3, + ) -> types.GenerateContentResponse | None: + """Generates content using the GenAI model asynchronously.""" + try: + result = await asyncio.wait_for(self._client.aio.models.generate_content( + model=model, + contents=prompt, + config=types.GenerateContentConfig( + response_mime_type=response_mime_type, + response_schema=response_schema, + temperature=temperature, + ), + ), timeout=self.timeout_secs) + return result + except Exception as e: + return None + diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py new file mode 100644 index 00000000..4a17d125 --- /dev/null +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py @@ -0,0 +1,36 @@ +from .genai_client import GenAiClient +from google.genai import types + + +class GeneratorBase: + """Base class for generators in NL2SQL Eval Dataset Generation.""" + pass + + def __init__(self, genai_client: GenAiClient, generator_model_id: str = "gemini-3-flash-preview"): + super().__init__() + self._genai_client = genai_client + self._generator_model_id = generator_model_id + + async def generate_content( + self, + prompt: str, + response_schema: types.SchemaUnion | None = None, + response_mime_type: str = "application/json", + model: str | None = None, + temperature: float = 0.3, + ) -> str | types.SchemaUnion | None: + """Generates content using the GenAI model.""" + if model is None: + model = self._generator_model_id + resp = await self._genai_client.generate_content_async( + prompt=prompt, + response_schema=response_schema, + response_mime_type=response_mime_type, + model=model, + temperature=temperature, + ) + if resp is None: + return None + if response_schema is not None: + return response_schema.model_validate_json(resp.text) + return resp.text diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py new file mode 100644 index 00000000..ad8f1677 --- /dev/null +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py @@ -0,0 +1,395 @@ +import random +import textwrap + +from pydantic import BaseModel, Field +from .generator_base import GeneratorBase + + +class GeneratedQuery(BaseModel): + """A generated SQL query or question.""" + + text: str + gen_id: str + + +class GeneratedSQLPool(BaseModel): + """A list of generated SQL queries.""" + + sqls: list[str] = Field( + description="A list of distinct SQL queries based on the schema." + ) + + +class TranslatedQuestion(BaseModel): + question: str = Field( + description=( + "A natural, realistic, unambigious business question that the SQL query answers." + ) + ) + +class QuestionReviewReport(BaseModel): + """The Reviewer Agent's assessment of a NL2SQL pair.""" + + is_golden: bool = Field( + description=( + "True if the question is perfectly aligned with the original SQL" + " intent." + ) + ) + red_flags: list[str] = Field( + description="List of issues: ambiguity, logical errors." + ) + ambiguity_score: float = Field( + description="0.0 (Clear) to 1.0 (Highly Ambiguous)." + ) + intent_mismatch: bool = Field( + description=( + "True if the NL question doesn't match the original SQL intent." + ) + ) + improvement_suggestion: str = Field( + description="Specific advice for the Repair Agent." + ) + summary: str = Field( + description="Concise and short summary of the assessment." + ) + + +class SQLCandidateGenerator(GeneratorBase): + """SQL generator for NL2SQL Eval Dataset Generation.""" + + async def generate_candidates( + self, schema_profile: str, dialect: str, complexity: str, count: int + ) -> list[GeneratedQuery]: + """Architect Agent: Generates candidate SQL queries (SQL2NL strategy).""" + strategy_id = random.randint(1, 4) + if strategy_id == 1: + sqls = await self._generate_candidates_1( + schema_profile=schema_profile, + dialect=dialect, + complexity=complexity, + count=count) + elif strategy_id == 2: + sqls = await self._generate_candidates_2( + schema_profile=schema_profile, + dialect=dialect, + complexity=complexity, + count=count + ) + elif strategy_id == 3: + sqls = await self.generate_candidates_3( + schema_profile=schema_profile, + dialect=dialect, + complexity=complexity, + count=count + ) + else: + sqls = await self.generate_candidates_4( + schema_profile=schema_profile, + dialect=dialect, + complexity=complexity, + count=count + ) + + sqls = [sql for sql in sqls if sql and 20 <= len(sql) <= 300] + + sqls = list(set(sqls)) + + return [GeneratedQuery(text=sql, gen_id=str(strategy_id)) for sql in sqls] + + async def _generate_candidates_1( + self, *, schema_profile: str, dialect: str, complexity: str, count: int + ) -> list[str]: + prompt = textwrap.dedent(f""" + You are an expert Database Architect. Given the following {dialect} database schema and sample data, + generate {count} {complexity}, diverse, highly realistic analytical SQL queries. + + Include a mix of: + - Simple SELECTs and aggregates + - Multi-table JOINs + - Subqueries and CTEs + - Window functions (if applicable) + + Ensure the queries are syntactically valid for {dialect} and are likely to return data based on the samples. + Ensure the queries are concise and each query is less than 300 characters. + + Below is the definition on SQL query complexity: + - easy: Involves single-table selections and basic relational filtering. + - medium: Requires multi-table joins, foundational data aggregations. + - hard: Demands deeply nested subqueries, complex window functions, or strict edge-case syntax constraints. + + Database Profile: + {schema_profile} + """) + + candidates = await self.generate_content( + prompt=prompt, + response_schema=GeneratedSQLPool, + temperature=0.7, + ) + return candidates.sqls if candidates else [] + + async def _generate_candidates_2( + self, + *, + schema_profile: str, + dialect: str, + complexity: str, + count: int + ) -> list[str]: + """Architect Agent: Generates candidate SQL queries (SQL2NL strategy).""" + sql_gen_prompt = textwrap.dedent(f""" + Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. + Inject real-world messiness: Use CTEs, window functions, or regex/string extraction if appropriate. + Ensure each query returns actual data from the samples provided. + Ensure the queries are concise and each query is less than 300 characters. + + Below is the definition on SQL query complexity: + - easy: Involves single-table selections and basic relational filtering. + - medium: Requires multi-table joins, foundational data aggregations. + - hard: Demands deeply nested subqueries, complex window functions, or strict edge-case syntax constraints. + + Database Profile: + {schema_profile} + """) + candidates = await self.generate_content( + prompt=sql_gen_prompt, + response_schema=GeneratedSQLPool, + temperature=0.7, + ) + return candidates.sqls if candidates else [] + + async def generate_candidates_3( + self, + *, + schema_profile: str, + dialect: str, + complexity: str, + count: int + ) -> list[str]: + """Generates a candidate NL2SQL pair using the RepairedPair schema.""" + prompt = textwrap.dedent(f""" + Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. + Think step-by-step: + - First generate {count} distinct, realistic business intents that users of this database might have. + - For each intent, generate a corresponding SQL query that would answer that intent. + + Ensure the queries are syntactically valid for {dialect} and are likely to return data based on the samples. + Ensure the queries are concise and each query is less than 300 characters. + + Below is the definition on SQL query complexity: + - easy: Involves single-table selections and basic relational filtering. + - medium: Requires multi-table joins, foundational data aggregations. + - hard: Demands deeply nested subqueries, complex window functions, or strict edge-case syntax constraints. + + Database Profile: + {schema_profile} + """) + candidates = await self.generate_content( + prompt=prompt, + response_schema=GeneratedSQLPool, + temperature=0.7, + ) + return candidates.sqls if candidates else [] + + async def generate_candidates_4( + self, + *, + schema_profile: str, + dialect: str, + complexity: str, + count: int + ) -> list[str]: + + + dev_prompt = textwrap.dedent(f""" + Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. + Think step-by-step: + - First, plan {count} {complexity} {dialect} SQL pattern for this database describing the tables, joins and the analytical goal. Focus on messy real-world scenarios. + - Next, generate {count} {complexity}, diverse, highly realistic, {dialect} queries, one for each SQL design. + Ensure it return non empty results when executed. Use exact table and column names from the context. + Ensure the queries are concise and each query is less than 300 characters. + Ensure you use exact table and column names from the context. + + Below is the definition on SQL query complexity: + - easy: Involves single-table selections and basic relational filtering. + - medium: Requires multi-table joins, foundational data aggregations. + - hard: Demands deeply nested subqueries, complex window functions, or strict edge-case syntax constraints. + + Database Profile: + {schema_profile} + """) + candidates = await self.generate_content( + prompt=dev_prompt, response_schema=GeneratedSQLPool, temperature=0.7 + ) + return candidates.sqls if candidates else [] + + +class QuestionGenerator(GeneratorBase): + """SQL2NL generator for NL2SQL Eval Dataset Generation.""" + + async def translate_sql_nl( + self, + *, + schema_profile: str, + sql: str + ) -> GeneratedQuery: + strategy_id = random.randint(1, 2) + if strategy_id == 1: + nlq = await self.translate_sql_nl_1( + schema_profile=schema_profile, + sql=sql + ) + else: + nlq = await self.translate_sql_nl_2( + schema_profile=schema_profile, + sql=sql + ) + return GeneratedQuery(text=nlq, gen_id=str(strategy_id)) + + + async def translate_sql_nl_1( + self, + *, + schema_profile: str, + sql: str + ) -> str | None: + """Translator Agent: Translates valid SQL into a business question.""" + prompt = textwrap.dedent(f""" + You are a Data Analyst. Look at the following SQL query, the database profile. + Translate this SQL query into a clear, natural-sounding business question that a CEO or Manager would ask. + Ensure that the translated question is concise and not vague and not ambiguous. + Avoid using technical jargon or SQL keywords in the question. + The question should be something that a non-technical business stakeholder might ask. + + SQL Query: + {sql} + + Database Profile: + {schema_profile} + """) + + result = await self.generate_content( + prompt=prompt, + response_schema=TranslatedQuestion, + temperature=0.4, + ) + return result.question if result else None + + async def translate_sql_nl_2( + self, + *, + schema_profile: str, + sql: str + ) -> str | None: + styles = [ + "formal", + "colloquial", + "imperative", + "interrogative", + "descriptive", + "concise", + "vague", + "metaphorical", + "conversational", + ] + selected_style = random.choice(styles) + nl_prompt = textwrap.dedent(f""" + You are a Data Analyst. Look at the following SQL query, the database profile. + Translate this SQL into a '{selected_style}' natural language question that is not ambiguous. + Ensure that the translated question is concise and not vague and not ambiguous. + Avoid using technical jargon or SQL keywords in the question. + The question should be something that a non-technical business stakeholder might ask. + + SQL Query: + {sql} + + Database Profile: + {schema_profile} + """) + question_data = await self.generate_content( + prompt=nl_prompt, + response_schema=TranslatedQuestion, + temperature=0.8, + ) + return question_data.question if question_data else None + + +class QuestionReviewer(GeneratorBase): + """Question reviewer agent for NL2SQL Eval Dataset Generation.""" + + async def review_question( + self, + *, + question: str, + sql: str, + ) -> QuestionReviewReport | None: + review_prompt = textwrap.dedent(f""" + You are the ReViSQL Reviewer Agent. Evaluate this NL2SQL question for inclusion in a Golden Dataset. + + NL Question: {question} + SQL Query: {sql} + + Check for: + 1. Ambiguity: Is the NL question too vague? + 2. Logical Correctness: Does the NL question accurately reflect the SQL intent? + 3. Filter Correctness: Does the NL question accurately reflect the filter condition of SQL query? + 4. Join Correctness: Does the NL question accurately reflect the join condition of SQL query? + 5. Output Correctness: Does the NL question accurately reflect the output of SQL query? + """) + + report = await self.generate_content( + prompt=review_prompt, + response_schema=QuestionReviewReport, + temperature=0.1, + ) + return report if report else None + + +class QuestionRefiner(GeneratorBase): + async def refine_question( + self, + *, + question: str, + sql: str, + question_report: QuestionReviewReport | str, + ) -> str | None: + """Refiner agent in the Verifier/Refiner Agent Loop. + + Args: + schema_profile: The database schema and sample data. + question: The business question to verify. + sql: The original SQL query that produced the business question. + sql_dialect: The dialect of the SQL query. + + Returns: + return verified and refined question + """ + + if isinstance(question_report, QuestionReviewReport): + question_report = question_report.model_dump_json() + + + refine_prompt = textwrap.dedent(f""" + The current business question is ambiguous and led to an incorrect SQL query. + + SQL Query: + {sql} + + Current Ambiguous or Incorrect Question: + {question} + + Reviewer Report: + {question_report} + + Please rewrite the Business Question to be perfectly precise, unambiguous, and aligned with the Intended SQL query. + Explicitly mention necessary filters or specific columns if needed to avoid ambiguity. + """) + + refined = await self.generate_content( + prompt=refine_prompt, + response_schema=TranslatedQuestion, + temperature=0.3, + ) + return refined.question if refined else None + diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py new file mode 100644 index 00000000..622bf54d --- /dev/null +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py @@ -0,0 +1,323 @@ +import textwrap +from typing import Any +import json + +from pydantic import BaseModel, Field + +from .generator_base import GeneratorBase + + +def truncate_length(s: str, max_length: int) -> str: + """Truncate the string to the maximum length.""" + if not isinstance(s, str): + return s + if len(s) > max_length: + return s[: max_length - 3] + "..." + return s + + +class Column(BaseModel): + name: str + data_type: str + is_primary_key: bool + is_foreign_key: bool + + +class SchemaDDL(BaseModel): + """Schema for a database table.""" + + ddl: str = '' + schema_name: str = '' + table_name: str = '' + full_table_name: str = '' + columns: list[Column] = [] + constraints: list[dict[str, Any]] = [] + indexes: list[dict[str, Any]] = [] + error: str = '' + + def get_table_name(self) -> str: + """Returns the table name.""" + if self.schema_name and self.schema_name != 'public': + return f'{self.schema_name}.{self.table_name}' + return self.table_name + + +class TableProfile(BaseModel): + """Schema profile for a database table.""" + + table_name: str + ddl: str + columns: list[str] + sample_data: list[dict[str, Any]] + sample_values_per_column: dict[str, list[Any]] + + def to_string(self): + profile = [] + profile.append(f"--- Table: {self.table_name} ---") + profile.append(f"DDL:\n{self.ddl}") + profile.append("\n") + + # remove columns with one unique value or less + filtered_columns = [] + for column in self.columns: + if self.sample_values_per_column: + values = self.sample_values_per_column.get(column, []) + if len(values) > 1: + filtered_columns.append(column) + else: + filtered_columns.append(column) + + if self.sample_data: + profile.append(f"Sample Data (Columns: {', '.join(filtered_columns)}):") + for row in self.sample_data: + new_row = { + k: truncate_length(v, 32 * 5) + for k, v in row.items() + if k in filtered_columns + } + profile.append(str(new_row)) + profile.append("\n") + + if self.sample_values_per_column: + for column, values in self.sample_values_per_column.items(): + if column not in filtered_columns: + continue + values = [truncate_length(v, 32 * 5) for v in values] + profile.append(f'Sample values for column "{column}": {values}') + return "\n".join(profile) + + +class SchemaProfile(BaseModel): + """Schema profile for a database.""" + + table_profiles: list[TableProfile] + general_context: str | None = None + + def to_string(self): + profile = [] + for table_profile in self.table_profiles: + profile.append(table_profile.to_string()) + profile.append("\n") + if self.general_context: + profile.append("--- General Context ---") + profile.append(truncate_length(self.general_context, 768 * 5)) + return "\n".join(profile) + + +def generate_ddls_from_json_schema(schema_json_string: str) -> list[SchemaDDL]: + try: + schema_data_list = json.loads(schema_json_string) + except json.JSONDecodeError as e: + return SchemaDDL(error=f'Error decoding JSON: {e}') + assert isinstance(schema_data_list, list) + ddls = [] + for schema_data in schema_data_list: + ddl = generate_ddl_from_dict(schema_data) + if ddl.error: + raise ValueError(f'Error generating DDL: {ddl.error}') + ddls.append(ddl) + return ddls + + +def generate_ddl_from_dict(schema_data: dict[str, Any]) -> SchemaDDL: + """Converts a JSON schema definition string into DDL statements. + + Args: + schema_json_string: A JSON string representing the database object schema. + + Returns: + A SchemaDdl object containing the generated DDL statements. + """ + + if 'object_details' not in schema_data: + return SchemaDDL(error="Error: 'object_details' not found in JSON schema.") + + details = schema_data['object_details'] + schema_name = schema_data.get('schema_name', 'public') + table_name = details.get('object_name') + + if not table_name: + return SchemaDDL(error='Error: "object_name" not found in object_details.') + + full_table_name = f'{schema_name}.{table_name}' + + ddl_statements = [] + + # 1. CREATE TABLE statement + columns = details.get('columns', []) + if not columns: + return SchemaDDL( + error='Error: No columns defined for table {full_table_name}.' + ) + + column_defs = [] + for col in columns: + col_name = col.get('column_name') + data_type = col.get('data_type') + if not col_name or not data_type: + return SchemaDDL( + error=( + 'Error: Column name or data_type missing in column definition:' + f' {col}' + ) + ) + + parts = [f'"{col_name}"', data_type] + if col.get('is_not_nullable', False): + parts.append('NOT NULL') + if col.get('column_default') is not None: + parts.append(f"DEFAULT {col['column_default']}") + column_defs.append(' '.join(parts)) + + create_table_sql = f'CREATE TABLE {full_table_name} (\n ' + create_table_sql += ',\n '.join(column_defs) + create_table_sql += '\n);' + ddl_statements.append(create_table_sql) + ddl_statements.append('') + + # 2. Add Constraints + constraints = details.get('constraints', []) + pk_constraints = [ + c for c in constraints if c.get('constraint_type') == 'PRIMARY KEY' + ] + fk_constraints = [ + c for c in constraints if c.get('constraint_type') == 'FOREIGN KEY' + ] + other_constraints = [ + c + for c in constraints + if c.get('constraint_type') not in ['PRIMARY KEY', 'FOREIGN KEY'] + ] + + def add_constraint_sql(constraint): + constraint_name = constraint.get('constraint_name') + constraint_def = constraint.get('constraint_definition') + if constraint_name and constraint_def: + return ( + f'ALTER TABLE {full_table_name} ADD CONSTRAINT "{constraint_name}"' + f' {constraint_def};' + ) + return None + + primary_keys = set() + foreign_keys = set() + + # Add Primary Key and other non-FK constraints + for c in pk_constraints: + sql = add_constraint_sql(c) + if sql: + ddl_statements.append(sql) + constraint_definition = c.get('constraint_definition') + pk_candidates = [] + for col in columns: + column_name = col.get('column_name') + if column_name in constraint_definition: + pk_candidates.append(column_name) + if pk_candidates: + selected_pk = list(sorted(pk_candidates, key=len, reverse=True))[0] + primary_keys.add(selected_pk) + + + for c in other_constraints: + sql = add_constraint_sql(c) + if sql: + ddl_statements.append(sql) + + if pk_constraints or other_constraints: + ddl_statements.append('') + + # Add Foreign Key constraints last + for c in fk_constraints: + sql = add_constraint_sql(c) + if sql: + ddl_statements.append(sql) + constraint_definition = c.get('constraint_definition') + fk_candidates = [] + for col in columns: + column_name = col.get('column_name') + if column_name in constraint_definition: + fk_candidates.append(column_name) + if fk_candidates: + selected_fk = list(sorted(fk_candidates, key=len, reverse=True))[0] + foreign_keys.add(selected_fk) + + if fk_constraints: + ddl_statements.append('') + + # 3. Add Indexes + indexes = details.get('indexes', []) + for index in indexes: + index_def = index.get('index_definition') + # Primary key constraints typically create their own unique index. + # Avoid duplicating index creation if it's for the primary key. + if not index.get('is_primary', False) and index_def: + # Ensure the index definition also uses quoted table name if needed + # Assuming index_definition is already correctly formatted. + ddl_statements.append(index_def + ';') + + ddl = '\n'.join(ddl_statements) + return SchemaDDL( + ddl=ddl, + schema_name=schema_name, + table_name=table_name, + full_table_name=full_table_name, + columns=[Column( + name=col.get('column_name'), + data_type=col.get('data_type'), + is_primary_key=col.get('column_name') in primary_keys, + is_foreign_key=col.get('column_name') in foreign_keys, + ) for col in columns], + constraints=constraints, + indexes=indexes, + ) + +class TableSelector(GeneratorBase): + async def select_tables( + self, + *, + tables: list[SchemaDDL], + top_k: int = 8 + ) -> list[str]: + + class SelectedTables(BaseModel): + table_names: list[str] = Field(description="List of selected table names, Each table name in the form of schema_name.table_name") + + profile = [] + for table in tables: + profile.append(f"--- Table: {table.full_table_name} ---") + profile.append(f"DDL:\n{table.ddl}") + profile.append("\n") + schema_profile = '\n'.join(profile) + + review_prompt = textwrap.dedent(f""" + You are experienced Database Analyst. You are given the schema of a database, and ask the pick at most {top_k} tables that + the end-user will most likely interact with. + + Below are some guidelines for picking the relevant tables (P0, P1, P2 refers to priority, which P0 being highest priority): + - P0: central tables in the schema. + - P1: tables whose primary keys are most frequently referenced in other tables's foreign keys. + - P2: tables which appear to be relevant for answering user questions in the application domain. + + Guildelines: + - When the two tables relevant, it means the two table are linked by reference or in the same application domain or under the same schema. + - You must not pick tables about schema information or table schema unless they are relevant to the application domain. + - After you pick the top 1 table, the other tables that you select must either have the same schema as the top 1 table or relevant to the top 1 table. + - Ensure that the tables selected are relevant to each other. + + You should return the full table names (in the form of schema_name.table_name). + You should return AT MOST {top_k} tables. + + Database schema: + {schema_profile} + """) + + report = await self.generate_content( + prompt=review_prompt, + response_schema=SelectedTables, + temperature=0.1, + ) + assert report is not None + assert isinstance(report, SelectedTables) + return report.table_names + + diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py new file mode 100644 index 00000000..dec952dc --- /dev/null +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py @@ -0,0 +1,431 @@ +import json +import os +import datetime +import asyncio +import shutil +from typing import Any +from pathlib import Path + + +class DatasetGenEventLogger: + def __init__(self, working_dir: str): + self._history_path = os.path.join(working_dir, 'mcp_history.json') + self._history = [] + if os.path.exists(self._history_path): + self._history = json.loads(Path(self._history_path).read_text(encoding='utf-8')) + + def log_event(self, action: str, request: dict[str, Any], response: dict[str, Any]) -> 'DatasetGenEventLogger': + evt = { + "action": action, + "time": datetime.datetime.now().isoformat(), + "request": request, + "response": response + } + if len(self._history) >= 10: + self._history.pop(0) + self._history.append(evt) + Path(self._history_path).parent.mkdir(parents=True, exist_ok=True) + Path(self._history_path).write_text(json.dumps(self._history, indent=2, ensure_ascii=False), encoding='utf-8') + return self + +class DatasetGenConfig: + def __init__(self, task_dir: str): + self._config_path = os.path.join(task_dir, "config.json") + self._config = None + + def _load(self) -> dict[str, Any]: + if self._config is not None: + return self._config + if not os.path.exists(self._config_path): + raise FileNotFoundError(f"Config file not found at {self._config_path}") + self._config = json.loads(Path(self._config_path).read_text(encoding='utf-8')) + return self._config + + @property + def dialect(self) -> str: + config = self._load() + return config["dialect"] + + @property + def complexity(self) -> str: + config = self._load() + return config["complexity"] + + @property + def database_name(self) -> str: + config = self._load() + return config["database_name"] + + @property + def desired_output_pairs(self) -> int: + config = self._load() + return int(config["size"]) + + @property + def constraints(self) -> str: + config = self._load() + return config["constraints"] + + @property + def parallelism(self) -> int: + return max(1, min(10, self.desired_output_pairs)) + + @property + def output_file_path(self) -> str: + config = self._load() + return config["output_file_path"] + + def delete(self) -> None: + Path(self._config_path).unlink(missing_ok=True) + self._config = None + + +class DatasetGenDbProfile: + def __init__(self, task_dir: str): + self._db_profile_path = os.path.join(task_dir, "db_profile.txt") + self._db_profile_internal_dir = os.path.join(task_dir, "_dbp") + self._db_profile = None + + def get_profile(self) -> str: + if self._db_profile is not None: + return self._db_profile + if os.path.exists(self._db_profile_path): + self._db_profile = Path(self._db_profile_path).read_text(encoding='utf-8') + else: + self._db_profile = "" + return self._db_profile + + def get_mini_profile(self) -> str: + # Remove the sample values from schema profile to reduce the context length + schema_profile = self.get_profile() + schema_profile_lines = schema_profile.split("\n") + schema_profile_lines = [line for line in schema_profile_lines if not line.startswith("Sample values for column")] + schema_profile = "\n".join(schema_profile_lines) + return schema_profile + + def delete(self) -> None: + Path(self._db_profile_path).unlink(missing_ok=True) + if os.path.exists(self._db_profile_internal_dir): + shutil.rmtree(self._db_profile_internal_dir, ignore_errors=True) + self._db_profile = None + + @property + def path(self) -> str: + return self._db_profile_path + + +class CommittedPairCollection: + def __init__(self, output_file_path: str): + self._committed_pairs_path = output_file_path + self._committed_pairs = None + + def _load(self) -> list[dict[str, Any]]: + if self._committed_pairs is not None: + return self._committed_pairs + if os.path.exists(self._committed_pairs_path): + self._committed_pairs = json.loads(Path(self._committed_pairs_path).read_text(encoding='utf-8')) + else: + self._committed_pairs = [] + return self._committed_pairs + + def add_verified_pairs(self, pairs: list[dict[str, Any]]) -> bool: + if not pairs: + return False + committed_pairs = self._load() + + index = 1 + num_new_pairs = 0 + seen = set() + if committed_pairs: + index = int(committed_pairs[-1]['id'].replace("eval_", "")) + 1 + seen = set([entry["golden_sql"] for entry in committed_pairs]) + + for entry in pairs: + if entry['golden_sql'] in seen: + continue + tags = entry.get('tags', []) + committed_pairs.append({ + "id": f"eval_{str(index).zfill(4)}", + "nlq": entry['nlq'], + "golden_sql": entry['golden_sql'], + "database": entry['database'], + "tags": tags + }) + num_new_pairs += 1 + index += 1 + + self._save() + return True + + def __len__(self) -> int: + committed_pairs = self._load() + return len(committed_pairs) + + def _save(self) -> None: + committed_pairs = self._load() + Path(self._committed_pairs_path).parent.mkdir(parents=True, exist_ok=True) + Path(self._committed_pairs_path).write_text(json.dumps(committed_pairs, indent=2), encoding='utf-8') + + def get_sql_set(self) -> set[str]: + committed_pairs = self._load() + return set([entry["golden_sql"] for entry in committed_pairs]) + + def exists(self) -> bool: + return os.path.exists(self._committed_pairs_path) + + def get_pairs(self) -> list[dict[str, Any]]: + return self._load() + + def delete(self) -> None: + Path(self._committed_pairs_path).unlink(missing_ok=True) + self._committed_pairs = None + + @property + def path(self) -> str: + return self._committed_pairs_path + + +class RejectedPairCollection: + def __init__(self, task_dir: str): + self._rejected_pairs_path = os.path.join(task_dir, "rejected_pairs.json") + self._rejected_pairs = None + + def _load(self) -> list[dict[str, Any]]: + if self._rejected_pairs is not None: + return self._rejected_pairs + if os.path.exists(self._rejected_pairs_path): + self._rejected_pairs = json.loads(Path(self._rejected_pairs_path).read_text(encoding='utf-8')) + else: + self._rejected_pairs = [] + return self._rejected_pairs + + def add_rejected_pairs(self, pairs: list[dict[str, Any]]) -> bool: + if not pairs: + return False + rejected_pairs = self._load() + rejected_pairs.extend(pairs) + self._save() + return True + + def pop_pairs(self, num_pairs: int) -> list[dict[str, Any]]: + rejected_pairs = self._load() + if not rejected_pairs: + return [] + pairs_to_return = rejected_pairs[:num_pairs] + self._rejected_pairs = rejected_pairs[num_pairs:] + self._save() + return pairs_to_return + + def __len__(self) -> int: + rejected_pairs = self._load() + return len(rejected_pairs) + + def _save(self) -> None: + rejected_pairs = self._load() + Path(self._rejected_pairs_path).parent.mkdir(parents=True, exist_ok=True) + Path(self._rejected_pairs_path).write_text(json.dumps(rejected_pairs, indent=2), encoding='utf-8') + + def delete(self) -> None: + Path(self._rejected_pairs_path).unlink(missing_ok=True) + self._rejected_pairs = None + + @property + def path(self) -> str: + return self._rejected_pairs_path + + def first(self) -> dict[str, Any] | None: + rejected_pairs = self._load() + return rejected_pairs[0] if rejected_pairs else None + + def is_empty(self) -> bool: + rejected_pairs = self._load() + return len(rejected_pairs) == 0 + + +class PendingPairCollection: + def __init__(self, task_dir: str): + self._pending_pairs_path = os.path.join(task_dir, "interim_generated_pairs.json") + self._pending_pairs = None + + def _load(self) -> list[dict[str, Any]]: + if self._pending_pairs is not None: + return self._pending_pairs + if os.path.exists(self._pending_pairs_path): + self._pending_pairs = json.loads(Path(self._pending_pairs_path).read_text(encoding='utf-8')) + else: + self._pending_pairs = [] + return self._pending_pairs + + @property + def path(self) -> str: + return self._pending_pairs_path + + def add_pending_pairs(self, pairs: list[dict[str, Any]]) -> bool: + if not pairs: + return False + pending_pairs = self._load() + pending_pairs.extend(pairs) + self._save() + return True + + def _save(self) -> None: + pending_pairs = self._load() + Path(self._pending_pairs_path).parent.mkdir(parents=True, exist_ok=True) + Path(self._pending_pairs_path).write_text(json.dumps(pending_pairs, indent=2), encoding='utf-8') + + def delete(self) -> None: + Path(self._pending_pairs_path).unlink(missing_ok=True) + self._pending_pairs = None + + def get_pairs(self, filter_func: callable) -> list[dict[str, Any]]: + pending_pairs = self._load() + if filter_func: + pending_pairs = [pair for pair in pending_pairs if filter_func(pair)] + return pending_pairs + + +class SqlCollection: + def __init__(self, task_dir: str): + self._sqls_path = os.path.join(task_dir, "interim_golden_sqls.json") + self._sqls = None + + def _load(self) -> list[str]: + if self._sqls is not None: + return self._sqls + if os.path.exists(self._sqls_path): + self._sqls = json.loads(Path(self._sqls_path).read_text(encoding='utf-8')) + else: + self._sqls = [] + return self._sqls + + def add_sqls(self, sqls: list[dict[str, Any]]) -> bool: + if not sqls: + return False + _sqls = self._load() + _sqls.extend(sqls) + self._save() + return True + + def delete(self) -> None: + Path(self._sqls_path).unlink(missing_ok=True) + self._sqls = None + + def get_sqls(self, filter_func: callable) -> list[str]: + sqls = self._load() + if filter_func: + sqls = [sql for sql in sqls if filter_func(sql)] + return sqls + + def is_empty(self) -> bool: + sqls = self._load() + return len(sqls) == 0 + + def _save(self) -> None: + sqls = self._load() + Path(self._sqls_path).parent.mkdir(parents=True, exist_ok=True) + Path(self._sqls_path).write_text(json.dumps(sqls, indent=2), encoding='utf-8') + + @property + def path(self) -> str: + return self._sqls_path + + +class DatasetGenStates: + def __init__(self, task_dir: str): + self._states_path = os.path.join(task_dir, "states.json") + self._states = None + self.config = DatasetGenConfig(task_dir) + + def _load(self) -> dict[str, Any]: + if self._states is not None: + return self._states + + if os.path.exists(self._states_path): + self._states = json.loads(Path(self._states_path).read_text(encoding='utf-8')) + else: + self._states = { + "is_done": False, + "completed_pairs": 0, + "remaining_pairs": self.config.desired_output_pairs, + "rejected_pairs": 0, + "iterations": 0, + } + return self._states + + def increment_iterations(self) -> int: + states = self._load() + iterations = states.get("iterations", 0) + 1 + states["iterations"] = iterations + return iterations + + def exists(self) -> bool: + return os.path.exists(self._states_path) + + def is_done(self) -> bool: + states = self._load() + return states.get("is_done", False) + + def delete(self) -> None: + Path(self._states_path).unlink(missing_ok=True) + self._states = None + + @property + def iterations(self) -> int: + states = self._load() + return states.get("iterations", 0) + + @property + def desired_output_pairs(self) -> int: + return self.config.desired_output_pairs + + def update_states_on_pairs_committed(self, committed_pairs: CommittedPairCollection, rejected_pairs: RejectedPairCollection) -> None: + num_verified_pairs = len(committed_pairs) + num_rejected_pairs = len(rejected_pairs) + remaining_pairs = max(0, self.desired_output_pairs - num_verified_pairs) + states = self._load() + states["is_done"] = self._is_data_generation_done(remaining_pairs, self.iterations) + states["remaining_pairs"] = remaining_pairs + states["completed_pairs"] = num_verified_pairs + states["rejected_pairs"] = num_rejected_pairs + if "start_time" not in states: + states["start_time"] = datetime.datetime.now().timestamp() + states["curr_time"] = datetime.datetime.now().timestamp() + states["elapsed_time_hr"] = round((states["curr_time"] - states["start_time"]) / 3600, 2) + + self._save() + + def elapsed_time_hr(self) -> float: + states = self._load() + return states.get("elapsed_time_hr", 0) + + def timestamp(self) -> float: + states = self._load() + return states.get("curr_time", datetime.datetime.now().timestamp()) + + def to_dict(self) -> dict[str, Any]: + return self._load() + + def _save(self) -> None: + states = self._load() + Path(self._states_path).parent.mkdir(parents=True, exist_ok=True) + Path(self._states_path).write_text(json.dumps(states, indent=2), encoding='utf-8') + + def _is_data_generation_done(self, remaining_pairs: int, iterations: int) -> bool: + if remaining_pairs <= 0 or iterations >= 500: + return True + return False + + +class BackgroundTaskCollection: + tasks = set() + + @classmethod + def add_task(cls, task: asyncio.Task) -> None: + cls.tasks.add(task) + task.add_done_callback(lambda t: cls.tasks.discard(t)) + + @classmethod + def cancel_tasks(cls) -> None: + for task in cls.tasks: + task.cancel() + cls.tasks.clear() diff --git a/src/google/cloud/db_context_enrichment/main.py b/src/google/cloud/db_context_enrichment/main.py index 476e0f23..ea6bac12 100644 --- a/src/google/cloud/db_context_enrichment/main.py +++ b/src/google/cloud/db_context_enrichment/main.py @@ -1,6 +1,7 @@ import datetime import json import os +from typing import Any from fastmcp import FastMCP @@ -17,6 +18,8 @@ from google.cloud.db_context_enrichment.template import template_generator from google.cloud.db_context_enrichment.value_search import generator as vi_generator from google.cloud.db_context_enrichment.value_search import match_templates +from google.cloud.db_context_enrichment.dataset.eval_seed_gen.dataset_generator import SeedEvalDatasetGenerator + mcp = FastMCP("Context Engineering Agent MCP") @@ -446,5 +449,18 @@ async def read_evaluation_result( return result_reader.read_eval_results(run_folder_path, offset, batch_size) +@mcp.tool +async def generate_seed_eval_dataset(payload: dict[str, Any]) -> dict[str, Any]: + """Reads the `task` field in the `payload` argument to determine which of a list of tasks to perform for generate_seed_eval_dataset. + + Args: + payload: A dictionary object which must have the fields `task`, `task_working_dir`, and other required fields based on the value of `task`. + + Returns: + A dictionary object which holds the results + """ + return await SeedEvalDatasetGenerator().run_task(payload) + + if __name__ == "__main__": mcp.run() # Uses STDIO transport by default From d8c8b76ad75b460a308aa13219cc4b62f70c6aca Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sat, 23 May 2026 12:02:18 -0700 Subject: [PATCH 02/23] remove the GEMINI_API_KEY environment variable --- .../eval_seed_gen/dataset_generator.py | 3 --- .../dataset/eval_seed_gen/genai_client.py | 24 +------------------ 2 files changed, 1 insertion(+), 26 deletions(-) diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py index 42351249..574de865 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py @@ -18,9 +18,6 @@ from .generators import SQLCandidateGenerator, QuestionGenerator, QuestionReviewer, QuestionRefiner -assert os.getenv('GEMINI_API_KEY'), "Missing GEMINI_API_KEY environment variable." - - def _prepare_sql_for_execution_accuracy_check(sql: str) -> str: if sql.strip().endswith(";"): sql = sql.strip()[:-1] diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py index 7b9f76eb..3f520b3f 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py @@ -10,42 +10,20 @@ BaseModel = pydantic.BaseModel -RETRYABLE_STATUS_CODES = [408, 429, 500, 502, 503, 504] -RETRYABLE_ATTEMPTS = 3 # can be set to 5 but we want to reduce it for faster failure in case of issues - class GenAiClient: """GenAI client for NL2SQL Eval Dataset Generator.""" def __init__(self, timeout_secs: int = 60): - # Ensure API key is set in environment, or set it here for local testing - api_key = os.environ.get("GEMINI_API_KEY", "") self.timeout_secs = timeout_secs - assert api_key, "GEMINI_API_KEY is not set in environment variables." - # http_options=types.HttpOptions( - # retry_options=types.HttpRetryOptions( - # initial_delay=1.0, - # attempts=RETRYABLE_ATTEMPTS, - # http_status_codes=RETRYABLE_STATUS_CODES, - # ), - # timeout=120 * 1000, - # ) self._client = genai.Client( - api_key=api_key, http_options=types.HttpOptions( retry_options=types.HttpRetryOptions( - attempts=1 # 1 attempt means 0 retries + attempts=1 # 1 attempt as we need genai to fail fast when throttling happens ), timeout=timeout_secs * 1000, ) ) - @staticmethod - def is_retryable_genai_error(exception): - """Return True if it's a rate limit (429) or server error (503).""" - if isinstance(exception, APIError): - return exception.code in RETRYABLE_STATUS_CODES - return False - async def generate_content_async( self, *, From 63480013fbcc6f913ab2f67510431fcbe0399b4c Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 17:16:04 -0700 Subject: [PATCH 03/23] update ci/cd to fix the gcrun issue --- .ci/build.sh | 12 +- .ci/cloudbuild.yaml | 129 ++++++++++-------- .ci/run_eval.sh | 48 +++++++ skills/autoctx-dataset-generation/SKILL.md | 2 + .../dataset/eval_seed_gen/state_manager.py | 4 +- 5 files changed, 134 insertions(+), 61 deletions(-) create mode 100644 .ci/run_eval.sh diff --git a/.ci/build.sh b/.ci/build.sh index 581c9c7a..26324fa9 100644 --- a/.ci/build.sh +++ b/.ci/build.sh @@ -42,7 +42,7 @@ rm "${ARCHIVE}" uv run pyinstaller pyinstaller.spec # 7. Validate binary -./dist/google-cloud-db-context-enrichment --help +./dist/google-cloud-db-context-engineering --help # 8. Prepare distribution (Staging) mkdir -p staging @@ -51,22 +51,22 @@ cp -r commands/ staging/commands/ mkdir -p staging/skills/autoctx-init/scripts/ mkdir -p staging/skills/autoctx-evaluate/scripts/ -mv dist/google-cloud-db-context-enrichment staging/ +mv dist/google-cloud-db-context-engineering staging/ mv toolbox staging/skills/autoctx-init/scripts/ mv evalbench staging/skills/autoctx-evaluate/scripts/ # Update gemini-extension.json -BINARY_NAME="\${extensionPath}/google-cloud-db-context-enrichment" +BINARY_NAME="\${extensionPath}/google-cloud-db-context-engineering" TOOLBOX_NAME="\${extensionPath}/skills/autoctx-init/scripts/toolbox" -jq ".contextFileName = \"GEMINI.md\" | .mcpServers.mcp_db_context_enrichment.command = \"$BINARY_NAME\" | .mcpServers.mcp_db_context_enrichment.args = [] | .mcpServers.mcp_toolbox = {\"command\": \"$TOOLBOX_NAME\", \"args\": [\"--stdio\", \"--config\", \"autoctx/tools.yaml\"]}" gemini-extension.json > staging/gemini-extension.json +jq ".contextFileName = \"GEMINI.md\" | .mcpServers.mcp_db_context_engineering.command = \"$BINARY_NAME\" | .mcpServers.mcp_db_context_engineering.args = [] | .mcpServers.mcp_toolbox = {\"command\": \"$TOOLBOX_NAME\", \"args\": [\"--stdio\", \"--config\", \"autoctx/tools.yaml\"]}" gemini-extension.json > staging/gemini-extension.json cp GEMINI.md staging/ cp LICENSE staging/ # Create tarball cd staging -tar -czf ../linux.x64.google-cloud-db-context-enrichment.tar.gz * +tar -czf ../linux.x64.google-cloud-db-context-engineering.tar.gz * cd .. -echo "Build completed successfully! Artifact at linux.x64.google-cloud-db-context-enrichment.tar.gz" +echo "Build completed successfully! Artifact at linux.x64.google-cloud-db-context-engineering.tar.gz" \ No newline at end of file diff --git a/.ci/cloudbuild.yaml b/.ci/cloudbuild.yaml index a59d77d5..26b75c95 100644 --- a/.ci/cloudbuild.yaml +++ b/.ci/cloudbuild.yaml @@ -1,6 +1,7 @@ steps: # --- Build Extension Step --- - - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + - id: build-extension + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' entrypoint: 'bash' args: - '-c' @@ -10,77 +11,97 @@ steps: chmod +x .ci/build.sh .ci/build.sh - # --- Evaluation Step --- - - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + # --- Pre-flight: gate evals on PR label --- + # Runs in parallel with build-extension; writes /workspace/SHOULD_RUN + # if evals should proceed (always for manual runs; only on 'ci:eval' label + # for PR-triggered runs). + - id: preflight + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' entrypoint: 'bash' - secretEnv: ['ADC_KEY', 'GITHUB_TOKEN'] + secretEnv: ['GITHUB_TOKEN'] + waitFor: ['-'] args: - '-c' - | set -e if [ -z "$_PR_NUMBER" ]; then - echo "No PR number provided. Skipping PR check (assuming local/manual run)." - else - echo "Fetching PR data from GitHub API..." - - # Fetch PR data and status code - HTTP_STATUS=$(curl -s -o pr_data.json -w "%{http_code}" -H "Authorization: token $$GITHUB_TOKEN" \ - "https://api.github.com/repos/$REPO_FULL_NAME/pulls/$_PR_NUMBER") - - if [ "$$HTTP_STATUS" -ne 200 ]; then - echo "Error fetching PR data: HTTP $$HTTP_STATUS" - cat pr_data.json - exit 1 - fi - - # Check if execution labels are present - if ! jq -e '.labels | any(.name == "ci:eval")' pr_data.json > /dev/null; then - echo "PR does not have 'ci:eval' label. Skipping execution." - exit 0 - fi - echo "Execution label detected. Processing release version context..." + echo "No PR number provided. Assuming local/manual run — enabling evals." + touch /workspace/SHOULD_RUN + exit 0 fi - # Write secret to file for ADC - echo "$$ADC_KEY" > /tmp/adc.json - chmod 600 /tmp/adc.json - export GOOGLE_APPLICATION_CREDENTIALS=/tmp/adc.json - - cd /workspace/evals - - # Override model.yaml to use the locally built extension instead of GitHub - sed -i 's|https://github.com/GoogleCloudPlatform/db-context-enrichment|/workspace/staging|g' core-cujs/model.yaml - - # Set environment variables for evalbench - export EVAL_GCP_PROJECT_ID=$PROJECT_ID - export EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION - export GOOGLE_CLOUD_PROJECT=$PROJECT_ID + echo "Fetching PR data from GitHub API..." + HTTP_STATUS=$(curl -s -o pr_data.json -w "%{http_code}" -H "Authorization: token $$GITHUB_TOKEN" \ + "https://api.github.com/repos/$REPO_FULL_NAME/pulls/$_PR_NUMBER") - # Setup Python path and run evalbench - export PYTHONPATH=/evalbench:/evalbench/evalproto - export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + if [ "$$HTTP_STATUS" -ne 200 ]; then + echo "Error fetching PR data: HTTP $$HTTP_STATUS" + cat pr_data.json + exit 1 + fi - echo "Launching Standalone Evaluation..." - python3 /evalbench/evalbench/evalbench.py --experiment_config=core-cujs/run.yaml + if ! jq -e '.labels | any(.name == "ci:eval")' pr_data.json > /dev/null; then + echo "PR does not have 'ci:eval' label. Skipping evals." + exit 0 + fi - # Mark that evaluation ran - touch /workspace/EVAL_RAN + echo "ci:eval label detected — enabling evals." + touch /workspace/SHOULD_RUN - # --- Upload Results and Workspaces --- - - name: 'gcr.io/cloud-builders/gsutil' + # --- Eval steps (parallel, isolated per suite) --- + # Both steps run the same .ci/run_eval.sh script with a different suite arg. + - id: eval-core-cujs + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + entrypoint: 'bash' + secretEnv: ['ADC_KEY'] + waitFor: ['build-extension', 'preflight'] + env: + - 'EVAL_GCP_PROJECT_ID=$PROJECT_ID' + - 'EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION' + args: ['.ci/run_eval.sh', 'core-cujs'] + + - id: eval-freeform-input + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + entrypoint: 'bash' + secretEnv: ['ADC_KEY'] + waitFor: ['build-extension', 'preflight'] + env: + - 'EVAL_GCP_PROJECT_ID=$PROJECT_ID' + - 'EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION' + args: ['.ci/run_eval.sh', 'freeform-input'] + + # --- Upload Results and Workspaces (after both eval steps) --- + - id: upload + name: 'gcr.io/cloud-builders/gsutil' entrypoint: 'bash' + waitFor: ['eval-core-cujs', 'eval-freeform-input'] args: - '-c' - | - if [ -f /workspace/EVAL_RAN ]; then - echo "Uploading results..." - gsutil -m cp -r evals/* gs://context-engineering-agent-eval-results/evals/$BUILD_ID/ + ANY_RAN=0 + for SUITE in core-cujs freeform-input; do + if [ ! -f /workspace/EVAL_RAN_$$SUITE ]; then + echo "$$SUITE did not run; skipping its upload." + continue + fi + ANY_RAN=1 + WORK_DIR=/workspace/eval-$$SUITE + DEST=gs://context-engineering-agent-eval-results/evals/$BUILD_ID/$$SUITE + + echo "Uploading $$SUITE results to $$DEST ..." + gsutil -m cp -r $$WORK_DIR/* $$DEST/ + + if [ -d $$WORK_DIR/.venv/fake_home/.gemini/tmp ]; then + echo "Uploading $$SUITE Gemini workspace..." + gsutil -m cp -r $$WORK_DIR/.venv/fake_home/.gemini/tmp/* $$DEST/gemini/ + fi + done - echo "Uploading Gemini workspace..." - gsutil -m cp -r evals/.venv/fake_home/.gemini/tmp/* gs://context-engineering-agent-eval-results/evals/$BUILD_ID/gemini/ + if [ "$$ANY_RAN" = "1" ]; then + echo "View uploaded results at: https://console.cloud.google.com/storage/browser/context-engineering-agent-eval-results/evals/$BUILD_ID" else - echo "Evaluation did not run. Skipping upload." + echo "No evaluations ran. Skipping upload." fi availableSecrets: @@ -93,4 +114,4 @@ availableSecrets: serviceAccount: 'projects/cloud-db-nl2sql/serviceAccounts/crema-eval@cloud-db-nl2sql.iam.gserviceaccount.com' options: - defaultLogsBucketBehavior: REGIONAL_USER_OWNED_BUCKET + defaultLogsBucketBehavior: REGIONAL_USER_OWNED_BUCKET \ No newline at end of file diff --git a/.ci/run_eval.sh b/.ci/run_eval.sh new file mode 100644 index 00000000..ee2a6fe4 --- /dev/null +++ b/.ci/run_eval.sh @@ -0,0 +1,48 @@ +#!/usr/bin/env bash +# Run a single evalbench suite in an isolated working directory. +# +# Invoked from .ci/cloudbuild.yaml. Required env vars: ADC_KEY, +# GEMINI_API_KEY, EVAL_GCP_PROJECT_ID, EVAL_GCP_PROJECT_REGION. +# Required argument: (e.g. core-cujs, freeform-input). + +set -e + +SUITE="${1:?usage: run_eval.sh }" + +if [ ! -f /workspace/SHOULD_RUN ]; then + echo "Evals disabled by preflight; skipping ${SUITE}." + exit 0 +fi + +# ADC setup +echo "${ADC_KEY}" > /tmp/adc.json +chmod 600 /tmp/adc.json +export GOOGLE_APPLICATION_CREDENTIALS=/tmp/adc.json + +# Isolate: copy this suite into its own working directory so parallel +# runs don't fight over the same .venv / fake_home / .gemini/tmp state. +WORK_DIR="/workspace/eval-${SUITE}" +mkdir -p "${WORK_DIR}" +cp -r "/workspace/evals/${SUITE}" "${WORK_DIR}/" +cd "${WORK_DIR}" + +# Point model.yaml at the locally built extension and inject the Gemini API +# key into the CLI's env (orchestrator auth — the extension itself no longer +# needs the key, so we no longer touch the `settings: {}` block). +sed -i 's|https://github.com/GoogleCloudPlatform/db-context-enrichment|/workspace/staging|g' "${SUITE}/model.yaml" + +# Inject Vertex project/location into the CLI env block so the extension can +# talk to the right GCP project without the values being committed to the repo. +sed -i \ + -e "/^ GEMINI_MODEL:/a\\ GOOGLE_CLOUD_PROJECT: \"${EVAL_GCP_PROJECT_ID}\"" \ + -e "/^ GEMINI_MODEL:/a\\ GOOGLE_CLOUD_LOCATION: \"${EVAL_GCP_PROJECT_REGION}\"" \ + "${SUITE}/model.yaml" + +# evalbench runtime +export PYTHONPATH=/evalbench:/evalbench/evalproto +export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + +echo "Launching ${SUITE} evaluation..." +uv run --no-sync --project /evalbench python /evalbench/evalbench/evalbench.py --experiment_config="${SUITE}/run.yaml" + +touch "/workspace/EVAL_RAN_${SUITE}" \ No newline at end of file diff --git a/skills/autoctx-dataset-generation/SKILL.md b/skills/autoctx-dataset-generation/SKILL.md index 6c1a365e..5304600a 100644 --- a/skills/autoctx-dataset-generation/SKILL.md +++ b/skills/autoctx-dataset-generation/SKILL.md @@ -26,6 +26,7 @@ Complete the following checklist before proceeding to step 1.2: * Check for `tools.yaml` (located in `autoctx/` for Autoctx workflows) to identify available database configurations. Prompt the user to select the target database for dataset generation. If `tools.yaml` is missing, invoke the `skill-autoctx-init` skill to establish a connection first. * Infer the database name `target_database_name` from the user and the `tools.yaml` file. If unsure, ask the user to confirm. * Infer the database dialect `database_dialect` from the user and the `tools.yaml` file. If unsure, ask the user to confirm. +* Infer the parallelism `internal_parallelism` from the user. If not provided, set `internal_parallelism` to `10`. * Check for the current workspace directory by running `pwd`. * You must provide the exact `output_file_path`. * Set the `evalset_working_dir` to the absolute path of the folder `.dataset_cache`, which is a hidden directory in the current workspace directory. @@ -47,6 +48,7 @@ Once all parameters are staged, you MUST explicitly ask the user to confirm the "constraints": "[generation_constraints]", "database_name": "[target_database_name]", "size": "[target_dataset_size]", + "parallelism": "[internal_parallelism]", "output_file_path": "[output_file_path]" } diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py index dec952dc..0a592069 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py @@ -68,7 +68,9 @@ def constraints(self) -> str: @property def parallelism(self) -> int: - return max(1, min(10, self.desired_output_pairs)) + config = self._load() + parallelism = config.get("parallelism", 10) + return max(1, min(parallelism, self.desired_output_pairs)) @property def output_file_path(self) -> str: From a845366d12489857e9fdfd60dc7d713dfcc09a37 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 17:29:56 -0700 Subject: [PATCH 04/23] revert ci/cd --- .ci/build.sh | 12 ++--- .ci/cloudbuild.yaml | 129 +++++++++++++++++++------------------------- .ci/run_eval.sh | 48 ----------------- 3 files changed, 60 insertions(+), 129 deletions(-) delete mode 100644 .ci/run_eval.sh diff --git a/.ci/build.sh b/.ci/build.sh index 26324fa9..581c9c7a 100644 --- a/.ci/build.sh +++ b/.ci/build.sh @@ -42,7 +42,7 @@ rm "${ARCHIVE}" uv run pyinstaller pyinstaller.spec # 7. Validate binary -./dist/google-cloud-db-context-engineering --help +./dist/google-cloud-db-context-enrichment --help # 8. Prepare distribution (Staging) mkdir -p staging @@ -51,22 +51,22 @@ cp -r commands/ staging/commands/ mkdir -p staging/skills/autoctx-init/scripts/ mkdir -p staging/skills/autoctx-evaluate/scripts/ -mv dist/google-cloud-db-context-engineering staging/ +mv dist/google-cloud-db-context-enrichment staging/ mv toolbox staging/skills/autoctx-init/scripts/ mv evalbench staging/skills/autoctx-evaluate/scripts/ # Update gemini-extension.json -BINARY_NAME="\${extensionPath}/google-cloud-db-context-engineering" +BINARY_NAME="\${extensionPath}/google-cloud-db-context-enrichment" TOOLBOX_NAME="\${extensionPath}/skills/autoctx-init/scripts/toolbox" -jq ".contextFileName = \"GEMINI.md\" | .mcpServers.mcp_db_context_engineering.command = \"$BINARY_NAME\" | .mcpServers.mcp_db_context_engineering.args = [] | .mcpServers.mcp_toolbox = {\"command\": \"$TOOLBOX_NAME\", \"args\": [\"--stdio\", \"--config\", \"autoctx/tools.yaml\"]}" gemini-extension.json > staging/gemini-extension.json +jq ".contextFileName = \"GEMINI.md\" | .mcpServers.mcp_db_context_enrichment.command = \"$BINARY_NAME\" | .mcpServers.mcp_db_context_enrichment.args = [] | .mcpServers.mcp_toolbox = {\"command\": \"$TOOLBOX_NAME\", \"args\": [\"--stdio\", \"--config\", \"autoctx/tools.yaml\"]}" gemini-extension.json > staging/gemini-extension.json cp GEMINI.md staging/ cp LICENSE staging/ # Create tarball cd staging -tar -czf ../linux.x64.google-cloud-db-context-engineering.tar.gz * +tar -czf ../linux.x64.google-cloud-db-context-enrichment.tar.gz * cd .. -echo "Build completed successfully! Artifact at linux.x64.google-cloud-db-context-engineering.tar.gz" \ No newline at end of file +echo "Build completed successfully! Artifact at linux.x64.google-cloud-db-context-enrichment.tar.gz" diff --git a/.ci/cloudbuild.yaml b/.ci/cloudbuild.yaml index 26b75c95..a59d77d5 100644 --- a/.ci/cloudbuild.yaml +++ b/.ci/cloudbuild.yaml @@ -1,7 +1,6 @@ steps: # --- Build Extension Step --- - - id: build-extension - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' entrypoint: 'bash' args: - '-c' @@ -11,97 +10,77 @@ steps: chmod +x .ci/build.sh .ci/build.sh - # --- Pre-flight: gate evals on PR label --- - # Runs in parallel with build-extension; writes /workspace/SHOULD_RUN - # if evals should proceed (always for manual runs; only on 'ci:eval' label - # for PR-triggered runs). - - id: preflight - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + # --- Evaluation Step --- + - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' entrypoint: 'bash' - secretEnv: ['GITHUB_TOKEN'] - waitFor: ['-'] + secretEnv: ['ADC_KEY', 'GITHUB_TOKEN'] args: - '-c' - | set -e if [ -z "$_PR_NUMBER" ]; then - echo "No PR number provided. Assuming local/manual run — enabling evals." - touch /workspace/SHOULD_RUN - exit 0 - fi + echo "No PR number provided. Skipping PR check (assuming local/manual run)." + else + echo "Fetching PR data from GitHub API..." - echo "Fetching PR data from GitHub API..." - HTTP_STATUS=$(curl -s -o pr_data.json -w "%{http_code}" -H "Authorization: token $$GITHUB_TOKEN" \ - "https://api.github.com/repos/$REPO_FULL_NAME/pulls/$_PR_NUMBER") + # Fetch PR data and status code + HTTP_STATUS=$(curl -s -o pr_data.json -w "%{http_code}" -H "Authorization: token $$GITHUB_TOKEN" \ + "https://api.github.com/repos/$REPO_FULL_NAME/pulls/$_PR_NUMBER") - if [ "$$HTTP_STATUS" -ne 200 ]; then - echo "Error fetching PR data: HTTP $$HTTP_STATUS" - cat pr_data.json - exit 1 - fi + if [ "$$HTTP_STATUS" -ne 200 ]; then + echo "Error fetching PR data: HTTP $$HTTP_STATUS" + cat pr_data.json + exit 1 + fi - if ! jq -e '.labels | any(.name == "ci:eval")' pr_data.json > /dev/null; then - echo "PR does not have 'ci:eval' label. Skipping evals." - exit 0 + # Check if execution labels are present + if ! jq -e '.labels | any(.name == "ci:eval")' pr_data.json > /dev/null; then + echo "PR does not have 'ci:eval' label. Skipping execution." + exit 0 + fi + echo "Execution label detected. Processing release version context..." fi - echo "ci:eval label detected — enabling evals." - touch /workspace/SHOULD_RUN + # Write secret to file for ADC + echo "$$ADC_KEY" > /tmp/adc.json + chmod 600 /tmp/adc.json + export GOOGLE_APPLICATION_CREDENTIALS=/tmp/adc.json - # --- Eval steps (parallel, isolated per suite) --- - # Both steps run the same .ci/run_eval.sh script with a different suite arg. - - id: eval-core-cujs - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' - entrypoint: 'bash' - secretEnv: ['ADC_KEY'] - waitFor: ['build-extension', 'preflight'] - env: - - 'EVAL_GCP_PROJECT_ID=$PROJECT_ID' - - 'EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION' - args: ['.ci/run_eval.sh', 'core-cujs'] - - - id: eval-freeform-input - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' - entrypoint: 'bash' - secretEnv: ['ADC_KEY'] - waitFor: ['build-extension', 'preflight'] - env: - - 'EVAL_GCP_PROJECT_ID=$PROJECT_ID' - - 'EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION' - args: ['.ci/run_eval.sh', 'freeform-input'] - - # --- Upload Results and Workspaces (after both eval steps) --- - - id: upload - name: 'gcr.io/cloud-builders/gsutil' + cd /workspace/evals + + # Override model.yaml to use the locally built extension instead of GitHub + sed -i 's|https://github.com/GoogleCloudPlatform/db-context-enrichment|/workspace/staging|g' core-cujs/model.yaml + + # Set environment variables for evalbench + export EVAL_GCP_PROJECT_ID=$PROJECT_ID + export EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION + export GOOGLE_CLOUD_PROJECT=$PROJECT_ID + + # Setup Python path and run evalbench + export PYTHONPATH=/evalbench:/evalbench/evalproto + export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + + echo "Launching Standalone Evaluation..." + python3 /evalbench/evalbench/evalbench.py --experiment_config=core-cujs/run.yaml + + # Mark that evaluation ran + touch /workspace/EVAL_RAN + + # --- Upload Results and Workspaces --- + - name: 'gcr.io/cloud-builders/gsutil' entrypoint: 'bash' - waitFor: ['eval-core-cujs', 'eval-freeform-input'] args: - '-c' - | - ANY_RAN=0 - for SUITE in core-cujs freeform-input; do - if [ ! -f /workspace/EVAL_RAN_$$SUITE ]; then - echo "$$SUITE did not run; skipping its upload." - continue - fi - ANY_RAN=1 - WORK_DIR=/workspace/eval-$$SUITE - DEST=gs://context-engineering-agent-eval-results/evals/$BUILD_ID/$$SUITE - - echo "Uploading $$SUITE results to $$DEST ..." - gsutil -m cp -r $$WORK_DIR/* $$DEST/ - - if [ -d $$WORK_DIR/.venv/fake_home/.gemini/tmp ]; then - echo "Uploading $$SUITE Gemini workspace..." - gsutil -m cp -r $$WORK_DIR/.venv/fake_home/.gemini/tmp/* $$DEST/gemini/ - fi - done + if [ -f /workspace/EVAL_RAN ]; then + echo "Uploading results..." + gsutil -m cp -r evals/* gs://context-engineering-agent-eval-results/evals/$BUILD_ID/ - if [ "$$ANY_RAN" = "1" ]; then - echo "View uploaded results at: https://console.cloud.google.com/storage/browser/context-engineering-agent-eval-results/evals/$BUILD_ID" + echo "Uploading Gemini workspace..." + gsutil -m cp -r evals/.venv/fake_home/.gemini/tmp/* gs://context-engineering-agent-eval-results/evals/$BUILD_ID/gemini/ else - echo "No evaluations ran. Skipping upload." + echo "Evaluation did not run. Skipping upload." fi availableSecrets: @@ -114,4 +93,4 @@ availableSecrets: serviceAccount: 'projects/cloud-db-nl2sql/serviceAccounts/crema-eval@cloud-db-nl2sql.iam.gserviceaccount.com' options: - defaultLogsBucketBehavior: REGIONAL_USER_OWNED_BUCKET \ No newline at end of file + defaultLogsBucketBehavior: REGIONAL_USER_OWNED_BUCKET diff --git a/.ci/run_eval.sh b/.ci/run_eval.sh deleted file mode 100644 index ee2a6fe4..00000000 --- a/.ci/run_eval.sh +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env bash -# Run a single evalbench suite in an isolated working directory. -# -# Invoked from .ci/cloudbuild.yaml. Required env vars: ADC_KEY, -# GEMINI_API_KEY, EVAL_GCP_PROJECT_ID, EVAL_GCP_PROJECT_REGION. -# Required argument: (e.g. core-cujs, freeform-input). - -set -e - -SUITE="${1:?usage: run_eval.sh }" - -if [ ! -f /workspace/SHOULD_RUN ]; then - echo "Evals disabled by preflight; skipping ${SUITE}." - exit 0 -fi - -# ADC setup -echo "${ADC_KEY}" > /tmp/adc.json -chmod 600 /tmp/adc.json -export GOOGLE_APPLICATION_CREDENTIALS=/tmp/adc.json - -# Isolate: copy this suite into its own working directory so parallel -# runs don't fight over the same .venv / fake_home / .gemini/tmp state. -WORK_DIR="/workspace/eval-${SUITE}" -mkdir -p "${WORK_DIR}" -cp -r "/workspace/evals/${SUITE}" "${WORK_DIR}/" -cd "${WORK_DIR}" - -# Point model.yaml at the locally built extension and inject the Gemini API -# key into the CLI's env (orchestrator auth — the extension itself no longer -# needs the key, so we no longer touch the `settings: {}` block). -sed -i 's|https://github.com/GoogleCloudPlatform/db-context-enrichment|/workspace/staging|g' "${SUITE}/model.yaml" - -# Inject Vertex project/location into the CLI env block so the extension can -# talk to the right GCP project without the values being committed to the repo. -sed -i \ - -e "/^ GEMINI_MODEL:/a\\ GOOGLE_CLOUD_PROJECT: \"${EVAL_GCP_PROJECT_ID}\"" \ - -e "/^ GEMINI_MODEL:/a\\ GOOGLE_CLOUD_LOCATION: \"${EVAL_GCP_PROJECT_REGION}\"" \ - "${SUITE}/model.yaml" - -# evalbench runtime -export PYTHONPATH=/evalbench:/evalbench/evalproto -export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python - -echo "Launching ${SUITE} evaluation..." -uv run --no-sync --project /evalbench python /evalbench/evalbench/evalbench.py --experiment_config="${SUITE}/run.yaml" - -touch "/workspace/EVAL_RAN_${SUITE}" \ No newline at end of file From b535616b8a34ec9dd54d8537dfc41193de400ea7 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 17:46:08 -0700 Subject: [PATCH 05/23] update the ci/cd based on main branch changes --- .ci/cloudbuild.yaml | 114 ++++++++++++++++++++++++-------------------- .ci/run_eval.sh | 48 +++++++++++++++++++ 2 files changed, 110 insertions(+), 52 deletions(-) create mode 100644 .ci/run_eval.sh diff --git a/.ci/cloudbuild.yaml b/.ci/cloudbuild.yaml index a59d77d5..cd9defa0 100644 --- a/.ci/cloudbuild.yaml +++ b/.ci/cloudbuild.yaml @@ -10,77 +10,87 @@ steps: chmod +x .ci/build.sh .ci/build.sh - # --- Evaluation Step --- - - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + # --- Pre-flight: gate evals on PR label --- + # Runs in parallel with build-extension; writes /workspace/SHOULD_RUN + # if evals should proceed (always for manual runs; only on 'ci:eval' label + # for PR-triggered runs). + - id: preflight + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' entrypoint: 'bash' - secretEnv: ['ADC_KEY', 'GITHUB_TOKEN'] + secretEnv: ['GITHUB_TOKEN'] + waitFor: ['-'] args: - '-c' - | set -e if [ -z "$_PR_NUMBER" ]; then - echo "No PR number provided. Skipping PR check (assuming local/manual run)." - else - echo "Fetching PR data from GitHub API..." - - # Fetch PR data and status code - HTTP_STATUS=$(curl -s -o pr_data.json -w "%{http_code}" -H "Authorization: token $$GITHUB_TOKEN" \ - "https://api.github.com/repos/$REPO_FULL_NAME/pulls/$_PR_NUMBER") - - if [ "$$HTTP_STATUS" -ne 200 ]; then - echo "Error fetching PR data: HTTP $$HTTP_STATUS" - cat pr_data.json - exit 1 - fi - - # Check if execution labels are present - if ! jq -e '.labels | any(.name == "ci:eval")' pr_data.json > /dev/null; then - echo "PR does not have 'ci:eval' label. Skipping execution." - exit 0 - fi - echo "Execution label detected. Processing release version context..." + echo "No PR number provided. Assuming local/manual run — enabling evals." + touch /workspace/SHOULD_RUN + exit 0 fi - # Write secret to file for ADC - echo "$$ADC_KEY" > /tmp/adc.json - chmod 600 /tmp/adc.json - export GOOGLE_APPLICATION_CREDENTIALS=/tmp/adc.json - - cd /workspace/evals - - # Override model.yaml to use the locally built extension instead of GitHub - sed -i 's|https://github.com/GoogleCloudPlatform/db-context-enrichment|/workspace/staging|g' core-cujs/model.yaml + echo "Fetching PR data from GitHub API..." + HTTP_STATUS=$(curl -s -o pr_data.json -w "%{http_code}" -H "Authorization: token $$GITHUB_TOKEN" \ + "https://api.github.com/repos/$REPO_FULL_NAME/pulls/$_PR_NUMBER") - # Set environment variables for evalbench - export EVAL_GCP_PROJECT_ID=$PROJECT_ID - export EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION - export GOOGLE_CLOUD_PROJECT=$PROJECT_ID - - # Setup Python path and run evalbench - export PYTHONPATH=/evalbench:/evalbench/evalproto - export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + if [ "$$HTTP_STATUS" -ne 200 ]; then + echo "Error fetching PR data: HTTP $$HTTP_STATUS" + cat pr_data.json + exit 1 + fi - echo "Launching Standalone Evaluation..." - python3 /evalbench/evalbench/evalbench.py --experiment_config=core-cujs/run.yaml + if ! jq -e '.labels | any(.name == "ci:eval")' pr_data.json > /dev/null; then + echo "PR does not have 'ci:eval' label. Skipping evals." + exit 0 + fi - # Mark that evaluation ran - touch /workspace/EVAL_RAN + echo "ci:eval label detected — enabling evals." + touch /workspace/SHOULD_RUN - # --- Upload Results and Workspaces --- - - name: 'gcr.io/cloud-builders/gsutil' + # --- Eval steps (parallel, isolated per suite) --- + # Both steps run the same .ci/run_eval.sh script with a different suite arg. + - id: eval-core-cujs + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + entrypoint: 'bash' + secretEnv: ['ADC_KEY'] + waitFor: ['build-extension', 'preflight'] + env: + - 'EVAL_GCP_PROJECT_ID=$PROJECT_ID' + - 'EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION' + args: ['.ci/run_eval.sh', 'core-cujs'] + + # --- Upload Results and Workspaces (after both eval steps) --- + - id: upload + name: 'gcr.io/cloud-builders/gsutil' entrypoint: 'bash' + waitFor: ['eval-core-cujs'] args: - '-c' - | - if [ -f /workspace/EVAL_RAN ]; then - echo "Uploading results..." - gsutil -m cp -r evals/* gs://context-engineering-agent-eval-results/evals/$BUILD_ID/ + ANY_RAN=0 + for SUITE in core-cujs; do + if [ ! -f /workspace/EVAL_RAN_$$SUITE ]; then + echo "$$SUITE did not run; skipping its upload." + continue + fi + ANY_RAN=1 + WORK_DIR=/workspace/eval-$$SUITE + DEST=gs://context-engineering-agent-eval-results/evals/$BUILD_ID/$$SUITE + + echo "Uploading $$SUITE results to $$DEST ..." + gsutil -m cp -r $$WORK_DIR/* $$DEST/ + + if [ -d $$WORK_DIR/.venv/fake_home/.gemini/tmp ]; then + echo "Uploading $$SUITE Gemini workspace..." + gsutil -m cp -r $$WORK_DIR/.venv/fake_home/.gemini/tmp/* $$DEST/gemini/ + fi + done - echo "Uploading Gemini workspace..." - gsutil -m cp -r evals/.venv/fake_home/.gemini/tmp/* gs://context-engineering-agent-eval-results/evals/$BUILD_ID/gemini/ + if [ "$$ANY_RAN" = "1" ]; then + echo "View uploaded results at: https://console.cloud.google.com/storage/browser/context-engineering-agent-eval-results/evals/$BUILD_ID" else - echo "Evaluation did not run. Skipping upload." + echo "No evaluations ran. Skipping upload." fi availableSecrets: diff --git a/.ci/run_eval.sh b/.ci/run_eval.sh new file mode 100644 index 00000000..ee2a6fe4 --- /dev/null +++ b/.ci/run_eval.sh @@ -0,0 +1,48 @@ +#!/usr/bin/env bash +# Run a single evalbench suite in an isolated working directory. +# +# Invoked from .ci/cloudbuild.yaml. Required env vars: ADC_KEY, +# GEMINI_API_KEY, EVAL_GCP_PROJECT_ID, EVAL_GCP_PROJECT_REGION. +# Required argument: (e.g. core-cujs, freeform-input). + +set -e + +SUITE="${1:?usage: run_eval.sh }" + +if [ ! -f /workspace/SHOULD_RUN ]; then + echo "Evals disabled by preflight; skipping ${SUITE}." + exit 0 +fi + +# ADC setup +echo "${ADC_KEY}" > /tmp/adc.json +chmod 600 /tmp/adc.json +export GOOGLE_APPLICATION_CREDENTIALS=/tmp/adc.json + +# Isolate: copy this suite into its own working directory so parallel +# runs don't fight over the same .venv / fake_home / .gemini/tmp state. +WORK_DIR="/workspace/eval-${SUITE}" +mkdir -p "${WORK_DIR}" +cp -r "/workspace/evals/${SUITE}" "${WORK_DIR}/" +cd "${WORK_DIR}" + +# Point model.yaml at the locally built extension and inject the Gemini API +# key into the CLI's env (orchestrator auth — the extension itself no longer +# needs the key, so we no longer touch the `settings: {}` block). +sed -i 's|https://github.com/GoogleCloudPlatform/db-context-enrichment|/workspace/staging|g' "${SUITE}/model.yaml" + +# Inject Vertex project/location into the CLI env block so the extension can +# talk to the right GCP project without the values being committed to the repo. +sed -i \ + -e "/^ GEMINI_MODEL:/a\\ GOOGLE_CLOUD_PROJECT: \"${EVAL_GCP_PROJECT_ID}\"" \ + -e "/^ GEMINI_MODEL:/a\\ GOOGLE_CLOUD_LOCATION: \"${EVAL_GCP_PROJECT_REGION}\"" \ + "${SUITE}/model.yaml" + +# evalbench runtime +export PYTHONPATH=/evalbench:/evalbench/evalproto +export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python + +echo "Launching ${SUITE} evaluation..." +uv run --no-sync --project /evalbench python /evalbench/evalbench/evalbench.py --experiment_config="${SUITE}/run.yaml" + +touch "/workspace/EVAL_RAN_${SUITE}" \ No newline at end of file From b0779589013d974640050e5d9476795b213889ec Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 17:52:02 -0700 Subject: [PATCH 06/23] minor update on substitutionOption --- .ci/cloudbuild.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.ci/cloudbuild.yaml b/.ci/cloudbuild.yaml index cd9defa0..840e4512 100644 --- a/.ci/cloudbuild.yaml +++ b/.ci/cloudbuild.yaml @@ -104,3 +104,4 @@ serviceAccount: 'projects/cloud-db-nl2sql/serviceAccounts/crema-eval@cloud-db-nl options: defaultLogsBucketBehavior: REGIONAL_USER_OWNED_BUCKET + substitutionOption: ALLOW_LOOSE From 4e592e117ce244d98af1afd02590a294d2c57cb4 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 17:56:22 -0700 Subject: [PATCH 07/23] fix the missing build-extension id --- .ci/cloudbuild.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.ci/cloudbuild.yaml b/.ci/cloudbuild.yaml index 840e4512..9ec6c928 100644 --- a/.ci/cloudbuild.yaml +++ b/.ci/cloudbuild.yaml @@ -1,6 +1,7 @@ steps: # --- Build Extension Step --- - - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + - id: build-extension + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' entrypoint: 'bash' args: - '-c' From 7c472ec1a27bcc091273aac27d14f41f76cf864d Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 20:04:06 -0700 Subject: [PATCH 08/23] make sure that the seed golden appears in the gcbrun output --- evals/core-cujs/dataset.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/evals/core-cujs/dataset.json b/evals/core-cujs/dataset.json index 969dd24e..e9d6f6b2 100644 --- a/evals/core-cujs/dataset.json +++ b/evals/core-cujs/dataset.json @@ -19,8 +19,8 @@ }, { "id": "autoctx:generate-dataset", - "starting_prompt": "generate a seed evaluation dataset for my alloydb database financial named golden.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 medium-level SQL/Question pairs for my alloydb database financial named golden.json. I prefer the generated SQL to have filter conditions on text or jsonb columns", + "starting_prompt": "generate a seed evaluation dataset for my alloydb database financial named golden_seed.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 medium-level SQL/Question pairs for my alloydb database financial named golden_seed.json. I prefer the generated SQL to have filter conditions on text or jsonb columns. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", "work_dir": "workspace_post_init/", From 459f130ac42171db39cdff2c99878b164ae70307 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 20:43:24 -0700 Subject: [PATCH 09/23] bug fix on ruff check and pytest --- .ci/cloudbuild.yaml | 4 +- .python-version | 2 +- pyproject.toml | 6 +-- .../eval_seed_gen/dataset_generator.py | 44 +++++++++++++------ .../dataset/eval_seed_gen/genai_client.py | 9 ++-- .../dataset/eval_seed_gen/generator_base.py | 3 +- .../dataset/eval_seed_gen/generators.py | 1 + .../dataset/eval_seed_gen/schema.py | 2 +- .../dataset/eval_seed_gen/state_manager.py | 6 +-- .../cloud/db_context_enrichment/main.py | 5 ++- 10 files changed, 49 insertions(+), 33 deletions(-) diff --git a/.ci/cloudbuild.yaml b/.ci/cloudbuild.yaml index 9ec6c928..cd9defa0 100644 --- a/.ci/cloudbuild.yaml +++ b/.ci/cloudbuild.yaml @@ -1,7 +1,6 @@ steps: # --- Build Extension Step --- - - id: build-extension - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' entrypoint: 'bash' args: - '-c' @@ -105,4 +104,3 @@ serviceAccount: 'projects/cloud-db-nl2sql/serviceAccounts/crema-eval@cloud-db-nl options: defaultLogsBucketBehavior: REGIONAL_USER_OWNED_BUCKET - substitutionOption: ALLOW_LOOSE diff --git a/.python-version b/.python-version index e4fba218..28d9a01b 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.12 +3.12.13 diff --git a/pyproject.toml b/pyproject.toml index 561fc22e..131bad5a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,14 +1,14 @@ [project] name = "google-cloud-db-context-enrichment" -version = "0.5.0" +version = "0.5.1" description = "A FastMCP server for generating natural language to SQL templates from database schemas." readme = "README.md" requires-python = ">=3.12" dependencies = [ - "fastmcp>=2.12.5", + "fastmcp==3.3.1", "google-genai>=1.46.0", "toolbox-core>=0.5.2", - "google-cloud-geminidataanalytics>=0.1.0", + "google-cloud-geminidataanalytics==0.11.0", ] [project.scripts] diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py index 574de865..793dc928 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py @@ -1,21 +1,37 @@ import asyncio +import collections import json import os -import collections -from typing import Any -from pathlib import Path - import warnings -warnings.filterwarnings("ignore") +from pathlib import Path +from typing import Any from .genai_client import GenAiClient -from .state_manager import CommittedPairCollection, PendingPairCollection, RejectedPairCollection -from .state_manager import DatasetGenConfig, DatasetGenDbProfile -from .state_manager import DatasetGenStates, DatasetGenEventLogger -from .state_manager import SqlCollection +from .generators import ( + QuestionGenerator, + QuestionRefiner, + QuestionReviewer, + SQLCandidateGenerator, +) +from .schema import ( + SchemaDDL, + SchemaProfile, + TableProfile, + TableSelector, + generate_ddls_from_json_schema, +) +from .state_manager import ( + CommittedPairCollection, + DatasetGenConfig, + DatasetGenDbProfile, + DatasetGenEventLogger, + DatasetGenStates, + PendingPairCollection, + RejectedPairCollection, + SqlCollection, +) -from .schema import generate_ddls_from_json_schema, SchemaDDL, TableProfile, SchemaProfile, TableSelector -from .generators import SQLCandidateGenerator, QuestionGenerator, QuestionReviewer, QuestionRefiner +warnings.filterwarnings("ignore") def _prepare_sql_for_execution_accuracy_check(sql: str) -> str: @@ -326,7 +342,7 @@ async def refine_nlq(self, task_working_dir: str) -> dict[str, Any]: if review_report.is_golden: pair["nlq"] = refined_nlq pair["tags"].append(f"nlq_review_report: {review_report.summary}") - pair["tags"].append(f"notes: refined_nlq") + pair["tags"].append("notes: refined_nlq") pair["tags"].append(f"timestamp: {states.timestamp()}") pair["tags"].append(f"elapsed_hr: {states.elapsed_time_hr()}") new_verified_pairs.append(pair) @@ -503,7 +519,7 @@ async def generate_database_profile_plan(self, task_working_dir: str) -> dict[st for column in columns: if column.is_primary_key or column.is_foreign_key: continue - query = f"SELECT DISTINCT {column.name} as val, '{column.name}' as name FROM {table.full_table_name} LIMIT 5"; + query = f"SELECT DISTINCT {column.name} as val, '{column.name}' as name FROM {table.full_table_name} LIMIT 5" qlist_by_cat[column.data_type].append(query) qlist.append(query) if len(qlist) > 10: @@ -521,7 +537,7 @@ async def generate_database_profile_plan(self, task_working_dir: str) -> dict[st row_sampling_queries.append({ 'table_name': table.full_table_name, 'sampling_query': f"SELECT * FROM {table.full_table_name} LIMIT 5" - }); + }) column_sampling_query_path = os.path.join(output_dir, 'column_sampling_queries.json') row_sampling_query_path = os.path.join(output_dir, 'row_sampling_queries.json') diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py index 3f520b3f..771cb459 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py @@ -1,12 +1,11 @@ """GenAI client for NL2SQL Eval Dataset Generator.""" -import os import asyncio -from google import genai -from google.genai import types -from google.genai.errors import APIError import pydantic +from google.genai import types + +from google import genai BaseModel = pydantic.BaseModel @@ -45,6 +44,6 @@ async def generate_content_async( ), ), timeout=self.timeout_secs) return result - except Exception as e: + except Exception: return None diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py index 4a17d125..1d2bac1a 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py @@ -1,6 +1,7 @@ -from .genai_client import GenAiClient from google.genai import types +from .genai_client import GenAiClient + class GeneratorBase: """Base class for generators in NL2SQL Eval Dataset Generation.""" diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py index ad8f1677..131c091c 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py @@ -2,6 +2,7 @@ import textwrap from pydantic import BaseModel, Field + from .generator_base import GeneratorBase diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py index 622bf54d..c6774cb0 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py @@ -1,6 +1,6 @@ +import json import textwrap from typing import Any -import json from pydantic import BaseModel, Field diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py index 0a592069..24797cc9 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py @@ -1,10 +1,10 @@ +import asyncio +import datetime import json import os -import datetime -import asyncio import shutil -from typing import Any from pathlib import Path +from typing import Any class DatasetGenEventLogger: diff --git a/src/google/cloud/db_context_enrichment/main.py b/src/google/cloud/db_context_enrichment/main.py index ea6bac12..70530fcd 100644 --- a/src/google/cloud/db_context_enrichment/main.py +++ b/src/google/cloud/db_context_enrichment/main.py @@ -9,6 +9,9 @@ from google.cloud.db_context_enrichment.bootstrap import bootstrap_generator from google.cloud.db_context_enrichment.common import context_mutator from google.cloud.db_context_enrichment.dataset import dataset_generator +from google.cloud.db_context_enrichment.dataset.eval_seed_gen.dataset_generator import ( + SeedEvalDatasetGenerator, +) from google.cloud.db_context_enrichment.evaluate import ( evaluate_generator, result_reader, @@ -18,8 +21,6 @@ from google.cloud.db_context_enrichment.template import template_generator from google.cloud.db_context_enrichment.value_search import generator as vi_generator from google.cloud.db_context_enrichment.value_search import match_templates -from google.cloud.db_context_enrichment.dataset.eval_seed_gen.dataset_generator import SeedEvalDatasetGenerator - mcp = FastMCP("Context Engineering Agent MCP") From f7ace072bd7e734010aab57af649beea627ffeda Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 20:54:35 -0700 Subject: [PATCH 10/23] more bug fix on ruff check and pytest --- .../eval_seed_gen/dataset_generator.py | 412 ++++++++------ .../dataset/eval_seed_gen/genai_client.py | 75 +-- .../dataset/eval_seed_gen/generator_base.py | 7 +- .../dataset/eval_seed_gen/generators.py | 433 +++++++------- .../dataset/eval_seed_gen/schema.py | 534 +++++++++--------- .../dataset/eval_seed_gen/state_manager.py | 199 ++++--- .../cloud/db_context_enrichment/main_test.py | 6 +- uv.lock | 446 +++++++-------- 8 files changed, 1082 insertions(+), 1030 deletions(-) diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py index 793dc928..6ba709aa 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py @@ -39,17 +39,15 @@ def _prepare_sql_for_execution_accuracy_check(sql: str) -> str: sql = sql.strip()[:-1] sql = sql.replace("\n", " ") sql = sql.strip() - sql = f'SELECT 1 FROM ({sql}) AS validate_target LIMIT 1;' + sql = f"SELECT 1 FROM ({sql}) AS validate_target LIMIT 1;" return sql class SeedEvalDatasetGenerator: - def __init__(self): self._genai_client = GenAiClient(timeout_secs=45) - async def generate_sql( - self, task_working_dir: str) -> list[dict]: + async def generate_sql(self, task_working_dir: str) -> list[dict]: """Reads database schema profile and use it to generate SQL candidates. Args: @@ -73,7 +71,7 @@ async def generate_sql( "dialect": dialect, "complexity": complexity, "database_name": database_name, - "parallelism": config.parallelism + "parallelism": config.parallelism, } elogger.log_event("generate_sql_triggered", payload, {}) @@ -85,7 +83,7 @@ async def generate_sql( schema_profile=schema_profile.get_profile(), dialect=dialect, complexity=complexity, - count=max_parallelism + count=max_parallelism, ) candidate_sqls = [ { @@ -94,8 +92,8 @@ async def generate_sql( "database": database_name, "tags": [ f"gen_id: {sql_entry.gen_id}", - f"complexity: {complexity}" - ] + f"complexity: {complexity}", + ], } for index, sql_entry in enumerate(candidate_sqls) ] @@ -104,26 +102,34 @@ async def generate_sql( candidate_sqls = sql_collection.get_sqls(None) response = { - "sqls": [{ - "qid": sql_entry["qid"], - "sql": _prepare_sql_for_execution_accuracy_check(sql_entry["golden_sql"]), - } - for sql_entry in candidate_sqls] + "sqls": [ + { + "qid": sql_entry["qid"], + "sql": _prepare_sql_for_execution_accuracy_check( + sql_entry["golden_sql"] + ), + } + for sql_entry in candidate_sqls + ] } - elogger.log_event("generate_sql", payload, { - "golden_sql_sample": candidate_sqls[0] if candidate_sqls else None, - "golden_sql_count": len(candidate_sqls), - "golden_sqls_path": sql_collection.path, - }) + elogger.log_event( + "generate_sql", + payload, + { + "golden_sql_sample": candidate_sqls[0] if candidate_sqls else None, + "golden_sql_count": len(candidate_sqls), + "golden_sqls_path": sql_collection.path, + }, + ) return response except Exception as e: - elogger.log_event("generate_sql_error", payload, { - "error": str(e) - }) + elogger.log_event("generate_sql_error", payload, {"error": str(e)}) return {"sqls": []} - async def generate_nlq(self, golden_sql_qids: str | list[dict], task_working_dir: str) -> dict[str, Any]: + async def generate_nlq( + self, golden_sql_qids: str | list[dict], task_working_dir: str + ) -> dict[str, Any]: """Save the generated SQL/Question pairs to the provided generated pairs path. Args: @@ -136,12 +142,12 @@ async def generate_nlq(self, golden_sql_qids: str | list[dict], task_working_dir if isinstance(golden_sql_qids, str): golden_sql_qids = json.loads(golden_sql_qids) - + if not isinstance(golden_sql_qids, list): raise ValueError(f"Unknown golden_sql_qids type: {type(golden_sql_qids)}") - + output_file_path = DatasetGenConfig(task_working_dir).output_file_path - + committed_pair_collection = CommittedPairCollection(output_file_path) pending_pair_collection = PendingPairCollection(task_working_dir) elogger = DatasetGenEventLogger(task_working_dir) @@ -149,10 +155,18 @@ async def generate_nlq(self, golden_sql_qids: str | list[dict], task_working_dir seen = committed_pair_collection.get_sql_set() sql_collection = SqlCollection(task_working_dir) - - golden_sqls = sql_collection.get_sqls(lambda entry: entry["qid"] in golden_sql_qids) - rejected_sqls = sql_collection.get_sqls(lambda entry: entry["qid"] not in golden_sql_qids) - golden_sqls = [sql_entry for sql_entry in golden_sqls if sql_entry.get("golden_sql") not in seen] + + golden_sqls = sql_collection.get_sqls( + lambda entry: entry["qid"] in golden_sql_qids + ) + rejected_sqls = sql_collection.get_sqls( + lambda entry: entry["qid"] not in golden_sql_qids + ) + golden_sqls = [ + sql_entry + for sql_entry in golden_sqls + if sql_entry.get("golden_sql") not in seen + ] payload = { "rejected_sql_sample": rejected_sqls[0] if rejected_sqls else None, @@ -162,34 +176,45 @@ async def generate_nlq(self, golden_sql_qids: str | list[dict], task_working_dir "schema_profile_path": schema_profile.path, "generated_pairs_path": pending_pair_collection.path, } - elogger.log_event("generate_nlq_triggered", payload, { - "generated_nlq_path": pending_pair_collection.path, - "golden_sqls_path": sql_collection.path, - }) - + elogger.log_event( + "generate_nlq_triggered", + payload, + { + "generated_nlq_path": pending_pair_collection.path, + "golden_sqls_path": sql_collection.path, + }, + ) + pairs = [] if not golden_sqls: - status = { - "generated_nlq_count": 0 - } + status = {"generated_nlq_count": 0} else: question_generator = QuestionGenerator(genai_client=self._genai_client) first_sql = golden_sqls[0] - assert "golden_sql" in first_sql, "golden_sqls must be a list of dict with 'golden_sql' key or a JSON string of such list." - assert "database" in first_sql, "golden_sqls must be a list of dict with 'database' key or a JSON string of such list." + assert "golden_sql" in first_sql, ( + "golden_sqls must be a list of dict with 'golden_sql' key or a JSON string of such list." + ) + assert "database" in first_sql, ( + "golden_sqls must be a list of dict with 'database' key or a JSON string of such list." + ) mini_schema_profile = schema_profile.get_mini_profile() tasks = [ question_generator.translate_sql_nl( - schema_profile=mini_schema_profile, - sql=sql_entry["golden_sql"]) - for sql_entry in golden_sqls] + schema_profile=mini_schema_profile, sql=sql_entry["golden_sql"] + ) + for sql_entry in golden_sqls + ] results = await asyncio.gather(*tasks) - elogger.log_event("generate_nlq_genai_done", payload, { - "generated_nlq_path": pending_pair_collection.path, - "golden_sqls_path": sql_collection.path, - }) + elogger.log_event( + "generate_nlq_genai_done", + payload, + { + "generated_nlq_path": pending_pair_collection.path, + "golden_sqls_path": sql_collection.path, + }, + ) for nlq_entry, sql_entry in zip(results, golden_sqls): if not nlq_entry: continue @@ -197,31 +222,33 @@ async def generate_nlq(self, golden_sql_qids: str | list[dict], task_working_dir tags = sql_entry.get("tags", []) current_question = nlq_entry.text pair = { - 'database': database_name, - 'nlq': current_question, - 'golden_sql': sql_entry["golden_sql"], - "tags": tags + [ + "database": database_name, + "nlq": current_question, + "golden_sql": sql_entry["golden_sql"], + "tags": tags + + [ f"sql2nl_gen_id: {nlq_entry.gen_id}", ], - "status": "nlq_review_pending" + "status": "nlq_review_pending", } pairs.append(pair) - + pending_pair_collection.add_pending_pairs(pairs) # clean up the interim golden SQLs since they are no longer needed after generating the NLQs sql_collection.delete() - status = { - "generated_nlq_count": len(pairs) - } - elogger.log_event("generate_nlq", payload, { - "generated_nlq_sample": pairs[0]["nlq"] if pairs else None, - "generated_nlq_count": status["generated_nlq_count"], - }) + status = {"generated_nlq_count": len(pairs)} + elogger.log_event( + "generate_nlq", + payload, + { + "generated_nlq_sample": pairs[0]["nlq"] if pairs else None, + "generated_nlq_count": status["generated_nlq_count"], + }, + ) return status - async def review_nlq(self, task_working_dir: str) -> dict[str, Any]: """Review the generated NLQs and update the tags in the generated pairs file. @@ -236,29 +263,33 @@ async def review_nlq(self, task_working_dir: str) -> dict[str, Any]: pending_pair_collection = PendingPairCollection(task_working_dir) rejected_pair_collection = RejectedPairCollection(task_working_dir) committed_pair_collection = CommittedPairCollection(output_file_path) - + states = DatasetGenStates(task_working_dir) elogger = DatasetGenEventLogger(task_working_dir) states.increment_iterations() - elogger.log_event("review_nlq_triggered", { - "generated_pairs_path": pending_pair_collection.path, - }, { - "verified_nlq_path": committed_pair_collection.path, - "rejected_nlq_path": rejected_pair_collection.path - }) - - pending_pairs = pending_pair_collection.get_pairs(lambda pair: pair.get("status") == "nlq_review_pending") + elogger.log_event( + "review_nlq_triggered", + { + "generated_pairs_path": pending_pair_collection.path, + }, + { + "verified_nlq_path": committed_pair_collection.path, + "rejected_nlq_path": rejected_pair_collection.path, + }, + ) + + pending_pairs = pending_pair_collection.get_pairs( + lambda pair: pair.get("status") == "nlq_review_pending" + ) verified_pairs = [] rejected_pairs = [] if pending_pairs: reviewer = QuestionReviewer(genai_client=self._genai_client) tasks = [ - reviewer.review_question( - sql=pair["golden_sql"], - question=pair["nlq"]) + reviewer.review_question(sql=pair["golden_sql"], question=pair["nlq"]) for pair in pending_pairs ] review_reports = await asyncio.gather(*tasks) @@ -277,30 +308,36 @@ async def review_nlq(self, task_working_dir: str) -> dict[str, Any]: pair["status"] = "nlq_review_rejected" pair["nlq_review"] = "" rejected_pairs.append(pair) - committed_pair_collection.add_verified_pairs(verified_pairs) rejected_pair_collection.add_rejected_pairs(rejected_pairs) - - states.update_states_on_pairs_committed(committed_pair_collection, rejected_pair_collection) - + + states.update_states_on_pairs_committed( + committed_pair_collection, rejected_pair_collection + ) + # clean up the pending pairs file since all pending pairs have been processed (either verified or rejected) pending_pair_collection.delete() self._sync_resources(task_working_dir=task_working_dir) - + status = states.to_dict() - elogger.log_event("review_nlq", { - "verified_pair_sample": verified_pairs[0]["nlq"] if verified_pairs else None, - "verified_pair_count": len(verified_pairs), - "desired_output_pairs": states.desired_output_pairs, - "verified_pairs_path": committed_pair_collection.path, - "rejected_pairs_path": rejected_pair_collection.path, - }, status) + elogger.log_event( + "review_nlq", + { + "verified_pair_sample": verified_pairs[0]["nlq"] + if verified_pairs + else None, + "verified_pair_count": len(verified_pairs), + "desired_output_pairs": states.desired_output_pairs, + "verified_pairs_path": committed_pair_collection.path, + "rejected_pairs_path": rejected_pair_collection.path, + }, + status, + ) return status - async def refine_nlq(self, task_working_dir: str) -> dict[str, Any]: """Refine the generated Question @@ -316,13 +353,17 @@ async def refine_nlq(self, task_working_dir: str) -> dict[str, Any]: elogger = DatasetGenEventLogger(task_working_dir) states = DatasetGenStates(task_working_dir) - elogger.log_event("refine_nlq_triggered", { + elogger.log_event( + "refine_nlq_triggered", + { "rejected_pairs_sample": rejected_pair_collection.first(), "rejected_pairs_count": len(rejected_pair_collection), "verified_pairs_path": committed_pair_collection.path, "rejected_pairs_path": rejected_pair_collection.path, - }, states.to_dict()) - + }, + states.to_dict(), + ) + if not rejected_pair_collection.is_empty(): pending_pairs = rejected_pair_collection.pop_pairs(3) new_rejected_pairs = [] @@ -332,46 +373,61 @@ async def refine_nlq(self, task_working_dir: str) -> dict[str, Any]: tasks = [ refiner.refine_question( sql=pair["golden_sql"], - question=pair["nlq"], - question_report=pair["nlq_review"]) - for pair in pending_pairs] + question=pair["nlq"], + question_report=pair["nlq_review"], + ) + for pair in pending_pairs + ] results = await asyncio.gather(*tasks) - + for (review_report, refined_nlq), pair in zip(results, pending_pairs): if review_report: if review_report.is_golden: pair["nlq"] = refined_nlq - pair["tags"].append(f"nlq_review_report: {review_report.summary}") + pair["tags"].append( + f"nlq_review_report: {review_report.summary}" + ) pair["tags"].append("notes: refined_nlq") pair["tags"].append(f"timestamp: {states.timestamp()}") pair["tags"].append(f"elapsed_hr: {states.elapsed_time_hr()}") new_verified_pairs.append(pair) else: pair["status"] = "nlq_review_rejected" - pair["nlq_review"] = review_report.model_dump_json() if review_report else pair.get("nlq_review", "") + pair["nlq_review"] = ( + review_report.model_dump_json() + if review_report + else pair.get("nlq_review", "") + ) new_rejected_pairs.append(pair) else: pair["status"] = "nlq_review_rejected" pair["nlq_review"] = pair.get("nlq_review", "") new_rejected_pairs.append(pair) - + committed_pair_collection.add_verified_pairs(new_verified_pairs) rejected_pair_collection.add_rejected_pairs(new_rejected_pairs) - states.update_states_on_pairs_committed(committed_pair_collection, rejected_pair_collection) + states.update_states_on_pairs_committed( + committed_pair_collection, rejected_pair_collection + ) self._sync_resources(task_working_dir=task_working_dir) - - elogger.log_event("refine_nlq", { - "verified_pair_sample": new_verified_pairs[0]["nlq"] if new_verified_pairs else None, - "verified_pair_count": len(new_verified_pairs), - "desired_output_pairs": states.desired_output_pairs, - "committed_pairs_path": committed_pair_collection.path, - "rejected_pairs_path": rejected_pair_collection.path - }, states.to_dict()) - return states.to_dict() + elogger.log_event( + "refine_nlq", + { + "verified_pair_sample": new_verified_pairs[0]["nlq"] + if new_verified_pairs + else None, + "verified_pair_count": len(new_verified_pairs), + "desired_output_pairs": states.desired_output_pairs, + "committed_pairs_path": committed_pair_collection.path, + "rejected_pairs_path": rejected_pair_collection.path, + }, + states.to_dict(), + ) + return states.to_dict() async def generate_database_profile(self, task_working_dir: str) -> dict[str, Any]: """Reads the contents from the input directory and assemble the database schema profile. @@ -383,17 +439,23 @@ async def generate_database_profile(self, task_working_dir: str) -> dict[str, An Returns: None """ - + input_dir = os.path.join(task_working_dir, "_dbp") output_path = os.path.join(task_working_dir, "db_profile.txt") config = DatasetGenConfig(task_working_dir) constraints = config.constraints - list_schema_result_path = os.path.join(input_dir, 'tables.json') - assert os.path.exists(list_schema_result_path), f"File not found: {list_schema_result_path}" - list_schema_result_text = Path(list_schema_result_path).read_text(encoding='utf-8') - schema_ddls: list[SchemaDDL] = generate_ddls_from_json_schema(list_schema_result_text) + list_schema_result_path = os.path.join(input_dir, "tables.json") + assert os.path.exists(list_schema_result_path), ( + f"File not found: {list_schema_result_path}" + ) + list_schema_result_text = Path(list_schema_result_path).read_text( + encoding="utf-8" + ) + schema_ddls: list[SchemaDDL] = generate_ddls_from_json_schema( + list_schema_result_text + ) output_dir: str = Path(output_path).parent.as_posix() @@ -417,18 +479,26 @@ async def generate_database_profile(self, task_working_dir: str) -> dict[str, An table_names.append(table_name) sample_values_per_column = collections.defaultdict(list) - - column_samples_path = os.path.join(input_dir, "column_samples", f"{full_table_name}.json") + + column_samples_path = os.path.join( + input_dir, "column_samples", f"{full_table_name}.json" + ) if os.path.exists(column_samples_path): - column_samples = json.loads(Path(column_samples_path).read_text(encoding='utf-8')) + column_samples = json.loads( + Path(column_samples_path).read_text(encoding="utf-8") + ) for row in column_samples: - column_name, column_value = row['name'], row['val'] + column_name, column_value = row["name"], row["val"] sample_values_per_column[column_name].append(column_value) - sample_rows_path = os.path.join(input_dir, "row_samples", f"{full_table_name}.json") + sample_rows_path = os.path.join( + input_dir, "row_samples", f"{full_table_name}.json" + ) sample_rows = [] if os.path.exists(sample_rows_path): - sample_rows = json.loads(Path(sample_rows_path).read_text(encoding='utf-8')) + sample_rows = json.loads( + Path(sample_rows_path).read_text(encoding="utf-8") + ) table_profile = TableProfile( table_name=table_name, @@ -444,20 +514,15 @@ async def generate_database_profile(self, task_working_dir: str) -> dict[str, An general_context=constraints, ).to_string() Path(output_path).parent.mkdir(parents=True, exist_ok=True) - Path(output_path).write_text(final_context, encoding='utf-8') - response = { - "schema_profile_path": output_path - } + Path(output_path).write_text(final_context, encoding="utf-8") + response = {"schema_profile_path": output_path} elogger.log_event("generate_database_profile", payload, response) return response except RuntimeError as e: - elogger.log_event("generate_database_profile", payload, { - "error": str(e) - }) + elogger.log_event("generate_database_profile", payload, {"error": str(e)}) return f"Error profiling database: {e}" - def _sync_resources(self, task_working_dir: str) -> dict[str, Any]: """Reads the content from the generated pairs path and merge them to the output path. @@ -469,10 +534,10 @@ def _sync_resources(self, task_working_dir: str) -> dict[str, Any]: A dictionary object which holds the results """ states = DatasetGenStates(task_working_dir) - response = { "is_done": states.is_done(), "status": states.to_dict() } + response = {"is_done": states.is_done(), "status": states.to_dict()} # remove check point files - if states.is_done(): # clean up + if states.is_done(): # clean up states.delete() RejectedPairCollection(task_working_dir).delete() PendingPairCollection(task_working_dir).delete() @@ -480,12 +545,13 @@ def _sync_resources(self, task_working_dir: str) -> dict[str, Any]: DatasetGenDbProfile(task_working_dir).delete() elogger = DatasetGenEventLogger(task_working_dir) - elogger.log_event("sync_resources", { }, response) + elogger.log_event("sync_resources", {}, response) return response - - async def generate_database_profile_plan(self, task_working_dir: str) -> dict[str, Any]: + async def generate_database_profile_plan( + self, task_working_dir: str + ) -> dict[str, Any]: """Read the full table schema and select the desired tables. Args: @@ -499,15 +565,30 @@ async def generate_database_profile_plan(self, task_working_dir: str) -> dict[st full_table_schema_path = os.path.join(task_working_dir, "_dbp/tables.json") output_dir = os.path.join(task_working_dir, "_dbp") - table_schema: list[SchemaDDL] = generate_ddls_from_json_schema(Path(full_table_schema_path).read_text(encoding='utf-8')) + table_schema: list[SchemaDDL] = generate_ddls_from_json_schema( + Path(full_table_schema_path).read_text(encoding="utf-8") + ) table_selector = TableSelector(genai_client=self._genai_client) - selected_tables: list[str] = await table_selector.select_tables(tables=table_schema, top_k=8) - - schema_data_list = json.loads(Path(full_table_schema_path).read_text(encoding='utf-8')) - schema_data_list = [item for item in schema_data_list if item['schema_name'] + '.' + item['object_details']['object_name'] in selected_tables] - Path(full_table_schema_path).write_text(json.dumps(schema_data_list, indent=2), encoding='utf-8') - - table_schema = [item for item in table_schema if item.full_table_name in selected_tables] + selected_tables: list[str] = await table_selector.select_tables( + tables=table_schema, top_k=8 + ) + + schema_data_list = json.loads( + Path(full_table_schema_path).read_text(encoding="utf-8") + ) + schema_data_list = [ + item + for item in schema_data_list + if item["schema_name"] + "." + item["object_details"]["object_name"] + in selected_tables + ] + Path(full_table_schema_path).write_text( + json.dumps(schema_data_list, indent=2), encoding="utf-8" + ) + + table_schema = [ + item for item in table_schema if item.full_table_name in selected_tables + ] column_sampling_queries = [] row_sampling_queries = [] @@ -530,42 +611,49 @@ async def generate_database_profile_plan(self, task_working_dir: str) -> dict[st qlist.append(q_by_cat) union_query = " UNION ALL ".join([f"({q})" for q in qlist]) if union_query: - column_sampling_queries.append({ - 'table_name': table.full_table_name, - 'sampling_query': union_query - }) - row_sampling_queries.append({ - 'table_name': table.full_table_name, - 'sampling_query': f"SELECT * FROM {table.full_table_name} LIMIT 5" - }) - - column_sampling_query_path = os.path.join(output_dir, 'column_sampling_queries.json') - row_sampling_query_path = os.path.join(output_dir, 'row_sampling_queries.json') + column_sampling_queries.append( + {"table_name": table.full_table_name, "sampling_query": union_query} + ) + row_sampling_queries.append( + { + "table_name": table.full_table_name, + "sampling_query": f"SELECT * FROM {table.full_table_name} LIMIT 5", + } + ) + + column_sampling_query_path = os.path.join( + output_dir, "column_sampling_queries.json" + ) + row_sampling_query_path = os.path.join(output_dir, "row_sampling_queries.json") Path(output_dir).mkdir(parents=True, exist_ok=True) - Path(column_sampling_query_path).write_text(json.dumps(column_sampling_queries, indent=2), encoding='utf-8') - Path(row_sampling_query_path).write_text(json.dumps(row_sampling_queries, indent=2), encoding='utf-8') + Path(column_sampling_query_path).write_text( + json.dumps(column_sampling_queries, indent=2), encoding="utf-8" + ) + Path(row_sampling_query_path).write_text( + json.dumps(row_sampling_queries, indent=2), encoding="utf-8" + ) return { - 'plan': f'Profile the database for the following tables: {selected_tables}', - 'column_sampling_query_path': column_sampling_query_path, - 'row_sampling_query_path': row_sampling_query_path, + "plan": f"Profile the database for the following tables: {selected_tables}", + "column_sampling_query_path": column_sampling_query_path, + "row_sampling_query_path": row_sampling_query_path, } - + async def run_task(self, payload: dict[str, Any]) -> dict[str, Any]: - if 'task' not in payload: + if "task" not in payload: raise KeyError(f"'task' must be in the payload {payload}") - task = payload.pop('task') - if task == 'generate_sql': + task = payload.pop("task") + if task == "generate_sql": return await self.generate_sql(**payload) - elif task == 'generate_nlq': + elif task == "generate_nlq": return await self.generate_nlq(**payload) - elif task == 'review_nlq': + elif task == "review_nlq": return await self.review_nlq(**payload) - elif task == 'refine_nlq': + elif task == "refine_nlq": return await self.refine_nlq(**payload) - elif task == 'generate_database_profile': + elif task == "generate_database_profile": return await self.generate_database_profile(**payload) - elif task == 'generate_database_profile_plan': + elif task == "generate_database_profile_plan": return await self.generate_database_profile_plan(**payload) else: raise ValueError(f"Unknown task: '{task}' from payload {payload}") diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py index 771cb459..8623b1e4 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py @@ -9,41 +9,44 @@ BaseModel = pydantic.BaseModel + class GenAiClient: - """GenAI client for NL2SQL Eval Dataset Generator.""" - - def __init__(self, timeout_secs: int = 60): - self.timeout_secs = timeout_secs - self._client = genai.Client( - http_options=types.HttpOptions( - retry_options=types.HttpRetryOptions( - attempts=1 # 1 attempt as we need genai to fail fast when throttling happens - ), - timeout=timeout_secs * 1000, + """GenAI client for NL2SQL Eval Dataset Generator.""" + + def __init__(self, timeout_secs: int = 60): + self.timeout_secs = timeout_secs + self._client = genai.Client( + http_options=types.HttpOptions( + retry_options=types.HttpRetryOptions( + attempts=1 # 1 attempt as we need genai to fail fast when throttling happens + ), + timeout=timeout_secs * 1000, + ) ) - ) - - async def generate_content_async( - self, - *, - prompt: str, - model: str, - response_schema: types.SchemaUnion | None = None, - response_mime_type: str = "application/json", - temperature: float = 0.3, - ) -> types.GenerateContentResponse | None: - """Generates content using the GenAI model asynchronously.""" - try: - result = await asyncio.wait_for(self._client.aio.models.generate_content( - model=model, - contents=prompt, - config=types.GenerateContentConfig( - response_mime_type=response_mime_type, - response_schema=response_schema, - temperature=temperature, - ), - ), timeout=self.timeout_secs) - return result - except Exception: - return None - + + async def generate_content_async( + self, + *, + prompt: str, + model: str, + response_schema: types.SchemaUnion | None = None, + response_mime_type: str = "application/json", + temperature: float = 0.3, + ) -> types.GenerateContentResponse | None: + """Generates content using the GenAI model asynchronously.""" + try: + result = await asyncio.wait_for( + self._client.aio.models.generate_content( + model=model, + contents=prompt, + config=types.GenerateContentConfig( + response_mime_type=response_mime_type, + response_schema=response_schema, + temperature=temperature, + ), + ), + timeout=self.timeout_secs, + ) + return result + except Exception: + return None diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py index 1d2bac1a..8a068396 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py @@ -5,9 +5,14 @@ class GeneratorBase: """Base class for generators in NL2SQL Eval Dataset Generation.""" + pass - def __init__(self, genai_client: GenAiClient, generator_model_id: str = "gemini-3-flash-preview"): + def __init__( + self, + genai_client: GenAiClient, + generator_model_id: str = "gemini-3-flash-preview", + ): super().__init__() self._genai_client = genai_client self._generator_model_id = generator_model_id diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py index 131c091c..b0f5c6b9 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py @@ -7,101 +7,96 @@ class GeneratedQuery(BaseModel): - """A generated SQL query or question.""" + """A generated SQL query or question.""" - text: str - gen_id: str + text: str + gen_id: str class GeneratedSQLPool(BaseModel): - """A list of generated SQL queries.""" + """A list of generated SQL queries.""" - sqls: list[str] = Field( - description="A list of distinct SQL queries based on the schema." - ) + sqls: list[str] = Field( + description="A list of distinct SQL queries based on the schema." + ) class TranslatedQuestion(BaseModel): - question: str = Field( - description=( - "A natural, realistic, unambigious business question that the SQL query answers." - ) - ) + question: str = Field( + description=( + "A natural, realistic, unambigious business question that the SQL query answers." + ) + ) + class QuestionReviewReport(BaseModel): - """The Reviewer Agent's assessment of a NL2SQL pair.""" - - is_golden: bool = Field( - description=( - "True if the question is perfectly aligned with the original SQL" - " intent." - ) - ) - red_flags: list[str] = Field( - description="List of issues: ambiguity, logical errors." - ) - ambiguity_score: float = Field( - description="0.0 (Clear) to 1.0 (Highly Ambiguous)." - ) - intent_mismatch: bool = Field( - description=( - "True if the NL question doesn't match the original SQL intent." - ) - ) - improvement_suggestion: str = Field( - description="Specific advice for the Repair Agent." - ) - summary: str = Field( - description="Concise and short summary of the assessment." - ) + """The Reviewer Agent's assessment of a NL2SQL pair.""" + + is_golden: bool = Field( + description=( + "True if the question is perfectly aligned with the original SQL intent." + ) + ) + red_flags: list[str] = Field( + description="List of issues: ambiguity, logical errors." + ) + ambiguity_score: float = Field(description="0.0 (Clear) to 1.0 (Highly Ambiguous).") + intent_mismatch: bool = Field( + description=("True if the NL question doesn't match the original SQL intent.") + ) + improvement_suggestion: str = Field( + description="Specific advice for the Repair Agent." + ) + summary: str = Field(description="Concise and short summary of the assessment.") class SQLCandidateGenerator(GeneratorBase): - """SQL generator for NL2SQL Eval Dataset Generation.""" - - async def generate_candidates( - self, schema_profile: str, dialect: str, complexity: str, count: int - ) -> list[GeneratedQuery]: - """Architect Agent: Generates candidate SQL queries (SQL2NL strategy).""" - strategy_id = random.randint(1, 4) - if strategy_id == 1: - sqls = await self._generate_candidates_1( - schema_profile=schema_profile, - dialect=dialect, - complexity=complexity, - count=count) - elif strategy_id == 2: - sqls = await self._generate_candidates_2( - schema_profile=schema_profile, - dialect=dialect, - complexity=complexity, - count=count - ) - elif strategy_id == 3: - sqls = await self.generate_candidates_3( - schema_profile=schema_profile, - dialect=dialect, - complexity=complexity, - count=count - ) - else: - sqls = await self.generate_candidates_4( - schema_profile=schema_profile, - dialect=dialect, - complexity=complexity, - count=count - ) - - sqls = [sql for sql in sqls if sql and 20 <= len(sql) <= 300] - - sqls = list(set(sqls)) - - return [GeneratedQuery(text=sql, gen_id=str(strategy_id)) for sql in sqls] - - async def _generate_candidates_1( - self, *, schema_profile: str, dialect: str, complexity: str, count: int - ) -> list[str]: - prompt = textwrap.dedent(f""" + """SQL generator for NL2SQL Eval Dataset Generation.""" + + async def generate_candidates( + self, schema_profile: str, dialect: str, complexity: str, count: int + ) -> list[GeneratedQuery]: + """Architect Agent: Generates candidate SQL queries (SQL2NL strategy).""" + strategy_id = random.randint(1, 4) + if strategy_id == 1: + sqls = await self._generate_candidates_1( + schema_profile=schema_profile, + dialect=dialect, + complexity=complexity, + count=count, + ) + elif strategy_id == 2: + sqls = await self._generate_candidates_2( + schema_profile=schema_profile, + dialect=dialect, + complexity=complexity, + count=count, + ) + elif strategy_id == 3: + sqls = await self.generate_candidates_3( + schema_profile=schema_profile, + dialect=dialect, + complexity=complexity, + count=count, + ) + else: + sqls = await self.generate_candidates_4( + schema_profile=schema_profile, + dialect=dialect, + complexity=complexity, + count=count, + ) + + sqls = [sql for sql in sqls if sql and 20 <= len(sql) <= 300] + + sqls = list(set(sqls)) + + return [GeneratedQuery(text=sql, gen_id=str(strategy_id)) for sql in sqls] + + async def _generate_candidates_1( + self, *, schema_profile: str, dialect: str, complexity: str, count: int + ) -> list[str]: + prompt = textwrap.dedent(f""" You are an expert Database Architect. Given the following {dialect} database schema and sample data, generate {count} {complexity}, diverse, highly realistic analytical SQL queries. @@ -123,23 +118,18 @@ async def _generate_candidates_1( {schema_profile} """) - candidates = await self.generate_content( - prompt=prompt, - response_schema=GeneratedSQLPool, - temperature=0.7, - ) - return candidates.sqls if candidates else [] - - async def _generate_candidates_2( - self, - *, - schema_profile: str, - dialect: str, - complexity: str, - count: int - ) -> list[str]: - """Architect Agent: Generates candidate SQL queries (SQL2NL strategy).""" - sql_gen_prompt = textwrap.dedent(f""" + candidates = await self.generate_content( + prompt=prompt, + response_schema=GeneratedSQLPool, + temperature=0.7, + ) + return candidates.sqls if candidates else [] + + async def _generate_candidates_2( + self, *, schema_profile: str, dialect: str, complexity: str, count: int + ) -> list[str]: + """Architect Agent: Generates candidate SQL queries (SQL2NL strategy).""" + sql_gen_prompt = textwrap.dedent(f""" Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. Inject real-world messiness: Use CTEs, window functions, or regex/string extraction if appropriate. Ensure each query returns actual data from the samples provided. @@ -153,23 +143,18 @@ async def _generate_candidates_2( Database Profile: {schema_profile} """) - candidates = await self.generate_content( - prompt=sql_gen_prompt, - response_schema=GeneratedSQLPool, - temperature=0.7, - ) - return candidates.sqls if candidates else [] - - async def generate_candidates_3( - self, - *, - schema_profile: str, - dialect: str, - complexity: str, - count: int - ) -> list[str]: - """Generates a candidate NL2SQL pair using the RepairedPair schema.""" - prompt = textwrap.dedent(f""" + candidates = await self.generate_content( + prompt=sql_gen_prompt, + response_schema=GeneratedSQLPool, + temperature=0.7, + ) + return candidates.sqls if candidates else [] + + async def generate_candidates_3( + self, *, schema_profile: str, dialect: str, complexity: str, count: int + ) -> list[str]: + """Generates a candidate NL2SQL pair using the RepairedPair schema.""" + prompt = textwrap.dedent(f""" Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. Think step-by-step: - First generate {count} distinct, realistic business intents that users of this database might have. @@ -186,24 +171,18 @@ async def generate_candidates_3( Database Profile: {schema_profile} """) - candidates = await self.generate_content( - prompt=prompt, - response_schema=GeneratedSQLPool, - temperature=0.7, - ) - return candidates.sqls if candidates else [] - - async def generate_candidates_4( - self, - *, - schema_profile: str, - dialect: str, - complexity: str, - count: int - ) -> list[str]: - - - dev_prompt = textwrap.dedent(f""" + candidates = await self.generate_content( + prompt=prompt, + response_schema=GeneratedSQLPool, + temperature=0.7, + ) + return candidates.sqls if candidates else [] + + async def generate_candidates_4( + self, *, schema_profile: str, dialect: str, complexity: str, count: int + ) -> list[str]: + + dev_prompt = textwrap.dedent(f""" Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. Think step-by-step: - First, plan {count} {complexity} {dialect} SQL pattern for this database describing the tables, joins and the analytical goal. Focus on messy real-world scenarios. @@ -220,43 +199,28 @@ async def generate_candidates_4( Database Profile: {schema_profile} """) - candidates = await self.generate_content( - prompt=dev_prompt, response_schema=GeneratedSQLPool, temperature=0.7 - ) - return candidates.sqls if candidates else [] - + candidates = await self.generate_content( + prompt=dev_prompt, response_schema=GeneratedSQLPool, temperature=0.7 + ) + return candidates.sqls if candidates else [] + class QuestionGenerator(GeneratorBase): - """SQL2NL generator for NL2SQL Eval Dataset Generation.""" - - async def translate_sql_nl( - self, - *, - schema_profile: str, - sql: str - ) -> GeneratedQuery: - strategy_id = random.randint(1, 2) - if strategy_id == 1: - nlq = await self.translate_sql_nl_1( - schema_profile=schema_profile, - sql=sql - ) - else: - nlq = await self.translate_sql_nl_2( - schema_profile=schema_profile, - sql=sql - ) - return GeneratedQuery(text=nlq, gen_id=str(strategy_id)) - - - async def translate_sql_nl_1( - self, - *, - schema_profile: str, - sql: str - ) -> str | None: - """Translator Agent: Translates valid SQL into a business question.""" - prompt = textwrap.dedent(f""" + """SQL2NL generator for NL2SQL Eval Dataset Generation.""" + + async def translate_sql_nl( + self, *, schema_profile: str, sql: str + ) -> GeneratedQuery: + strategy_id = random.randint(1, 2) + if strategy_id == 1: + nlq = await self.translate_sql_nl_1(schema_profile=schema_profile, sql=sql) + else: + nlq = await self.translate_sql_nl_2(schema_profile=schema_profile, sql=sql) + return GeneratedQuery(text=nlq, gen_id=str(strategy_id)) + + async def translate_sql_nl_1(self, *, schema_profile: str, sql: str) -> str | None: + """Translator Agent: Translates valid SQL into a business question.""" + prompt = textwrap.dedent(f""" You are a Data Analyst. Look at the following SQL query, the database profile. Translate this SQL query into a clear, natural-sounding business question that a CEO or Manager would ask. Ensure that the translated question is concise and not vague and not ambiguous. @@ -270,20 +234,15 @@ async def translate_sql_nl_1( {schema_profile} """) - result = await self.generate_content( - prompt=prompt, - response_schema=TranslatedQuestion, - temperature=0.4, - ) - return result.question if result else None - - async def translate_sql_nl_2( - self, - *, - schema_profile: str, - sql: str - ) -> str | None: - styles = [ + result = await self.generate_content( + prompt=prompt, + response_schema=TranslatedQuestion, + temperature=0.4, + ) + return result.question if result else None + + async def translate_sql_nl_2(self, *, schema_profile: str, sql: str) -> str | None: + styles = [ "formal", "colloquial", "imperative", @@ -294,8 +253,8 @@ async def translate_sql_nl_2( "metaphorical", "conversational", ] - selected_style = random.choice(styles) - nl_prompt = textwrap.dedent(f""" + selected_style = random.choice(styles) + nl_prompt = textwrap.dedent(f""" You are a Data Analyst. Look at the following SQL query, the database profile. Translate this SQL into a '{selected_style}' natural language question that is not ambiguous. Ensure that the translated question is concise and not vague and not ambiguous. @@ -308,24 +267,24 @@ async def translate_sql_nl_2( Database Profile: {schema_profile} """) - question_data = await self.generate_content( - prompt=nl_prompt, - response_schema=TranslatedQuestion, - temperature=0.8, - ) - return question_data.question if question_data else None - + question_data = await self.generate_content( + prompt=nl_prompt, + response_schema=TranslatedQuestion, + temperature=0.8, + ) + return question_data.question if question_data else None + class QuestionReviewer(GeneratorBase): - """Question reviewer agent for NL2SQL Eval Dataset Generation.""" - - async def review_question( - self, - *, - question: str, - sql: str, - ) -> QuestionReviewReport | None: - review_prompt = textwrap.dedent(f""" + """Question reviewer agent for NL2SQL Eval Dataset Generation.""" + + async def review_question( + self, + *, + question: str, + sql: str, + ) -> QuestionReviewReport | None: + review_prompt = textwrap.dedent(f""" You are the ReViSQL Reviewer Agent. Evaluate this NL2SQL question for inclusion in a Golden Dataset. NL Question: {question} @@ -339,39 +298,38 @@ async def review_question( 5. Output Correctness: Does the NL question accurately reflect the output of SQL query? """) - report = await self.generate_content( - prompt=review_prompt, - response_schema=QuestionReviewReport, - temperature=0.1, - ) - return report if report else None + report = await self.generate_content( + prompt=review_prompt, + response_schema=QuestionReviewReport, + temperature=0.1, + ) + return report if report else None class QuestionRefiner(GeneratorBase): - async def refine_question( - self, - *, - question: str, - sql: str, - question_report: QuestionReviewReport | str, - ) -> str | None: - """Refiner agent in the Verifier/Refiner Agent Loop. - - Args: - schema_profile: The database schema and sample data. - question: The business question to verify. - sql: The original SQL query that produced the business question. - sql_dialect: The dialect of the SQL query. - - Returns: - return verified and refined question - """ - - if isinstance(question_report, QuestionReviewReport): - question_report = question_report.model_dump_json() - - - refine_prompt = textwrap.dedent(f""" + async def refine_question( + self, + *, + question: str, + sql: str, + question_report: QuestionReviewReport | str, + ) -> str | None: + """Refiner agent in the Verifier/Refiner Agent Loop. + + Args: + schema_profile: The database schema and sample data. + question: The business question to verify. + sql: The original SQL query that produced the business question. + sql_dialect: The dialect of the SQL query. + + Returns: + return verified and refined question + """ + + if isinstance(question_report, QuestionReviewReport): + question_report = question_report.model_dump_json() + + refine_prompt = textwrap.dedent(f""" The current business question is ambiguous and led to an incorrect SQL query. SQL Query: @@ -387,10 +345,9 @@ async def refine_question( Explicitly mention necessary filters or specific columns if needed to avoid ambiguity. """) - refined = await self.generate_content( - prompt=refine_prompt, - response_schema=TranslatedQuestion, - temperature=0.3, - ) - return refined.question if refined else None - + refined = await self.generate_content( + prompt=refine_prompt, + response_schema=TranslatedQuestion, + temperature=0.3, + ) + return refined.question if refined else None diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py index c6774cb0..9d5a5edd 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py @@ -8,288 +8,288 @@ def truncate_length(s: str, max_length: int) -> str: - """Truncate the string to the maximum length.""" - if not isinstance(s, str): + """Truncate the string to the maximum length.""" + if not isinstance(s, str): + return s + if len(s) > max_length: + return s[: max_length - 3] + "..." return s - if len(s) > max_length: - return s[: max_length - 3] + "..." - return s class Column(BaseModel): - name: str - data_type: str - is_primary_key: bool - is_foreign_key: bool + name: str + data_type: str + is_primary_key: bool + is_foreign_key: bool class SchemaDDL(BaseModel): - """Schema for a database table.""" - - ddl: str = '' - schema_name: str = '' - table_name: str = '' - full_table_name: str = '' - columns: list[Column] = [] - constraints: list[dict[str, Any]] = [] - indexes: list[dict[str, Any]] = [] - error: str = '' - - def get_table_name(self) -> str: - """Returns the table name.""" - if self.schema_name and self.schema_name != 'public': - return f'{self.schema_name}.{self.table_name}' - return self.table_name - + """Schema for a database table.""" -class TableProfile(BaseModel): - """Schema profile for a database table.""" - - table_name: str - ddl: str - columns: list[str] - sample_data: list[dict[str, Any]] - sample_values_per_column: dict[str, list[Any]] - - def to_string(self): - profile = [] - profile.append(f"--- Table: {self.table_name} ---") - profile.append(f"DDL:\n{self.ddl}") - profile.append("\n") - - # remove columns with one unique value or less - filtered_columns = [] - for column in self.columns: - if self.sample_values_per_column: - values = self.sample_values_per_column.get(column, []) - if len(values) > 1: - filtered_columns.append(column) - else: - filtered_columns.append(column) - - if self.sample_data: - profile.append(f"Sample Data (Columns: {', '.join(filtered_columns)}):") - for row in self.sample_data: - new_row = { - k: truncate_length(v, 32 * 5) - for k, v in row.items() - if k in filtered_columns - } - profile.append(str(new_row)) - profile.append("\n") - - if self.sample_values_per_column: - for column, values in self.sample_values_per_column.items(): - if column not in filtered_columns: - continue - values = [truncate_length(v, 32 * 5) for v in values] - profile.append(f'Sample values for column "{column}": {values}') - return "\n".join(profile) - - -class SchemaProfile(BaseModel): - """Schema profile for a database.""" - - table_profiles: list[TableProfile] - general_context: str | None = None - - def to_string(self): - profile = [] - for table_profile in self.table_profiles: - profile.append(table_profile.to_string()) - profile.append("\n") - if self.general_context: - profile.append("--- General Context ---") - profile.append(truncate_length(self.general_context, 768 * 5)) - return "\n".join(profile) - + ddl: str = "" + schema_name: str = "" + table_name: str = "" + full_table_name: str = "" + columns: list[Column] = [] + constraints: list[dict[str, Any]] = [] + indexes: list[dict[str, Any]] = [] + error: str = "" -def generate_ddls_from_json_schema(schema_json_string: str) -> list[SchemaDDL]: - try: - schema_data_list = json.loads(schema_json_string) - except json.JSONDecodeError as e: - return SchemaDDL(error=f'Error decoding JSON: {e}') - assert isinstance(schema_data_list, list) - ddls = [] - for schema_data in schema_data_list: - ddl = generate_ddl_from_dict(schema_data) - if ddl.error: - raise ValueError(f'Error generating DDL: {ddl.error}') - ddls.append(ddl) - return ddls + def get_table_name(self) -> str: + """Returns the table name.""" + if self.schema_name and self.schema_name != "public": + return f"{self.schema_name}.{self.table_name}" + return self.table_name -def generate_ddl_from_dict(schema_data: dict[str, Any]) -> SchemaDDL: - """Converts a JSON schema definition string into DDL statements. +class TableProfile(BaseModel): + """Schema profile for a database table.""" + + table_name: str + ddl: str + columns: list[str] + sample_data: list[dict[str, Any]] + sample_values_per_column: dict[str, list[Any]] + + def to_string(self): + profile = [] + profile.append(f"--- Table: {self.table_name} ---") + profile.append(f"DDL:\n{self.ddl}") + profile.append("\n") + + # remove columns with one unique value or less + filtered_columns = [] + for column in self.columns: + if self.sample_values_per_column: + values = self.sample_values_per_column.get(column, []) + if len(values) > 1: + filtered_columns.append(column) + else: + filtered_columns.append(column) + + if self.sample_data: + profile.append(f"Sample Data (Columns: {', '.join(filtered_columns)}):") + for row in self.sample_data: + new_row = { + k: truncate_length(v, 32 * 5) + for k, v in row.items() + if k in filtered_columns + } + profile.append(str(new_row)) + profile.append("\n") + + if self.sample_values_per_column: + for column, values in self.sample_values_per_column.items(): + if column not in filtered_columns: + continue + values = [truncate_length(v, 32 * 5) for v in values] + profile.append(f'Sample values for column "{column}": {values}') + return "\n".join(profile) - Args: - schema_json_string: A JSON string representing the database object schema. - Returns: - A SchemaDdl object containing the generated DDL statements. - """ +class SchemaProfile(BaseModel): + """Schema profile for a database.""" - if 'object_details' not in schema_data: - return SchemaDDL(error="Error: 'object_details' not found in JSON schema.") + table_profiles: list[TableProfile] + general_context: str | None = None - details = schema_data['object_details'] - schema_name = schema_data.get('schema_name', 'public') - table_name = details.get('object_name') + def to_string(self): + profile = [] + for table_profile in self.table_profiles: + profile.append(table_profile.to_string()) + profile.append("\n") + if self.general_context: + profile.append("--- General Context ---") + profile.append(truncate_length(self.general_context, 768 * 5)) + return "\n".join(profile) - if not table_name: - return SchemaDDL(error='Error: "object_name" not found in object_details.') - full_table_name = f'{schema_name}.{table_name}' +def generate_ddls_from_json_schema(schema_json_string: str) -> list[SchemaDDL]: + try: + schema_data_list = json.loads(schema_json_string) + except json.JSONDecodeError as e: + return SchemaDDL(error=f"Error decoding JSON: {e}") + assert isinstance(schema_data_list, list) + ddls = [] + for schema_data in schema_data_list: + ddl = generate_ddl_from_dict(schema_data) + if ddl.error: + raise ValueError(f"Error generating DDL: {ddl.error}") + ddls.append(ddl) + return ddls - ddl_statements = [] - # 1. CREATE TABLE statement - columns = details.get('columns', []) - if not columns: +def generate_ddl_from_dict(schema_data: dict[str, Any]) -> SchemaDDL: + """Converts a JSON schema definition string into DDL statements. + + Args: + schema_json_string: A JSON string representing the database object schema. + + Returns: + A SchemaDdl object containing the generated DDL statements. + """ + + if "object_details" not in schema_data: + return SchemaDDL(error="Error: 'object_details' not found in JSON schema.") + + details = schema_data["object_details"] + schema_name = schema_data.get("schema_name", "public") + table_name = details.get("object_name") + + if not table_name: + return SchemaDDL(error='Error: "object_name" not found in object_details.') + + full_table_name = f"{schema_name}.{table_name}" + + ddl_statements = [] + + # 1. CREATE TABLE statement + columns = details.get("columns", []) + if not columns: + return SchemaDDL(error="Error: No columns defined for table {full_table_name}.") + + column_defs = [] + for col in columns: + col_name = col.get("column_name") + data_type = col.get("data_type") + if not col_name or not data_type: + return SchemaDDL( + error=( + "Error: Column name or data_type missing in column definition:" + f" {col}" + ) + ) + + parts = [f'"{col_name}"', data_type] + if col.get("is_not_nullable", False): + parts.append("NOT NULL") + if col.get("column_default") is not None: + parts.append(f"DEFAULT {col['column_default']}") + column_defs.append(" ".join(parts)) + + create_table_sql = f"CREATE TABLE {full_table_name} (\n " + create_table_sql += ",\n ".join(column_defs) + create_table_sql += "\n);" + ddl_statements.append(create_table_sql) + ddl_statements.append("") + + # 2. Add Constraints + constraints = details.get("constraints", []) + pk_constraints = [ + c for c in constraints if c.get("constraint_type") == "PRIMARY KEY" + ] + fk_constraints = [ + c for c in constraints if c.get("constraint_type") == "FOREIGN KEY" + ] + other_constraints = [ + c + for c in constraints + if c.get("constraint_type") not in ["PRIMARY KEY", "FOREIGN KEY"] + ] + + def add_constraint_sql(constraint): + constraint_name = constraint.get("constraint_name") + constraint_def = constraint.get("constraint_definition") + if constraint_name and constraint_def: + return ( + f'ALTER TABLE {full_table_name} ADD CONSTRAINT "{constraint_name}"' + f" {constraint_def};" + ) + return None + + primary_keys = set() + foreign_keys = set() + + # Add Primary Key and other non-FK constraints + for c in pk_constraints: + sql = add_constraint_sql(c) + if sql: + ddl_statements.append(sql) + constraint_definition = c.get("constraint_definition") + pk_candidates = [] + for col in columns: + column_name = col.get("column_name") + if column_name in constraint_definition: + pk_candidates.append(column_name) + if pk_candidates: + selected_pk = list(sorted(pk_candidates, key=len, reverse=True))[0] + primary_keys.add(selected_pk) + + for c in other_constraints: + sql = add_constraint_sql(c) + if sql: + ddl_statements.append(sql) + + if pk_constraints or other_constraints: + ddl_statements.append("") + + # Add Foreign Key constraints last + for c in fk_constraints: + sql = add_constraint_sql(c) + if sql: + ddl_statements.append(sql) + constraint_definition = c.get("constraint_definition") + fk_candidates = [] + for col in columns: + column_name = col.get("column_name") + if column_name in constraint_definition: + fk_candidates.append(column_name) + if fk_candidates: + selected_fk = list(sorted(fk_candidates, key=len, reverse=True))[0] + foreign_keys.add(selected_fk) + + if fk_constraints: + ddl_statements.append("") + + # 3. Add Indexes + indexes = details.get("indexes", []) + for index in indexes: + index_def = index.get("index_definition") + # Primary key constraints typically create their own unique index. + # Avoid duplicating index creation if it's for the primary key. + if not index.get("is_primary", False) and index_def: + # Ensure the index definition also uses quoted table name if needed + # Assuming index_definition is already correctly formatted. + ddl_statements.append(index_def + ";") + + ddl = "\n".join(ddl_statements) return SchemaDDL( - error='Error: No columns defined for table {full_table_name}.' + ddl=ddl, + schema_name=schema_name, + table_name=table_name, + full_table_name=full_table_name, + columns=[ + Column( + name=col.get("column_name"), + data_type=col.get("data_type"), + is_primary_key=col.get("column_name") in primary_keys, + is_foreign_key=col.get("column_name") in foreign_keys, + ) + for col in columns + ], + constraints=constraints, + indexes=indexes, ) - column_defs = [] - for col in columns: - col_name = col.get('column_name') - data_type = col.get('data_type') - if not col_name or not data_type: - return SchemaDDL( - error=( - 'Error: Column name or data_type missing in column definition:' - f' {col}' - ) - ) - - parts = [f'"{col_name}"', data_type] - if col.get('is_not_nullable', False): - parts.append('NOT NULL') - if col.get('column_default') is not None: - parts.append(f"DEFAULT {col['column_default']}") - column_defs.append(' '.join(parts)) - - create_table_sql = f'CREATE TABLE {full_table_name} (\n ' - create_table_sql += ',\n '.join(column_defs) - create_table_sql += '\n);' - ddl_statements.append(create_table_sql) - ddl_statements.append('') - - # 2. Add Constraints - constraints = details.get('constraints', []) - pk_constraints = [ - c for c in constraints if c.get('constraint_type') == 'PRIMARY KEY' - ] - fk_constraints = [ - c for c in constraints if c.get('constraint_type') == 'FOREIGN KEY' - ] - other_constraints = [ - c - for c in constraints - if c.get('constraint_type') not in ['PRIMARY KEY', 'FOREIGN KEY'] - ] - - def add_constraint_sql(constraint): - constraint_name = constraint.get('constraint_name') - constraint_def = constraint.get('constraint_definition') - if constraint_name and constraint_def: - return ( - f'ALTER TABLE {full_table_name} ADD CONSTRAINT "{constraint_name}"' - f' {constraint_def};' - ) - return None - - primary_keys = set() - foreign_keys = set() - - # Add Primary Key and other non-FK constraints - for c in pk_constraints: - sql = add_constraint_sql(c) - if sql: - ddl_statements.append(sql) - constraint_definition = c.get('constraint_definition') - pk_candidates = [] - for col in columns: - column_name = col.get('column_name') - if column_name in constraint_definition: - pk_candidates.append(column_name) - if pk_candidates: - selected_pk = list(sorted(pk_candidates, key=len, reverse=True))[0] - primary_keys.add(selected_pk) - - - for c in other_constraints: - sql = add_constraint_sql(c) - if sql: - ddl_statements.append(sql) - - if pk_constraints or other_constraints: - ddl_statements.append('') - - # Add Foreign Key constraints last - for c in fk_constraints: - sql = add_constraint_sql(c) - if sql: - ddl_statements.append(sql) - constraint_definition = c.get('constraint_definition') - fk_candidates = [] - for col in columns: - column_name = col.get('column_name') - if column_name in constraint_definition: - fk_candidates.append(column_name) - if fk_candidates: - selected_fk = list(sorted(fk_candidates, key=len, reverse=True))[0] - foreign_keys.add(selected_fk) - - if fk_constraints: - ddl_statements.append('') - - # 3. Add Indexes - indexes = details.get('indexes', []) - for index in indexes: - index_def = index.get('index_definition') - # Primary key constraints typically create their own unique index. - # Avoid duplicating index creation if it's for the primary key. - if not index.get('is_primary', False) and index_def: - # Ensure the index definition also uses quoted table name if needed - # Assuming index_definition is already correctly formatted. - ddl_statements.append(index_def + ';') - - ddl = '\n'.join(ddl_statements) - return SchemaDDL( - ddl=ddl, - schema_name=schema_name, - table_name=table_name, - full_table_name=full_table_name, - columns=[Column( - name=col.get('column_name'), - data_type=col.get('data_type'), - is_primary_key=col.get('column_name') in primary_keys, - is_foreign_key=col.get('column_name') in foreign_keys, - ) for col in columns], - constraints=constraints, - indexes=indexes, - ) class TableSelector(GeneratorBase): - async def select_tables( - self, - *, - tables: list[SchemaDDL], - top_k: int = 8 - ) -> list[str]: - - class SelectedTables(BaseModel): - table_names: list[str] = Field(description="List of selected table names, Each table name in the form of schema_name.table_name") - - profile = [] - for table in tables: - profile.append(f"--- Table: {table.full_table_name} ---") - profile.append(f"DDL:\n{table.ddl}") - profile.append("\n") - schema_profile = '\n'.join(profile) - - review_prompt = textwrap.dedent(f""" + async def select_tables( + self, *, tables: list[SchemaDDL], top_k: int = 8 + ) -> list[str]: + + class SelectedTables(BaseModel): + table_names: list[str] = Field( + description="List of selected table names, Each table name in the form of schema_name.table_name" + ) + + profile = [] + for table in tables: + profile.append(f"--- Table: {table.full_table_name} ---") + profile.append(f"DDL:\n{table.ddl}") + profile.append("\n") + schema_profile = "\n".join(profile) + + review_prompt = textwrap.dedent(f""" You are experienced Database Analyst. You are given the schema of a database, and ask the pick at most {top_k} tables that the end-user will most likely interact with. @@ -311,13 +311,11 @@ class SelectedTables(BaseModel): {schema_profile} """) - report = await self.generate_content( - prompt=review_prompt, - response_schema=SelectedTables, - temperature=0.1, - ) - assert report is not None - assert isinstance(report, SelectedTables) - return report.table_names - - + report = await self.generate_content( + prompt=review_prompt, + response_schema=SelectedTables, + temperature=0.1, + ) + assert report is not None + assert isinstance(report, SelectedTables) + return report.table_names diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py index 24797cc9..38ba98ba 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py @@ -9,25 +9,32 @@ class DatasetGenEventLogger: def __init__(self, working_dir: str): - self._history_path = os.path.join(working_dir, 'mcp_history.json') + self._history_path = os.path.join(working_dir, "mcp_history.json") self._history = [] if os.path.exists(self._history_path): - self._history = json.loads(Path(self._history_path).read_text(encoding='utf-8')) + self._history = json.loads( + Path(self._history_path).read_text(encoding="utf-8") + ) - def log_event(self, action: str, request: dict[str, Any], response: dict[str, Any]) -> 'DatasetGenEventLogger': + def log_event( + self, action: str, request: dict[str, Any], response: dict[str, Any] + ) -> "DatasetGenEventLogger": evt = { "action": action, "time": datetime.datetime.now().isoformat(), "request": request, - "response": response + "response": response, } if len(self._history) >= 10: self._history.pop(0) self._history.append(evt) Path(self._history_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._history_path).write_text(json.dumps(self._history, indent=2, ensure_ascii=False), encoding='utf-8') + Path(self._history_path).write_text( + json.dumps(self._history, indent=2, ensure_ascii=False), encoding="utf-8" + ) return self + class DatasetGenConfig: def __init__(self, task_dir: str): self._config_path = os.path.join(task_dir, "config.json") @@ -38,49 +45,49 @@ def _load(self) -> dict[str, Any]: return self._config if not os.path.exists(self._config_path): raise FileNotFoundError(f"Config file not found at {self._config_path}") - self._config = json.loads(Path(self._config_path).read_text(encoding='utf-8')) + self._config = json.loads(Path(self._config_path).read_text(encoding="utf-8")) return self._config - + @property def dialect(self) -> str: config = self._load() return config["dialect"] - + @property def complexity(self) -> str: config = self._load() return config["complexity"] - + @property def database_name(self) -> str: config = self._load() return config["database_name"] - + @property def desired_output_pairs(self) -> int: config = self._load() return int(config["size"]) - + @property def constraints(self) -> str: config = self._load() return config["constraints"] - + @property def parallelism(self) -> int: config = self._load() - parallelism = config.get("parallelism", 10) + parallelism = config.get("parallelism", 10) return max(1, min(parallelism, self.desired_output_pairs)) - + @property def output_file_path(self) -> str: config = self._load() return config["output_file_path"] - + def delete(self) -> None: Path(self._config_path).unlink(missing_ok=True) self._config = None - + class DatasetGenDbProfile: def __init__(self, task_dir: str): @@ -92,19 +99,23 @@ def get_profile(self) -> str: if self._db_profile is not None: return self._db_profile if os.path.exists(self._db_profile_path): - self._db_profile = Path(self._db_profile_path).read_text(encoding='utf-8') + self._db_profile = Path(self._db_profile_path).read_text(encoding="utf-8") else: self._db_profile = "" return self._db_profile - + def get_mini_profile(self) -> str: # Remove the sample values from schema profile to reduce the context length schema_profile = self.get_profile() schema_profile_lines = schema_profile.split("\n") - schema_profile_lines = [line for line in schema_profile_lines if not line.startswith("Sample values for column")] + schema_profile_lines = [ + line + for line in schema_profile_lines + if not line.startswith("Sample values for column") + ] schema_profile = "\n".join(schema_profile_lines) return schema_profile - + def delete(self) -> None: Path(self._db_profile_path).unlink(missing_ok=True) if os.path.exists(self._db_profile_internal_dir): @@ -114,7 +125,7 @@ def delete(self) -> None: @property def path(self) -> str: return self._db_profile_path - + class CommittedPairCollection: def __init__(self, output_file_path: str): @@ -125,11 +136,13 @@ def _load(self) -> list[dict[str, Any]]: if self._committed_pairs is not None: return self._committed_pairs if os.path.exists(self._committed_pairs_path): - self._committed_pairs = json.loads(Path(self._committed_pairs_path).read_text(encoding='utf-8')) + self._committed_pairs = json.loads( + Path(self._committed_pairs_path).read_text(encoding="utf-8") + ) else: self._committed_pairs = [] return self._committed_pairs - + def add_verified_pairs(self, pairs: list[dict[str, Any]]) -> bool: if not pairs: return False @@ -139,53 +152,57 @@ def add_verified_pairs(self, pairs: list[dict[str, Any]]) -> bool: num_new_pairs = 0 seen = set() if committed_pairs: - index = int(committed_pairs[-1]['id'].replace("eval_", "")) + 1 + index = int(committed_pairs[-1]["id"].replace("eval_", "")) + 1 seen = set([entry["golden_sql"] for entry in committed_pairs]) - + for entry in pairs: - if entry['golden_sql'] in seen: + if entry["golden_sql"] in seen: continue - tags = entry.get('tags', []) - committed_pairs.append({ - "id": f"eval_{str(index).zfill(4)}", - "nlq": entry['nlq'], - "golden_sql": entry['golden_sql'], - "database": entry['database'], - "tags": tags - }) + tags = entry.get("tags", []) + committed_pairs.append( + { + "id": f"eval_{str(index).zfill(4)}", + "nlq": entry["nlq"], + "golden_sql": entry["golden_sql"], + "database": entry["database"], + "tags": tags, + } + ) num_new_pairs += 1 index += 1 - + self._save() return True def __len__(self) -> int: committed_pairs = self._load() return len(committed_pairs) - + def _save(self) -> None: committed_pairs = self._load() Path(self._committed_pairs_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._committed_pairs_path).write_text(json.dumps(committed_pairs, indent=2), encoding='utf-8') - + Path(self._committed_pairs_path).write_text( + json.dumps(committed_pairs, indent=2), encoding="utf-8" + ) + def get_sql_set(self) -> set[str]: committed_pairs = self._load() return set([entry["golden_sql"] for entry in committed_pairs]) - + def exists(self) -> bool: return os.path.exists(self._committed_pairs_path) - + def get_pairs(self) -> list[dict[str, Any]]: return self._load() - + def delete(self) -> None: Path(self._committed_pairs_path).unlink(missing_ok=True) self._committed_pairs = None - + @property def path(self) -> str: return self._committed_pairs_path - + class RejectedPairCollection: def __init__(self, task_dir: str): @@ -196,11 +213,13 @@ def _load(self) -> list[dict[str, Any]]: if self._rejected_pairs is not None: return self._rejected_pairs if os.path.exists(self._rejected_pairs_path): - self._rejected_pairs = json.loads(Path(self._rejected_pairs_path).read_text(encoding='utf-8')) + self._rejected_pairs = json.loads( + Path(self._rejected_pairs_path).read_text(encoding="utf-8") + ) else: self._rejected_pairs = [] return self._rejected_pairs - + def add_rejected_pairs(self, pairs: list[dict[str, Any]]) -> bool: if not pairs: return False @@ -208,7 +227,7 @@ def add_rejected_pairs(self, pairs: list[dict[str, Any]]) -> bool: rejected_pairs.extend(pairs) self._save() return True - + def pop_pairs(self, num_pairs: int) -> list[dict[str, Any]]: rejected_pairs = self._load() if not rejected_pairs: @@ -221,47 +240,53 @@ def pop_pairs(self, num_pairs: int) -> list[dict[str, Any]]: def __len__(self) -> int: rejected_pairs = self._load() return len(rejected_pairs) - + def _save(self) -> None: rejected_pairs = self._load() Path(self._rejected_pairs_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._rejected_pairs_path).write_text(json.dumps(rejected_pairs, indent=2), encoding='utf-8') + Path(self._rejected_pairs_path).write_text( + json.dumps(rejected_pairs, indent=2), encoding="utf-8" + ) def delete(self) -> None: Path(self._rejected_pairs_path).unlink(missing_ok=True) self._rejected_pairs = None - + @property def path(self) -> str: return self._rejected_pairs_path - + def first(self) -> dict[str, Any] | None: rejected_pairs = self._load() return rejected_pairs[0] if rejected_pairs else None - + def is_empty(self) -> bool: rejected_pairs = self._load() return len(rejected_pairs) == 0 - + class PendingPairCollection: def __init__(self, task_dir: str): - self._pending_pairs_path = os.path.join(task_dir, "interim_generated_pairs.json") + self._pending_pairs_path = os.path.join( + task_dir, "interim_generated_pairs.json" + ) self._pending_pairs = None def _load(self) -> list[dict[str, Any]]: if self._pending_pairs is not None: return self._pending_pairs if os.path.exists(self._pending_pairs_path): - self._pending_pairs = json.loads(Path(self._pending_pairs_path).read_text(encoding='utf-8')) + self._pending_pairs = json.loads( + Path(self._pending_pairs_path).read_text(encoding="utf-8") + ) else: self._pending_pairs = [] return self._pending_pairs - + @property def path(self) -> str: return self._pending_pairs_path - + def add_pending_pairs(self, pairs: list[dict[str, Any]]) -> bool: if not pairs: return False @@ -269,11 +294,13 @@ def add_pending_pairs(self, pairs: list[dict[str, Any]]) -> bool: pending_pairs.extend(pairs) self._save() return True - + def _save(self) -> None: pending_pairs = self._load() Path(self._pending_pairs_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._pending_pairs_path).write_text(json.dumps(pending_pairs, indent=2), encoding='utf-8') + Path(self._pending_pairs_path).write_text( + json.dumps(pending_pairs, indent=2), encoding="utf-8" + ) def delete(self) -> None: Path(self._pending_pairs_path).unlink(missing_ok=True) @@ -295,11 +322,11 @@ def _load(self) -> list[str]: if self._sqls is not None: return self._sqls if os.path.exists(self._sqls_path): - self._sqls = json.loads(Path(self._sqls_path).read_text(encoding='utf-8')) + self._sqls = json.loads(Path(self._sqls_path).read_text(encoding="utf-8")) else: self._sqls = [] return self._sqls - + def add_sqls(self, sqls: list[dict[str, Any]]) -> bool: if not sqls: return False @@ -307,7 +334,7 @@ def add_sqls(self, sqls: list[dict[str, Any]]) -> bool: _sqls.extend(sqls) self._save() return True - + def delete(self) -> None: Path(self._sqls_path).unlink(missing_ok=True) self._sqls = None @@ -317,20 +344,20 @@ def get_sqls(self, filter_func: callable) -> list[str]: if filter_func: sqls = [sql for sql in sqls if filter_func(sql)] return sqls - + def is_empty(self) -> bool: sqls = self._load() return len(sqls) == 0 - + def _save(self) -> None: sqls = self._load() Path(self._sqls_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._sqls_path).write_text(json.dumps(sqls, indent=2), encoding='utf-8') - + Path(self._sqls_path).write_text(json.dumps(sqls, indent=2), encoding="utf-8") + @property def path(self) -> str: return self._sqls_path - + class DatasetGenStates: def __init__(self, task_dir: str): @@ -343,7 +370,9 @@ def _load(self) -> dict[str, Any]: return self._states if os.path.exists(self._states_path): - self._states = json.loads(Path(self._states_path).read_text(encoding='utf-8')) + self._states = json.loads( + Path(self._states_path).read_text(encoding="utf-8") + ) else: self._states = { "is_done": False, @@ -359,47 +388,55 @@ def increment_iterations(self) -> int: iterations = states.get("iterations", 0) + 1 states["iterations"] = iterations return iterations - + def exists(self) -> bool: return os.path.exists(self._states_path) - + def is_done(self) -> bool: states = self._load() return states.get("is_done", False) - + def delete(self) -> None: Path(self._states_path).unlink(missing_ok=True) self._states = None - + @property def iterations(self) -> int: states = self._load() return states.get("iterations", 0) - + @property def desired_output_pairs(self) -> int: return self.config.desired_output_pairs - - def update_states_on_pairs_committed(self, committed_pairs: CommittedPairCollection, rejected_pairs: RejectedPairCollection) -> None: + + def update_states_on_pairs_committed( + self, + committed_pairs: CommittedPairCollection, + rejected_pairs: RejectedPairCollection, + ) -> None: num_verified_pairs = len(committed_pairs) num_rejected_pairs = len(rejected_pairs) remaining_pairs = max(0, self.desired_output_pairs - num_verified_pairs) states = self._load() - states["is_done"] = self._is_data_generation_done(remaining_pairs, self.iterations) + states["is_done"] = self._is_data_generation_done( + remaining_pairs, self.iterations + ) states["remaining_pairs"] = remaining_pairs states["completed_pairs"] = num_verified_pairs states["rejected_pairs"] = num_rejected_pairs if "start_time" not in states: states["start_time"] = datetime.datetime.now().timestamp() states["curr_time"] = datetime.datetime.now().timestamp() - states["elapsed_time_hr"] = round((states["curr_time"] - states["start_time"]) / 3600, 2) + states["elapsed_time_hr"] = round( + (states["curr_time"] - states["start_time"]) / 3600, 2 + ) self._save() def elapsed_time_hr(self) -> float: states = self._load() return states.get("elapsed_time_hr", 0) - + def timestamp(self) -> float: states = self._load() return states.get("curr_time", datetime.datetime.now().timestamp()) @@ -410,13 +447,15 @@ def to_dict(self) -> dict[str, Any]: def _save(self) -> None: states = self._load() Path(self._states_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._states_path).write_text(json.dumps(states, indent=2), encoding='utf-8') + Path(self._states_path).write_text( + json.dumps(states, indent=2), encoding="utf-8" + ) def _is_data_generation_done(self, remaining_pairs: int, iterations: int) -> bool: if remaining_pairs <= 0 or iterations >= 500: return True return False - + class BackgroundTaskCollection: tasks = set() @@ -425,7 +464,7 @@ class BackgroundTaskCollection: def add_task(cls, task: asyncio.Task) -> None: cls.tasks.add(task) task.add_done_callback(lambda t: cls.tasks.discard(t)) - + @classmethod def cancel_tasks(cls) -> None: for task in cls.tasks: diff --git a/tests/google/cloud/db_context_enrichment/main_test.py b/tests/google/cloud/db_context_enrichment/main_test.py index b42b8ed6..f0adad68 100644 --- a/tests/google/cloud/db_context_enrichment/main_test.py +++ b/tests/google/cloud/db_context_enrichment/main_test.py @@ -43,7 +43,7 @@ def test_attach_context_set_legacy_compatibility(tmp_path, clean_context_set_jso legacy_file.write_text(json.dumps(legacy_content)) # Call the tool - attach_context_set.fn( + attach_context_set( context_set_json=clean_context_set_json, file_path=str(legacy_file) ) @@ -83,7 +83,7 @@ def test_attach_context_set_standard(tmp_path, clean_context_set_json): standard_file.write_text(json.dumps(standard_content)) # Call the tool - attach_context_set.fn( + attach_context_set( context_set_json=clean_context_set_json, file_path=str(standard_file) ) @@ -109,7 +109,7 @@ def test_attach_context_set_file_not_exist(tmp_path, clean_context_set_json): assert not new_file.exists() # Call the tool (should not raise FileNotFoundError) - attach_context_set.fn( + attach_context_set( context_set_json=clean_context_set_json, file_path=str(new_file) ) diff --git a/uv.lock b/uv.lock index ce98f2b2..dc949491 100644 --- a/uv.lock +++ b/uv.lock @@ -7,6 +7,18 @@ resolution-markers = [ "python_full_version < '3.13'", ] +[[package]] +name = "aiofile" +version = "3.11.1" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "caio" }, +] +sdist = { url = "https://files.pythonhosted.org/packages/48/41/2fea7e193e061ce54eacc3b7bc0e6a99e4fcff43c78cf0a76dd781ed8334/aiofile-3.11.1.tar.gz", hash = "sha256:1f91912c6643d2a4e49ca4ae3514f0bf3867ce948a36d99a6411b8f4755f4cf9", size = 19342, upload-time = "2026-05-16T08:18:33.538Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/67/cd/0d76dfc5de72bde52f55f53e925c7d152d9c7906634ec1e0cbc7e8d4ad93/aiofile-3.11.1-py3-none-any.whl", hash = "sha256:ce77d14ac07f77bc2b757834a5c129321f3f705c474593deed5ab209079a52c9", size = 20446, upload-time = "2026-05-16T08:18:32.051Z" }, +] + [[package]] name = "aiohappyeyeballs" version = "2.6.1" @@ -123,15 +135,6 @@ wheels = [ { 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a/tests/google/cloud/db_context_enrichment/main_test.py b/tests/google/cloud/db_context_enrichment/main_test.py index f0adad68..a9953ca1 100644 --- a/tests/google/cloud/db_context_enrichment/main_test.py +++ b/tests/google/cloud/db_context_enrichment/main_test.py @@ -109,9 +109,7 @@ def test_attach_context_set_file_not_exist(tmp_path, clean_context_set_json): assert not new_file.exists() # Call the tool (should not raise FileNotFoundError) - attach_context_set( - context_set_json=clean_context_set_json, file_path=str(new_file) - ) + attach_context_set(context_set_json=clean_context_set_json, file_path=str(new_file)) # Verify the file was created and holds the new content assert new_file.exists() From 26c4a241c11f6b6d9b6e3651bcdb1b84cbc7b1e6 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 21:43:38 -0700 Subject: [PATCH 12/23] fix cloudbuild --- .ci/cloudbuild.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.ci/cloudbuild.yaml b/.ci/cloudbuild.yaml index cd9defa0..d5ac50ea 100644 --- a/.ci/cloudbuild.yaml +++ b/.ci/cloudbuild.yaml @@ -1,6 +1,7 @@ steps: # --- Build Extension Step --- - - name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + - id: build-extension + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' entrypoint: 'bash' args: - '-c' From 64c399ffa3c44d5ed1e92151519d6ba0368e0508 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 22:18:58 -0700 Subject: [PATCH 13/23] address code agent comments --- .../dataset/eval_seed_gen/genai_client.py | 10 +++++++++- .../dataset/eval_seed_gen/generators.py | 7 ++++++- 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py index 8623b1e4..0ea52df3 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py @@ -1,9 +1,11 @@ """GenAI client for NL2SQL Eval Dataset Generator.""" import asyncio +import logging import pydantic from google.genai import types +from google.genai.errors import APIError from google import genai @@ -48,5 +50,11 @@ async def generate_content_async( timeout=self.timeout_secs, ) return result - except Exception: + except (TimeoutError, APIError) as e: + logging.warning( + f"generate_content is not able to complete due to timeout or api error: {e}" + ) + return None + except Exception as e: + logging.warning(f"generate_content was not able to complete due to: {e}") return None diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py index b0f5c6b9..65203883 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py @@ -5,6 +5,9 @@ from .generator_base import GeneratorBase +MIN_SQL_LENGTH = 20 +MAX_SQL_LENGTH = 300 + class GeneratedQuery(BaseModel): """A generated SQL query or question.""" @@ -87,7 +90,9 @@ async def generate_candidates( count=count, ) - sqls = [sql for sql in sqls if sql and 20 <= len(sql) <= 300] + sqls = [ + sql for sql in sqls if sql and MIN_SQL_LENGTH <= len(sql) <= MAX_SQL_LENGTH + ] sqls = list(set(sqls)) From 6498f2afe56c2d9b56fe30a373f4f91214c97110 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Sun, 24 May 2026 22:24:57 -0700 Subject: [PATCH 14/23] address code agent comments --- .../dataset/eval_seed_gen/dataset_generator.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py index 6ba709aa..fa5b1266 100644 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py +++ b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py @@ -33,6 +33,8 @@ warnings.filterwarnings("ignore") +MAX_NLQ_REFINES_PER_ITERATION = 3 + def _prepare_sql_for_execution_accuracy_check(sql: str) -> str: if sql.strip().endswith(";"): @@ -47,7 +49,7 @@ class SeedEvalDatasetGenerator: def __init__(self): self._genai_client = GenAiClient(timeout_secs=45) - async def generate_sql(self, task_working_dir: str) -> list[dict]: + async def generate_sql(self, task_working_dir: str) -> dict[str, Any]: """Reads database schema profile and use it to generate SQL candidates. Args: @@ -365,7 +367,9 @@ async def refine_nlq(self, task_working_dir: str) -> dict[str, Any]: ) if not rejected_pair_collection.is_empty(): - pending_pairs = rejected_pair_collection.pop_pairs(3) + pending_pairs = rejected_pair_collection.pop_pairs( + MAX_NLQ_REFINES_PER_ITERATION + ) new_rejected_pairs = [] new_verified_pairs = [] From 7f9ee9a8334189361a7781287e7e00320df6ea02 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Tue, 26 May 2026 12:30:17 -0700 Subject: [PATCH 15/23] debug skill not invoked --- skills/autoctx-dataset-expansion/SKILL.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skills/autoctx-dataset-expansion/SKILL.md b/skills/autoctx-dataset-expansion/SKILL.md index 12a563b9..aafc4088 100644 --- a/skills/autoctx-dataset-expansion/SKILL.md +++ b/skills/autoctx-dataset-expansion/SKILL.md @@ -3,7 +3,7 @@ name: skill-autoctx-dataset-expansion description: "Expand seed evaluation datasets of Natural Language Questions (NLQ) and SQL pairs for evaluation." --- -You are an agent that helps a user expand evaluation datasets of Natural Language Questions (NLQ) and their corresponding SQL queries. Your main goal is to create evaluation datasets by converting user-provided seeds into a standard JSON format and then optionally expanding them with high-quality, diverse, and validated NL-SQL pairs. +You are an agent that helps a user expand evaluation datasets of Natural Language Questions (NLQ) and their corresponding SQL queries. Your main goal is to convert user-provided seed eval dataset into a standard JSON format and then optionally expanding them with high-quality, diverse, and validated NL-SQL pairs. ## Workflow From ab85419351daf1b3dc09f1c06304a5f04c59d243 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Wed, 27 May 2026 11:59:48 -0700 Subject: [PATCH 16/23] pure skill implementation --- commands/autoctx/expand-dataset.toml | 5 - evals/core-cujs/dataset.json | 78 ++- .../user_data/binary_data/ER_Diagram.jpeg | Bin 0 -> 3286046 bytes .../user_data/binary_data/desgin_doc.pdf | Bin 0 -> 30802 bytes .../ecommerce_app/business_blueprint.md | 66 ++ .../user_data/ecommerce_app/db_schema.sql | 272 +++++++ .../user_data/hr_app/db_schema.sql | 103 +++ .../user_data/hr_app/hr_agent.py | 158 +++++ .../user_data/rust_app/main.rs | 154 ++++ skills/autoctx-dataset-expansion/SKILL.md | 50 -- skills/autoctx-dataset-generation/SKILL.md | 284 ++------ .../dataset/eval_seed_gen/__init__.py | 0 .../eval_seed_gen/dataset_generator.py | 663 ------------------ .../dataset/eval_seed_gen/genai_client.py | 60 -- .../dataset/eval_seed_gen/generator_base.py | 42 -- .../dataset/eval_seed_gen/generators.py | 358 ---------- .../dataset/eval_seed_gen/schema.py | 321 --------- .../dataset/eval_seed_gen/state_manager.py | 472 ------------- .../cloud/db_context_enrichment/main.py | 17 - 19 files changed, 891 insertions(+), 2212 deletions(-) delete mode 100644 commands/autoctx/expand-dataset.toml create mode 100644 evals/core-cujs/workspace_post_init/user_data/binary_data/ER_Diagram.jpeg create mode 100644 evals/core-cujs/workspace_post_init/user_data/binary_data/desgin_doc.pdf create mode 100644 evals/core-cujs/workspace_post_init/user_data/ecommerce_app/business_blueprint.md create mode 100644 evals/core-cujs/workspace_post_init/user_data/ecommerce_app/db_schema.sql create mode 100644 evals/core-cujs/workspace_post_init/user_data/hr_app/db_schema.sql create mode 100644 evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py create mode 100644 evals/core-cujs/workspace_post_init/user_data/rust_app/main.rs delete mode 100644 skills/autoctx-dataset-expansion/SKILL.md delete mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/__init__.py delete mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py delete mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py delete mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py delete mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py delete mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py delete mode 100644 src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py diff --git a/commands/autoctx/expand-dataset.toml b/commands/autoctx/expand-dataset.toml deleted file mode 100644 index 8486b73d..00000000 --- a/commands/autoctx/expand-dataset.toml +++ /dev/null @@ -1,5 +0,0 @@ -description = "Expands seed evaluation datasets of Natural Language Questions (NLQ) and SQL pairs for evaluation." -prompt = """ -Your task is to expand a seed evaluation dataset of Natural Language Questions and their corresponding SQL queries. -Please use the `activate_skill` tool to activate the `skill-autoctx-dataset-expansion` skill, and strictly follow its workflow. -""" diff --git a/evals/core-cujs/dataset.json b/evals/core-cujs/dataset.json index e9d6f6b2..dad7cd98 100644 --- a/evals/core-cujs/dataset.json +++ b/evals/core-cujs/dataset.json @@ -18,9 +18,81 @@ "max_turns": 15 }, { - "id": "autoctx:generate-dataset", - "starting_prompt": "generate a seed evaluation dataset for my alloydb database financial named golden_seed.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 medium-level SQL/Question pairs for my alloydb database financial named golden_seed.json. I prefer the generated SQL to have filter conditions on text or jsonb columns. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "id": "autoctx:generate-dataset-bfd-simple", + "starting_prompt": "generate a evaluation dataset consisting of simple SQL/Question pairs for my alloydb database financial named golden_bfd_simple.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 simple SQL/Question pairs for my alloydb database financial named golden_bfd_simple.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-bfd-advanced", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for my alloydb database financial named golden_bfd_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs for my alloydb database financial named golden_bfd_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-bfd-expert", + "starting_prompt": "generate a seed evaluation dataset consisting of expert SQL/Question pairs for my alloydb database financial named golden_bfd_expert.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 expert SQL/Question pairs for my alloydb database financial named golden_bfd_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-app-property", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for property search application named golden_property_search_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the application at https://github.com/kupp0/multi-db-property-search-data-agents, save the generated pairs in output file named golden_property_search_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-app-flight", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for flight booking application named golden_flight_booking_expert.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 expert SQL/Question pairs in postgres dialect for the flight booking application at https://docs.cloud.google.com/alloydb/docs/ai/integrate-applications-data-agent, save the generated pairs in output file named golden_flight_booking_expert.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-hr-advanced", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for hr application named golden_hr_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the hr application whose source codes can be found in the directory `user_data/hr_app`, save the generated pairs in output file named golden_hr_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-ecomm-advanced", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for ecommerce application named golden_ecomm_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the ecommerce application whose design and schema can be found in the directory `user_data/ecommerce_app`, save the generated pairs in output file named golden_ecomm_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-blog-advanced", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for blog application named golden_blog_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the blog application whose er diagram and design doc can be found in the directory `user_data/binary_data`, save the generated pairs in output file named golden_blog_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_post_init/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-rust-advanced", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for blog application named golden_rust_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the blog application whose source codes can be found in the directory `user_data/rust_app`, save the generated pairs in output file named golden_rust_app_advanced.json. 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zF{oI2nf|-PplomNLik@bXXSrSn%V!m`CrHXDRi;2{wLcYn{0;y20|5^`x*Ad8u2wi zQ9+6sLZVg1&cCQ0QEd(`%ECfbUEd+P95d3Gs8y3foz+!6v)oI_qcmASxDr7LyY7c_h zFIk>%|2v}>)r@;5DScw|65;wDUG)EcE6y%PPA(o!re@GAjBJd|&}3v{3gXcJ4~H^N A1^@s6 literal 0 HcmV?d00001 diff --git a/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/business_blueprint.md b/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/business_blueprint.md new file mode 100644 index 00000000..a50cffde --- /dev/null +++ b/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/business_blueprint.md @@ -0,0 +1,66 @@ +## E-Commerce Platform Blueprint & Cryptographic Data Dictionary + +### Executive Summary + +This document serves as the master key for the e-commerce platform's data architecture. The physical database schema utilizes a heavily abbreviated, cryptic naming convention (`t_` for tables, consonant-heavy shorthand for columns) to prevent immediate comprehension by unauthorized actors. This blueprint maps those cryptic entities back to their respective business domains and operational logic. + +### 1. The Rosetta Stone: Global Naming Convention + +To read the database, developers must apply the following global translation rules. + +**Table Prefixes:** All tables begin with `t_`. +**Common Suffixes/Shorthand:** + +* `_id`: Identifier (Primary/Foreign Key) +* `_nm`: Name +* `_dt`: Date / Timestamp +* `_amt`: Amount / Financial Value +* `_sts`: Status +* `_qty`: Quantity +* `_mthd`: Method + +**Domain Mappings:** + +| Business Concept | Cryptic Table Name | Cryptic Column Examples | +| --- | --- | --- | +| **Users / Customers** | `t_usr` | `u_id` (User ID), `eml` (Email), `gndr` (Gender) | +| **Products / Catalog** | `t_prd` | `p_id` (Product ID), `prc` (Price), `stk_qty` (Stock Quantity) | +| **Categories** | `t_cat` | `c_id` (Category ID), `c_nm` (Category Name) | +| **Orders** | `t_ord` | `o_id` (Order ID), `t_amt` (Total Amount), `sts` (Order Status) | +| **Order Items** | `t_o_itm` | `oi_id` (Order Item ID), `p_id` (Product ID) | +| **Returns** | `t_rtn_req` | `r_id` (Return ID), `r_rsn` (Return Reason), `r_sts` (Return Status) | +| **Shipping** | `t_shp` | `s_id` (Shipping ID), `c_nm` (Carrier Name) | + +--- + +### 2. Business Domain Architecture + +#### A. User & Identity Management Domain (Table: `t_usr`) + +This domain handles the complete lifecycle and segmentation of all platform actors. + +* **Demographic Tracking:** Captures rich demographic data (`dob`, `gndr`, `occ` for occupation) to drive personalized recommendations. +* **Security & Access:** Tracks account security through failed login attempts (`f_log_a`) and account lockouts (`a_lck_dt`). +* **Location Management (Table: `t_addr`):** Manages user addresses (`s_addr` for street, `pst_c` for postal code), categorized by type (`a_typ`) to optimize shipping logistics. + +#### B. Catalog & Inventory Domain (Tables: `t_prd`, `t_cat`, `t_brnd`) + +This is the core of the platform's offering, tracking inventory health and hierarchical taxonomies. + +* **Taxonomy (`t_cat`):** Utilizes a nested model (`l_lvl`, `r_val`) for deep, highly performant sub-category trees. +* **Inventory Lifecycle (`t_prd`):** Tracks stock quantities (`stk_qty`) and availability statuses (`a_sts`). The system triggers restocking alerts when `stk_qty` drops below thresholds. +* **Financials:** Tracks base price (`prc`), discounted price (`d_prc`), and discount percentages (`d_pct`) for margin calculations. + +#### C. Sales & Order Fulfillment Domain (Tables: `t_ord`, `t_o_itm`, `t_shp`) + +Manages the customer's purchasing journey from intent to physical delivery. + +* **Transaction Processing (`t_ord`):** Orders are linked to specific payment methods (`p_mthd`) and shipping methods (`s_mthd`). Total revenue calculations rely heavily on the `t_amt` column. +* **Logistics Tracking (`t_shp`):** Integrates shipping details directly with orders. It measures estimated (`e_d_dt`) versus actual delivery dates (`a_d_dt`) to evaluate external carrier (`c_nm`) performance. + +#### D. Reverse Logistics & Customer Support (Tables: `t_rtn_req`, `t_rfnd`) + +Handles post-purchase customer satisfaction and issue resolution. + +* **Returns Management (`t_rtn_req`):** Tracks the reason for return (`r_rsn`), current operational status (`r_sts`), and resolution notes (`r_nts`). This allows the business to identify recurring product defects. +* **Refund Processing (`t_rfnd`):** Tracks the financial return to the customer, logging the refund amount (`r_amt`) and the method used (`r_mthd`). diff --git a/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/db_schema.sql b/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/db_schema.sql new file mode 100644 index 00000000..0256de4e --- /dev/null +++ b/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/db_schema.sql @@ -0,0 +1,272 @@ +CREATE TABLE t_addr ( + a_id integer NOT NULL, + u_id integer, + s_addr character varying(255), + cty character varying(100), + st character varying(100), + cntry character varying(100), + pst_c character varying(20), + is_dflt boolean, + lbl character varying(100), + lat numeric(10,8), + lon numeric(11,8), + phn character varying(20), + c_nm character varying(100), + cmp_nm character varying(100), + apt_num character varying(50), + bld_nm character varying(100), + flr_num character varying(50), + gt_c character varying(50), + d_inst text, + a_typ character varying(50), + c_dt timestamp without time zone, + u_dt timestamp without time zone +); + +CREATE TABLE t_brnd ( + b_id integer NOT NULL, + b_nm character varying(255), + desc text, + w_url character varying(255), + l_url character varying(255), + is_act boolean, + c_dt timestamp without time zone, + u_dt timestamp without time zone +); + +CREATE TABLE t_cmp ( + cmp_id integer NOT NULL, + cmp_nm character varying(255), + dur integer, + s_dt date, + e_dt date +); + +CREATE TABLE t_crt ( + crt_id integer NOT NULL, + u_id integer, + p_id integer, + qty integer, + a_dt timestamp without time zone +); + +CREATE TABLE t_cat ( + c_id integer NOT NULL, + p_c_id integer, + c_nm character varying(255), + desc text, + i_url character varying(255), + is_act boolean, + c_dt timestamp without time zone, + u_dt timestamp without time zone, + m_ttl character varying(255), + m_desc text, + slg character varying(255), + d_ord integer, + lvl integer, + pth character varying(255), + l_val integer, + r_val integer +); + +CREATE TABLE t_cpn ( + cpn_id integer NOT NULL, + c_cde character varying(255), + d_amt numeric(10,2), + d_typ character varying(50), + min_sp numeric(10,2), + max_sp numeric(10,2), + s_dt timestamp without time zone, + e_dt timestamp without time zone, + is_act boolean, + c_dt timestamp without time zone, + u_dt timestamp without time zone +); + +CREATE TABLE t_ord_itm ( + oi_id integer NOT NULL, + o_id integer, + u_id integer, + o_dt timestamp without time zone, + t_amt numeric(10,2), + sts character varying(100), + p_mthd character varying(100), + s_mthd character varying(100), + s_a_id integer, + b_a_id integer, + p_sts character varying(100), + d_sts character varying(100), + d_dt date, + c_rsn character varying(255), + c_dt timestamp without time zone, + p_id integer +); + +CREATE TABLE t_ord ( + o_id integer NOT NULL, + u_id integer, + o_dt timestamp without time zone, + t_amt numeric(10,2), + sts character varying(100), + p_mthd character varying(100), + s_mthd character varying(100), + s_a_id integer, + b_a_id integer, + r_req boolean, + r_app boolean, + r_rej boolean, + rf_prc boolean, + rf_cmp boolean, + cmp_id integer +); + +CREATE TABLE t_pay ( + pay_id integer NOT NULL, + o_id integer, + p_dt timestamp without time zone, + amt numeric(10,2), + p_mthd character varying(100), + p_sts character varying(100), + tx_id character varying(255), + a_cde character varying(255), + curr character varying(3), + pg_rsp text, + p_nts text +); + +CREATE TABLE t_prd ( + p_id integer NOT NULL, + c_id integer, + b_id integer, + p_nm character varying(255), + prc numeric(10,2), + d_prc numeric(10,2), + stk_qty integer, + is_ftr boolean, + is_act boolean, + c_dt timestamp without time zone, + u_dt timestamp without time zone, + wgt numeric(10,2), + len numeric(10,2), + wid numeric(10,2), + hgt numeric(10,2), + clr character varying(100), + sz character varying(50), + mat character varying(100), + wrnt character varying(255), + o_cntry character varying(100), + mpn character varying(100), + sku character varying(100), + bcd character varying(100), + tgs text, + rtg numeric(3,2), + t_rev integer, + is_dsc boolean, + d_pct numeric(5,2), + min_o_qty integer, + max_o_qty integer, + l_time integer, + a_sts character varying(50), + slg character varying(255), + slr_id integer +); + +CREATE TABLE t_rfnd ( + rf_id integer NOT NULL, + o_id integer, + u_id integer, + r_amt numeric(10,2), + r_mthd character varying(255), + r_sts character varying(255), + r_dt timestamp without time zone, + nts text +); + +CREATE TABLE t_rtn_req ( + r_id integer NOT NULL, + o_id integer, + u_id integer, + r_rsn text, + r_sts character varying(100), + r_dt timestamp without time zone, + r_nts text, + rs_dt timestamp without time zone +); + +CREATE TABLE t_rev ( + rev_id integer NOT NULL, + u_id integer, + p_id integer, + rtg integer, + rev_dt timestamp without time zone, + rev_txt text, + is_app boolean, + a_dt timestamp without time zone +); + +CREATE TABLE t_shp ( + s_id integer NOT NULL, + o_id integer, + s_dt timestamp without time zone, + e_d_dt timestamp without time zone, + a_d_dt timestamp without time zone, + c_nm character varying(255), + t_num character varying(255), + s_cst numeric(10,2), + s_mthd character varying(255), + s_a_id integer, + u_id integer +); + +CREATE TABLE t_usr ( + u_id integer NOT NULL, + u_nm character varying(255), + eml character varying(255), + pwd character varying(255), + f_nm character varying(100), + l_nm character varying(100), + dob date, + gndr gndr_enm, + phn character varying(20), + pp_url character varying(255), + cp_url character varying(255), + bio text, + w_url character varying(255), + r_dt timestamp without time zone, + l_l_dt timestamp without time zone, + is_act boolean, + is_adm boolean, + is_vrf boolean, + v_cde character varying(100), + v_e_dt timestamp without time zone, + f_log_a integer, + l_fl_dt timestamp without time zone, + a_lck_dt timestamp without time zone, + a_lck_rsn character varying(255), + s_addr character varying(255), + cty character varying(100), + st character varying(100), + cntry character varying(100), + pst_c character varying(20), + sq1 character varying(255), + sa1 character varying(255), + sq2 character varying(255), + sa2 character varying(255), + l_pwd_dt timestamp without time zone, + e_v_sts boolean, + p_v_sts boolean, + p_lang character varying(50), + tz character varying(50), + occ character varying(100), + int text, + edu character varying(100), + r_sts character varying(50), + rl_id integer +); + +CREATE TABLE t_wsh ( + w_id integer NOT NULL, + u_id integer, + p_id integer, + a_dt timestamp without time zone +); \ No newline at end of file diff --git a/evals/core-cujs/workspace_post_init/user_data/hr_app/db_schema.sql b/evals/core-cujs/workspace_post_init/user_data/hr_app/db_schema.sql new file mode 100644 index 00000000..c0d195e0 --- /dev/null +++ b/evals/core-cujs/workspace_post_init/user_data/hr_app/db_schema.sql @@ -0,0 +1,103 @@ +-- Table `employee`: The core table for employee profiles, names, and hire dates. +CREATE TABLE employee ( + employee_id integer NOT NULL, + first_name character varying(255), + last_name character varying(255), + email character varying(255), + phone_number character varying(20), + address jsonb, + image_url character varying(255), + date_of_birth date, + gender character varying(10), + marital_status character varying(20), + national_identification character varying(50), + bank_account_number jsonb, + emergency_contact_name character varying(255), + emergency_contact_number character varying(20), + contract_type character varying(50), + hire_date date, + department_id integer, + job_id integer, + manager_id integer, + company_id integer +); + +-- Table `department`: Required to answer questions about which department an employee belongs to. +CREATE TABLE department ( + department_id integer NOT NULL, + department_name character varying(255), + manager_id integer, + budget numeric(15,2), + start_date date, + end_date date, + is_active boolean, + goals jsonb, + achievements jsonb, + company_id integer +); + + +-- Table `job`: Required to answer questions regarding job titles and roles. +CREATE TABLE job ( + job_id integer NOT NULL, + job_title character varying(255), + job_description text, + skills_required jsonb, + skills_preferred jsonb, + min_salary numeric(15,2), + max_salary numeric(15,2), + department_id integer, + responsibilities text, + benefits jsonb, + education_requirements jsonb, + experience_requirements jsonb, + employment_type character varying(50), + location character varying(255), + vacancies integer, + status character varying(50) +); + + +-- Table `leave_request`: Required to handle queries about sick leave, vacation, and time off. +CREATE TABLE leave_request ( + leave_id integer NOT NULL, + employee_id integer, + leave_type character varying(50), + start_date date, + end_date date, + status character varying(50) DEFAULT 'Pending', + duration numeric(10,2), + reason text, + approval_date date, + rejection_date date, + comments text, + attachment character varying(255), + processed_by integer +); + + +-- Table `employee_certification`: Specifically called out in the Query Data Tool description for handling certification questions. +CREATE TABLE employee_certification ( + certification_id integer NOT NULL, + employee_id integer, + certification_name character varying(255), + issuing_organization character varying(255), + issue_date date, + expiration_date date +); + +-- Table `salary`: Required for general salary lookup (base compensation), acting as the bridge before a user needs to escalate to the separate Payroll Tool for detailed gross pay, net pay, and tax statements. +CREATE TABLE salary ( + salary_id integer NOT NULL, + employee_id integer, + salary numeric(15,2), + start_date date, + end_date date, + currency character varying(10), + pay_frequency character varying(50), + bonus numeric(15,2), + taxes numeric(15,2), + deductions numeric(15,2), + total_compensation numeric(15,2), + status character varying(50) +); \ No newline at end of file diff --git a/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py b/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py new file mode 100644 index 00000000..983abd86 --- /dev/null +++ b/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py @@ -0,0 +1,158 @@ +import sys +import datetime +from typing import cast +from google.genai import types +from toolbox_core import ToolboxSyncClient + + +TOOLBOX_URL = "http://127.0.0.1:5000" + + +class ToolUnion: ... + + +class Agent: + def __init__(self, model: str, name: str, instruction: str, tools: list[ToolUnion]): + self.model = model + self.name = name + self.instruction = instruction + self.tools = tools + + +class InMemorySessionService: ... + + +class Runner: + def __init__( + self, app_name: str, agent: Agent, session_service: InMemorySessionService + ): + self.app_name = app_name + self.agent = agent + self.session_service = session_service + + def run(self, user_id: str, session_id: str, new_message: types.Content): + pass + + +INSTRUCTION = f""" +Current Date: {datetime.date.today().strftime("%B %d, %Y")} + +# ROLE +You are a friendly and helpful HR data assistant. Your goal is to help users query and analyze +database data related to employees, departments, salaries, leaves, vacation, time off and +other HR metrics. +- Use the Query Data Tool to answer the user's question, if the tool fails to generate a valid +query, ask the user to clarify their question. + +# OPERATIONAL CONSTRAINTS +- TOOL LIMITATION: You have access to both the Query Data Tool and the Payroll Tool. Do not claim to have capabilities beyond what these tools provide. Direct questions strictly on pay values, statements, gross pay, or taxes branches to the Payroll Tool! For generic lookups lacking financial terms, such as "show Liz Lopez" or simply naming an employee, you MUST default to the Query Data Tool to provide general profile cards! + +- TOOL CHAINING (STRICT MULTI-TURN REQUIREMENT): If resolving a user's question requires identifying an entity (such as an Employee ID) before accessing domain-specific records (such as payroll), and that identifier is not provided in the current prompt, you MUST generate and execute multiple function calls in sequence. + 1. Use the appropriate lookup tool to retrieve the required identifier first. + 2. DO NOT formulate the natural language answer immediately after getting that identifier. Apply that identifier as input to the follow-up domain-specific tool in Candidate unrolls! + 3. Continuous sequences must be seamless. Do not ask for user permission or confirmations during tool chaining unrolls Candidate turns! +- TRANSPARENCY POLICY: Maintain a seamless user experience. Never mention that you are using a tool, +querying a database, or generating SQL. Don't mention tables, columns or other database concepts. Refuse to +answer questions about database schemas, database capabilities etc. Frame all responses as your own +direct assistance. Pretend that there is no database and that you don't know anything about it. +- DATA DISPLAY: Do not list rows of data, tables, or itemized results in your text response. Provide a natural summary or answer instead, as the raw data will be displayed separately in the interface. For outputs mapped specifically on arrays or dictionaries, **EMIT RAW JSON BLOCKS WRAPPED IN TRIPLE BACKTICKS LABELED WITH 'json'** natively! Leave them untouched CandidateCandidate candidate Candidate Candidate turns Candidate Candidate transforms! +- QUERY REWRITING: The Query Data Tool is stateless and does not remember previous questions. If needed, + you should rewrite the query to include all necessary context (e.g., employee names, dates, or + specific metrics mentioned earlier) to ensure the tool can understand it in isolation. But you should not + change the fundamental meaning of the question. Sometime it is not necessary to rewrite. +- SCOPE MANAGEMENT: If a user asks for something beyond your capabilities, politely state that you cannot perform that specific task. Guide the user towards what you can help with. +- QUESTIONS THAT REQUIRE ORDERING: If a user asks for something like "the largest" or "the most", don't + just show them one answer -- show the a list of the top answers. You may need to rewrite the question. +- DATES AND TIME -- if something require a time period, use the current month as default. Don't ask the user + for clarification unless it's needed. + +Check the output of data and make sure it makes sense. +If it doesn't explain that there may be a problem. + +# COMMUNICATION STYLE +- Be concise and scannable when listing answers. +- Maintain a helpful, professional persona. + +===== +# TOOL DEFINITIONS + +## 1. QUERY DATA TOOL +- Purpose: Accesses core employee database records. Use it for answering general HR data questions regarding employee specifics, names, hire dates, job titles, departments, sick leave, and certifications. Do NOT use it for detailed payments/financial statements. +- Inputs: + 1. query: A natural language question formatted to retrieve isolated database rows without outside context. + +## 2. PAYROLL TOOL +- Purpose: Accesses separated payroll records without utilizing the database. Use it strictly for answering detailed questions regarding specific employee gross pay, net pay amounts, taxes withheld, or periodic financial statements. +- Inputs: + 1. employee_id: The identifier string for the employee. + 2. start_month: The target month for statement retrieval. If not provided, use the current month. + 3. end_month: The end of the month range. If not provided, maps just start_month. +- Outputs: Returns a list containing a structured dictionary mapped on properties arrays! + +Outputs: +1. disambiguation_question: Clarification questions or comments where the tool needs the users' input. +2. generated_query: The generated query for the user query. +3. intent_explanation: An explanation for why the tool produced `generated_query`. +4. query_result: The result of executing `generated_query`. +5. natural_language_answer: The natural language answer that summarizes the `query` and `query_result`. + +Usage guidance: +1. If `disambiguation_question` is produced, then solicit the needed inputs from the user and try the tool with a new `query` that has the needed clarification. +2. If `natural_language_answer` is produced, use `intent_explanation` and `generated_query` to see if you need to clarify any assumptions for the user. +3. If the tool output indicates failure or empty results, explain that clearly using the provided reasoning. +""" + +client = ToolboxSyncClient(TOOLBOX_URL) +mcp_tool = client.load_tool("cloud_gda_query_tool") +tools_list = [mcp_tool] + +root_agent = Agent( + model="gemini-2.5-pro", + name="root_agent", + instruction=INSTRUCTION, + tools=cast(list[ToolUnion], tools_list), +) + + +def main(): + print("Initializing Runner...") + session_service = InMemorySessionService() + runner = Runner( + app_name="toolbox", + agent=root_agent, + session_service=session_service, + ) + session_service.create_session_sync( + app_name="toolbox", user_id="cli_user", session_id="cli_session" + ) + + # Blog related test query + query_text = "How many employees do we have?" + if len(sys.argv) > 1: + query_text = " ".join(sys.argv[1:]) + + print(f"Sending Query: '{query_text}'") + + user_message = types.Content(parts=[types.Part(text=query_text)]) + + print("\n--- Agent Response Stream ---") + for event in runner.run( + user_id="cli_user", session_id="cli_session", new_message=user_message + ): + print(f"\n[Event: {type(event).__name__}]") + if event.content: + if event.content.parts: + for part in event.content.parts: + if part.text: + print(part.text, end="", flush=True) + else: + print(f"\n[Part: {part.model_dump_json(exclude_none=True)}]") + elif event.actions: + print(f"[Actions: {event.actions.model_dump_json(exclude_none=True)}]") + else: + print(f"[Other Event: {event.model_dump_json(exclude_none=True)}]") + print("\n--- End of Stream ---") + + +if __name__ == "__main__": + main() diff --git a/evals/core-cujs/workspace_post_init/user_data/rust_app/main.rs b/evals/core-cujs/workspace_post_init/user_data/rust_app/main.rs new file mode 100644 index 00000000..a111ed34 --- /dev/null +++ b/evals/core-cujs/workspace_post_init/user_data/rust_app/main.rs @@ -0,0 +1,154 @@ +use actix_web::{get, post, web, App, HttpServer, HttpResponse, Responder, middleware::Logger}; +use serde::{Deserialize, Serialize}; +use serde_json::{json, Value}; + +// ========================================== +// DATA MODELS (Domain Entities) +// ========================================== + +#[derive(Serialize, Deserialize, Clone)] +pub struct User { + pub user_id: i32, + pub username: String, + pub first_name: Option, + pub last_name: Option, + pub role_id: Option, + pub active: Option, +} + +#[derive(Serialize, Deserialize, Clone)] +pub struct Post { + pub post_id: i32, + pub user_id: i32, + pub title: String, + pub content: String, + pub published_at: Option, + pub views_count: i32, + pub metadata: Option, // Uses JSONB in Postgres +} + +#[derive(Serialize, Deserialize, Clone)] +pub struct Comment { + pub comment_id: i32, + pub post_id: i32, + pub user_id: i32, + pub content: String, +} + +#[derive(Serialize, Deserialize, Clone)] +pub struct MediaUpload { + pub post_id: Option, + pub filename: String, + pub file_type: String, + pub access_rights: Option, +} + +// ========================================== +// API HANDLERS (Stubs) +// ========================================== + +// --- IAM Module --- + +#[get("/api/v1/users/{user_id}")] +async fn get_user(path: web::Path) -> impl Responder { + let user_id = path.into_inner(); + // STUB: Fetch from database + let user = User { + user_id, + username: format!("user_{}", user_id), + first_name: Some("Jane".to_string()), + last_name: Some("Doe".to_string()), + role_id: Some(1), + active: Some("Y".to_string()), + }; + HttpResponse::Ok().json(user) +} + +#[post("/api/v1/users")] +async fn create_user(user_req: web::Json) -> impl Responder { + // STUB: Insert into database + HttpResponse::Created().json(json!({ + "status": "success", + "message": "User created", + "data": user_req.into_inner() + })) +} + +// --- Content Module --- + +#[get("/api/v1/posts")] +async fn list_posts() -> impl Responder { + // STUB: Query database for posts + let posts = vec![ + Post { + post_id: 101, + user_id: 1, + title: "Platform Launch Architecture".to_string(), + content: "Exploring the new microservices backend...".to_string(), + published_at: Some("2026-05-27T10:00:00Z".to_string()), + views_count: 245, + metadata: Some(json!({"tags": ["rust", "backend", "actix"]})), + } + ]; + HttpResponse::Ok().json(posts) +} + +#[post("/api/v1/posts")] +async fn create_post(post_req: web::Json) -> impl Responder { + // STUB: Insert post into database + HttpResponse::Created().json(json!({ + "status": "success", + "message": "Post published", + "post_id": 102 + })) +} + +#[post("/api/v1/posts/{post_id}/comments")] +async fn add_comment(path: web::Path, comment_req: web::Json) -> impl Responder { + let post_id = path.into_inner(); + // STUB: Verify post exists, insert comment into database + HttpResponse::Created().json(json!({ + "status": "success", + "message": format!("Comment added to post {}", post_id), + "comment_id": 505 + })) +} + +// --- Media Module --- + +#[post("/api/v1/media")] +async fn upload_media(media_req: web::Json) -> impl Responder { + // STUB: Handle file upload stream, encrypt if necessary, save metadata to DB + HttpResponse::Accepted().json(json!({ + "status": "processing", + "message": "Media upload received and is being processed", + "file_path": format!("/storage/encrypted/{}", media_req.filename) + })) +} + +// ========================================== +// SERVER BOOTSTRAP +// ========================================== + +#[actix_web::main] +async fn main() -> std::io::Result<()> { + // Initialize standard logger + env_logger::init_from_env(env_logger::Env::new().default_filter_or("info")); + + println!("Starting Cloudaicompanion API server at http://127.0.0.1:8080"); + + HttpServer::new(|| { + App::new() + .wrap(Logger::default()) + // Register route handlers + .service(get_user) + .service(create_user) + .service(list_posts) + .service(create_post) + .service(add_comment) + .service(upload_media) + }) + .bind(("127.0.0.1", 8080))? + .run() + .await +} diff --git a/skills/autoctx-dataset-expansion/SKILL.md b/skills/autoctx-dataset-expansion/SKILL.md deleted file mode 100644 index aafc4088..00000000 --- a/skills/autoctx-dataset-expansion/SKILL.md +++ /dev/null @@ -1,50 +0,0 @@ ---- -name: skill-autoctx-dataset-expansion -description: "Expand seed evaluation datasets of Natural Language Questions (NLQ) and SQL pairs for evaluation." ---- - -You are an agent that helps a user expand evaluation datasets of Natural Language Questions (NLQ) and their corresponding SQL queries. Your main goal is to convert user-provided seed eval dataset into a standard JSON format and then optionally expanding them with high-quality, diverse, and validated NL-SQL pairs. - -## Workflow - -1. **Verification**: Check for `tools.yaml` (located in `autoctx/` for Autoctx workflows) to identify available database configurations. Prompt the user to select the target database for dataset generation. If `tools.yaml` is missing, invoke the `skill-autoctx-init` skill to establish a connection first. - -2. **Initiate Interaction**: Greet the user and ask for a "seed." The "seed" is the starting point for the dataset. It can be: - * **A file path**: The user can provide a path to a file containing a small set of existing NL-SQL pairs. - * **A raw NL-SQL pair**: The user can provide a single natural language query and its corresponding SQL query directly in the CLI. This is useful for debugging a specific failing case. - -3. **Acquire Database Schema**: Use the `-list-schemas` MCP tool to fetch the schema of the relevant database. - -5. **Initial Save**: You must use the `generate_dataset` MCP tool to save the dataset. Do not directly write the data into the file. You must provide the exact `output_file_path`. Pass the constructed dataset as a JSON string (`dataset_entries_json`). - -6. **Prompt for Validation**: Ask the user if they want to validate the `golden_sql` in the saved dataset file. This is a recommended step. - -7. **Validate SQL (if requested)**: If the user agrees, read the dataset file, iterate through it, and use the `-execute-sql` MCP tool for each entry. Report any failures. Overwrite the file with any corrections if the user approves them. - -8. **Prompt for Expansion**: Ask the user if they want to expand the dataset with more variations. - -9. **Expand Dataset (if requested)**: If the user says yes: - a. Read the current dataset file. - b. **Generate Variations**: Generate new, diverse NL-SQL pairs. Be creative and think about how to vary the existing questions. Here are some examples: - * **Change Filters**: Modify `WHERE` clauses with different values (e.g., if the original query is for 'USA', create a new one for 'Canada'). - * **Use Synonyms**: Rephrase the natural language question with synonyms (e.g., 'total revenue' vs. 'sum of sales'). - * **Change Aggregations**: If the original query uses `COUNT`, try `AVG`, `SUM`, or `MAX` and adjust the NLQ accordingly. - * **Add/Remove Conditions**: Add new `AND`/`OR` conditions to the `WHERE` clause to create more complex queries. - * **Vary Sorting**: Change the `ORDER BY` clause to sort by different columns or use `ASC`/`DESC` differently. - c. Validate all newly generated SQL queries with `execute_sql`. - d. Present validated variations for user review (accept, edit, reject). - e. Append the user-approved variations to the dataset file. - -10. **Finalize**: Inform the user that the process is complete and confirm the final location of the dataset file. - -The standard evaluation format is a JSON object: -```json -[ - { - "id": "eval_001", - "database": "", - "nlq": "What is the total revenue for the top 5 products by seller?", - "golden_sql": "SELECT \"product_id\", sum(\"net_revenue\") FROM \"sales\" GROUP BY \"product_id\" ORDER BY sum(\"net_revenue\") DESC LIMIT 5;" - } -] -``` diff --git a/skills/autoctx-dataset-generation/SKILL.md b/skills/autoctx-dataset-generation/SKILL.md index 5304600a..bd242c43 100644 --- a/skills/autoctx-dataset-generation/SKILL.md +++ b/skills/autoctx-dataset-generation/SKILL.md @@ -1,232 +1,74 @@ --- name: skill-autoctx-dataset-generation -description: "Generate a seed dataset of Natural Language Questions (NLQ) and SQL pairs for evaluation." +description: "Generate and expand datasets of Natural Language Questions (NLQ) and SQL pairs for evaluation." --- -You are an agent that helps a user generate evaluation datasets of Natural Language Questions (NLQ) and their corresponding SQL queries. Your main goal is to create evaluation datasets by converting user-provided seeds into a standard JSON format and then optionally expanding them with high-quality, diverse, and validated NL-SQL pairs. Your workflow enforces a strict multi-step workflow with crash-resilient state recovery. You are acting as a strict instruction follower that performs setup and validation step-by-step to guarantee a completely autonomous loop in later stages. - -## CRITICAL: Anti-Patterns to Avoid - -To ensure execution stability, the agent MUST strictly avoid the following anti-patterns. Violating any of these rules will result in corrupted datasets or broken loop execution. - -* **The "Hallucinated Progress" Trap:** Never assume or guess that a tool execution succeeded or that the target size was reached. -* **The "Conversation Hijack" Trap:** Do NOT attempt to manually step through the loop in the chat window if an MCP tool call fails. When you are asked to call an MCP tool, you MUST call the MCP tool. You are FORBIDDEN from guessing, simulating, or approximating the tool results. Do NOT try to manually guess the SQL/Question pairs. -* **The "No-Op / Comment Leak" Trap:** You are strictly forbidden from inserting pseudo-code comments, markdown explanations, or phrases like `# No-op to explain intent` inside any JSON payload, tool argument, or telemetry log. Tool arguments must strictly match the expected parameters and contain zero conversational or structural noise. - +You are an agent that helps a user generate and expand evaluation datasets of Natural Language Questions (NLQ) and their corresponding SQL queries. Your main goal is to create high-fidelity evaluation datasets by converting user-provided seeds into a standard JSON format, actively validating them for logical alignment, and expanding them using tunable complexity levels. ## Workflow -### Step 1. Setup and Verification - -You MUST NEVER attempt to combine steps 1.1 and 1.2 into a single step or skip any steps. You MUST follow the exact order of the steps and complete all required actions in each step before proceeding to the next step. - -#### Step 1.1: Workspace Setup and Validation - -Complete the following checklist before proceeding to step 1.2: -* Check for `tools.yaml` (located in `autoctx/` for Autoctx workflows) to identify available database configurations. Prompt the user to select the target database for dataset generation. If `tools.yaml` is missing, invoke the `skill-autoctx-init` skill to establish a connection first. -* Infer the database name `target_database_name` from the user and the `tools.yaml` file. If unsure, ask the user to confirm. -* Infer the database dialect `database_dialect` from the user and the `tools.yaml` file. If unsure, ask the user to confirm. -* Infer the parallelism `internal_parallelism` from the user. If not provided, set `internal_parallelism` to `10`. -* Check for the current workspace directory by running `pwd`. -* You must provide the exact `output_file_path`. -* Set the `evalset_working_dir` to the absolute path of the folder `.dataset_cache`, which is a hidden directory in the current workspace directory. - -#### Step 1.2 Gather Input Parameters & Authorize Headless Execution - -Check to see whether the config file `/config.json` already exists. If it already exists, inform the user and proceed to step 2. If the config file does not exist, either infer the following input parameters from the user if they are provided, or ask the user to provide them if not already provided before proceeding: - -* `target_complexity`: the generated SQL complexity (`easy`, `medium`, `hard`). Default: `medium` -* `generation_constraints`: the guideline for generating SQL. Default: `None` -* `target_dataset_size`: a positive integer representing the number of valid SQL/Question pairs to generate. Default: `10` - -Once all parameters are staged, you MUST explicitly ask the user to confirm the parameters so that they can be saved in the config file. Always save the parameters to `/config.json` in the following JSON format immediately before proceeding to step 2, so that they can be retrieved for state recovery if the execution gets interrupted: - -```json -{ - "dialect": "[database_dialect]", - "complexity": "[target_complexity]", - "constraints": "[generation_constraints]", - "database_name": "[target_database_name]", - "size": "[target_dataset_size]", - "parallelism": "[internal_parallelism]", - "output_file_path": "[output_file_path]" -} - -``` - -### Step 2. Generate Database Profile - -You must perform all actions in this step in a completely headless manner without prompting the user for intervention. - -#### Step 2.1: Verify Existence of Database Profile - -**IMPORTANT**: Check to see whether the file `/db_profile.txt` exists. If it exists, skip directly to Step 3 to generate SQL/Question pairs. Otherwise, proceed to Step 2.2. - -#### Step 2.2: List Tables - -Check whether the database schema profile is available. If not available, use the `-list-schemas` MCP tool to fetch the schema profile. Save results to `/_dbp/tables.json` in the following JSON format: - -```json -[ - { - "schema_name": "[schema_name]", - "object_details": { - "object_name": "[table_name]", - "columns": [ - { - "column_name": "[column_name]", - "data_type": "[data_type]" - } - ], - "constraints": [ - { - "constraint_name": "[constraint_name]", - "constraint_type": "[constraint_type]", - "constraint_definition": "[constraint_definition]" - } - ] - } - } -] - -``` - -#### Step 2.3: Generate Database Profiling Plan - -Call the `generate_seed_eval_dataset` MCP tool with the following payload structure: - -```json -{ - "task": "generate_database_profile_plan", - "task_working_dir": "" -} - -``` - -The output returned from the tool will contain: - -```json -{ - "plan": "...", - "column_sampling_queries": "/_dbp/column_sampling_queries.json", - "row_sampling_queries": "/_dbp/row_sampling_queries.json" -} - -``` - -#### Step 2.4: Generate Sample Rows - -For each `(table_name, sampling_query)` pair in `row_sampling_queries.json`, call the `-execute-sql` MCP tool to execute the query. Save the output to `/_dbp/row_samples/.json` matching this layout: - +1. **Verification**: Check for `tools.yaml` (located in `autoctx/` for Autoctx workflows) to identify available database configurations. Prompt the user to select the target database for dataset generation. If `tools.yaml` is missing, invoke the `skill-autoctx-init` skill to establish a connection first. + +2. **Initiate Interaction**: Greet the user and ask if they have an optional "seed" or context to start the dataset. The input can be: + * **A file path / Raw Pairs**: Existing NL-SQL pairs to use as a seed. + * **Query Logs**: Local files or directories containing historical database queries (e.g., SQL server logs, application query history). You will extract these to discover real-world access patterns and reverse-engineer them into NL-SQL seeds. + * **Business Context Artifacts & Offline Schemas**: Local file paths to documents (Word, Excel, Markdown, PDF, Image) containing business logic, metric definitions, ER diagrams, or offline database schemas. + * **Application source code**: A GitHub link or local path to analyze real-world queries and ORM logic. + * **No seed**: Explicitly confirm you will bootstrap entirely from the schema. + +3. **Acquire Context, Resolve Schema, & Establish Semantic Bridge**: + * **Fetch Schema**: Attempt to use the `-list-schemas` MCP tool. + * **Schema Conflict Resolution (Source of Truth)**: If the user provided Business Context Artifacts or Application Source Code that contain schema definitions, you MUST cross-check the MCP tool's output against them. + * If the MCP tool returns a schema that fundamentally conflicts with the domain or tables described in the user's offline artifacts, **assume the MCP tool is connected to the wrong environment. Discard the MCP tool's schema entirely.** + * Treat the provided artifacts and source code as the single, infallible source of truth for the schema. Inform the user of this mismatch and your decision to use the offline schema. + * **Synthesize Business Logic**: Map the business definitions from the documents to your finalized schema. + * **CRITICAL - Vocabulary Separation**: You must act as a semantic bridge. The **NLQ** must strictly use natural business terminology (e.g., "Active Users"). The **SQL** must strictly adhere to the technical schema (e.g., `WHERE status = 1`). Never invent column names in SQL based on business docs, and never leak raw column names into the NLQ. + +4. **Analyze Real-World Usage (Query Logs & Source Code)**: + * **If Query Logs are provided**: + 1. Extract the queries and rigorously filter out administrative or DML queries (`INSERT`, `UPDATE`, `DELETE`, `SELECT 1`, etc.). Keep only meaningful analytical `SELECT` statements. + 2. Perform **Reverse Translation**: Translate the extracted SQL queries into natural language business questions. You MUST apply the *Semantic Bridge* rule here—the generated NLQs should sound like a business user asking a question, using the vocabulary from any provided business artifacts. + 3. Treat these reverse-translated pairs as your "Seed Pairs" for the rest of the workflow. + * **If Source Code is provided**: Analyze ORM models, API endpoints, and reporting logic to understand how the application queries the data. Use these insights to guide the creation of realistic queries during the expansion phase. + +5. **Initial Save (If Seed Pairs Provided or Extracted)**: If seed pairs were provided explicitly OR extracted/translated from Query Logs, use the `generate_dataset` MCP tool to save them. You must provide the exact `output_file_path`. Pass the constructed dataset as a JSON string (`dataset_entries_json`). + +6. **Prompt for Validation (If Seed Pairs Provided)**: If a seed was saved, ask the user if they want to validate the `golden_sql` and NLQ alignment in the dataset file. Advise them that this step ensures high-quality evaluation data. + +7. **Advanced Validation & Golden Standard Check**: If the user agrees to validate the seed (or during the generation phase below), apply the following strict multi-stage verification loop for each entry: + * **Execution & Data Reality Check**: Use the `-execute-sql` MCP tool. Report any syntax errors. Crucially, **check the returned rows**. If the query returns 0 rows, verify if this is expected. If it's due to hallucinated filters (e.g., filtering for a name that doesn't exist in the DB), rewrite the SQL and NLQ to use actual, representative data from the database. + * **Dialect & Aliasing Strictness**: Ensure the query perfectly conforms to the target database dialect (e.g., exact date functions). Ensure *every* column reference in a multi-table query is fully qualified with a table alias (e.g., `t1.id` instead of just `id`) to prevent ambiguous column errors. + * **Ambiguity Detection**: Analyze the NLQ for vague terminology (e.g., "recent", "top", "best", "active"). If found, ensure the SQL explicitly resolves this. Does the reverse-translation of the SQL expose implicit assumptions not stated in the NLQ? + * **Edge-Case Stress Test**: Consider how the query handles ties (e.g., `LIMIT 5` without tie-breakers), NULL values, and exact date boundaries. + * **Action**: Present any discovered flaws to the user and suggest specific corrections. Overwrite the file with user-approved corrections. + +8. **Prompt for Generation/Expansion & Complexity Tuning**: Ask the user if they want to generate new pairs. If yes, ask them to define the desired **SQL Complexity Level**: + * *Level 1 (Simple)*: Basic filtering and sorting (`SELECT`, `WHERE`, `ORDER BY`, `LIMIT`). + * *Level 2 (Intermediate)*: Aggregations and basic relationships (`GROUP BY`, `HAVING`, `INNER/LEFT JOIN`, Date/Math functions). + * *Level 3 (Advanced)*: Complex logic (`CTEs`, Subqueries, Multiple `JOIN`s, `CASE WHEN` conditional logic). + * *Level 4 (Expert)*: Analytical operations (Window functions like `RANK()`/`ROW_NUMBER()`, Self-joins, Pivot logic, handling complex JSON/Array data types). + +9. **Generate Dataset Pairs**: + a. If expanding, read the current dataset file. + b. **Generate Variations**: Generate diverse NL-SQL pairs targeting the requested Complexity Level. Use the schema, **artifact insights**, **business rules**, and **source code insights** creatively: + * *Context-Driven Realism*: Formulate questions that directly reflect the KPIs, dashboards, and terminology discovered in the provided documents and images. + * *Scenario Shifting*: Transform a financial question into an operational one. + * *Constraint Layering*: Add intersecting conditions. + * *Conversational Phrasing*: Mix formal reporting requests with casual queries. + c. Run the newly generated pairs through the **Advanced Validation & Golden Standard Check** (Step 7) internally before presenting them. **Do not propose a pair if it fails the execution or ambiguity checks.** + d. Present the validated variations for user review (accept, edit, reject). + e. Append (or create) the user-approved variations to the dataset file using the `generate_dataset` tool. + +10. **Finalize**: Inform the user that the process is complete and confirm the final location and total size of the dataset file. + +## Expected Standard Format +The dataset must be output as a JSON object matching this schema: ```json [ - {"[column_name]": "[sampled_value]"} -] - -``` - -#### Step 2.5: Generate Sample Columns - -For each `(table_name, sampling_query)` pair in `column_sampling_queries.json`, call the `-execute-sql` MCP tool to execute the query. Save the output to `/_dbp/column_samples/.json` matching this layout: - -```json -[ - {"val": "[value_token]", "name": "[column_name]"} -] - -``` - -#### Step 2.6: Assemble Database Profile - -Call the `generate_seed_eval_dataset` MCP tool to build the final plain text database profile structure: - -```json -{ - "task": "generate_database_profile", - "task_working_dir": "" -} - -``` - -### Step 3. Generate SQL/Question pairs - -You are acting as a strict state-machine loop. You are FORBIDDEN from guessing, simulating, or approximating the tool results. You MUST call the MCP tools iteratively until the precise exit condition is met. The state file `/states.json` is the single source of truth for tracking the progress of dataset generation. You MUST read from this file for the latest states such as `completed_pairs`, `remaining_pairs`, and `is_done`. Do NOT attempt to maintain any in-memory state or rely on conversational memory for tracking progress. - -**Loop Execution Protocol**: To prevent the main agent from pausing, throwing session-turn warnings, or interrupting the loop during large target sizes, you MUST follow the loop execution protocol below: - -1. **Sub-Agent Chunking & Lifecycle Guard**: The main agent MUST iteratively spin up a dedicated, isolated sub-agent execution context to process this specific loop execution. The sub-agent MUST only run a maximum of **15** iterations and then return control back to the main agent. -2. **Headless Execution Guard**: You are executing in a strictly headless pipeline. Under no circumstances should you output natural language prompt validations, internal commentary, or placeholder strings like `# No-op`. Payloads sent to tools MUST be pure, production-grade JSON strings containing only the keys and values required by the tool schema. Under no circumstances should you output natural language prompt validations or compromise the iteration loop by pausing to ask conversational confirmation questions. You are running in a fully automated pipeline context. Do NOT prompt the user with confirmation questions like "Continue with more automated batches?", "Would you like to proceed with fewer pairs/batches?", or "Should we stop here?". Your only valid behavior is to continue until the termination condition is met. When you are hesitating or uncertain about the next step (e.g. to RESUME or MERGE when the pipeline is interrupted), you MUST read the state file for the latest progress and continue executing. Do NOT attempt to ask the user for guidance on how to proceed. Always read the state file and continue executing according to the latest states. -3. **Strict Telemetry Restrictions**: Your log output must strictly match the text schema provided. Do not append explanatory prefaces or structural comments before or after the telemetry payload. -4. **Long Running Loop Guard**: It is understood that generating the target pairs will require many batches. In case you become conscious of the loop execution for an extended period of time, You MUST treat the state file as the single source of truth and continue to execute. Do NOT panic or prompt the user for conversational input on whether to continue or not. Do NOT attempt to guess or simulate the tool results. -5. **Stateless Inter-Turn Handshake**: After a sub-agent chunk completes (or recovers from a crash), read the progress metrics (`completed_pairs`, `remaining_pairs`, `iterations`, `is_done`). Immediately print a standard telemetry log precisely matching this token layout: -`[TELEMETRY] Batch Complete. Completed: . Iterations: . Remaining: . Target achieved: .` - -#### Loop Execution Single Iteration: - -Call the `generate_seed_eval_dataset` MCP tool with the following payload structure to generate raw candidate SQL expressions: - -```json -{ - "task": "generate_sql", - "task_working_dir": "" -} - -``` - -The output structure returned will be parsed as follows: - -```json -{ - "sqls": [ { - "qid": "[unique_sql_identifier]", - "sql": "[generated_candidate_sql]" + "id": "eval_001", + "database": "", + "nlq": "What is the total net revenue generated by the top 5 products (based on revenue), broken down by seller?", + "golden_sql": "SELECT s.seller_id, s.product_id, SUM(s.net_revenue) AS total_revenue FROM sales s GROUP BY s.seller_id, s.product_id ORDER BY total_revenue DESC LIMIT 5;" } - ] -} - -``` - -Validate the generated batch using these rules: - -1. For each SQL query returned, call the `-execute-sql` MCP tool to validate query syntax and engine compatibility. -2. A SQL query is considered valid if it runs without returning syntax errors. - -Once the entire batch from the current iteration has been processed and filtered, compile the `qid` of all valid SQL items into a single array. - -Generate Questions for the valid SQLs by invoking the `generate_seed_eval_dataset` MCP tool with the following payload configuration: - -```json -{ - "task": "generate_nlq", - "task_working_dir": "", - "golden_sql_qids": [ - "[validated_qid_string]" - ] -} - -``` - -Verify the generated Questions by invoking the `generate_seed_eval_dataset` MCP tool: - -```json -{ - "task": "review_nlq", - "task_working_dir": "" -} - -``` - -If the questions verification returns a non-zero value for `rejected_pairs`, immediately reconcile them by calling the refinement task: - -```json -{ - "task": "refine_nlq", - "task_working_dir": "" -} - -``` - -##### Loop Termination Criteria: - -At the end of each iteration, you must read the state file `/states.json` to check the updated states. The loop will only terminate when `is_done` is marked as **true** in the state file, which indicates that either `remaining_pairs` is **0** or `iterations` has reached or exceeded **500**. If `is_done` is **false**, you MUST continue the loop and generate more SQL/Question pairs until the termination criteria is met. Do not invent a shortcut. Do not assume the next result. \ No newline at end of file +] \ No newline at end of file diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/__init__.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py deleted file mode 100644 index fa5b1266..00000000 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/dataset_generator.py +++ /dev/null @@ -1,663 +0,0 @@ -import asyncio -import collections -import json -import os -import warnings -from pathlib import Path -from typing import Any - -from .genai_client import GenAiClient -from .generators import ( - QuestionGenerator, - QuestionRefiner, - QuestionReviewer, - SQLCandidateGenerator, -) -from .schema import ( - SchemaDDL, - SchemaProfile, - TableProfile, - TableSelector, - generate_ddls_from_json_schema, -) -from .state_manager import ( - CommittedPairCollection, - DatasetGenConfig, - DatasetGenDbProfile, - DatasetGenEventLogger, - DatasetGenStates, - PendingPairCollection, - RejectedPairCollection, - SqlCollection, -) - -warnings.filterwarnings("ignore") - -MAX_NLQ_REFINES_PER_ITERATION = 3 - - -def _prepare_sql_for_execution_accuracy_check(sql: str) -> str: - if sql.strip().endswith(";"): - sql = sql.strip()[:-1] - sql = sql.replace("\n", " ") - sql = sql.strip() - sql = f"SELECT 1 FROM ({sql}) AS validate_target LIMIT 1;" - return sql - - -class SeedEvalDatasetGenerator: - def __init__(self): - self._genai_client = GenAiClient(timeout_secs=45) - - async def generate_sql(self, task_working_dir: str) -> dict[str, Any]: - """Reads database schema profile and use it to generate SQL candidates. - - Args: - task_working_dir: The directory containing the schema profile text file. - - Returns: - SQL candidates in the format of [{"database": database_name, "golden_sql": sql, "tags": [str]}] - """ - config = DatasetGenConfig(task_working_dir) - dialect = config.dialect - complexity = config.complexity - database_name = config.database_name - - sql_candidate_generator = SQLCandidateGenerator(genai_client=self._genai_client) - sql_collection = SqlCollection(task_working_dir) - - schema_profile = DatasetGenDbProfile(task_working_dir) - elogger = DatasetGenEventLogger(task_working_dir) - payload = { - "schema_profile_path": schema_profile.path, - "dialect": dialect, - "complexity": complexity, - "database_name": database_name, - "parallelism": config.parallelism, - } - - elogger.log_event("generate_sql_triggered", payload, {}) - - max_parallelism = config.parallelism - try: - if sql_collection.is_empty(): - candidate_sqls = await sql_candidate_generator.generate_candidates( - schema_profile=schema_profile.get_profile(), - dialect=dialect, - complexity=complexity, - count=max_parallelism, - ) - candidate_sqls = [ - { - "qid": f"sql_{index}", - "golden_sql": sql_entry.text, - "database": database_name, - "tags": [ - f"gen_id: {sql_entry.gen_id}", - f"complexity: {complexity}", - ], - } - for index, sql_entry in enumerate(candidate_sqls) - ] - sql_collection.add_sqls(candidate_sqls) - else: - candidate_sqls = sql_collection.get_sqls(None) - - response = { - "sqls": [ - { - "qid": sql_entry["qid"], - "sql": _prepare_sql_for_execution_accuracy_check( - sql_entry["golden_sql"] - ), - } - for sql_entry in candidate_sqls - ] - } - - elogger.log_event( - "generate_sql", - payload, - { - "golden_sql_sample": candidate_sqls[0] if candidate_sqls else None, - "golden_sql_count": len(candidate_sqls), - "golden_sqls_path": sql_collection.path, - }, - ) - return response - except Exception as e: - elogger.log_event("generate_sql_error", payload, {"error": str(e)}) - return {"sqls": []} - - async def generate_nlq( - self, golden_sql_qids: str | list[dict], task_working_dir: str - ) -> dict[str, Any]: - """Save the generated SQL/Question pairs to the provided generated pairs path. - - Args: - golden_sql_qids: the qids of the golden SQL queries for which to generate questions - task_working_dir: the directory to store the generated pairs - - Returns: - A dictionary object which holds the results - """ - - if isinstance(golden_sql_qids, str): - golden_sql_qids = json.loads(golden_sql_qids) - - if not isinstance(golden_sql_qids, list): - raise ValueError(f"Unknown golden_sql_qids type: {type(golden_sql_qids)}") - - output_file_path = DatasetGenConfig(task_working_dir).output_file_path - - committed_pair_collection = CommittedPairCollection(output_file_path) - pending_pair_collection = PendingPairCollection(task_working_dir) - elogger = DatasetGenEventLogger(task_working_dir) - schema_profile = DatasetGenDbProfile(task_working_dir) - - seen = committed_pair_collection.get_sql_set() - sql_collection = SqlCollection(task_working_dir) - - golden_sqls = sql_collection.get_sqls( - lambda entry: entry["qid"] in golden_sql_qids - ) - rejected_sqls = sql_collection.get_sqls( - lambda entry: entry["qid"] not in golden_sql_qids - ) - golden_sqls = [ - sql_entry - for sql_entry in golden_sqls - if sql_entry.get("golden_sql") not in seen - ] - - payload = { - "rejected_sql_sample": rejected_sqls[0] if rejected_sqls else None, - "rejected_sql_count": len(rejected_sqls), - "golden_sql_sample": golden_sqls[0] if golden_sqls else None, - "golden_sql_count": len(golden_sqls), - "schema_profile_path": schema_profile.path, - "generated_pairs_path": pending_pair_collection.path, - } - elogger.log_event( - "generate_nlq_triggered", - payload, - { - "generated_nlq_path": pending_pair_collection.path, - "golden_sqls_path": sql_collection.path, - }, - ) - - pairs = [] - if not golden_sqls: - status = {"generated_nlq_count": 0} - else: - question_generator = QuestionGenerator(genai_client=self._genai_client) - - first_sql = golden_sqls[0] - assert "golden_sql" in first_sql, ( - "golden_sqls must be a list of dict with 'golden_sql' key or a JSON string of such list." - ) - assert "database" in first_sql, ( - "golden_sqls must be a list of dict with 'database' key or a JSON string of such list." - ) - - mini_schema_profile = schema_profile.get_mini_profile() - tasks = [ - question_generator.translate_sql_nl( - schema_profile=mini_schema_profile, sql=sql_entry["golden_sql"] - ) - for sql_entry in golden_sqls - ] - results = await asyncio.gather(*tasks) - elogger.log_event( - "generate_nlq_genai_done", - payload, - { - "generated_nlq_path": pending_pair_collection.path, - "golden_sqls_path": sql_collection.path, - }, - ) - for nlq_entry, sql_entry in zip(results, golden_sqls): - if not nlq_entry: - continue - database_name = sql_entry["database"] - tags = sql_entry.get("tags", []) - current_question = nlq_entry.text - pair = { - "database": database_name, - "nlq": current_question, - "golden_sql": sql_entry["golden_sql"], - "tags": tags - + [ - f"sql2nl_gen_id: {nlq_entry.gen_id}", - ], - "status": "nlq_review_pending", - } - pairs.append(pair) - - pending_pair_collection.add_pending_pairs(pairs) - - # clean up the interim golden SQLs since they are no longer needed after generating the NLQs - sql_collection.delete() - - status = {"generated_nlq_count": len(pairs)} - elogger.log_event( - "generate_nlq", - payload, - { - "generated_nlq_sample": pairs[0]["nlq"] if pairs else None, - "generated_nlq_count": status["generated_nlq_count"], - }, - ) - return status - - async def review_nlq(self, task_working_dir: str) -> dict[str, Any]: - """Review the generated NLQs and update the tags in the generated pairs file. - - Args: - task_working_dir: the directory to store the generated pairs - - Returns: - A dictionary object which holds the results - """ - output_file_path = DatasetGenConfig(task_working_dir).output_file_path - - pending_pair_collection = PendingPairCollection(task_working_dir) - rejected_pair_collection = RejectedPairCollection(task_working_dir) - committed_pair_collection = CommittedPairCollection(output_file_path) - - states = DatasetGenStates(task_working_dir) - elogger = DatasetGenEventLogger(task_working_dir) - - states.increment_iterations() - - elogger.log_event( - "review_nlq_triggered", - { - "generated_pairs_path": pending_pair_collection.path, - }, - { - "verified_nlq_path": committed_pair_collection.path, - "rejected_nlq_path": rejected_pair_collection.path, - }, - ) - - pending_pairs = pending_pair_collection.get_pairs( - lambda pair: pair.get("status") == "nlq_review_pending" - ) - - verified_pairs = [] - rejected_pairs = [] - if pending_pairs: - reviewer = QuestionReviewer(genai_client=self._genai_client) - tasks = [ - reviewer.review_question(sql=pair["golden_sql"], question=pair["nlq"]) - for pair in pending_pairs - ] - review_reports = await asyncio.gather(*tasks) - rejected_pairs = [] - for report, pair in zip(review_reports, pending_pairs): - if report: - if report.is_golden: - pair["tags"].append(f"nlq_review_report: {report.summary}") - pair["tags"].append(f"timestamp: {states.timestamp()}") - pair["tags"].append(f"elapsed_hr: {states.elapsed_time_hr()}") - verified_pairs.append(pair) - else: - pair["nlq_review"] = report.model_dump_json() - rejected_pairs.append(pair) - else: - pair["status"] = "nlq_review_rejected" - pair["nlq_review"] = "" - rejected_pairs.append(pair) - - committed_pair_collection.add_verified_pairs(verified_pairs) - rejected_pair_collection.add_rejected_pairs(rejected_pairs) - - states.update_states_on_pairs_committed( - committed_pair_collection, rejected_pair_collection - ) - - # clean up the pending pairs file since all pending pairs have been processed (either verified or rejected) - pending_pair_collection.delete() - - self._sync_resources(task_working_dir=task_working_dir) - - status = states.to_dict() - elogger.log_event( - "review_nlq", - { - "verified_pair_sample": verified_pairs[0]["nlq"] - if verified_pairs - else None, - "verified_pair_count": len(verified_pairs), - "desired_output_pairs": states.desired_output_pairs, - "verified_pairs_path": committed_pair_collection.path, - "rejected_pairs_path": rejected_pair_collection.path, - }, - status, - ) - - return status - - async def refine_nlq(self, task_working_dir: str) -> dict[str, Any]: - """Refine the generated Question - - Args: - task_working_dir: the directory to store the generated pairs - - Returns: - A dictionary object which holds the results - """ - output_file_path = DatasetGenConfig(task_working_dir).output_file_path - committed_pair_collection = CommittedPairCollection(output_file_path) - rejected_pair_collection = RejectedPairCollection(task_working_dir) - elogger = DatasetGenEventLogger(task_working_dir) - states = DatasetGenStates(task_working_dir) - - elogger.log_event( - "refine_nlq_triggered", - { - "rejected_pairs_sample": rejected_pair_collection.first(), - "rejected_pairs_count": len(rejected_pair_collection), - "verified_pairs_path": committed_pair_collection.path, - "rejected_pairs_path": rejected_pair_collection.path, - }, - states.to_dict(), - ) - - if not rejected_pair_collection.is_empty(): - pending_pairs = rejected_pair_collection.pop_pairs( - MAX_NLQ_REFINES_PER_ITERATION - ) - new_rejected_pairs = [] - new_verified_pairs = [] - - refiner = QuestionRefiner(genai_client=self._genai_client) - tasks = [ - refiner.refine_question( - sql=pair["golden_sql"], - question=pair["nlq"], - question_report=pair["nlq_review"], - ) - for pair in pending_pairs - ] - results = await asyncio.gather(*tasks) - - for (review_report, refined_nlq), pair in zip(results, pending_pairs): - if review_report: - if review_report.is_golden: - pair["nlq"] = refined_nlq - pair["tags"].append( - f"nlq_review_report: {review_report.summary}" - ) - pair["tags"].append("notes: refined_nlq") - pair["tags"].append(f"timestamp: {states.timestamp()}") - pair["tags"].append(f"elapsed_hr: {states.elapsed_time_hr()}") - new_verified_pairs.append(pair) - else: - pair["status"] = "nlq_review_rejected" - pair["nlq_review"] = ( - review_report.model_dump_json() - if review_report - else pair.get("nlq_review", "") - ) - new_rejected_pairs.append(pair) - else: - pair["status"] = "nlq_review_rejected" - pair["nlq_review"] = pair.get("nlq_review", "") - new_rejected_pairs.append(pair) - - committed_pair_collection.add_verified_pairs(new_verified_pairs) - rejected_pair_collection.add_rejected_pairs(new_rejected_pairs) - - states.update_states_on_pairs_committed( - committed_pair_collection, rejected_pair_collection - ) - - self._sync_resources(task_working_dir=task_working_dir) - - elogger.log_event( - "refine_nlq", - { - "verified_pair_sample": new_verified_pairs[0]["nlq"] - if new_verified_pairs - else None, - "verified_pair_count": len(new_verified_pairs), - "desired_output_pairs": states.desired_output_pairs, - "committed_pairs_path": committed_pair_collection.path, - "rejected_pairs_path": rejected_pair_collection.path, - }, - states.to_dict(), - ) - - return states.to_dict() - - async def generate_database_profile(self, task_working_dir: str) -> dict[str, Any]: - """Reads the contents from the input directory and assemble the database schema profile. - The input directory will be removed after the database schema profile is generated and save to the output path. - - Args: - task_working_dir: the directory to store the generated pairs - - Returns: - None - """ - - input_dir = os.path.join(task_working_dir, "_dbp") - output_path = os.path.join(task_working_dir, "db_profile.txt") - - config = DatasetGenConfig(task_working_dir) - constraints = config.constraints - - list_schema_result_path = os.path.join(input_dir, "tables.json") - assert os.path.exists(list_schema_result_path), ( - f"File not found: {list_schema_result_path}" - ) - list_schema_result_text = Path(list_schema_result_path).read_text( - encoding="utf-8" - ) - schema_ddls: list[SchemaDDL] = generate_ddls_from_json_schema( - list_schema_result_text - ) - - output_dir: str = Path(output_path).parent.as_posix() - - table_profiles = [] - table_names = [] - - payload = { - "input_dir": input_dir, - "output_path": output_path, - "constraints": constraints, - } - elogger = DatasetGenEventLogger(output_dir) - - try: - for schema_ddl in schema_ddls: - table_name = schema_ddl.get_table_name() - ddl = schema_ddl.ddl - columns = schema_ddl.columns - full_table_name = schema_ddl.full_table_name - - table_names.append(table_name) - - sample_values_per_column = collections.defaultdict(list) - - column_samples_path = os.path.join( - input_dir, "column_samples", f"{full_table_name}.json" - ) - if os.path.exists(column_samples_path): - column_samples = json.loads( - Path(column_samples_path).read_text(encoding="utf-8") - ) - for row in column_samples: - column_name, column_value = row["name"], row["val"] - sample_values_per_column[column_name].append(column_value) - - sample_rows_path = os.path.join( - input_dir, "row_samples", f"{full_table_name}.json" - ) - sample_rows = [] - if os.path.exists(sample_rows_path): - sample_rows = json.loads( - Path(sample_rows_path).read_text(encoding="utf-8") - ) - - table_profile = TableProfile( - table_name=table_name, - ddl=ddl, - columns=[c.name for c in columns], - sample_data=sample_rows, - sample_values_per_column=sample_values_per_column, - ) - table_profiles.append(table_profile) - - final_context = SchemaProfile( - table_profiles=table_profiles, - general_context=constraints, - ).to_string() - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - Path(output_path).write_text(final_context, encoding="utf-8") - response = {"schema_profile_path": output_path} - - elogger.log_event("generate_database_profile", payload, response) - - return response - except RuntimeError as e: - elogger.log_event("generate_database_profile", payload, {"error": str(e)}) - return f"Error profiling database: {e}" - - def _sync_resources(self, task_working_dir: str) -> dict[str, Any]: - """Reads the content from the generated pairs path and merge them to the output path. - - Args: - task_working_dir: The directory containing the generated pairs. - - Returns: - A dictionary object which holds the results - """ - states = DatasetGenStates(task_working_dir) - response = {"is_done": states.is_done(), "status": states.to_dict()} - - # remove check point files - if states.is_done(): # clean up - states.delete() - RejectedPairCollection(task_working_dir).delete() - PendingPairCollection(task_working_dir).delete() - DatasetGenConfig(task_working_dir).delete() - DatasetGenDbProfile(task_working_dir).delete() - - elogger = DatasetGenEventLogger(task_working_dir) - elogger.log_event("sync_resources", {}, response) - - return response - - async def generate_database_profile_plan( - self, task_working_dir: str - ) -> dict[str, Any]: - """Read the full table schema and select the desired tables. - - Args: - full_table_schema_path: The absolute path to the full table schema. - output_dir: The absolute path to the output directory. - output_summary_path: The absolute path to the output summary. - - Returns: - A dictionary object which holds the results - """ - full_table_schema_path = os.path.join(task_working_dir, "_dbp/tables.json") - output_dir = os.path.join(task_working_dir, "_dbp") - - table_schema: list[SchemaDDL] = generate_ddls_from_json_schema( - Path(full_table_schema_path).read_text(encoding="utf-8") - ) - table_selector = TableSelector(genai_client=self._genai_client) - selected_tables: list[str] = await table_selector.select_tables( - tables=table_schema, top_k=8 - ) - - schema_data_list = json.loads( - Path(full_table_schema_path).read_text(encoding="utf-8") - ) - schema_data_list = [ - item - for item in schema_data_list - if item["schema_name"] + "." + item["object_details"]["object_name"] - in selected_tables - ] - Path(full_table_schema_path).write_text( - json.dumps(schema_data_list, indent=2), encoding="utf-8" - ) - - table_schema = [ - item for item in table_schema if item.full_table_name in selected_tables - ] - - column_sampling_queries = [] - row_sampling_queries = [] - - for table in table_schema: - columns = table.columns - qlist_by_cat = collections.defaultdict(list) - qlist = [] - for column in columns: - if column.is_primary_key or column.is_foreign_key: - continue - query = f"SELECT DISTINCT {column.name} as val, '{column.name}' as name FROM {table.full_table_name} LIMIT 5" - qlist_by_cat[column.data_type].append(query) - qlist.append(query) - if len(qlist) > 10: - qlist = qlist[:10] - for dtype in qlist_by_cat: - q_by_cat = qlist_by_cat[dtype][0] - if q_by_cat not in qlist: - qlist.append(q_by_cat) - union_query = " UNION ALL ".join([f"({q})" for q in qlist]) - if union_query: - column_sampling_queries.append( - {"table_name": table.full_table_name, "sampling_query": union_query} - ) - row_sampling_queries.append( - { - "table_name": table.full_table_name, - "sampling_query": f"SELECT * FROM {table.full_table_name} LIMIT 5", - } - ) - - column_sampling_query_path = os.path.join( - output_dir, "column_sampling_queries.json" - ) - row_sampling_query_path = os.path.join(output_dir, "row_sampling_queries.json") - Path(output_dir).mkdir(parents=True, exist_ok=True) - Path(column_sampling_query_path).write_text( - json.dumps(column_sampling_queries, indent=2), encoding="utf-8" - ) - Path(row_sampling_query_path).write_text( - json.dumps(row_sampling_queries, indent=2), encoding="utf-8" - ) - - return { - "plan": f"Profile the database for the following tables: {selected_tables}", - "column_sampling_query_path": column_sampling_query_path, - "row_sampling_query_path": row_sampling_query_path, - } - - async def run_task(self, payload: dict[str, Any]) -> dict[str, Any]: - if "task" not in payload: - raise KeyError(f"'task' must be in the payload {payload}") - task = payload.pop("task") - if task == "generate_sql": - return await self.generate_sql(**payload) - elif task == "generate_nlq": - return await self.generate_nlq(**payload) - elif task == "review_nlq": - return await self.review_nlq(**payload) - elif task == "refine_nlq": - return await self.refine_nlq(**payload) - elif task == "generate_database_profile": - return await self.generate_database_profile(**payload) - elif task == "generate_database_profile_plan": - return await self.generate_database_profile_plan(**payload) - else: - raise ValueError(f"Unknown task: '{task}' from payload {payload}") diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py deleted file mode 100644 index 0ea52df3..00000000 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/genai_client.py +++ /dev/null @@ -1,60 +0,0 @@ -"""GenAI client for NL2SQL Eval Dataset Generator.""" - -import asyncio -import logging - -import pydantic -from google.genai import types -from google.genai.errors import APIError - -from google import genai - -BaseModel = pydantic.BaseModel - - -class GenAiClient: - """GenAI client for NL2SQL Eval Dataset Generator.""" - - def __init__(self, timeout_secs: int = 60): - self.timeout_secs = timeout_secs - self._client = genai.Client( - http_options=types.HttpOptions( - retry_options=types.HttpRetryOptions( - attempts=1 # 1 attempt as we need genai to fail fast when throttling happens - ), - timeout=timeout_secs * 1000, - ) - ) - - async def generate_content_async( - self, - *, - prompt: str, - model: str, - response_schema: types.SchemaUnion | None = None, - response_mime_type: str = "application/json", - temperature: float = 0.3, - ) -> types.GenerateContentResponse | None: - """Generates content using the GenAI model asynchronously.""" - try: - result = await asyncio.wait_for( - self._client.aio.models.generate_content( - model=model, - contents=prompt, - config=types.GenerateContentConfig( - response_mime_type=response_mime_type, - response_schema=response_schema, - temperature=temperature, - ), - ), - timeout=self.timeout_secs, - ) - return result - except (TimeoutError, APIError) as e: - logging.warning( - f"generate_content is not able to complete due to timeout or api error: {e}" - ) - return None - except Exception as e: - logging.warning(f"generate_content was not able to complete due to: {e}") - return None diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py deleted file mode 100644 index 8a068396..00000000 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generator_base.py +++ /dev/null @@ -1,42 +0,0 @@ -from google.genai import types - -from .genai_client import GenAiClient - - -class GeneratorBase: - """Base class for generators in NL2SQL Eval Dataset Generation.""" - - pass - - def __init__( - self, - genai_client: GenAiClient, - generator_model_id: str = "gemini-3-flash-preview", - ): - super().__init__() - self._genai_client = genai_client - self._generator_model_id = generator_model_id - - async def generate_content( - self, - prompt: str, - response_schema: types.SchemaUnion | None = None, - response_mime_type: str = "application/json", - model: str | None = None, - temperature: float = 0.3, - ) -> str | types.SchemaUnion | None: - """Generates content using the GenAI model.""" - if model is None: - model = self._generator_model_id - resp = await self._genai_client.generate_content_async( - prompt=prompt, - response_schema=response_schema, - response_mime_type=response_mime_type, - model=model, - temperature=temperature, - ) - if resp is None: - return None - if response_schema is not None: - return response_schema.model_validate_json(resp.text) - return resp.text diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py deleted file mode 100644 index 65203883..00000000 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/generators.py +++ /dev/null @@ -1,358 +0,0 @@ -import random -import textwrap - -from pydantic import BaseModel, Field - -from .generator_base import GeneratorBase - -MIN_SQL_LENGTH = 20 -MAX_SQL_LENGTH = 300 - - -class GeneratedQuery(BaseModel): - """A generated SQL query or question.""" - - text: str - gen_id: str - - -class GeneratedSQLPool(BaseModel): - """A list of generated SQL queries.""" - - sqls: list[str] = Field( - description="A list of distinct SQL queries based on the schema." - ) - - -class TranslatedQuestion(BaseModel): - question: str = Field( - description=( - "A natural, realistic, unambigious business question that the SQL query answers." - ) - ) - - -class QuestionReviewReport(BaseModel): - """The Reviewer Agent's assessment of a NL2SQL pair.""" - - is_golden: bool = Field( - description=( - "True if the question is perfectly aligned with the original SQL intent." - ) - ) - red_flags: list[str] = Field( - description="List of issues: ambiguity, logical errors." - ) - ambiguity_score: float = Field(description="0.0 (Clear) to 1.0 (Highly Ambiguous).") - intent_mismatch: bool = Field( - description=("True if the NL question doesn't match the original SQL intent.") - ) - improvement_suggestion: str = Field( - description="Specific advice for the Repair Agent." - ) - summary: str = Field(description="Concise and short summary of the assessment.") - - -class SQLCandidateGenerator(GeneratorBase): - """SQL generator for NL2SQL Eval Dataset Generation.""" - - async def generate_candidates( - self, schema_profile: str, dialect: str, complexity: str, count: int - ) -> list[GeneratedQuery]: - """Architect Agent: Generates candidate SQL queries (SQL2NL strategy).""" - strategy_id = random.randint(1, 4) - if strategy_id == 1: - sqls = await self._generate_candidates_1( - schema_profile=schema_profile, - dialect=dialect, - complexity=complexity, - count=count, - ) - elif strategy_id == 2: - sqls = await self._generate_candidates_2( - schema_profile=schema_profile, - dialect=dialect, - complexity=complexity, - count=count, - ) - elif strategy_id == 3: - sqls = await self.generate_candidates_3( - schema_profile=schema_profile, - dialect=dialect, - complexity=complexity, - count=count, - ) - else: - sqls = await self.generate_candidates_4( - schema_profile=schema_profile, - dialect=dialect, - complexity=complexity, - count=count, - ) - - sqls = [ - sql for sql in sqls if sql and MIN_SQL_LENGTH <= len(sql) <= MAX_SQL_LENGTH - ] - - sqls = list(set(sqls)) - - return [GeneratedQuery(text=sql, gen_id=str(strategy_id)) for sql in sqls] - - async def _generate_candidates_1( - self, *, schema_profile: str, dialect: str, complexity: str, count: int - ) -> list[str]: - prompt = textwrap.dedent(f""" - You are an expert Database Architect. Given the following {dialect} database schema and sample data, - generate {count} {complexity}, diverse, highly realistic analytical SQL queries. - - Include a mix of: - - Simple SELECTs and aggregates - - Multi-table JOINs - - Subqueries and CTEs - - Window functions (if applicable) - - Ensure the queries are syntactically valid for {dialect} and are likely to return data based on the samples. - Ensure the queries are concise and each query is less than 300 characters. - - Below is the definition on SQL query complexity: - - easy: Involves single-table selections and basic relational filtering. - - medium: Requires multi-table joins, foundational data aggregations. - - hard: Demands deeply nested subqueries, complex window functions, or strict edge-case syntax constraints. - - Database Profile: - {schema_profile} - """) - - candidates = await self.generate_content( - prompt=prompt, - response_schema=GeneratedSQLPool, - temperature=0.7, - ) - return candidates.sqls if candidates else [] - - async def _generate_candidates_2( - self, *, schema_profile: str, dialect: str, complexity: str, count: int - ) -> list[str]: - """Architect Agent: Generates candidate SQL queries (SQL2NL strategy).""" - sql_gen_prompt = textwrap.dedent(f""" - Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. - Inject real-world messiness: Use CTEs, window functions, or regex/string extraction if appropriate. - Ensure each query returns actual data from the samples provided. - Ensure the queries are concise and each query is less than 300 characters. - - Below is the definition on SQL query complexity: - - easy: Involves single-table selections and basic relational filtering. - - medium: Requires multi-table joins, foundational data aggregations. - - hard: Demands deeply nested subqueries, complex window functions, or strict edge-case syntax constraints. - - Database Profile: - {schema_profile} - """) - candidates = await self.generate_content( - prompt=sql_gen_prompt, - response_schema=GeneratedSQLPool, - temperature=0.7, - ) - return candidates.sqls if candidates else [] - - async def generate_candidates_3( - self, *, schema_profile: str, dialect: str, complexity: str, count: int - ) -> list[str]: - """Generates a candidate NL2SQL pair using the RepairedPair schema.""" - prompt = textwrap.dedent(f""" - Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. - Think step-by-step: - - First generate {count} distinct, realistic business intents that users of this database might have. - - For each intent, generate a corresponding SQL query that would answer that intent. - - Ensure the queries are syntactically valid for {dialect} and are likely to return data based on the samples. - Ensure the queries are concise and each query is less than 300 characters. - - Below is the definition on SQL query complexity: - - easy: Involves single-table selections and basic relational filtering. - - medium: Requires multi-table joins, foundational data aggregations. - - hard: Demands deeply nested subqueries, complex window functions, or strict edge-case syntax constraints. - - Database Profile: - {schema_profile} - """) - candidates = await self.generate_content( - prompt=prompt, - response_schema=GeneratedSQLPool, - temperature=0.7, - ) - return candidates.sqls if candidates else [] - - async def generate_candidates_4( - self, *, schema_profile: str, dialect: str, complexity: str, count: int - ) -> list[str]: - - dev_prompt = textwrap.dedent(f""" - Generate {count} {complexity}, diverse, highly realistic, {dialect} SQL queries. - Think step-by-step: - - First, plan {count} {complexity} {dialect} SQL pattern for this database describing the tables, joins and the analytical goal. Focus on messy real-world scenarios. - - Next, generate {count} {complexity}, diverse, highly realistic, {dialect} queries, one for each SQL design. - Ensure it return non empty results when executed. Use exact table and column names from the context. - Ensure the queries are concise and each query is less than 300 characters. - Ensure you use exact table and column names from the context. - - Below is the definition on SQL query complexity: - - easy: Involves single-table selections and basic relational filtering. - - medium: Requires multi-table joins, foundational data aggregations. - - hard: Demands deeply nested subqueries, complex window functions, or strict edge-case syntax constraints. - - Database Profile: - {schema_profile} - """) - candidates = await self.generate_content( - prompt=dev_prompt, response_schema=GeneratedSQLPool, temperature=0.7 - ) - return candidates.sqls if candidates else [] - - -class QuestionGenerator(GeneratorBase): - """SQL2NL generator for NL2SQL Eval Dataset Generation.""" - - async def translate_sql_nl( - self, *, schema_profile: str, sql: str - ) -> GeneratedQuery: - strategy_id = random.randint(1, 2) - if strategy_id == 1: - nlq = await self.translate_sql_nl_1(schema_profile=schema_profile, sql=sql) - else: - nlq = await self.translate_sql_nl_2(schema_profile=schema_profile, sql=sql) - return GeneratedQuery(text=nlq, gen_id=str(strategy_id)) - - async def translate_sql_nl_1(self, *, schema_profile: str, sql: str) -> str | None: - """Translator Agent: Translates valid SQL into a business question.""" - prompt = textwrap.dedent(f""" - You are a Data Analyst. Look at the following SQL query, the database profile. - Translate this SQL query into a clear, natural-sounding business question that a CEO or Manager would ask. - Ensure that the translated question is concise and not vague and not ambiguous. - Avoid using technical jargon or SQL keywords in the question. - The question should be something that a non-technical business stakeholder might ask. - - SQL Query: - {sql} - - Database Profile: - {schema_profile} - """) - - result = await self.generate_content( - prompt=prompt, - response_schema=TranslatedQuestion, - temperature=0.4, - ) - return result.question if result else None - - async def translate_sql_nl_2(self, *, schema_profile: str, sql: str) -> str | None: - styles = [ - "formal", - "colloquial", - "imperative", - "interrogative", - "descriptive", - "concise", - "vague", - "metaphorical", - "conversational", - ] - selected_style = random.choice(styles) - nl_prompt = textwrap.dedent(f""" - You are a Data Analyst. Look at the following SQL query, the database profile. - Translate this SQL into a '{selected_style}' natural language question that is not ambiguous. - Ensure that the translated question is concise and not vague and not ambiguous. - Avoid using technical jargon or SQL keywords in the question. - The question should be something that a non-technical business stakeholder might ask. - - SQL Query: - {sql} - - Database Profile: - {schema_profile} - """) - question_data = await self.generate_content( - prompt=nl_prompt, - response_schema=TranslatedQuestion, - temperature=0.8, - ) - return question_data.question if question_data else None - - -class QuestionReviewer(GeneratorBase): - """Question reviewer agent for NL2SQL Eval Dataset Generation.""" - - async def review_question( - self, - *, - question: str, - sql: str, - ) -> QuestionReviewReport | None: - review_prompt = textwrap.dedent(f""" - You are the ReViSQL Reviewer Agent. Evaluate this NL2SQL question for inclusion in a Golden Dataset. - - NL Question: {question} - SQL Query: {sql} - - Check for: - 1. Ambiguity: Is the NL question too vague? - 2. Logical Correctness: Does the NL question accurately reflect the SQL intent? - 3. Filter Correctness: Does the NL question accurately reflect the filter condition of SQL query? - 4. Join Correctness: Does the NL question accurately reflect the join condition of SQL query? - 5. Output Correctness: Does the NL question accurately reflect the output of SQL query? - """) - - report = await self.generate_content( - prompt=review_prompt, - response_schema=QuestionReviewReport, - temperature=0.1, - ) - return report if report else None - - -class QuestionRefiner(GeneratorBase): - async def refine_question( - self, - *, - question: str, - sql: str, - question_report: QuestionReviewReport | str, - ) -> str | None: - """Refiner agent in the Verifier/Refiner Agent Loop. - - Args: - schema_profile: The database schema and sample data. - question: The business question to verify. - sql: The original SQL query that produced the business question. - sql_dialect: The dialect of the SQL query. - - Returns: - return verified and refined question - """ - - if isinstance(question_report, QuestionReviewReport): - question_report = question_report.model_dump_json() - - refine_prompt = textwrap.dedent(f""" - The current business question is ambiguous and led to an incorrect SQL query. - - SQL Query: - {sql} - - Current Ambiguous or Incorrect Question: - {question} - - Reviewer Report: - {question_report} - - Please rewrite the Business Question to be perfectly precise, unambiguous, and aligned with the Intended SQL query. - Explicitly mention necessary filters or specific columns if needed to avoid ambiguity. - """) - - refined = await self.generate_content( - prompt=refine_prompt, - response_schema=TranslatedQuestion, - temperature=0.3, - ) - return refined.question if refined else None diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py deleted file mode 100644 index 9d5a5edd..00000000 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/schema.py +++ /dev/null @@ -1,321 +0,0 @@ -import json -import textwrap -from typing import Any - -from pydantic import BaseModel, Field - -from .generator_base import GeneratorBase - - -def truncate_length(s: str, max_length: int) -> str: - """Truncate the string to the maximum length.""" - if not isinstance(s, str): - return s - if len(s) > max_length: - return s[: max_length - 3] + "..." - return s - - -class Column(BaseModel): - name: str - data_type: str - is_primary_key: bool - is_foreign_key: bool - - -class SchemaDDL(BaseModel): - """Schema for a database table.""" - - ddl: str = "" - schema_name: str = "" - table_name: str = "" - full_table_name: str = "" - columns: list[Column] = [] - constraints: list[dict[str, Any]] = [] - indexes: list[dict[str, Any]] = [] - error: str = "" - - def get_table_name(self) -> str: - """Returns the table name.""" - if self.schema_name and self.schema_name != "public": - return f"{self.schema_name}.{self.table_name}" - return self.table_name - - -class TableProfile(BaseModel): - """Schema profile for a database table.""" - - table_name: str - ddl: str - columns: list[str] - sample_data: list[dict[str, Any]] - sample_values_per_column: dict[str, list[Any]] - - def to_string(self): - profile = [] - profile.append(f"--- Table: {self.table_name} ---") - profile.append(f"DDL:\n{self.ddl}") - profile.append("\n") - - # remove columns with one unique value or less - filtered_columns = [] - for column in self.columns: - if self.sample_values_per_column: - values = self.sample_values_per_column.get(column, []) - if len(values) > 1: - filtered_columns.append(column) - else: - filtered_columns.append(column) - - if self.sample_data: - profile.append(f"Sample Data (Columns: {', '.join(filtered_columns)}):") - for row in self.sample_data: - new_row = { - k: truncate_length(v, 32 * 5) - for k, v in row.items() - if k in filtered_columns - } - profile.append(str(new_row)) - profile.append("\n") - - if self.sample_values_per_column: - for column, values in self.sample_values_per_column.items(): - if column not in filtered_columns: - continue - values = [truncate_length(v, 32 * 5) for v in values] - profile.append(f'Sample values for column "{column}": {values}') - return "\n".join(profile) - - -class SchemaProfile(BaseModel): - """Schema profile for a database.""" - - table_profiles: list[TableProfile] - general_context: str | None = None - - def to_string(self): - profile = [] - for table_profile in self.table_profiles: - profile.append(table_profile.to_string()) - profile.append("\n") - if self.general_context: - profile.append("--- General Context ---") - profile.append(truncate_length(self.general_context, 768 * 5)) - return "\n".join(profile) - - -def generate_ddls_from_json_schema(schema_json_string: str) -> list[SchemaDDL]: - try: - schema_data_list = json.loads(schema_json_string) - except json.JSONDecodeError as e: - return SchemaDDL(error=f"Error decoding JSON: {e}") - assert isinstance(schema_data_list, list) - ddls = [] - for schema_data in schema_data_list: - ddl = generate_ddl_from_dict(schema_data) - if ddl.error: - raise ValueError(f"Error generating DDL: {ddl.error}") - ddls.append(ddl) - return ddls - - -def generate_ddl_from_dict(schema_data: dict[str, Any]) -> SchemaDDL: - """Converts a JSON schema definition string into DDL statements. - - Args: - schema_json_string: A JSON string representing the database object schema. - - Returns: - A SchemaDdl object containing the generated DDL statements. - """ - - if "object_details" not in schema_data: - return SchemaDDL(error="Error: 'object_details' not found in JSON schema.") - - details = schema_data["object_details"] - schema_name = schema_data.get("schema_name", "public") - table_name = details.get("object_name") - - if not table_name: - return SchemaDDL(error='Error: "object_name" not found in object_details.') - - full_table_name = f"{schema_name}.{table_name}" - - ddl_statements = [] - - # 1. CREATE TABLE statement - columns = details.get("columns", []) - if not columns: - return SchemaDDL(error="Error: No columns defined for table {full_table_name}.") - - column_defs = [] - for col in columns: - col_name = col.get("column_name") - data_type = col.get("data_type") - if not col_name or not data_type: - return SchemaDDL( - error=( - "Error: Column name or data_type missing in column definition:" - f" {col}" - ) - ) - - parts = [f'"{col_name}"', data_type] - if col.get("is_not_nullable", False): - parts.append("NOT NULL") - if col.get("column_default") is not None: - parts.append(f"DEFAULT {col['column_default']}") - column_defs.append(" ".join(parts)) - - create_table_sql = f"CREATE TABLE {full_table_name} (\n " - create_table_sql += ",\n ".join(column_defs) - create_table_sql += "\n);" - ddl_statements.append(create_table_sql) - ddl_statements.append("") - - # 2. Add Constraints - constraints = details.get("constraints", []) - pk_constraints = [ - c for c in constraints if c.get("constraint_type") == "PRIMARY KEY" - ] - fk_constraints = [ - c for c in constraints if c.get("constraint_type") == "FOREIGN KEY" - ] - other_constraints = [ - c - for c in constraints - if c.get("constraint_type") not in ["PRIMARY KEY", "FOREIGN KEY"] - ] - - def add_constraint_sql(constraint): - constraint_name = constraint.get("constraint_name") - constraint_def = constraint.get("constraint_definition") - if constraint_name and constraint_def: - return ( - f'ALTER TABLE {full_table_name} ADD CONSTRAINT "{constraint_name}"' - f" {constraint_def};" - ) - return None - - primary_keys = set() - foreign_keys = set() - - # Add Primary Key and other non-FK constraints - for c in pk_constraints: - sql = add_constraint_sql(c) - if sql: - ddl_statements.append(sql) - constraint_definition = c.get("constraint_definition") - pk_candidates = [] - for col in columns: - column_name = col.get("column_name") - if column_name in constraint_definition: - pk_candidates.append(column_name) - if pk_candidates: - selected_pk = list(sorted(pk_candidates, key=len, reverse=True))[0] - primary_keys.add(selected_pk) - - for c in other_constraints: - sql = add_constraint_sql(c) - if sql: - ddl_statements.append(sql) - - if pk_constraints or other_constraints: - ddl_statements.append("") - - # Add Foreign Key constraints last - for c in fk_constraints: - sql = add_constraint_sql(c) - if sql: - ddl_statements.append(sql) - constraint_definition = c.get("constraint_definition") - fk_candidates = [] - for col in columns: - column_name = col.get("column_name") - if column_name in constraint_definition: - fk_candidates.append(column_name) - if fk_candidates: - selected_fk = list(sorted(fk_candidates, key=len, reverse=True))[0] - foreign_keys.add(selected_fk) - - if fk_constraints: - ddl_statements.append("") - - # 3. Add Indexes - indexes = details.get("indexes", []) - for index in indexes: - index_def = index.get("index_definition") - # Primary key constraints typically create their own unique index. - # Avoid duplicating index creation if it's for the primary key. - if not index.get("is_primary", False) and index_def: - # Ensure the index definition also uses quoted table name if needed - # Assuming index_definition is already correctly formatted. - ddl_statements.append(index_def + ";") - - ddl = "\n".join(ddl_statements) - return SchemaDDL( - ddl=ddl, - schema_name=schema_name, - table_name=table_name, - full_table_name=full_table_name, - columns=[ - Column( - name=col.get("column_name"), - data_type=col.get("data_type"), - is_primary_key=col.get("column_name") in primary_keys, - is_foreign_key=col.get("column_name") in foreign_keys, - ) - for col in columns - ], - constraints=constraints, - indexes=indexes, - ) - - -class TableSelector(GeneratorBase): - async def select_tables( - self, *, tables: list[SchemaDDL], top_k: int = 8 - ) -> list[str]: - - class SelectedTables(BaseModel): - table_names: list[str] = Field( - description="List of selected table names, Each table name in the form of schema_name.table_name" - ) - - profile = [] - for table in tables: - profile.append(f"--- Table: {table.full_table_name} ---") - profile.append(f"DDL:\n{table.ddl}") - profile.append("\n") - schema_profile = "\n".join(profile) - - review_prompt = textwrap.dedent(f""" - You are experienced Database Analyst. You are given the schema of a database, and ask the pick at most {top_k} tables that - the end-user will most likely interact with. - - Below are some guidelines for picking the relevant tables (P0, P1, P2 refers to priority, which P0 being highest priority): - - P0: central tables in the schema. - - P1: tables whose primary keys are most frequently referenced in other tables's foreign keys. - - P2: tables which appear to be relevant for answering user questions in the application domain. - - Guildelines: - - When the two tables relevant, it means the two table are linked by reference or in the same application domain or under the same schema. - - You must not pick tables about schema information or table schema unless they are relevant to the application domain. - - After you pick the top 1 table, the other tables that you select must either have the same schema as the top 1 table or relevant to the top 1 table. - - Ensure that the tables selected are relevant to each other. - - You should return the full table names (in the form of schema_name.table_name). - You should return AT MOST {top_k} tables. - - Database schema: - {schema_profile} - """) - - report = await self.generate_content( - prompt=review_prompt, - response_schema=SelectedTables, - temperature=0.1, - ) - assert report is not None - assert isinstance(report, SelectedTables) - return report.table_names diff --git a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py b/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py deleted file mode 100644 index 38ba98ba..00000000 --- a/src/google/cloud/db_context_enrichment/dataset/eval_seed_gen/state_manager.py +++ /dev/null @@ -1,472 +0,0 @@ -import asyncio -import datetime -import json -import os -import shutil -from pathlib import Path -from typing import Any - - -class DatasetGenEventLogger: - def __init__(self, working_dir: str): - self._history_path = os.path.join(working_dir, "mcp_history.json") - self._history = [] - if os.path.exists(self._history_path): - self._history = json.loads( - Path(self._history_path).read_text(encoding="utf-8") - ) - - def log_event( - self, action: str, request: dict[str, Any], response: dict[str, Any] - ) -> "DatasetGenEventLogger": - evt = { - "action": action, - "time": datetime.datetime.now().isoformat(), - "request": request, - "response": response, - } - if len(self._history) >= 10: - self._history.pop(0) - self._history.append(evt) - Path(self._history_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._history_path).write_text( - json.dumps(self._history, indent=2, ensure_ascii=False), encoding="utf-8" - ) - return self - - -class DatasetGenConfig: - def __init__(self, task_dir: str): - self._config_path = os.path.join(task_dir, "config.json") - self._config = None - - def _load(self) -> dict[str, Any]: - if self._config is not None: - return self._config - if not os.path.exists(self._config_path): - raise FileNotFoundError(f"Config file not found at {self._config_path}") - self._config = json.loads(Path(self._config_path).read_text(encoding="utf-8")) - return self._config - - @property - def dialect(self) -> str: - config = self._load() - return config["dialect"] - - @property - def complexity(self) -> str: - config = self._load() - return config["complexity"] - - @property - def database_name(self) -> str: - config = self._load() - return config["database_name"] - - @property - def desired_output_pairs(self) -> int: - config = self._load() - return int(config["size"]) - - @property - def constraints(self) -> str: - config = self._load() - return config["constraints"] - - @property - def parallelism(self) -> int: - config = self._load() - parallelism = config.get("parallelism", 10) - return max(1, min(parallelism, self.desired_output_pairs)) - - @property - def output_file_path(self) -> str: - config = self._load() - return config["output_file_path"] - - def delete(self) -> None: - Path(self._config_path).unlink(missing_ok=True) - self._config = None - - -class DatasetGenDbProfile: - def __init__(self, task_dir: str): - self._db_profile_path = os.path.join(task_dir, "db_profile.txt") - self._db_profile_internal_dir = os.path.join(task_dir, "_dbp") - self._db_profile = None - - def get_profile(self) -> str: - if self._db_profile is not None: - return self._db_profile - if os.path.exists(self._db_profile_path): - self._db_profile = Path(self._db_profile_path).read_text(encoding="utf-8") - else: - self._db_profile = "" - return self._db_profile - - def get_mini_profile(self) -> str: - # Remove the sample values from schema profile to reduce the context length - schema_profile = self.get_profile() - schema_profile_lines = schema_profile.split("\n") - schema_profile_lines = [ - line - for line in schema_profile_lines - if not line.startswith("Sample values for column") - ] - schema_profile = "\n".join(schema_profile_lines) - return schema_profile - - def delete(self) -> None: - Path(self._db_profile_path).unlink(missing_ok=True) - if os.path.exists(self._db_profile_internal_dir): - shutil.rmtree(self._db_profile_internal_dir, ignore_errors=True) - self._db_profile = None - - @property - def path(self) -> str: - return self._db_profile_path - - -class CommittedPairCollection: - def __init__(self, output_file_path: str): - self._committed_pairs_path = output_file_path - self._committed_pairs = None - - def _load(self) -> list[dict[str, Any]]: - if self._committed_pairs is not None: - return self._committed_pairs - if os.path.exists(self._committed_pairs_path): - self._committed_pairs = json.loads( - Path(self._committed_pairs_path).read_text(encoding="utf-8") - ) - else: - self._committed_pairs = [] - return self._committed_pairs - - def add_verified_pairs(self, pairs: list[dict[str, Any]]) -> bool: - if not pairs: - return False - committed_pairs = self._load() - - index = 1 - num_new_pairs = 0 - seen = set() - if committed_pairs: - index = int(committed_pairs[-1]["id"].replace("eval_", "")) + 1 - seen = set([entry["golden_sql"] for entry in committed_pairs]) - - for entry in pairs: - if entry["golden_sql"] in seen: - continue - tags = entry.get("tags", []) - committed_pairs.append( - { - "id": f"eval_{str(index).zfill(4)}", - "nlq": entry["nlq"], - "golden_sql": entry["golden_sql"], - "database": entry["database"], - "tags": tags, - } - ) - num_new_pairs += 1 - index += 1 - - self._save() - return True - - def __len__(self) -> int: - committed_pairs = self._load() - return len(committed_pairs) - - def _save(self) -> None: - committed_pairs = self._load() - Path(self._committed_pairs_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._committed_pairs_path).write_text( - json.dumps(committed_pairs, indent=2), encoding="utf-8" - ) - - def get_sql_set(self) -> set[str]: - committed_pairs = self._load() - return set([entry["golden_sql"] for entry in committed_pairs]) - - def exists(self) -> bool: - return os.path.exists(self._committed_pairs_path) - - def get_pairs(self) -> list[dict[str, Any]]: - return self._load() - - def delete(self) -> None: - Path(self._committed_pairs_path).unlink(missing_ok=True) - self._committed_pairs = None - - @property - def path(self) -> str: - return self._committed_pairs_path - - -class RejectedPairCollection: - def __init__(self, task_dir: str): - self._rejected_pairs_path = os.path.join(task_dir, "rejected_pairs.json") - self._rejected_pairs = None - - def _load(self) -> list[dict[str, Any]]: - if self._rejected_pairs is not None: - return self._rejected_pairs - if os.path.exists(self._rejected_pairs_path): - self._rejected_pairs = json.loads( - Path(self._rejected_pairs_path).read_text(encoding="utf-8") - ) - else: - self._rejected_pairs = [] - return self._rejected_pairs - - def add_rejected_pairs(self, pairs: list[dict[str, Any]]) -> bool: - if not pairs: - return False - rejected_pairs = self._load() - rejected_pairs.extend(pairs) - self._save() - return True - - def pop_pairs(self, num_pairs: int) -> list[dict[str, Any]]: - rejected_pairs = self._load() - if not rejected_pairs: - return [] - pairs_to_return = rejected_pairs[:num_pairs] - self._rejected_pairs = rejected_pairs[num_pairs:] - self._save() - return pairs_to_return - - def __len__(self) -> int: - rejected_pairs = self._load() - return len(rejected_pairs) - - def _save(self) -> None: - rejected_pairs = self._load() - Path(self._rejected_pairs_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._rejected_pairs_path).write_text( - json.dumps(rejected_pairs, indent=2), encoding="utf-8" - ) - - def delete(self) -> None: - Path(self._rejected_pairs_path).unlink(missing_ok=True) - self._rejected_pairs = None - - @property - def path(self) -> str: - return self._rejected_pairs_path - - def first(self) -> dict[str, Any] | None: - rejected_pairs = self._load() - return rejected_pairs[0] if rejected_pairs else None - - def is_empty(self) -> bool: - rejected_pairs = self._load() - return len(rejected_pairs) == 0 - - -class PendingPairCollection: - def __init__(self, task_dir: str): - self._pending_pairs_path = os.path.join( - task_dir, "interim_generated_pairs.json" - ) - self._pending_pairs = None - - def _load(self) -> list[dict[str, Any]]: - if self._pending_pairs is not None: - return self._pending_pairs - if os.path.exists(self._pending_pairs_path): - self._pending_pairs = json.loads( - Path(self._pending_pairs_path).read_text(encoding="utf-8") - ) - else: - self._pending_pairs = [] - return self._pending_pairs - - @property - def path(self) -> str: - return self._pending_pairs_path - - def add_pending_pairs(self, pairs: list[dict[str, Any]]) -> bool: - if not pairs: - return False - pending_pairs = self._load() - pending_pairs.extend(pairs) - self._save() - return True - - def _save(self) -> None: - pending_pairs = self._load() - Path(self._pending_pairs_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._pending_pairs_path).write_text( - json.dumps(pending_pairs, indent=2), encoding="utf-8" - ) - - def delete(self) -> None: - Path(self._pending_pairs_path).unlink(missing_ok=True) - self._pending_pairs = None - - def get_pairs(self, filter_func: callable) -> list[dict[str, Any]]: - pending_pairs = self._load() - if filter_func: - pending_pairs = [pair for pair in pending_pairs if filter_func(pair)] - return pending_pairs - - -class SqlCollection: - def __init__(self, task_dir: str): - self._sqls_path = os.path.join(task_dir, "interim_golden_sqls.json") - self._sqls = None - - def _load(self) -> list[str]: - if self._sqls is not None: - return self._sqls - if os.path.exists(self._sqls_path): - self._sqls = json.loads(Path(self._sqls_path).read_text(encoding="utf-8")) - else: - self._sqls = [] - return self._sqls - - def add_sqls(self, sqls: list[dict[str, Any]]) -> bool: - if not sqls: - return False - _sqls = self._load() - _sqls.extend(sqls) - self._save() - return True - - def delete(self) -> None: - Path(self._sqls_path).unlink(missing_ok=True) - self._sqls = None - - def get_sqls(self, filter_func: callable) -> list[str]: - sqls = self._load() - if filter_func: - sqls = [sql for sql in sqls if filter_func(sql)] - return sqls - - def is_empty(self) -> bool: - sqls = self._load() - return len(sqls) == 0 - - def _save(self) -> None: - sqls = self._load() - Path(self._sqls_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._sqls_path).write_text(json.dumps(sqls, indent=2), encoding="utf-8") - - @property - def path(self) -> str: - return self._sqls_path - - -class DatasetGenStates: - def __init__(self, task_dir: str): - self._states_path = os.path.join(task_dir, "states.json") - self._states = None - self.config = DatasetGenConfig(task_dir) - - def _load(self) -> dict[str, Any]: - if self._states is not None: - return self._states - - if os.path.exists(self._states_path): - self._states = json.loads( - Path(self._states_path).read_text(encoding="utf-8") - ) - else: - self._states = { - "is_done": False, - "completed_pairs": 0, - "remaining_pairs": self.config.desired_output_pairs, - "rejected_pairs": 0, - "iterations": 0, - } - return self._states - - def increment_iterations(self) -> int: - states = self._load() - iterations = states.get("iterations", 0) + 1 - states["iterations"] = iterations - return iterations - - def exists(self) -> bool: - return os.path.exists(self._states_path) - - def is_done(self) -> bool: - states = self._load() - return states.get("is_done", False) - - def delete(self) -> None: - Path(self._states_path).unlink(missing_ok=True) - self._states = None - - @property - def iterations(self) -> int: - states = self._load() - return states.get("iterations", 0) - - @property - def desired_output_pairs(self) -> int: - return self.config.desired_output_pairs - - def update_states_on_pairs_committed( - self, - committed_pairs: CommittedPairCollection, - rejected_pairs: RejectedPairCollection, - ) -> None: - num_verified_pairs = len(committed_pairs) - num_rejected_pairs = len(rejected_pairs) - remaining_pairs = max(0, self.desired_output_pairs - num_verified_pairs) - states = self._load() - states["is_done"] = self._is_data_generation_done( - remaining_pairs, self.iterations - ) - states["remaining_pairs"] = remaining_pairs - states["completed_pairs"] = num_verified_pairs - states["rejected_pairs"] = num_rejected_pairs - if "start_time" not in states: - states["start_time"] = datetime.datetime.now().timestamp() - states["curr_time"] = datetime.datetime.now().timestamp() - states["elapsed_time_hr"] = round( - (states["curr_time"] - states["start_time"]) / 3600, 2 - ) - - self._save() - - def elapsed_time_hr(self) -> float: - states = self._load() - return states.get("elapsed_time_hr", 0) - - def timestamp(self) -> float: - states = self._load() - return states.get("curr_time", datetime.datetime.now().timestamp()) - - def to_dict(self) -> dict[str, Any]: - return self._load() - - def _save(self) -> None: - states = self._load() - Path(self._states_path).parent.mkdir(parents=True, exist_ok=True) - Path(self._states_path).write_text( - json.dumps(states, indent=2), encoding="utf-8" - ) - - def _is_data_generation_done(self, remaining_pairs: int, iterations: int) -> bool: - if remaining_pairs <= 0 or iterations >= 500: - return True - return False - - -class BackgroundTaskCollection: - tasks = set() - - @classmethod - def add_task(cls, task: asyncio.Task) -> None: - cls.tasks.add(task) - task.add_done_callback(lambda t: cls.tasks.discard(t)) - - @classmethod - def cancel_tasks(cls) -> None: - for task in cls.tasks: - task.cancel() - cls.tasks.clear() diff --git a/src/google/cloud/db_context_enrichment/main.py b/src/google/cloud/db_context_enrichment/main.py index 70530fcd..476e0f23 100644 --- a/src/google/cloud/db_context_enrichment/main.py +++ b/src/google/cloud/db_context_enrichment/main.py @@ -1,7 +1,6 @@ import datetime import json import os -from typing import Any from fastmcp import FastMCP @@ -9,9 +8,6 @@ from google.cloud.db_context_enrichment.bootstrap import bootstrap_generator from google.cloud.db_context_enrichment.common import context_mutator from google.cloud.db_context_enrichment.dataset import dataset_generator -from google.cloud.db_context_enrichment.dataset.eval_seed_gen.dataset_generator import ( - SeedEvalDatasetGenerator, -) from google.cloud.db_context_enrichment.evaluate import ( evaluate_generator, result_reader, @@ -450,18 +446,5 @@ async def read_evaluation_result( return result_reader.read_eval_results(run_folder_path, offset, batch_size) -@mcp.tool -async def generate_seed_eval_dataset(payload: dict[str, Any]) -> dict[str, Any]: - """Reads the `task` field in the `payload` argument to determine which of a list of tasks to perform for generate_seed_eval_dataset. - - Args: - payload: A dictionary object which must have the fields `task`, `task_working_dir`, and other required fields based on the value of `task`. - - Returns: - A dictionary object which holds the results - """ - return await SeedEvalDatasetGenerator().run_task(payload) - - if __name__ == "__main__": mcp.run() # Uses STDIO transport by default From 571dfce4e2d5929f35e150cb1ed2f03e7a14374f Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Wed, 27 May 2026 12:06:42 -0700 Subject: [PATCH 17/23] simplified mock app code --- .../user_data/hr_app/hr_agent.py | 22 ++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py b/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py index 983abd86..80c543f7 100644 --- a/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py +++ b/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py @@ -1,8 +1,6 @@ import sys import datetime from typing import cast -from google.genai import types -from toolbox_core import ToolboxSyncClient TOOLBOX_URL = "http://127.0.0.1:5000" @@ -11,6 +9,24 @@ class ToolUnion: ... +class ToolboxSyncClient: + ... + + +class Tool: + ... + + +class Part: + def __init__(self, text: str | None = None): + self.text = text + + +class Content: + def __init__(self, parts: list[Part]): + self.parts = parts + + class Agent: def __init__(self, model: str, name: str, instruction: str, tools: list[ToolUnion]): self.model = model @@ -133,7 +149,7 @@ def main(): print(f"Sending Query: '{query_text}'") - user_message = types.Content(parts=[types.Part(text=query_text)]) + user_message = Content(parts=[Part(text=query_text)]) print("\n--- Agent Response Stream ---") for event in runner.run( From fbc7ba9f5372c7779938627be6c11e4d26fd5cb2 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Wed, 27 May 2026 12:09:27 -0700 Subject: [PATCH 18/23] simplified mock app code --- .../core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py b/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py index 80c543f7..90244472 100644 --- a/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py +++ b/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py @@ -46,7 +46,7 @@ def __init__( self.agent = agent self.session_service = session_service - def run(self, user_id: str, session_id: str, new_message: types.Content): + def run(self, user_id: str, session_id: str, new_message: Content): pass From a378686ebe45ec35de056196cee218676be31def Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Wed, 27 May 2026 12:12:15 -0700 Subject: [PATCH 19/23] simplified mock app code --- .../workspace_post_init/user_data/hr_app/hr_agent.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py b/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py index 90244472..7dc8f07b 100644 --- a/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py +++ b/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py @@ -1,20 +1,17 @@ -import sys import datetime +import sys from typing import cast - TOOLBOX_URL = "http://127.0.0.1:5000" class ToolUnion: ... -class ToolboxSyncClient: - ... +class ToolboxSyncClient: ... -class Tool: - ... +class Tool: ... class Part: From c079ed287cb6e9c00e912bea30985ff03ca1f3a5 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Wed, 27 May 2026 17:34:14 -0700 Subject: [PATCH 20/23] restructure the cuj folders and update skill to make it more selective based on its knowledge derived from app code and business context artifacts --- evals/core-cujs/dataset.json | 30 +- .../user_data/hr_app/db_schema.sql | 103 ------ .../user_data/rust_app/main.rs | 154 --------- .../workspace_post_init_seed/README.md | 49 +++ .../case_bdf_ql/app_data/querylog.txt | 153 +++++++++ .../case_bdf_ql/autoctx/experiments/.gitkeep | 0 .../case_bdf_ql/autoctx/state.md | 1 + .../case_bdf_ql/autoctx/tools.yaml | 33 ++ .../case_blog/app_data}/ER_Diagram.jpeg | Bin .../case_blog/app_data}/desgin_doc.pdf | Bin .../case_blog/autoctx/experiments/.gitkeep | 0 .../case_blog/autoctx/state.md | 1 + .../case_blog/autoctx/tools.yaml | 33 ++ .../app_data}/business_blueprint.md | 0 .../app_data}/db_schema.sql | 6 +- .../autoctx/experiments/.gitkeep | 0 .../case_ecommerce_cryptic/autoctx/state.md | 1 + .../case_ecommerce_cryptic/autoctx/tools.yaml | 33 ++ .../case_flights/app_data/db_schema.sql | 19 ++ .../case_flights/autoctx/experiments/.gitkeep | 0 .../case_flights/autoctx/state.md | 1 + .../case_flights/autoctx/tools.yaml | 33 ++ .../case_hr/app_data/db_schema.sql | 301 ++++++++++++++++++ .../case_hr/app_data}/hr_agent.py | 0 .../case_hr/autoctx/experiments/.gitkeep | 0 .../case_hr/autoctx/state.md | 1 + .../case_hr/autoctx/tools.yaml | 33 ++ .../app_data/db_schema.sql | 61 ++++ .../autoctx/experiments/.gitkeep | 0 .../case_property_search/autoctx/state.md | 1 + .../case_property_search/autoctx/tools.yaml | 33 ++ golden_flight_booking_expert.json | 32 ++ golden_property_search_advanced.json | 32 ++ skills/autoctx-dataset-generation/SKILL.md | 21 +- 34 files changed, 883 insertions(+), 282 deletions(-) delete mode 100644 evals/core-cujs/workspace_post_init/user_data/hr_app/db_schema.sql delete mode 100644 evals/core-cujs/workspace_post_init/user_data/rust_app/main.rs create mode 100644 evals/core-cujs/workspace_post_init_seed/README.md create mode 100644 evals/core-cujs/workspace_post_init_seed/case_bdf_ql/app_data/querylog.txt create mode 100644 evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/experiments/.gitkeep create mode 100644 evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/state.md create mode 100644 evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/tools.yaml rename evals/core-cujs/{workspace_post_init/user_data/binary_data => workspace_post_init_seed/case_blog/app_data}/ER_Diagram.jpeg (100%) rename evals/core-cujs/{workspace_post_init/user_data/binary_data => workspace_post_init_seed/case_blog/app_data}/desgin_doc.pdf (100%) create mode 100644 evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/experiments/.gitkeep create mode 100644 evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/state.md create mode 100644 evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/tools.yaml rename evals/core-cujs/{workspace_post_init/user_data/ecommerce_app => workspace_post_init_seed/case_ecommerce_cryptic/app_data}/business_blueprint.md (100%) rename evals/core-cujs/{workspace_post_init/user_data/ecommerce_app => workspace_post_init_seed/case_ecommerce_cryptic/app_data}/db_schema.sql (98%) create mode 100644 evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/experiments/.gitkeep create mode 100644 evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/state.md create mode 100644 evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/tools.yaml create mode 100644 evals/core-cujs/workspace_post_init_seed/case_flights/app_data/db_schema.sql create mode 100644 evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/experiments/.gitkeep create mode 100644 evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/state.md create mode 100644 evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/tools.yaml create mode 100644 evals/core-cujs/workspace_post_init_seed/case_hr/app_data/db_schema.sql rename evals/core-cujs/{workspace_post_init/user_data/hr_app => workspace_post_init_seed/case_hr/app_data}/hr_agent.py (100%) create mode 100644 evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/experiments/.gitkeep create mode 100644 evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/state.md create mode 100644 evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/tools.yaml create mode 100644 evals/core-cujs/workspace_post_init_seed/case_property_search/app_data/db_schema.sql create mode 100644 evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/experiments/.gitkeep create mode 100644 evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/state.md create mode 100644 evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/tools.yaml create mode 100644 golden_flight_booking_expert.json create mode 100644 golden_property_search_advanced.json diff --git a/evals/core-cujs/dataset.json b/evals/core-cujs/dataset.json index dad7cd98..26a2455f 100644 --- a/evals/core-cujs/dataset.json +++ b/evals/core-cujs/dataset.json @@ -50,7 +50,7 @@ "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the application at https://github.com/kupp0/multi-db-property-search-data-agents, save the generated pairs in output file named golden_property_search_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", - "work_dir": "workspace_post_init/", + "work_dir": "workspace_post_init_seed/case_property_search", "max_turns": 15 }, { @@ -59,43 +59,43 @@ "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 expert SQL/Question pairs in postgres dialect for the flight booking application at https://docs.cloud.google.com/alloydb/docs/ai/integrate-applications-data-agent, save the generated pairs in output file named golden_flight_booking_expert.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", - "work_dir": "workspace_post_init/", + "work_dir": "workspace_post_init_seed/case_flights", "max_turns": 15 }, { - "id": "autoctx:generate-dataset-hr-advanced", + "id": "autoctx:generate-dataset-app-hr", "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for hr application named golden_hr_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the hr application whose source codes can be found in the directory `user_data/hr_app`, save the generated pairs in output file named golden_hr_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the hr application whose source codes can be found in the directory `./app_data`, save the generated pairs in output file named golden_hr_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", - "work_dir": "workspace_post_init/", + "work_dir": "workspace_post_init_seed/case_hr", "max_turns": 15 }, { - "id": "autoctx:generate-dataset-ecomm-advanced", + "id": "autoctx:generate-dataset-ecommmerce-cryptic", "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for ecommerce application named golden_ecomm_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the ecommerce application whose design and schema can be found in the directory `user_data/ecommerce_app`, save the generated pairs in output file named golden_ecomm_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the ecommerce application whose business context doc can be found in the directory `./app_data`, save the generated pairs in output file named golden_ecomm_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", - "work_dir": "workspace_post_init/", + "work_dir": "workspace_post_init_seed/case_ecommerce_cryptic", "max_turns": 15 }, { - "id": "autoctx:generate-dataset-blog-advanced", + "id": "autoctx:generate-dataset-blog", "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for blog application named golden_blog_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the blog application whose er diagram and design doc can be found in the directory `user_data/binary_data`, save the generated pairs in output file named golden_blog_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the blog application whose er diagram and design doc can be found in the directory `./app_data`, save the generated pairs in output file named golden_blog_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", - "work_dir": "workspace_post_init/", + "work_dir": "workspace_post_init_seed/case_blog", "max_turns": 15 }, { - "id": "autoctx:generate-dataset-rust-advanced", - "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for blog application named golden_rust_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the blog application whose source codes can be found in the directory `user_data/rust_app`, save the generated pairs in output file named golden_rust_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "id": "autoctx:generate-dataset-bdf-querylog", + "starting_prompt": "generate a seed evaluation dataset consisting of SQL/Question pairs for my alloydb database financials named golden_bdf_querylog_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 SQL/Question pairs in postgres dialect for my alloydb database financial whose query log can be found in the file `./app_data/querylog.txt`, save the generated pairs in output file named golden_rust_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", - "work_dir": "workspace_post_init/", + "work_dir": "workspace_post_init_seed/case_bdf_ql", "max_turns": 15 }, { diff --git a/evals/core-cujs/workspace_post_init/user_data/hr_app/db_schema.sql b/evals/core-cujs/workspace_post_init/user_data/hr_app/db_schema.sql deleted file mode 100644 index c0d195e0..00000000 --- a/evals/core-cujs/workspace_post_init/user_data/hr_app/db_schema.sql +++ /dev/null @@ -1,103 +0,0 @@ --- Table `employee`: The core table for employee profiles, names, and hire dates. -CREATE TABLE employee ( - employee_id integer NOT NULL, - first_name character varying(255), - last_name character varying(255), - email character varying(255), - phone_number character varying(20), - address jsonb, - image_url character varying(255), - date_of_birth date, - gender character varying(10), - marital_status character varying(20), - national_identification character varying(50), - bank_account_number jsonb, - emergency_contact_name character varying(255), - emergency_contact_number character varying(20), - contract_type character varying(50), - hire_date date, - department_id integer, - job_id integer, - manager_id integer, - company_id integer -); - --- Table `department`: Required to answer questions about which department an employee belongs to. -CREATE TABLE department ( - department_id integer NOT NULL, - department_name character varying(255), - manager_id integer, - budget numeric(15,2), - start_date date, - end_date date, - is_active boolean, - goals jsonb, - achievements jsonb, - company_id integer -); - - --- Table `job`: Required to answer questions regarding job titles and roles. -CREATE TABLE job ( - job_id integer NOT NULL, - job_title character varying(255), - job_description text, - skills_required jsonb, - skills_preferred jsonb, - min_salary numeric(15,2), - max_salary numeric(15,2), - department_id integer, - responsibilities text, - benefits jsonb, - education_requirements jsonb, - experience_requirements jsonb, - employment_type character varying(50), - location character varying(255), - vacancies integer, - status character varying(50) -); - - --- Table `leave_request`: Required to handle queries about sick leave, vacation, and time off. -CREATE TABLE leave_request ( - leave_id integer NOT NULL, - employee_id integer, - leave_type character varying(50), - start_date date, - end_date date, - status character varying(50) DEFAULT 'Pending', - duration numeric(10,2), - reason text, - approval_date date, - rejection_date date, - comments text, - attachment character varying(255), - processed_by integer -); - - --- Table `employee_certification`: Specifically called out in the Query Data Tool description for handling certification questions. -CREATE TABLE employee_certification ( - certification_id integer NOT NULL, - employee_id integer, - certification_name character varying(255), - issuing_organization character varying(255), - issue_date date, - expiration_date date -); - --- Table `salary`: Required for general salary lookup (base compensation), acting as the bridge before a user needs to escalate to the separate Payroll Tool for detailed gross pay, net pay, and tax statements. -CREATE TABLE salary ( - salary_id integer NOT NULL, - employee_id integer, - salary numeric(15,2), - start_date date, - end_date date, - currency character varying(10), - pay_frequency character varying(50), - bonus numeric(15,2), - taxes numeric(15,2), - deductions numeric(15,2), - total_compensation numeric(15,2), - status character varying(50) -); \ No newline at end of file diff --git a/evals/core-cujs/workspace_post_init/user_data/rust_app/main.rs b/evals/core-cujs/workspace_post_init/user_data/rust_app/main.rs deleted file mode 100644 index a111ed34..00000000 --- a/evals/core-cujs/workspace_post_init/user_data/rust_app/main.rs +++ /dev/null @@ -1,154 +0,0 @@ -use actix_web::{get, post, web, App, HttpServer, HttpResponse, Responder, middleware::Logger}; -use serde::{Deserialize, Serialize}; -use serde_json::{json, Value}; - -// ========================================== -// DATA MODELS (Domain Entities) -// ========================================== - -#[derive(Serialize, Deserialize, Clone)] -pub struct User { - pub user_id: i32, - pub username: String, - pub first_name: Option, - pub last_name: Option, - pub role_id: Option, - pub active: Option, -} - -#[derive(Serialize, Deserialize, Clone)] -pub struct Post { - pub post_id: i32, - pub user_id: i32, - pub title: String, - pub content: String, - pub published_at: Option, - pub views_count: i32, - pub metadata: Option, // Uses JSONB in Postgres -} - -#[derive(Serialize, Deserialize, Clone)] -pub struct Comment { - pub comment_id: i32, - pub post_id: i32, - pub user_id: i32, - pub content: String, -} - -#[derive(Serialize, Deserialize, Clone)] -pub struct MediaUpload { - pub post_id: Option, - pub filename: String, - pub file_type: String, - pub access_rights: Option, -} - -// ========================================== -// API HANDLERS (Stubs) -// ========================================== - -// --- IAM Module --- - -#[get("/api/v1/users/{user_id}")] -async fn get_user(path: web::Path) -> impl Responder { - let user_id = path.into_inner(); - // STUB: Fetch from database - let user = User { - user_id, - username: format!("user_{}", user_id), - first_name: Some("Jane".to_string()), - last_name: Some("Doe".to_string()), - role_id: Some(1), - active: Some("Y".to_string()), - }; - HttpResponse::Ok().json(user) -} - -#[post("/api/v1/users")] -async fn create_user(user_req: web::Json) -> impl Responder { - // STUB: Insert into database - HttpResponse::Created().json(json!({ - "status": "success", - "message": "User created", - "data": user_req.into_inner() - })) -} - -// --- Content Module --- - -#[get("/api/v1/posts")] -async fn list_posts() -> impl Responder { - // STUB: Query database for posts - let posts = vec![ - Post { - post_id: 101, - user_id: 1, - title: "Platform Launch Architecture".to_string(), - content: "Exploring the new microservices backend...".to_string(), - published_at: Some("2026-05-27T10:00:00Z".to_string()), - views_count: 245, - metadata: Some(json!({"tags": ["rust", "backend", "actix"]})), - } - ]; - HttpResponse::Ok().json(posts) -} - -#[post("/api/v1/posts")] -async fn create_post(post_req: web::Json) -> impl Responder { - // STUB: Insert post into database - HttpResponse::Created().json(json!({ - "status": "success", - "message": "Post published", - "post_id": 102 - })) -} - -#[post("/api/v1/posts/{post_id}/comments")] -async fn add_comment(path: web::Path, comment_req: web::Json) -> impl Responder { - let post_id = path.into_inner(); - // STUB: Verify post exists, insert comment into database - HttpResponse::Created().json(json!({ - "status": "success", - "message": format!("Comment added to post {}", post_id), - "comment_id": 505 - })) -} - -// --- Media Module --- - -#[post("/api/v1/media")] -async fn upload_media(media_req: web::Json) -> impl Responder { - // STUB: Handle file upload stream, encrypt if necessary, save metadata to DB - HttpResponse::Accepted().json(json!({ - "status": "processing", - "message": "Media upload received and is being processed", - "file_path": format!("/storage/encrypted/{}", media_req.filename) - })) -} - -// ========================================== -// SERVER BOOTSTRAP -// ========================================== - -#[actix_web::main] -async fn main() -> std::io::Result<()> { - // Initialize standard logger - env_logger::init_from_env(env_logger::Env::new().default_filter_or("info")); - - println!("Starting Cloudaicompanion API server at http://127.0.0.1:8080"); - - HttpServer::new(|| { - App::new() - .wrap(Logger::default()) - // Register route handlers - .service(get_user) - .service(create_user) - .service(list_posts) - .service(create_post) - .service(add_comment) - .service(upload_media) - }) - .bind(("127.0.0.1", 8080))? - .run() - .await -} diff --git a/evals/core-cujs/workspace_post_init_seed/README.md b/evals/core-cujs/workspace_post_init_seed/README.md new file mode 100644 index 00000000..c5588a46 --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/README.md @@ -0,0 +1,49 @@ + +## CUJ: case_flights + +**Setup**: In this CUJ, the user only provides the https://docs.cloud.google.com/alloydb/docs/ai/integrate-applications-data-agent documentation link. + +**Source**: database is `db_dummy`, which does not contain any table. + +**Expected Outcome**: The eval dataset generator needs to figure out based on the documentation at https://docs.cloud.google.com/alloydb/docs/ai/integrate-applications-data-agent what NL-SQL pairs to generate. + + +## CUJ: case_property_search + +**Setup**: In this CUJ, the user only provides the github link https://github.com/kupp0/multi-db-property-search-data-agents. + +**Source**: database is `search`, which contains test data for the app. + +**Expected Outcome**: The eval dataset generator needs to figure out based on the application source codes at https://github.com/kupp0/multi-db-property-search-data-agents what NL-SQL pairs to generate. + +## CUJ: case_blog + +**Setup**: In this CUJ, the user provides the `app_data` folder containing a `ER_Diagram.jpg` which sketch the ER diagram for blog application as well as its `design_doc.pdf`. + +**Source**: database is `db_dummy`, which does not contain any table. + +**Expected Outcome**: The eval dataset generator need to figure out that the `-list-schemas` returns the empty schema (since the user did not create any table in the schema), infers the schema from the `ER_Diagram.jpg` and makes use of the content in the `design_doc.pdf` to generate NL-SQL pairs. + +## CUJ: case_ecommerce_cryptic + +**Setup**: In this CUJ, the user provides a business context doc containing explaination over the cryptic column names in the database + +**Source**: database is `db_ecommerce_cryptic`, which contains cryptic column names + +**Expected Outcome**: The eval dataset generator needs to figure out how to generate NL which leverage domain knowledge from the business context doc which explains the cryptic column names, and its corresponding SQL only knows the cryptic column names. + +## CUJ: case_bdf_ql + +**Setup**: In this CUJ, the user provides the querylog file `querylog.txt` which simulates the query log for the `financials` database. + +**Source**: database is `financials` from BIRD. + +**Expected Outcome**: The eval dataset generator needs to figure out how to extract the SQL from the query log, and then translate them to the corresponding NL while ensuring the NL is unambiguous and logically align with the SQL. In the case the number of pairs user ask is more than the SQLs in the querylog, the eval dataset generator needs to uses its knowledge and inspection of querylog to come up with more NL-SQL pairs. + +## CUJ: case_hr + +**Setup**: In this CUJ, the user provides the `app_data` directory containing the application codes. + +**Source**: database is the `db_hr`. + +**Expected Outcome**: The eval dataset generator needs to figure out which tables and columns to use to generate NL-SQL so that it alighns with the application codes. \ No newline at end of file diff --git a/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/app_data/querylog.txt b/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/app_data/querylog.txt new file mode 100644 index 00000000..0cb09b96 --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/app_data/querylog.txt @@ -0,0 +1,153 @@ +2026-05-27 10:00:00.000 PDT [1042] LOG: starting PostgreSQL 15.3 on x86_64-pc-linux-gnu, compiled by gcc (GCC) 11.3.0, 64-bit +2026-05-27 10:00:00.005 PDT [1042] LOG: listening on IPv4 address "0.0.0.0", port 5432 +2026-05-27 10:00:00.010 PDT [1042] LOG: listening on IPv6 address "::", port 5432 +2026-05-27 10:00:00.015 PDT [1042] LOG: listening on Unix socket "/var/run/postgresql/.s.PGSQL.5432" +2026-05-27 10:00:00.050 PDT [1044] LOG: database system was shut down at 2026-05-27 09:59:58 PDT +2026-05-27 10:00:00.100 PDT [1042] LOG: database system is ready to accept connections +2026-05-27 10:01:12.345 PDT [1201] app_user@financial LOG: connection authorized: user=app_user database=financial +2026-05-27 10:01:12.350 PDT [1201] app_user@financial LOG: statement: SELECT COUNT(DISTINCT "T1"."account_id") FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."A3" = 'east Bohemia' AND "T1"."frequency" = 'POPLATEK PO OBRATU' +2026-05-27 10:01:13.102 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."district_id") FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."A3" = 'East Bohemia' AND "T2"."frequency" = 'POPLATEK PO OBRATU' +2026-05-27 10:01:14.450 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "account" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "district" AS "T3" ON "T1"."district_id" = "T3"."district_id" WHERE "T3"."A3" = 'Prague' +2026-05-27 10:01:15.890 PDT [1201] app_user@financial LOG: statement: SELECT DISTINCT CASE WHEN AVG("A13") > AVG("A12") THEN '1996' ELSE '1995' END FROM "district" +2026-05-27 10:01:16.120 PDT [1201] app_user@financial LOG: statement: SELECT COUNT(DISTINCT "T1"."district_id") FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."gender" = 'F' AND "T1"."A11" BETWEEN 6000 AND 10000; +2026-05-27 10:01:17.333 PDT [1201] app_user@financial LOG: statement: SELECT DISTINCT "T2"."district_id" FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."gender" = 'F' AND "T2"."A11" BETWEEN 6000 AND 10000 +2026-05-27 10:01:18.005 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."gender" = 'M' AND "T2"."A3" = 'north Bohemia' AND "T2"."A11" > 8000 +2026-05-27 10:01:19.412 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."gender" = 'M' AND "T2"."A3" = 'North Bohemia' AND "T2"."A11" > 8000 +2026-05-27 10:01:21.500 PDT [1201] app_user@financial LOG: statement: SELECT "disp"."account_id", (SELECT MAX("A11") - MIN("A11") FROM "district") as gap FROM "disp" INNER JOIN "client" ON "disp"."client_id" = "client"."client_id" WHERE "client"."client_id" IN ( SELECT "c"."client_id" FROM "client" AS "c" INNER JOIN "district" AS "d" ON "c"."district_id" = "d"."district_id" WHERE "c"."gender" = 'F' ORDER BY "d"."A11" ASC, "c"."birth_date" ASC LIMIT 1 ) +2026-05-27 10:01:22.750 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id", ( SELECT max("A11") - min("A11") FROM "district" ) FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."district_id" = ( SELECT "district_id" FROM "client" WHERE "gender" = 'F' ORDER BY "birth_date" ASC LIMIT 1 ) ORDER BY "T2"."A11" DESC NULLS LAST LIMIT 1 +2026-05-27 10:01:23.010 PDT [1201] app_user@financial LOG: statement: SELECT T1.account_id FROM account AS T1 INNER JOIN client AS T2 ON T1.district_id = T2.district_id INNER JOIN district AS T3 ON T2.district_id = T3.district_id WHERE T2.birth_date = (SELECT MAX(birth_date) FROM client) AND T3."A11" = (SELECT MAX("A11") FROM district); -- Youngest client(s) and district with highest avg salary +2026-05-27 10:01:23.880 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."district_id" = ( SELECT "district_id" FROM "client" ORDER BY "birth_date" DESC LIMIT 1 ) ORDER BY "T2"."A11" DESC NULLS LAST LIMIT 1 +2026-05-27 10:01:24.102 PDT [1201] app_user@financial LOG: statement: SELECT COUNT(DISTINCT "T1"."client_id") FROM "disp" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."frequency" = 'POPLATEK TYDNE' AND "T1"."type" = 'OWNER' +2026-05-27 10:01:25.550 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "account" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."type" = 'Owner' AND "T1"."frequency" = 'POPLATEK TYDNE' +2026-05-27 10:01:26.772 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."client_id" FROM "account" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."frequency" = 'POPLATEK PO OBRATU' AND "T2"."type" = 'DISPONENT' +2026-05-27 10:01:27.100 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."account_id" FROM loan AS "T1" INNER JOIN account AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE to_char("T1"."date"::timestamp, 'YYYY') = '1997' AND "T2"."frequency" = 'POPLATEK TYDNE' ORDER BY "T1"."amount" NULLS LAST LIMIT 1 +2026-05-27 10:01:28.450 PDT [1201] app_user@financial LOG: statement: SELECT T1.account_id FROM loan AS T1 INNER JOIN account AS T2 ON T1.account_id = T2.account_id WHERE T1.duration > 12 AND EXTRACT(YEAR FROM T2.date) = 1993 ORDER BY T1.amount DESC LIMIT 1 +2026-05-27 10:01:29.900 PDT [1201] app_user@financial LOG: statement: SELECT T1.account_id FROM loan AS T1 INNER JOIN accounts AS T2 ON T1.account_id = T2.account_id WHERE T1.duration > 12 AND EXTRACT(YEAR FROM T2.opening_date) = 1993 ORDER BY T1.amount DESC LIMIT 1 +2026-05-27 10:01:30.120 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id" FROM "loan" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE to_char("T1"."date"::timestamp, 'YYYY') = '1993' AND "T1"."duration" > 12 ORDER BY "T1"."amount" DESC LIMIT 1 +2026-05-27 10:01:31.005 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T2"."client_id") FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."gender" = 'F' AND to_char("T2"."birth_date"::timestamp, 'YYYY') < '1950' AND "T1"."A2" = 'Slokolov' +2026-05-27 10:01:32.410 PDT [1201] app_user@financial LOG: statement: SELECT "account_id" FROM "trans" WHERE to_char("date"::timestamp, 'YYYY') = '1995' ORDER BY "date" ASC LIMIT 1 +2026-05-27 10:01:33.750 PDT [1201] app_user@financial LOG: statement: SELECT DISTINCT "T2"."account_id" FROM "trans" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE to_char("T2"."date"::timestamp, 'YYYY') < '1997' AND "T1"."amount" > 3000 +2026-05-27 10:01:34.900 PDT [1201] app_user@financial LOG: statement: SELECT DISTINCT "T1"."account_id" FROM "accounts" AS "T1" INNER JOIN "transactions" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."opening_date" < '1997-01-01' AND "T2"."amount" > 3000 +2026-05-27 10:01:35.120 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."client_id" FROM "client" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "card" AS "T3" ON "T2"."disp_id" = "T3"."disp_id" WHERE "T3"."issued" = '1994-03-03' +2026-05-27 10:01:36.050 PDT [1201] app_user@financial LOG: statement: SELECT T1.date FROM account AS T1 INNER JOIN trans AS T2 ON T1.account_id = T2.account_id WHERE T2.amount = 840 AND T2.date = '1998-10-14' +2026-05-27 10:01:37.400 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."date" FROM account AS "T1" INNER JOIN trans AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."amount" = 840 AND "T2"."date" = '1998-10-14' +2026-05-27 10:01:38.700 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."district_id" FROM "account" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."date" = '1994-08-25' +2026-05-27 10:01:39.110 PDT [1201] app_user@financial LOG: statement: SELECT MAX("T1"."amount") FROM "trans" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "card" AS "T3" ON "T2"."disp_id" = "T3"."disp_id" WHERE "T3"."issued" = '1996-10-21' +2026-05-27 10:01:40.005 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."amount" FROM "account" AS "T1" INNER JOIN "trans" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."date" = '1996-10-21' ORDER BY "T2"."amount" DESC NULLS LAST LIMIT 1 +2026-05-27 10:01:41.450 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."gender" FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" ORDER BY "T1"."A11" DESC, "T2"."birth_date" ASC LIMIT 1 +2026-05-27 10:01:42.800 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."amount" FROM "loan" AS "T1" INNER JOIN "trans" AS "T2" ON "T1"."account_id" = "T2"."account_id" ORDER BY "T1"."amount" DESC, "T2"."date" ASC LIMIT 1 +2026-05-27 10:01:43.120 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."gender" = 'F' AND "T2"."A2" = 'Jesenik' +2026-05-27 10:01:44.050 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."disp_id" FROM "disp" AS "T1" INNER JOIN "trans" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."date" = '1998-09-02' AND "T2"."amount" = 5100 +2026-05-27 10:01:45.400 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T2"."account_id") FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE to_char("T2"."date"::timestamp, 'YYYY') = '1996' AND "T1"."A2" = 'Litomerice' +2026-05-27 10:01:46.750 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."A2" FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."birth_date" = '1976-01-29' AND "T2"."gender" = 'F' +2026-05-27 10:01:47.110 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."birth_date" FROM "loan" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "client" AS "T3" ON "T2"."client_id" = "T3"."client_id" WHERE "T1"."amount" = 98832 AND "T1"."date" = '1996-01-03'; +2026-05-27 10:01:48.005 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."birth_date" FROM loan AS "T1" INNER JOIN account AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN client AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T1"."date" = '1996-01-03' AND "T1"."amount" = 98832 +2026-05-27 10:01:49.450 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."A3" = 'Prague' ORDER BY "T1"."date" ASC LIMIT 1 +2026-05-27 10:01:50.800 PDT [1201] app_user@financial LOG: statement: SELECT (CAST(SUM(CASE WHEN "T1"."gender" = 'M' THEN 1 ELSE 0 END) AS REAL) * 100) / COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."A3" = 'south Bohemia' GROUP BY "T2"."A4" ORDER BY "T2"."A4" DESC LIMIT 1 +2026-05-27 10:01:51.120 PDT [1201] app_user@financial LOG: statement: WITH client_account AS ( SELECT "account_id" FROM "loan" WHERE "date" = '1993-07-05' ORDER BY "loan_id" LIMIT 1 ) SELECT (("T1"."balance" - "T2"."balance") / "T2"."balance") * 100 FROM "trans" AS "T1" INNER JOIN "trans" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN client_account AS "ca" ON "T1"."account_id" = "ca"."account_id" WHERE "T1"."date" = '1998-12-27' AND "T2"."date" = '1993-03-22'; +2026-05-27 10:01:52.050 PDT [1201] app_user@financial LOG: statement: SELECT CAST(((SUM(CASE WHEN "T3"."date" = '1998-12-27' THEN "T3"."balance" ELSE 0 END) - SUM(CASE WHEN "T3"."date" = '1993-03-22' THEN "T3"."balance" ELSE 0 END)) * 100.0 / SUM(CASE WHEN "T3"."date" = '1993-03-22' THEN "T3"."balance" ELSE 0 END)) AS FLOAT) FROM "loan" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "trans" AS "T3" ON "T3"."account_id" = "T2"."account_id" WHERE "T1"."date" = '1993-07-05' +2026-05-27 10:01:53.400 PDT [1201] app_user@financial LOG: statement: SELECT (CAST(SUM(CASE WHEN "status" = 'A' THEN "amount" ELSE 0 END) AS NUMERIC) * 100) / SUM("amount") FROM "loan" +2026-05-27 10:01:54.750 PDT [1201] app_user@financial LOG: statement: SELECT CAST(SUM(CASE WHEN "status" = 'C' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT("amount") FROM loan WHERE "amount" < 100000 +2026-05-27 10:01:55.110 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id", "T2"."A2", "T2"."A3" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."frequency" = 'POPLATEK PO OBRATU' AND to_char("T1"."date"::timestamp, 'YYYY') = '1993' +2026-05-27 10:01:56.005 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id", "T1"."frequency" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."A3" = 'east Bohemia' AND to_char("T1"."date"::timestamp, 'YYYY') BETWEEN '1995' AND '2000' +2026-05-27 10:01:57.450 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id", "T1"."date" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."A2" = 'Prachatice' +2026-05-27 10:01:58.800 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."A2", "T2"."A3" FROM account AS "T1" INNER JOIN district AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN loan AS "T3" ON "T1"."account_id" = "T3"."account_id" WHERE "T3"."loan_id" = 4990 +2026-05-27 10:01:59.120 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id", "T2"."A2", "T2"."A3" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "loan" AS "T3" ON "T1"."account_id" = "T3"."account_id" WHERE "T3"."amount" > 300000 +2026-05-27 10:02:00.050 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."loan_id", "T3"."A3", "T3"."A11" FROM "loan" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "district" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T1"."duration" = 60; +2026-05-27 10:02:01.400 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."loan_id", "T2"."A2", "T2"."A11" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "loan" AS "T3" ON "T1"."account_id" = "T3"."account_id" WHERE "T3"."duration" = 60 +2026-05-27 10:02:02.750 PDT [1201] app_user@financial LOG: statement: SELECT (CAST(("T3"."A13" - "T3"."A12") AS numeric) * 100) / "T3"."A12" FROM "loan" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "district" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T1"."status" = 'D' +2026-05-27 10:02:03.110 PDT [1201] app_user@financial LOG: statement: SELECT CAST(SUM(CASE WHEN "T1"."A2" = 'Decin' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT("account_id") FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE to_char("T2"."date"::timestamp, 'YYYY') = '1993' +2026-05-27 10:02:04.005 PDT [1201] app_user@financial LOG: statement: SELECT "account_id" FROM "account" WHERE "frequency" = 'POPLATEK MESICNE' +2026-05-27 10:02:05.450 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."A2" FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."gender" = 'F' GROUP BY "T1"."A2" ORDER BY COUNT("T2"."client_id") DESC LIMIT 10 +2026-05-27 10:02:06.800 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."A2", COUNT("T1"."client_id") FROM client AS "T1" INNER JOIN district AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."gender" = 'F' GROUP BY "T2"."district_id", "T2"."A2" ORDER BY COUNT("T1"."client_id") DESC NULLS LAST LIMIT 10 +2026-05-27 10:02:07.120 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."A2", SUM("T1"."amount") FROM "trans" AS "T1" INNER JOIN "account" AS "T3" ON "T1"."account_id" = "T3"."account_id" INNER JOIN "district" AS "T2" ON "T3"."district_id" = "T2"."district_id" WHERE "T1"."type" = 'VYDAJ' AND EXTRACT(YEAR FROM "T1"."date") = 1996 AND EXTRACT(MONTH FROM "T1"."date") = 1 GROUP BY "T2"."A2" ORDER BY SUM("T1"."amount") DESC LIMIT 10 +2026-05-27 10:02:08.050 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."district_id" FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "trans" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T3"."type" = 'VYDAJ' AND CAST("T2"."date" AS TEXT) LIKE '1996-01%' ORDER BY "T1"."district_id" ASC LIMIT 10 +2026-05-27 10:02:09.400 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T3"."account_id") FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "disp" AS "T3" ON "T2"."client_id" = "T3"."client_id" WHERE "T1"."A3" = 'south Bohemia' AND "T3"."type" != 'OWNER' +2026-05-27 10:02:10.750 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."A3" FROM account AS "T1" INNER JOIN district AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN loan AS "T3" ON "T1"."account_id" = "T3"."account_id" WHERE "T3"."status" IN ('C', 'D') GROUP BY "T2"."A3" ORDER BY SUM("T3"."amount") DESC NULLS LAST LIMIT 1 +2026-05-27 10:02:11.110 PDT [1201] app_user@financial LOG: statement: SELECT AVG(l.amount) FROM loan AS l INNER JOIN disp AS d ON l.account_id = d.account_id INNER JOIN client AS c ON d.client_id = c.client_id WHERE c.gender = 'M' +2026-05-27 10:02:12.005 PDT [1201] app_user@financial LOG: statement: SELECT AVG("T3"."amount") FROM "client" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "loan" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T1"."gender" = 'M' +2026-05-27 10:02:13.450 PDT [1201] app_user@financial LOG: statement: SELECT "A2", "A3" FROM "district" WHERE "A13" = (SELECT MAX("A13") FROM "district") +2026-05-27 10:02:14.800 PDT [1201] app_user@financial LOG: statement: SELECT "district_id", "A2" FROM "district" ORDER BY "A13" DESC LIMIT 1 +2026-05-27 10:02:15.120 PDT [1201] app_user@financial LOG: statement: SELECT count("T2"."account_id") FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" GROUP BY "T1"."A16" ORDER BY "T1"."A16" DESC NULLS LAST LIMIT 1 +2026-05-27 10:02:16.050 PDT [1201] app_user@financial LOG: statement: SELECT COUNT(DISTINCT "T1"."account_id") FROM "trans" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."balance" < 0 AND "T1"."operation" = 'VYBER KARTOU' AND "T2"."frequency" = 'POPLATEK MESICNE' +2026-05-27 10:02:17.400 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "account" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."date" BETWEEN '1995-01-01' AND '1997-12-31' AND "T1"."frequency" = 'POPLATEK MESICNE' AND "T2"."amount" >= 250000 +2026-05-27 10:02:18.750 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "account" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."date" BETWEEN '1995-01-01' AND '1997-12-31' AND "T1"."frequency" = 'POPLATEK MESICNE' AND "T2"."amount" > 250000 +2026-05-27 10:02:19.110 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "loan" AS "T3" ON "T1"."account_id" = "T3"."account_id" WHERE "T1"."district_id" = 1 AND ("T3"."status" = 'C' OR "T3"."status" = 'D') +2026-05-27 10:02:20.005 PDT [1201] app_user@financial LOG: statement: SELECT count("T1"."client_id") FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."gender" = 'M' AND "T2"."A15" = (SELECT "T3"."A15" FROM "district" AS "T3" ORDER BY "T3"."A15" DESC OFFSET 1 LIMIT 1) +2026-05-27 10:02:21.450 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."card_id") FROM "card" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."disp_id" = "T2"."disp_id" WHERE "T1"."type" = 'gold' AND "T2"."type" = 'DISPONENT' +2026-05-27 10:02:22.800 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."A2" = 'Pisek' +2026-05-27 10:02:23.120 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."district_id" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "trans" AS "T3" ON "T1"."account_id" = "T3"."account_id" WHERE to_char("T3"."date"::timestamp, 'YYYY') = '1997' GROUP BY "T1"."district_id" HAVING SUM("T3"."amount") > 10000 +2026-05-27 10:02:24.050 PDT [1201] app_user@financial LOG: statement: SELECT DISTINCT "T1"."account_id" FROM "order" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "district" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T1"."k_symbol" = 'SIPO' AND "T3"."A2" = 'Pisek' +2026-05-27 10:02:25.400 PDT [1201] app_user@financial LOG: statement: SELECT DISTINCT "T2"."account_id" FROM "trans" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "district" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T1"."k_symbol" = 'SIPO' AND "T3"."A2" = 'Pisek' +2026-05-27 10:02:26.750 PDT [1201] app_user@financial LOG: statement: SELECT DISTINCT "T1"."account_id" FROM "disp" AS "T1" INNER JOIN "card" AS "T2" ON "T1"."disp_id" = "T2"."disp_id" INNER JOIN "card" AS "T3" ON "T1"."disp_id" = "T3"."disp_id" WHERE "T2"."type" = 'gold' AND "T3"."type" = 'junior' +2026-05-27 10:02:27.110 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."account_id" FROM "card" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."disp_id" = "T2"."disp_id" WHERE "T1"."type" IN ('gold', 'junior') +2026-05-27 10:02:28.005 PDT [1201] app_user@financial LOG: statement: SELECT AVG("T3"."amount") FROM "card" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."disp_id" = "T2"."disp_id" INNER JOIN "trans" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE to_char("T3"."date"::timestamp, 'YYYY') = '2021' AND "T3"."operation" = 'VYBER KARTOU' +2026-05-27 10:02:29.450 PDT [1201] app_user@financial LOG: statement: WITH MonthlySpending AS ( SELECT account_id, EXTRACT(MONTH FROM date) as month, SUM(amount) as monthly_amount FROM trans WHERE operation = 'VYBER KARTOU' AND EXTRACT(YEAR FROM date) = 1998 GROUP BY account_id, EXTRACT(MONTH FROM date) ) SELECT DISTINCT account_id FROM MonthlySpending WHERE monthly_amount < ( SELECT AVG(monthly_amount) FROM MonthlySpending ) +2026-05-27 10:02:30.800 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id" FROM "trans" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE to_char("T1"."date"::timestamp, 'YYYY') = '1998' AND "T1"."operation" = 'VYBER KARTOU' AND "T1"."amount" < (SELECT AVG("amount") FROM "trans" WHERE to_char("date"::timestamp, 'YYYY') = '1998') +2026-05-27 10:02:31.120 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."client_id" FROM "client" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "loan" AS "T3" ON "T2"."account_id" = "T3"."account_id" INNER JOIN "card" AS "T4" ON "T2"."disp_id" = "T4"."disp_id" WHERE "T1"."gender" = 'F' +2026-05-27 10:02:32.050 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."gender" = 'F' AND "T2"."A3" = 'south Bohemia' +2026-05-27 10:02:33.400 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."account_id" FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "disp" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T3"."type" = 'OWNER' AND "T1"."A2" = 'Tabor' +2026-05-27 10:02:34.750 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."type" FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "disp" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T3"."type" != 'OWNER' AND "T1"."A11" BETWEEN 8000 AND 9000 +2026-05-27 10:02:35.110 PDT [1201] app_user@financial LOG: statement: SELECT COUNT(DISTINCT "T1"."account_id") FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "trans" AS "T3" ON "T1"."account_id" = "T3"."account_id" WHERE "T2"."A3" = 'north Bohemia' AND "T3"."bank" = 'AB' +2026-05-27 10:02:36.005 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T2"."account_id") FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "trans" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T3"."bank" = 'AB' AND "T1"."A3" = 'north Bohemia' +2026-05-27 10:02:37.450 PDT [1201] app_user@financial LOG: statement: SELECT DISTINCT "T1"."A2" FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "trans" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T3"."type" = 'VYDAJ' +2026-05-27 10:02:38.800 PDT [1201] app_user@financial LOG: statement: SELECT AVG("T1"."A15") FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE to_char("T2"."date"::timestamp, 'YYYY') >= '1997' AND "T1"."A15" > 4000 +2026-05-27 10:02:39.120 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."card_id") FROM "card" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."disp_id" = "T2"."disp_id" WHERE "T1"."type" = 'classic' AND "T2"."type" = 'OWNER' +2026-05-27 10:02:40.050 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."gender" = 'M' AND "T2"."A2" = 'Hl.m. Praha' +2026-05-27 10:02:41.400 PDT [1201] app_user@financial LOG: statement: SELECT CAST(SUM(CASE WHEN "type" = 'gold' AND "issued" < '1998-01-01' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM "card"; +2026-05-27 10:02:42.750 PDT [1201] app_user@financial LOG: statement: SELECT (CAST(SUM(CASE WHEN "type" = 'gold' THEN 1 ELSE 0 END) AS REAL) * 100) / COUNT("card_id") FROM "card" WHERE to_char("issued"::timestamp, 'YYYY') < '1998' +2026-05-27 10:02:43.110 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."client_id" FROM "disp" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."type" = 'OWNER' ORDER BY "T2"."amount" DESC NULLS LAST LIMIT 1 +2026-05-27 10:02:44.005 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."A15" FROM district AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."account_id" = 532 +2026-05-27 10:02:45.450 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."district_id" FROM "order" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "district" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T1"."order_id" = 33333 +2026-05-27 10:02:46.800 PDT [1201] app_user@financial LOG: statement: SELECT "T4"."trans_id" FROM "client" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "account" AS "T3" ON "T2"."account_id" = "T3"."account_id" INNER JOIN "trans" AS "T4" ON "T3"."account_id" = "T4"."account_id" WHERE "T1"."client_id" = 3356 AND "T4"."operation" = 'VYBER' +2026-05-27 10:02:47.120 PDT [1201] app_user@financial LOG: statement: SELECT COUNT(DISTINCT "T1"."account_id") FROM "account" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."frequency" = 'POPLATEK TYDNE' AND "T2"."amount" < 200000 +2026-05-27 10:02:48.050 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "loan" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."frequency" = 'POPLATEK TYDNE' AND "T1"."amount" < 200000 +2026-05-27 10:02:49.400 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."type" FROM "disp" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "card" AS "T3" ON "T1"."disp_id" = "T3"."disp_id" WHERE "T2"."client_id" = 13539 +2026-05-27 10:02:50.750 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."A3" FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."client_id" = 3541 +2026-05-27 10:02:51.110 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."district_id" FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "loan" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T3"."status" = 'A' GROUP BY "T1"."district_id" ORDER BY count("T2"."account_id") DESC NULLS LAST LIMIT 1 +2026-05-27 10:02:52.005 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."client_id" FROM "order" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "client" AS "T3" ON "T2"."client_id" = "T3"."client_id" WHERE "T1"."order_id" = 32423 +2026-05-27 10:02:53.450 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."client_id" FROM "order" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "client" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T1"."order_id" = 32423 +2026-05-27 10:02:54.800 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."trans_id" FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "trans" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T1"."district_id" = 5 +2026-05-27 10:02:55.120 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T2"."account_id") FROM "district" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."A2" = 'Jesenik' +2026-05-27 10:02:56.050 PDT [1201] app_user@financial LOG: statement: SELECT "T2"."client_id" FROM "card" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."disp_id" = "T2"."disp_id" WHERE "T1"."type" = 'junior' AND "T1"."issued" >= '1997-01-01' +2026-05-27 10:02:57.400 PDT [1201] app_user@financial LOG: statement: SELECT CAST(SUM(CASE WHEN "T2"."gender" = 'F' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT("T2"."client_id") FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."A11" > 10000 +2026-05-27 10:02:58.750 PDT [1201] app_user@financial LOG: statement: SELECT CAST(((SUM(CASE WHEN to_char("T1"."date"::timestamp, 'YYYY') = '1997' THEN "T1"."amount" ELSE 0 END) - SUM(CASE WHEN to_char("T1"."date"::timestamp, 'YYYY') = '1996' THEN "T1"."amount" ELSE 0 END)) * 100 / SUM(CASE WHEN to_char("T1"."date"::timestamp, 'YYYY') = '1996' THEN "T1"."amount" ELSE 0 END)) AS REAL) FROM "loan" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "disp" AS "T3" ON "T3"."account_id" = "T2"."account_id" INNER JOIN "client" AS "T4" ON "T4"."client_id" = "T3"."client_id" WHERE "T4"."gender" = 'M' AND "T3"."type" = 'OWNER'; +2026-05-27 10:02:59.110 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("account_id") FROM "trans" WHERE to_char("date"::timestamp, 'YYYY') > '1995' AND "operation" = 'VYBER KARTOU' +2026-05-27 10:03:00.005 PDT [1201] app_user@financial LOG: statement: SELECT SUM(CASE WHEN "A3" = 'north Bohemia' THEN "A16" ELSE 0 END) - SUM(CASE WHEN "A3" = 'east Bohemia' THEN "A16" ELSE 0 END) FROM "district" +2026-05-27 10:03:01.450 PDT [1201] app_user@financial LOG: statement: SELECT SUM(CASE WHEN "A3" = 'east Bohemia' THEN "A16" ELSE 0 END) - SUM(CASE WHEN "A3" = 'north Bohemia' THEN "A16" ELSE 0 END) FROM district +2026-05-27 10:03:02.800 PDT [1201] app_user@financial LOG: statement: SELECT SUM(CASE WHEN "type" = 'OWNER' THEN 1 ELSE 0 END) AS owner_count, SUM(CASE WHEN "type" = 'DISPONENT' THEN 1 ELSE 0 END) AS disponent_count FROM disp WHERE "account_id" BETWEEN 1 AND 10 +2026-05-27 10:03:03.120 PDT [1201] app_user@financial LOG: statement: SELECT SUM(CASE WHEN "type" = 'Owner' THEN 1 ELSE 0 END) AS owner_count, SUM(CASE WHEN "type" = 'Disponent' THEN 1 ELSE 0 END) AS disponent_count FROM disp WHERE "account_id" BETWEEN 1 AND 10 +2026-05-27 10:03:04.050 PDT [1201] app_user@financial LOG: statement: SELECT frequency FROM account WHERE account_id = 3; SELECT k_symbol FROM trans WHERE amount = 3539 AND type = 'VYDAJ'; +2026-05-27 10:03:05.400 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."frequency", "T2"."k_symbol" FROM account AS "T1" INNER JOIN "order" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."account_id" = 3 AND "T2"."amount" = 3539 +2026-05-27 10:03:06.750 PDT [1201] app_user@financial LOG: statement: SELECT to_char("T1"."birth_date"::timestamp, 'YYYY') FROM "client" AS "T1" INNER JOIN "disp" AS "T3" ON "T1"."client_id" = "T3"."client_id" INNER JOIN "account" AS "T2" ON "T3"."account_id" = "T2"."account_id" WHERE "T2"."account_id" = 130 +2026-05-27 10:03:07.110 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "account" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."type" = 'OWNER' AND "T1"."frequency" = 'POPLATEK PO OBRATU' +2026-05-27 10:03:08.005 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."amount", "T3"."status" FROM "client" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "loan" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T1"."client_id" = 992 +2026-05-27 10:03:09.450 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."balance", "T1"."gender" FROM "client" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "trans" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T1"."client_id" = 4 AND "T3"."trans_id" = 851 +2026-05-27 10:03:10.800 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."type" FROM "client" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "card" AS "T3" ON "T2"."disp_id" = "T3"."disp_id" WHERE "T1"."client_id" = 9 +2026-05-27 10:03:11.120 PDT [1201] app_user@financial LOG: statement: SELECT SUM(T1.amount) FROM trans AS T1 INNER JOIN disp AS T2 ON T1.account_id = T2.account_id WHERE T2.client_id = 617 AND EXTRACT(YEAR FROM T1.date) = 1998 +2026-05-27 10:03:12.050 PDT [1201] app_user@financial LOG: statement: SELECT SUM("T3"."amount") FROM "client" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "trans" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE to_char("T3"."date"::timestamp, 'YYYY') = '1998' AND "T1"."client_id" = 617 +2026-05-27 10:03:13.400 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."client_id" FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE EXTRACT(YEAR FROM "T1"."birth_date") BETWEEN 1983 AND 1987 AND "T2"."A3" = 'east Bohemia'; +2026-05-27 10:03:14.750 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."client_id", "T3"."account_id" FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "account" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T2"."A3" = 'east Bohemia' AND to_char("T1"."birth_date"::timestamp, 'YYYY') BETWEEN '1983' AND '1987' +2026-05-27 10:03:15.110 PDT [1201] app_user@financial LOG: statement: SELECT T1.client_id FROM client AS T1 INNER JOIN disp AS T2 ON T1.client_id = T2.client_id INNER JOIN loan AS T3 ON T2.account_id = T3.account_id WHERE T1.gender = 'F' GROUP BY T1.client_id ORDER BY MAX(T3.amount) DESC LIMIT 3 +2026-05-27 10:03:16.005 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."client_id" FROM "client" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "loan" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T1"."gender" = 'F' ORDER BY "T3"."amount" DESC NULLS LAST LIMIT 3 +2026-05-27 10:03:17.450 PDT [1201] app_user@financial LOG: statement: SELECT COUNT(DISTINCT T1.client_id) FROM client AS T1 INNER JOIN disp AS T2 ON T1.client_id = T2.client_id INNER JOIN trans AS T3 ON T2.account_id = T3.account_id WHERE T1.gender = 'M' AND T1.birth_date BETWEEN '1974-01-01' AND '1976-12-31' AND T3.k_symbol = 'SIPO' AND T3.amount > 4000 +2026-05-27 10:03:18.800 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "trans" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" INNER JOIN "client" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE to_char("T3"."birth_date"::timestamp, 'YYYY') BETWEEN '1974' AND '1976' AND "T3"."gender" = 'M' AND "T1"."amount" > 4000 AND "T1"."k_symbol" = 'SIPO' +2026-05-27 10:03:19.120 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."account_id") FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE to_char("T1"."date"::timestamp, 'YYYY') > '1996' AND "T2"."A2" = 'Beroun'; +2026-05-27 10:03:20.050 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "card" AS "T3" ON "T2"."disp_id" = "T3"."disp_id" WHERE "T1"."gender" = 'F' AND "T3"."type" = 'junior' +2026-05-27 10:03:21.400 PDT [1201] app_user@financial LOG: statement: SELECT CAST(SUM(CASE WHEN "T1"."gender" = 'F' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(*) FROM "client" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "account" AS "T3" ON "T2"."account_id" = "T3"."account_id" INNER JOIN "district" AS "T4" ON "T3"."district_id" = "T4"."district_id" WHERE "T4"."A3" LIKE '%Prague%'; +2026-05-27 10:03:22.750 PDT [1201] app_user@financial LOG: statement: SELECT (CAST(SUM(CASE WHEN "T2"."gender" = 'F' THEN 1 ELSE 0 END) AS REAL) / CAST(COUNT("T2"."client_id") AS REAL)) * 100 FROM "district" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T1"."A3" = 'Prague' +2026-05-27 10:03:23.110 PDT [1201] app_user@financial LOG: statement: SELECT CAST(SUM(CASE WHEN "T1"."gender" = 'M' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "disp" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "account" AS "T3" ON "T2"."account_id" = "T3"."account_id" WHERE "T3"."frequency" = 'POPLATEK TYDNE' +2026-05-27 10:03:24.005 PDT [1201] app_user@financial LOG: statement: SELECT CAST(SUM(CASE WHEN "T1"."gender" = 'M' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE "T2"."frequency" = 'POPLATEK TYDNE' +2026-05-27 10:03:25.450 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T2"."account_id") FROM "account" AS "T1" INNER JOIN "disp" AS "T2" ON "T2"."account_id" = "T1"."account_id" WHERE "T1"."frequency" = 'POPLATEK TYDNE' AND "T2"."type" = 'USER' +2026-05-27 10:03:26.800 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id" FROM "loan" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."duration" > 24 AND "T2"."date" < '1997-01-01' ORDER BY "T1"."amount" ASC LIMIT 1; +2026-05-27 10:03:27.120 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id" FROM "loan" AS "T1" INNER JOIN "accounts" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."duration" > 24 AND "T2"."opening_date" < '1997-01-01' ORDER BY "T1"."amount" ASC LIMIT 1; +2026-05-27 10:03:28.050 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id" FROM "loan" AS "T1" INNER JOIN "account" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."duration" > 24 AND to_char("T2"."date"::timestamp, 'YYYY') < '1997' ORDER BY "T1"."amount" ASC LIMIT 1 +2026-05-27 10:03:29.400 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."account_id" FROM "disp" AS "T1" INNER JOIN "client" AS "T2" ON "T1"."client_id" = "T2"."client_id" INNER JOIN "district" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T2"."gender" = 'F' ORDER BY "T2"."birth_date" ASC, "T3"."A11" ASC LIMIT 1; +2026-05-27 10:03:30.750 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."account_id" FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "account" AS "T3" ON "T2"."district_id" = "T3"."district_id" WHERE "T1"."gender" = 'F' ORDER BY "T1"."birth_date" ASC, "T2"."A11" ASC NULLS LAST LIMIT 1 +2026-05-27 10:03:31.110 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T1"."client_id") FROM "client" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" WHERE to_char("T1"."birth_date"::timestamp, 'YYYY') = '1920' AND "T2"."A3" = 'east Bohemia' +2026-05-27 10:03:32.005 PDT [1201] app_user@financial LOG: statement: SELECT COUNT(DISTINCT "T1"."account_id") FROM "account" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T1"."frequency" = 'POPLATEK TYDNE' AND "T2"."duration" = 24 +2026-05-27 10:03:33.450 PDT [1201] app_user@financial LOG: statement: SELECT COUNT("T2"."account_id") FROM "account" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."duration" = 24 AND "T1"."frequency" = 'POPLATEK TYDNE' +2026-05-27 10:03:34.800 PDT [1201] app_user@financial LOG: statement: SELECT AVG("T2"."amount") FROM "account" AS "T1" INNER JOIN "loan" AS "T2" ON "T1"."account_id" = "T2"."account_id" WHERE "T2"."status" IN ('C', 'D') AND "T1"."frequency" = 'POPLATEK PO OBRATU' +2026-05-27 10:03:35.120 PDT [1201] app_user@financial LOG: statement: SELECT client_id, district_id FROM client WHERE client_id IN (SELECT client_id FROM disp WHERE type = 'OWNER') EXCEPT SELECT client_id, district_id FROM client WHERE client_id IN (SELECT client_id FROM disp WHERE type != 'OWNER') +2026-05-27 10:03:36.050 PDT [1201] app_user@financial LOG: statement: SELECT "T3"."client_id", "T2"."district_id", "T2"."A2" FROM "account" AS "T1" INNER JOIN "district" AS "T2" ON "T1"."district_id" = "T2"."district_id" INNER JOIN "disp" AS "T3" ON "T1"."account_id" = "T3"."account_id" WHERE "T3"."type" = 'OWNER' +2026-05-27 10:03:37.400 PDT [1201] app_user@financial LOG: statement: SELECT "T1"."client_id", (CAST(to_char(CURRENT_TIMESTAMP, 'YYYY') AS integer) - CAST(to_char("T3"."birth_date"::timestamp, 'YYYY') AS integer)) AS age FROM "disp" AS "T1" INNER JOIN "card" AS "T2" ON "T2"."disp_id" = "T1"."disp_id" INNER JOIN "client" AS "T3" ON "T1"."client_id" = "T3"."client_id" WHERE "T2"."type" = 'gold' AND "T1"."type" = 'OWNER'; \ No newline at end of file diff --git a/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/experiments/.gitkeep b/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/experiments/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/state.md b/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/state.md new file mode 100644 index 00000000..d5ca461c --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/state.md @@ -0,0 +1 @@ +# context authoring experiment state tracking diff --git a/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/tools.yaml b/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/tools.yaml new file mode 100644 index 00000000..2624bc1e --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/tools.yaml @@ -0,0 +1,33 @@ +kind: source +name: my-alloydb +type: alloydb-postgres +project: cloud-db-nl2sql +region: us-central1 +cluster: whaoyu-test +instance: whaoyu-test-primary +database: financial +--- +kind: tool +name: my-alloydb-list-schemas +type: postgres-list-tables +source: my-alloydb +description: | + Use this tool to list tables and their schemas in the my-alloydb database. + + Progressive Schema Discovery (Recommended): + 1) Fetch structure first (output_format='simple'), + 2) Go deep on specific parts if interested, + 3) Use batching if info is too large. + + Scope: + - The tool can fetch system/extension schemas. Agents should ignore them and focus on user data. + + Behavior: + - Omit 'table_names' to fetch all tables. + - Omit 'output_format' for detailed schema (default). +--- +kind: tool +name: my-alloydb-execute-sql +type: postgres-execute-sql +source: my-alloydb +description: Use this tool to execute SQL statements against the my-alloydb database. diff --git a/evals/core-cujs/workspace_post_init/user_data/binary_data/ER_Diagram.jpeg b/evals/core-cujs/workspace_post_init_seed/case_blog/app_data/ER_Diagram.jpeg similarity index 100% rename from evals/core-cujs/workspace_post_init/user_data/binary_data/ER_Diagram.jpeg rename to evals/core-cujs/workspace_post_init_seed/case_blog/app_data/ER_Diagram.jpeg diff --git a/evals/core-cujs/workspace_post_init/user_data/binary_data/desgin_doc.pdf b/evals/core-cujs/workspace_post_init_seed/case_blog/app_data/desgin_doc.pdf similarity index 100% rename from evals/core-cujs/workspace_post_init/user_data/binary_data/desgin_doc.pdf rename to evals/core-cujs/workspace_post_init_seed/case_blog/app_data/desgin_doc.pdf diff --git a/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/experiments/.gitkeep b/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/experiments/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/state.md b/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/state.md new file mode 100644 index 00000000..d5ca461c --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/state.md @@ -0,0 +1 @@ +# context authoring experiment state tracking diff --git a/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/tools.yaml b/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/tools.yaml new file mode 100644 index 00000000..10c505c1 --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/tools.yaml @@ -0,0 +1,33 @@ +kind: source +name: my-alloydb +type: alloydb-postgres +project: cloud-db-nl2sql +region: us-central1 +cluster: whaoyu-test +instance: whaoyu-test-primary +database: db_dummy +--- +kind: tool +name: my-alloydb-list-schemas +type: postgres-list-tables +source: my-alloydb +description: | + Use this tool to list tables and their schemas in the my-alloydb database. + + Progressive Schema Discovery (Recommended): + 1) Fetch structure first (output_format='simple'), + 2) Go deep on specific parts if interested, + 3) Use batching if info is too large. + + Scope: + - The tool can fetch system/extension schemas. Agents should ignore them and focus on user data. + + Behavior: + - Omit 'table_names' to fetch all tables. + - Omit 'output_format' for detailed schema (default). +--- +kind: tool +name: my-alloydb-execute-sql +type: postgres-execute-sql +source: my-alloydb +description: Use this tool to execute SQL statements against the my-alloydb database. diff --git a/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/business_blueprint.md b/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/business_blueprint.md similarity index 100% rename from evals/core-cujs/workspace_post_init/user_data/ecommerce_app/business_blueprint.md rename to evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/business_blueprint.md diff --git a/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/db_schema.sql b/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/db_schema.sql similarity index 98% rename from evals/core-cujs/workspace_post_init/user_data/ecommerce_app/db_schema.sql rename to evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/db_schema.sql index 0256de4e..46d5f5b4 100644 --- a/evals/core-cujs/workspace_post_init/user_data/ecommerce_app/db_schema.sql +++ b/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/db_schema.sql @@ -26,7 +26,7 @@ CREATE TABLE t_addr ( CREATE TABLE t_brnd ( b_id integer NOT NULL, b_nm character varying(255), - desc text, + description text, w_url character varying(255), l_url character varying(255), is_act boolean, @@ -54,7 +54,7 @@ CREATE TABLE t_cat ( c_id integer NOT NULL, p_c_id integer, c_nm character varying(255), - desc text, + description text, i_url character varying(255), is_act boolean, c_dt timestamp without time zone, @@ -226,7 +226,7 @@ CREATE TABLE t_usr ( f_nm character varying(100), l_nm character varying(100), dob date, - gndr gndr_enm, + gndr character VARYING(255), phn character varying(20), pp_url character varying(255), cp_url character varying(255), diff --git a/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/experiments/.gitkeep b/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/experiments/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/state.md b/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/state.md new file mode 100644 index 00000000..d5ca461c --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/state.md @@ -0,0 +1 @@ +# context authoring experiment state tracking diff --git a/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/tools.yaml b/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/tools.yaml new file mode 100644 index 00000000..6cc51853 --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/tools.yaml @@ -0,0 +1,33 @@ +kind: source +name: my-alloydb +type: alloydb-postgres +project: cloud-db-nl2sql +region: us-central1 +cluster: whaoyu-test +instance: whaoyu-test-primary +database: db_ecommerce_cryptic +--- +kind: tool +name: my-alloydb-list-schemas +type: postgres-list-tables +source: my-alloydb +description: | + Use this tool to list tables and their schemas in the my-alloydb database. + + Progressive Schema Discovery (Recommended): + 1) Fetch structure first (output_format='simple'), + 2) Go deep on specific parts if interested, + 3) Use batching if info is too large. + + Scope: + - The tool can fetch system/extension schemas. Agents should ignore them and focus on user data. + + Behavior: + - Omit 'table_names' to fetch all tables. + - Omit 'output_format' for detailed schema (default). +--- +kind: tool +name: my-alloydb-execute-sql +type: postgres-execute-sql +source: my-alloydb +description: Use this tool to execute SQL statements against the my-alloydb database. diff --git a/evals/core-cujs/workspace_post_init_seed/case_flights/app_data/db_schema.sql b/evals/core-cujs/workspace_post_init_seed/case_flights/app_data/db_schema.sql new file mode 100644 index 00000000..6bbb303c --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_flights/app_data/db_schema.sql @@ -0,0 +1,19 @@ +CREATE TABLE IF NOT EXISTS airports ( + id INT PRIMARY KEY, + iata TEXT, + name TEXT, + city TEXT, + country TEXT + ); + +CREATE TABLE IF NOT EXISTS flights ( + id INT PRIMARY KEY, + airline VARCHAR(10), + flight_number INT, + departure_airport VARCHAR(5), + arrival_airport VARCHAR(5), + departure_time TIMESTAMP, + arrival_time TIMESTAMP, + departure_gate VARCHAR(10), + arrival_gate VARCHAR(10) +); diff --git a/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/experiments/.gitkeep b/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/experiments/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/state.md b/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/state.md new file mode 100644 index 00000000..d5ca461c --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/state.md @@ -0,0 +1 @@ +# context authoring experiment state tracking diff --git a/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/tools.yaml b/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/tools.yaml new file mode 100644 index 00000000..10c505c1 --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/tools.yaml @@ -0,0 +1,33 @@ +kind: source +name: my-alloydb +type: alloydb-postgres +project: cloud-db-nl2sql +region: us-central1 +cluster: whaoyu-test +instance: whaoyu-test-primary +database: db_dummy +--- +kind: tool +name: my-alloydb-list-schemas +type: postgres-list-tables +source: my-alloydb +description: | + Use this tool to list tables and their schemas in the my-alloydb database. + + Progressive Schema Discovery (Recommended): + 1) Fetch structure first (output_format='simple'), + 2) Go deep on specific parts if interested, + 3) Use batching if info is too large. + + Scope: + - The tool can fetch system/extension schemas. Agents should ignore them and focus on user data. + + Behavior: + - Omit 'table_names' to fetch all tables. + - Omit 'output_format' for detailed schema (default). +--- +kind: tool +name: my-alloydb-execute-sql +type: postgres-execute-sql +source: my-alloydb +description: Use this tool to execute SQL statements against the my-alloydb database. diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/app_data/db_schema.sql b/evals/core-cujs/workspace_post_init_seed/case_hr/app_data/db_schema.sql new file mode 100644 index 00000000..acd97421 --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_hr/app_data/db_schema.sql @@ -0,0 +1,301 @@ +CREATE TABLE allowances ( + allowance_id integer NOT NULL, + allowance character varying(255), + allowance_description text +); + +CREATE TABLE attendance ( + attendance_id integer NOT NULL, + employee_id integer, + date date, + check_in_time time without time zone, + check_out_time time without time zone, + check_in_location character varying(255), + check_out_location character varying(255), + check_in_method character varying(50), + check_out_method character varying(50), + work_hours numeric(10,2), + overtime_hours numeric(10,2), + shift character varying(50), + notes text +); + +CREATE TABLE benefit ( + benefit_id integer NOT NULL, + benefit_name character varying(255), + description text, + coverage character varying(255), + provider character varying(255), + start_date date, + end_date date, + eligibility_criteria jsonb, + usage_instructions text, + contact_info character varying(255) +); + +CREATE TABLE company ( + company_id integer NOT NULL, + company_name character varying(255), + industry character varying(255), + size character varying(50), + location character varying(255), + website character varying(255), + contact_person character varying(255), + contact_email character varying(255), + contact_phone character varying(20), + founded_date date, + ownership_type character varying(50), + social_media_facebook character varying(255), + social_media_instagram character varying(255), + social_media_linkedin character varying(255), + social_media_x character varying(255), + clients jsonb, + products jsonb, + partners jsonb, + competitors jsonb, + financial_year_end date +); + +CREATE TABLE department ( + department_id integer NOT NULL, + department_name character varying(255), + manager_id integer, + budget numeric(15,2), + start_date date, + end_date date, + is_active boolean, + goals jsonb, + achievements jsonb, + company_id integer +); + +CREATE TABLE employee ( + employee_id integer NOT NULL, + first_name character varying(255), + last_name character varying(255), + email character varying(255), + phone_number character varying(20), + address jsonb, + image_url character varying(255), + date_of_birth date, + gender character varying(10), + marital_status character varying(20), + national_identification character varying(50), + bank_account_number jsonb, + emergency_contact_name character varying(255), + emergency_contact_number character varying(20), + contract_type character varying(50), + hire_date date, + department_id integer, + job_id integer, + manager_id integer, + company_id integer +); + +CREATE TABLE employee_allowances ( + employee_allowance_id integer NOT NULL, + employee_id integer, + allowance_id integer, + type character varying(255), + amount numeric(15,2), + effective_date date, + date_created date +); + +CREATE TABLE employee_business_expense ( + expense_id integer NOT NULL, + employee_id integer, + expense_date date, + expense_category character varying(255), + expense_description text, + expense_amount numeric(15,2), + expense_receipt character varying(255) +); + +CREATE TABLE employee_certification ( + certification_id integer NOT NULL, + employee_id integer, + certification_name character varying(255), + issuing_organization character varying(255), + issue_date date, + expiration_date date +); + +CREATE TABLE employee_computer ( + computer_id integer NOT NULL, + employee_id integer, + computer_name character varying(255), + serial_number character varying(255), + model character varying(255), + purchase_date date, + warranty_expiry_date date, + status character varying(50), + assigned_date date, + returned_date date +); + +CREATE TABLE employee_termination ( + termination_id integer NOT NULL, + employee_id integer, + termination_date date, + reason character varying(255), + description text +); + +CREATE TABLE employee_training ( + employee_id integer, + training_id integer, + start_date date, + end_date date +); + +CREATE TABLE employee_travel ( + travel_id integer NOT NULL, + employee_id integer, + travel_purpose character varying(255), + travel_destination character varying(255), + travel_start_date date, + travel_end_date date, + travel_expenses numeric(15,2) +); + +CREATE TABLE employee_x_benefit ( + employee_id integer, + benefit_id integer, + start_date date, + end_date date +); + +CREATE TABLE holiday ( + date date NOT NULL, + day character varying(255), + occasion character varying(255), + geographic_location character varying(255), + city character varying(255) NOT NULL, + project character varying(255) NOT NULL +); + +CREATE TABLE invoice ( + invoice_id integer NOT NULL, + invoice_date date, + due_date date, + total_amount numeric(15,2), + employee_id integer, + status character varying(50) +); + +CREATE TABLE job ( + job_id integer NOT NULL, + job_title character varying(255), + job_description text, + skills_required jsonb, + skills_preferred jsonb, + min_salary numeric(15,2), + max_salary numeric(15,2), + department_id integer, + responsibilities text, + benefits jsonb, + education_requirements jsonb, + experience_requirements jsonb, + employment_type character varying(50), + location character varying(255), + vacancies integer, + status character varying(50) +); + +CREATE TABLE job_history ( + job_history_id integer NOT NULL, + employee_id integer, + start_date date, + end_date date, + job_id integer, + department_id integer +); + +CREATE TABLE leave_request ( + leave_id integer NOT NULL, + employee_id integer, + leave_type character varying(50), + start_date date, + end_date date, + status character varying(50) DEFAULT 'Pending', + duration numeric(10,2), + reason text, + approval_date date, + rejection_date date, + comments text, + attachment character varying(255), + processed_by integer +); + +CREATE TABLE manager ( + manager_id integer NOT NULL, + start_date date, + end_date date +); + +CREATE TABLE payment ( + payment_id integer NOT NULL, + payment_date date, + amount numeric(15,2), + payment_method character varying(255), + invoice_id integer +); + +CREATE TABLE performance_review ( + review_id integer NOT NULL, + employee_id integer, + reviewer_id integer, + review_date date, + performance_rating numeric(5,2), + comments text +); + +CREATE TABLE salary ( + salary_id integer NOT NULL, + employee_id integer, + salary numeric(15,2), + start_date date, + end_date date, + currency character varying(10), + pay_frequency character varying(50), + bonus numeric(15,2), + taxes numeric(15,2), + deductions numeric(15,2), + total_compensation numeric(15,2), + status character varying(50) +); + +CREATE TABLE timesheet ( + timesheet_id integer NOT NULL, + date date, + working_hours numeric(10,2), + overtime numeric(10,2), + leave_type character varying(255) +); + +CREATE TABLE timesheet_employee ( + timesheet_id integer NOT NULL, + employee_id integer, + month integer, + year integer, + fort_night1_flag boolean, + fort_night2_flag boolean +); + +CREATE TABLE training ( + training_id integer NOT NULL, + title character varying(255), + description text, + date date, + location character varying(255), + link character varying(255), + trainer character varying(255), + duration numeric(10,2), + attendance_required boolean, + registration_deadline_date date, + registration_link character varying(255), + status character varying(50), + feedback_link character varying(255), + materials text +); \ No newline at end of file diff --git a/evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py b/evals/core-cujs/workspace_post_init_seed/case_hr/app_data/hr_agent.py similarity index 100% rename from evals/core-cujs/workspace_post_init/user_data/hr_app/hr_agent.py rename to evals/core-cujs/workspace_post_init_seed/case_hr/app_data/hr_agent.py diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/experiments/.gitkeep b/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/experiments/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/state.md b/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/state.md new file mode 100644 index 00000000..d5ca461c --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/state.md @@ -0,0 +1 @@ +# context authoring experiment state tracking diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/tools.yaml b/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/tools.yaml new file mode 100644 index 00000000..b01c55fd --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/tools.yaml @@ -0,0 +1,33 @@ +kind: source +name: my-alloydb +type: alloydb-postgres +project: cloud-db-nl2sql +region: us-central1 +cluster: whaoyu-test +instance: whaoyu-test-primary +database: db_hr +--- +kind: tool +name: my-alloydb-list-schemas +type: postgres-list-tables +source: my-alloydb +description: | + Use this tool to list tables and their schemas in the my-alloydb database. + + Progressive Schema Discovery (Recommended): + 1) Fetch structure first (output_format='simple'), + 2) Go deep on specific parts if interested, + 3) Use batching if info is too large. + + Scope: + - The tool can fetch system/extension schemas. Agents should ignore them and focus on user data. + + Behavior: + - Omit 'table_names' to fetch all tables. + - Omit 'output_format' for detailed schema (default). +--- +kind: tool +name: my-alloydb-execute-sql +type: postgres-execute-sql +source: my-alloydb +description: Use this tool to execute SQL statements against the my-alloydb database. diff --git a/evals/core-cujs/workspace_post_init_seed/case_property_search/app_data/db_schema.sql b/evals/core-cujs/workspace_post_init_seed/case_property_search/app_data/db_schema.sql new file mode 100644 index 00000000..c5ecbf80 --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_property_search/app_data/db_schema.sql @@ -0,0 +1,61 @@ +CREATE EXTENSION IF NOT EXISTS vector CASCADE; +CREATE EXTENSION IF NOT EXISTS alloydb_scann CASCADE; + +-- 2. TABLE CREATION +DROP TABLE IF EXISTS user_prompt_history CASCADE; + +CREATE TABLE public.user_prompt_history ( + id SERIAL PRIMARY KEY, + "timestamp" timestamp without time zone DEFAULT CURRENT_TIMESTAMP, + user_prompt text, + prompt_embedded vector(3072), + query_template_used boolean, + query_template_id integer, + query_explanation text +); + +DROP TABLE IF EXISTS property_listings CASCADE; + +CREATE TABLE property_listings ( + id SERIAL PRIMARY KEY, + title VARCHAR(255) NOT NULL, + description TEXT, + price DECIMAL(12, 2) NOT NULL, + bedrooms INT, + city VARCHAR(100), + image_gcs_uri TEXT, + country VARCHAR(100) DEFAULT 'Switzerland', + canton VARCHAR(100), + -- Embeddings are generated externally and inserted directly + description_embedding VECTOR(3072) , + image_embedding VECTOR(1408) +); + +-- 2.1 COLUMN METADATA COMMENTS (Gemini Context Enrichment) +COMMENT ON COLUMN property_listings.bedrooms IS 'Examples: [''4'', ''6'', ''3''] | Distinct Values: 7 | Null Count: 0 |'; +COMMENT ON COLUMN property_listings.canton IS 'Examples: [''Solothurn'', ''Ticino'', ''Zug''] | Distinct Values: 27 | Null Count: 0 |'; +COMMENT ON COLUMN property_listings.city IS 'Examples: [''Stans'', ''Altdorf'', ''Kilchberg''] | Distinct Values: 89 | Null Count: 0 |'; +COMMENT ON COLUMN property_listings.country IS 'Examples: [''Switzerland''] | Distinct Values: 1 | Null Count: 0 |'; +COMMENT ON COLUMN property_listings.description IS 'Examples: [''The central rail crossroad of Switzerland. Reach anywhere fast. Modern functional apartment.'', ''Cozy retreat for weekend getaways or permanent living.''] | Distinct Values: 250 | Null Count: 0 |'; +COMMENT ON COLUMN property_listings.id IS 'Examples: [''75'', ''247'', ''13''] | Distinct Values: 250 | Null Count: 0 |'; +COMMENT ON COLUMN property_listings.image_gcs_uri IS 'Examples: [''https://storage.googleapis.com/property-images-data-agent-ai-powered-search-alloydb-1542/listings/10.jpg''] | Distinct Values: 250 | Null Count: 0 |'; +COMMENT ON COLUMN property_listings.price IS 'Examples: [''11878.00'', ''4869.00'', ''2792.00''] | Distinct Values: 189 | Null Count: 0 |'; +COMMENT ON COLUMN property_listings.title IS 'Examples: [''Rustic Studio in Landquart'', ''Renovated Villa in Herisau'', ''Quiet Home in Appenzell''] | Distinct Values: 248 | Null Count: 0 |'; + + +-- 3. INDEX CREATION (ScaNN). Follow index sql files after data is loaded. + +-- 4. MODEL ALIASING (Vertex AI Integration) +CREATE EXTENSION IF NOT EXISTS google_ml_integration CASCADE; + + +-- 4.1 Test Text Embeddings in Database Vertex AI integration +SELECT google_ml.embedding( + model_id => 'gemini-embedding-001', + content => 'This is the text to embed.' +); +SELECT google_ml.embedding( + model_id => 'multimodalembedding@001', + content => 'This is the text to embed.' +); + diff --git a/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/experiments/.gitkeep b/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/experiments/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/state.md b/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/state.md new file mode 100644 index 00000000..d5ca461c --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/state.md @@ -0,0 +1 @@ +# context authoring experiment state tracking diff --git a/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/tools.yaml b/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/tools.yaml new file mode 100644 index 00000000..cac5a034 --- /dev/null +++ b/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/tools.yaml @@ -0,0 +1,33 @@ +kind: source +name: my-alloydb +type: alloydb-postgres +project: cloud-db-nl2sql +region: us-central1 +cluster: whaoyu-test +instance: whaoyu-test-primary +database: search +--- +kind: tool +name: my-alloydb-list-schemas +type: postgres-list-tables +source: my-alloydb +description: | + Use this tool to list tables and their schemas in the my-alloydb database. + + Progressive Schema Discovery (Recommended): + 1) Fetch structure first (output_format='simple'), + 2) Go deep on specific parts if interested, + 3) Use batching if info is too large. + + Scope: + - The tool can fetch system/extension schemas. Agents should ignore them and focus on user data. + + Behavior: + - Omit 'table_names' to fetch all tables. + - Omit 'output_format' for detailed schema (default). +--- +kind: tool +name: my-alloydb-execute-sql +type: postgres-execute-sql +source: my-alloydb +description: Use this tool to execute SQL statements against the my-alloydb database. diff --git a/golden_flight_booking_expert.json b/golden_flight_booking_expert.json new file mode 100644 index 00000000..30a3927a --- /dev/null +++ b/golden_flight_booking_expert.json @@ -0,0 +1,32 @@ +[ + { + "id": "flight_expert_001", + "database": "flight_booking", + "nlq": "Find all flights departing from San Francisco that avoid the evening rush hour between 5 PM and 7 PM.", + "golden_sql": "SELECT * FROM flights WHERE departure_airport = 'SFO' AND (departure_time::time < '17:00:00' OR departure_time::time > '19:00:00');" + }, + { + "id": "flight_expert_002", + "database": "flight_booking", + "nlq": "Which airlines have the most flights arriving at Orlando (MCO) on January 2nd, 2025?", + "golden_sql": "SELECT airline, COUNT(*) as flight_count FROM flights WHERE arrival_airport = 'MCO' AND arrival_time::date = '2025-01-02' GROUP BY airline ORDER BY flight_count DESC;" + }, + { + "id": "flight_expert_003", + "database": "flight_booking", + "nlq": "List the full names of airports that have more than 100 departing flights scheduled.", + "golden_sql": "SELECT a.name, COUNT(f.flight_id) as total_departures FROM airports a JOIN flights f ON a.code = f.departure_airport GROUP BY a.name HAVING COUNT(f.flight_id) > 100;" + }, + { + "id": "flight_expert_004", + "database": "flight_booking", + "nlq": "What is the average flight duration for United Airlines (UA) flights between San Francisco (SFO) and New York (JFK)?", + "golden_sql": "SELECT AVG(arrival_time - departure_time) as avg_duration FROM flights WHERE airline = 'UA' AND departure_airport = 'SFO' AND arrival_airport = 'JFK';" + }, + { + "id": "flight_expert_005", + "database": "flight_booking", + "nlq": "Find flights to Disney World (Orlando) departing from the West Coast (SFO, LAX, or SEA) tomorrow.", + "golden_sql": "SELECT f.* FROM flights f JOIN airports a ON f.arrival_airport = a.code WHERE a.city = 'Orlando' AND f.departure_airport IN ('SFO', 'LAX', 'SEA') AND f.departure_time::date = CURRENT_DATE + 1;" + } +] \ No newline at end of file diff --git a/golden_property_search_advanced.json b/golden_property_search_advanced.json new file mode 100644 index 00000000..3ee3f382 --- /dev/null +++ b/golden_property_search_advanced.json @@ -0,0 +1,32 @@ +[ + { + "id": "adv_001", + "database": "search", + "nlq": "Find the average price and total count of 3-bedroom properties for each canton, but only include cantons with at least 5 listings, sorted by average price descending.", + "golden_sql": "SELECT canton, AVG(price) as avg_price, COUNT(*) as property_count FROM property_listings WHERE bedrooms = 3 GROUP BY canton HAVING COUNT(*) >= 5 ORDER BY avg_price DESC" + }, + { + "id": "adv_002", + "database": "search", + "nlq": "Show the top 5 most expensive cities in terms of average property price where there are listings with more than 4 bedrooms.", + "golden_sql": "SELECT city, AVG(price) as avg_city_price FROM property_listings WHERE bedrooms > 4 GROUP BY city ORDER BY avg_city_price DESC LIMIT 5" + }, + { + "id": "adv_003", + "database": "search", + "nlq": "List all properties in 'Zurich' or 'Geneva' that have a price lower than the overall average price of all properties in Switzerland, ordered by price.", + "golden_sql": "SELECT * FROM property_listings WHERE city IN ('Zurich', 'Geneva') AND price < (SELECT AVG(price) FROM property_listings) ORDER BY price ASC" + }, + { + "id": "adv_004", + "database": "search", + "nlq": "Find cantons where the difference between the maximum and minimum property price is greater than 10000, and show the count of properties in those cantons.", + "golden_sql": "SELECT canton, COUNT(*) as property_count, (MAX(price) - MIN(price)) as price_range FROM property_listings GROUP BY canton HAVING (MAX(price) - MIN(price)) > 10000" + }, + { + "id": "adv_005", + "database": "search", + "nlq": "Retrieve properties that have the same number of bedrooms and are in the same city as the property with ID 10, excluding property 10 itself.", + "golden_sql": "SELECT p1.* FROM property_listings p1 JOIN property_listings p2 ON p1.bedrooms = p2.bedrooms AND p1.city = p2.city WHERE p2.id = 10 AND p1.id <> 10" + } +] \ No newline at end of file diff --git a/skills/autoctx-dataset-generation/SKILL.md b/skills/autoctx-dataset-generation/SKILL.md index bd242c43..9126945b 100644 --- a/skills/autoctx-dataset-generation/SKILL.md +++ b/skills/autoctx-dataset-generation/SKILL.md @@ -19,17 +19,18 @@ You are an agent that helps a user generate and expand evaluation datasets of Na 3. **Acquire Context, Resolve Schema, & Establish Semantic Bridge**: * **Fetch Schema**: Attempt to use the `-list-schemas` MCP tool. * **Schema Conflict Resolution (Source of Truth)**: If the user provided Business Context Artifacts or Application Source Code that contain schema definitions, you MUST cross-check the MCP tool's output against them. - * If the MCP tool returns a schema that fundamentally conflicts with the domain or tables described in the user's offline artifacts, **assume the MCP tool is connected to the wrong environment. Discard the MCP tool's schema entirely.** - * Treat the provided artifacts and source code as the single, infallible source of truth for the schema. Inform the user of this mismatch and your decision to use the offline schema. + * If the MCP tool returns an empty schema or a schema that fundamentally conflicts with the domain or tables described in the user's offline artifacts, treat the provided artifacts and source code as the single, infallible source of truth for the schema. Inform the user of this mismatch and your decision to use the offline schema. * **Synthesize Business Logic**: Map the business definitions from the documents to your finalized schema. * **CRITICAL - Vocabulary Separation**: You must act as a semantic bridge. The **NLQ** must strictly use natural business terminology (e.g., "Active Users"). The **SQL** must strictly adhere to the technical schema (e.g., `WHERE status = 1`). Never invent column names in SQL based on business docs, and never leak raw column names into the NLQ. -4. **Analyze Real-World Usage (Query Logs & Source Code)**: + +4. **Analyze Real-World Usage & Table Prioritization (Source Code & Artifacts)**: + * **If Source Code or Artifacts are provided**, treat them as a "heatmap" for the database. Analyze ORM models, BI dashboards, API endpoints, and reporting logic to identify the **core business tables, relationships, highly utilized columns, and filter criteria**. + * **Intelligent Selection**: Use this knowledge to actively filter the schema. Prioritize generating NL-SQL pairs involving these highly-accessed tables and frequently joined relationships and filter criteria. Ignore system tables (e.g., migrations, raw audit logs) or deprecated columns that do not appear in the application code or business docs. * **If Query Logs are provided**: 1. Extract the queries and rigorously filter out administrative or DML queries (`INSERT`, `UPDATE`, `DELETE`, `SELECT 1`, etc.). Keep only meaningful analytical `SELECT` statements. 2. Perform **Reverse Translation**: Translate the extracted SQL queries into natural language business questions. You MUST apply the *Semantic Bridge* rule here—the generated NLQs should sound like a business user asking a question, using the vocabulary from any provided business artifacts. 3. Treat these reverse-translated pairs as your "Seed Pairs" for the rest of the workflow. - * **If Source Code is provided**: Analyze ORM models, API endpoints, and reporting logic to understand how the application queries the data. Use these insights to guide the creation of realistic queries during the expansion phase. 5. **Initial Save (If Seed Pairs Provided or Extracted)**: If seed pairs were provided explicitly OR extracted/translated from Query Logs, use the `generate_dataset` MCP tool to save them. You must provide the exact `output_file_path`. Pass the constructed dataset as a JSON string (`dataset_entries_json`). @@ -52,12 +53,18 @@ You are an agent that helps a user generate and expand evaluation datasets of Na a. If expanding, read the current dataset file. b. **Generate Variations**: Generate diverse NL-SQL pairs targeting the requested Complexity Level. Use the schema, **artifact insights**, **business rules**, and **source code insights** creatively: * *Context-Driven Realism*: Formulate questions that directly reflect the KPIs, dashboards, and terminology discovered in the provided documents and images. + * *Targeted Generation*: Align your generations with the high-priority tables, columns, and JOIN relationships identified during your Source Code/Artifact analysis in Step 4 if they are available. * *Scenario Shifting*: Transform a financial question into an operational one. * *Constraint Layering*: Add intersecting conditions. * *Conversational Phrasing*: Mix formal reporting requests with casual queries. - c. Run the newly generated pairs through the **Advanced Validation & Golden Standard Check** (Step 7) internally before presenting them. **Do not propose a pair if it fails the execution or ambiguity checks.** - d. Present the validated variations for user review (accept, edit, reject). - e. Append (or create) the user-approved variations to the dataset file using the `generate_dataset` tool. + c. **Execute CoT Generation**: For each new pair targeting the requested Complexity Level, you can follow this internal Chain of Thought: + * **Step c1: Draft SQL (Schema-First)**: Based on the prioritized tables, columns and filter criteria from Step 4 and the requested Complexity Level, write a syntactically perfect, dialect-compliant SQL query. Ensure it relies ONLY on existing schema columns. + * **Step c2: Literal Translation (SQL -> NL)**: Translate the SQL query literally into English to ensure no logical constraints (like a specific `WHERE` clause or `JOIN` condition) are missed. + * **Step c3: Humanize & Bridge (Refinement)**: Rewrite the literal translation into natural business language. Apply the **Semantic Bridge**: replace table/column names with the business terms discovered in the context artifacts (e.g., instead of "where is_active = 1", use "active customers"). Ensure it sounds like a non-technical stakeholder asking a real-world question. + * **Step c4: Verification (The "Blind" Test)**: Look at your refined NLQ from Step c3. If a different agent were given ONLY this NLQ and the , would they have enough context to generate the exact SQL from Step c1? If the NLQ is too vague, refine it to add necessary precision without sounding robotic. + d. Run the newly generated pairs through the **Advanced Validation & Golden Standard Check** (Step 7) internally before presenting them. **Do not propose a pair if it fails the execution or ambiguity checks.**. + e. Present the validated variations for user review (accept, edit, reject). + f. Append (or create) the user-approved variations to the dataset file using the `generate_dataset` tool. Always provide the **absolute path** for the `output_file_path`. 10. **Finalize**: Inform the user that the process is complete and confirm the final location and total size of the dataset file. From 29c08edbf690423c9487a4585a52778406e54323 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Wed, 27 May 2026 17:40:35 -0700 Subject: [PATCH 21/23] minor update to improve the skill.md --- evals/core-cujs/dataset.json | 2 +- skills/autoctx-dataset-generation/SKILL.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/evals/core-cujs/dataset.json b/evals/core-cujs/dataset.json index 26a2455f..a6f645f6 100644 --- a/evals/core-cujs/dataset.json +++ b/evals/core-cujs/dataset.json @@ -92,7 +92,7 @@ { "id": "autoctx:generate-dataset-bdf-querylog", "starting_prompt": "generate a seed evaluation dataset consisting of SQL/Question pairs for my alloydb database financials named golden_bdf_querylog_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 SQL/Question pairs in postgres dialect for my alloydb database financial whose query log can be found in the file `./app_data/querylog.txt`, save the generated pairs in output file named golden_rust_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 SQL/Question pairs in postgres dialect for my alloydb database financial whose query log can be found in the file `./app_data/querylog.txt`, save the generated pairs in output file named golden_hr_querylog_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", "work_dir": "workspace_post_init_seed/case_bdf_ql", diff --git a/skills/autoctx-dataset-generation/SKILL.md b/skills/autoctx-dataset-generation/SKILL.md index 9126945b..a4324463 100644 --- a/skills/autoctx-dataset-generation/SKILL.md +++ b/skills/autoctx-dataset-generation/SKILL.md @@ -61,7 +61,7 @@ You are an agent that helps a user generate and expand evaluation datasets of Na * **Step c1: Draft SQL (Schema-First)**: Based on the prioritized tables, columns and filter criteria from Step 4 and the requested Complexity Level, write a syntactically perfect, dialect-compliant SQL query. Ensure it relies ONLY on existing schema columns. * **Step c2: Literal Translation (SQL -> NL)**: Translate the SQL query literally into English to ensure no logical constraints (like a specific `WHERE` clause or `JOIN` condition) are missed. * **Step c3: Humanize & Bridge (Refinement)**: Rewrite the literal translation into natural business language. Apply the **Semantic Bridge**: replace table/column names with the business terms discovered in the context artifacts (e.g., instead of "where is_active = 1", use "active customers"). Ensure it sounds like a non-technical stakeholder asking a real-world question. - * **Step c4: Verification (The "Blind" Test)**: Look at your refined NLQ from Step c3. If a different agent were given ONLY this NLQ and the , would they have enough context to generate the exact SQL from Step c1? If the NLQ is too vague, refine it to add necessary precision without sounding robotic. + * **Step c4: Verification (The "Blind" Test)**: Look at your refined NLQ from Step c3. If a different agent were given ONLY this NLQ and the Source Codes & Artifacts from step 2, would they have enough context to generate the exact SQL from Step c1? If the NLQ is too vague, refine it to add necessary precision without sounding robotic. d. Run the newly generated pairs through the **Advanced Validation & Golden Standard Check** (Step 7) internally before presenting them. **Do not propose a pair if it fails the execution or ambiguity checks.**. e. Present the validated variations for user review (accept, edit, reject). f. Append (or create) the user-approved variations to the dataset file using the `generate_dataset` tool. Always provide the **absolute path** for the `output_file_path`. From 18fad806098cc01c7e5316e1c2f7689ee69f5fac Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Wed, 27 May 2026 17:54:56 -0700 Subject: [PATCH 22/23] improve the execution guard for edge case --- skills/autoctx-dataset-generation/SKILL.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/skills/autoctx-dataset-generation/SKILL.md b/skills/autoctx-dataset-generation/SKILL.md index a4324463..c060c0ca 100644 --- a/skills/autoctx-dataset-generation/SKILL.md +++ b/skills/autoctx-dataset-generation/SKILL.md @@ -37,7 +37,7 @@ You are an agent that helps a user generate and expand evaluation datasets of Na 6. **Prompt for Validation (If Seed Pairs Provided)**: If a seed was saved, ask the user if they want to validate the `golden_sql` and NLQ alignment in the dataset file. Advise them that this step ensures high-quality evaluation data. 7. **Advanced Validation & Golden Standard Check**: If the user agrees to validate the seed (or during the generation phase below), apply the following strict multi-stage verification loop for each entry: - * **Execution & Data Reality Check**: Use the `-execute-sql` MCP tool. Report any syntax errors. Crucially, **check the returned rows**. If the query returns 0 rows, verify if this is expected. If it's due to hallucinated filters (e.g., filtering for a name that doesn't exist in the DB), rewrite the SQL and NLQ to use actual, representative data from the database. + * **Execution & Data Reality Check**: Use the `-execute-sql` MCP tool. Report any syntax errors. Crucially, **check the returned rows**. If the query returns 0 rows, verify if this is expected. If it's due to hallucinated filters (e.g., filtering for a name that doesn't exist in the DB), rewrite the SQL and NLQ to use actual, representative data from the database. **CRITICAL EXCEPTION: If the live database connection was marked "INVALID" in Step 3, or if no live connection exists, you MUST SKIP this execution step.** Instead, rely entirely on the static analysis steps below. * **Dialect & Aliasing Strictness**: Ensure the query perfectly conforms to the target database dialect (e.g., exact date functions). Ensure *every* column reference in a multi-table query is fully qualified with a table alias (e.g., `t1.id` instead of just `id`) to prevent ambiguous column errors. * **Ambiguity Detection**: Analyze the NLQ for vague terminology (e.g., "recent", "top", "best", "active"). If found, ensure the SQL explicitly resolves this. Does the reverse-translation of the SQL expose implicit assumptions not stated in the NLQ? * **Edge-Case Stress Test**: Consider how the query handles ties (e.g., `LIMIT 5` without tie-breakers), NULL values, and exact date boundaries. From 627ff13f7315585730d374050c6894901aecdb07 Mon Sep 17 00:00:00 2001 From: Xianshun Chen Date: Wed, 27 May 2026 20:41:07 -0700 Subject: [PATCH 23/23] separate evals for seed-eval-gen for ci/cd --- .ci/cloudbuild.yaml | 14 ++- evals/core-cujs/dataset.json | 78 +-------------- .../README.md | 2 +- evals/seed-eval-gen/dataset.json | 94 ++++++++++++++++++ evals/seed-eval-gen/gemini_2.5_pro_model.yaml | 4 + evals/seed-eval-gen/model.yaml | 11 ++ evals/seed-eval-gen/run.yaml | 34 +++++++ .../workspace_blog}/app_data/ER_Diagram.jpeg | Bin .../workspace_blog}/app_data/desgin_doc.pdf | Bin .../autoctx/experiments/.gitkeep | 0 .../workspace_blog}/autoctx/state.md | 0 .../workspace_blog}/autoctx/tools.yaml | 0 .../app_data/business_blueprint.md | 0 .../app_data/db_schema.sql | 0 .../autoctx/experiments/.gitkeep | 0 .../autoctx/state.md | 0 .../autoctx/tools.yaml | 0 .../autoctx/experiments/.gitkeep | 0 .../workspace_financials}/autoctx/state.md | 0 .../workspace_financials}/autoctx/tools.yaml | 0 .../app_data/querylog.txt | 0 .../autoctx/experiments/.gitkeep | 0 .../workspace_financials_ql}/autoctx/state.md | 0 .../autoctx/tools.yaml | 33 ++++++ .../workspace_flights}/app_data/db_schema.sql | 0 .../autoctx/experiments/.gitkeep | 0 .../workspace_flights}/autoctx/state.md | 0 .../workspace_flights}/autoctx/tools.yaml | 0 .../workspace_hr}/app_data/db_schema.sql | 0 .../workspace_hr}/app_data/hr_agent.py | 0 .../autoctx/experiments/.gitkeep | 0 .../workspace_hr}/autoctx/state.md | 0 .../workspace_hr}/autoctx/tools.yaml | 0 .../app_data/db_schema.sql | 0 .../autoctx/experiments/.gitkeep | 0 .../autoctx/state.md | 1 + .../autoctx/tools.yaml | 0 37 files changed, 193 insertions(+), 78 deletions(-) rename evals/{core-cujs/workspace_post_init_seed => seed-eval-gen}/README.md (99%) create mode 100644 evals/seed-eval-gen/dataset.json create mode 100644 evals/seed-eval-gen/gemini_2.5_pro_model.yaml create mode 100644 evals/seed-eval-gen/model.yaml create mode 100644 evals/seed-eval-gen/run.yaml rename evals/{core-cujs/workspace_post_init_seed/case_blog => seed-eval-gen/workspace_blog}/app_data/ER_Diagram.jpeg (100%) rename evals/{core-cujs/workspace_post_init_seed/case_blog => seed-eval-gen/workspace_blog}/app_data/desgin_doc.pdf (100%) rename evals/{core-cujs/workspace_post_init_seed/case_bdf_ql => seed-eval-gen/workspace_blog}/autoctx/experiments/.gitkeep (100%) rename evals/{core-cujs/workspace_post_init_seed/case_bdf_ql => seed-eval-gen/workspace_blog}/autoctx/state.md (100%) rename evals/{core-cujs/workspace_post_init_seed/case_blog => seed-eval-gen/workspace_blog}/autoctx/tools.yaml (100%) rename evals/{core-cujs/workspace_post_init_seed/case_ecommerce_cryptic => seed-eval-gen/workspace_ecommerce_cryptic}/app_data/business_blueprint.md (100%) rename evals/{core-cujs/workspace_post_init_seed/case_ecommerce_cryptic => seed-eval-gen/workspace_ecommerce_cryptic}/app_data/db_schema.sql (100%) rename evals/{core-cujs/workspace_post_init_seed/case_blog => seed-eval-gen/workspace_ecommerce_cryptic}/autoctx/experiments/.gitkeep (100%) rename evals/{core-cujs/workspace_post_init_seed/case_blog => seed-eval-gen/workspace_ecommerce_cryptic}/autoctx/state.md (100%) rename evals/{core-cujs/workspace_post_init_seed/case_ecommerce_cryptic => seed-eval-gen/workspace_ecommerce_cryptic}/autoctx/tools.yaml (100%) rename evals/{core-cujs/workspace_post_init_seed/case_ecommerce_cryptic => seed-eval-gen/workspace_financials}/autoctx/experiments/.gitkeep (100%) rename evals/{core-cujs/workspace_post_init_seed/case_ecommerce_cryptic => seed-eval-gen/workspace_financials}/autoctx/state.md (100%) rename evals/{core-cujs/workspace_post_init_seed/case_bdf_ql => seed-eval-gen/workspace_financials}/autoctx/tools.yaml (100%) rename evals/{core-cujs/workspace_post_init_seed/case_bdf_ql => seed-eval-gen/workspace_financials_ql}/app_data/querylog.txt (100%) rename evals/{core-cujs/workspace_post_init_seed/case_flights => seed-eval-gen/workspace_financials_ql}/autoctx/experiments/.gitkeep (100%) rename evals/{core-cujs/workspace_post_init_seed/case_flights => seed-eval-gen/workspace_financials_ql}/autoctx/state.md (100%) create mode 100644 evals/seed-eval-gen/workspace_financials_ql/autoctx/tools.yaml rename evals/{core-cujs/workspace_post_init_seed/case_flights => seed-eval-gen/workspace_flights}/app_data/db_schema.sql (100%) rename evals/{core-cujs/workspace_post_init_seed/case_hr => seed-eval-gen/workspace_flights}/autoctx/experiments/.gitkeep (100%) rename evals/{core-cujs/workspace_post_init_seed/case_hr => seed-eval-gen/workspace_flights}/autoctx/state.md (100%) rename evals/{core-cujs/workspace_post_init_seed/case_flights => seed-eval-gen/workspace_flights}/autoctx/tools.yaml (100%) rename evals/{core-cujs/workspace_post_init_seed/case_hr => seed-eval-gen/workspace_hr}/app_data/db_schema.sql (100%) rename evals/{core-cujs/workspace_post_init_seed/case_hr => seed-eval-gen/workspace_hr}/app_data/hr_agent.py (100%) rename evals/{core-cujs/workspace_post_init_seed/case_property_search => seed-eval-gen/workspace_hr}/autoctx/experiments/.gitkeep (100%) rename evals/{core-cujs/workspace_post_init_seed/case_property_search => seed-eval-gen/workspace_hr}/autoctx/state.md (100%) rename evals/{core-cujs/workspace_post_init_seed/case_hr => seed-eval-gen/workspace_hr}/autoctx/tools.yaml (100%) rename evals/{core-cujs/workspace_post_init_seed/case_property_search => seed-eval-gen/workspace_property_search}/app_data/db_schema.sql (100%) create mode 100644 evals/seed-eval-gen/workspace_property_search/autoctx/experiments/.gitkeep create mode 100644 evals/seed-eval-gen/workspace_property_search/autoctx/state.md rename evals/{core-cujs/workspace_post_init_seed/case_property_search => seed-eval-gen/workspace_property_search}/autoctx/tools.yaml (100%) diff --git a/.ci/cloudbuild.yaml b/.ci/cloudbuild.yaml index d5ac50ea..5c718b60 100644 --- a/.ci/cloudbuild.yaml +++ b/.ci/cloudbuild.yaml @@ -61,16 +61,26 @@ steps: - 'EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION' args: ['.ci/run_eval.sh', 'core-cujs'] + - id: eval-seed-eval-gen + name: 'us-central1-docker.pkg.dev/cloud-db-nl2sql/evalbench/eval_server:latest' + entrypoint: 'bash' + secretEnv: ['ADC_KEY'] + waitFor: ['build-extension', 'preflight'] + env: + - 'EVAL_GCP_PROJECT_ID=$PROJECT_ID' + - 'EVAL_GCP_PROJECT_REGION=$_PROJECT_LOCATION' + args: ['.ci/run_eval.sh', 'seed-eval-gen'] + # --- Upload Results and Workspaces (after both eval steps) --- - id: upload name: 'gcr.io/cloud-builders/gsutil' entrypoint: 'bash' - waitFor: ['eval-core-cujs'] + waitFor: ['eval-core-cujs', 'eval-seed-eval-gen'] args: - '-c' - | ANY_RAN=0 - for SUITE in core-cujs; do + for SUITE in core-cujs seed-eval-gen; do if [ ! -f /workspace/EVAL_RAN_$$SUITE ]; then echo "$$SUITE did not run; skipping its upload." continue diff --git a/evals/core-cujs/dataset.json b/evals/core-cujs/dataset.json index a6f645f6..33acc5e1 100644 --- a/evals/core-cujs/dataset.json +++ b/evals/core-cujs/dataset.json @@ -18,86 +18,14 @@ "max_turns": 15 }, { - "id": "autoctx:generate-dataset-bfd-simple", - "starting_prompt": "generate a evaluation dataset consisting of simple SQL/Question pairs for my alloydb database financial named golden_bfd_simple.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 simple SQL/Question pairs for my alloydb database financial named golden_bfd_simple.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "id": "autoctx:generate-dataset", + "starting_prompt": "generate a evaluation dataset consisting of simple SQL/Question pairs for my alloydb database financial named golden_seed.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 simple SQL/Question pairs for my alloydb database financial named golden_seed.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", "expected_trajectory": [], "kind": "agents", "work_dir": "workspace_post_init/", "max_turns": 15 }, - { - "id": "autoctx:generate-dataset-bfd-advanced", - "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for my alloydb database financial named golden_bfd_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs for my alloydb database financial named golden_bfd_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", - "expected_trajectory": [], - "kind": "agents", - "work_dir": "workspace_post_init/", - "max_turns": 15 - }, - { - "id": "autoctx:generate-dataset-bfd-expert", - "starting_prompt": "generate a seed evaluation dataset consisting of expert SQL/Question pairs for my alloydb database financial named golden_bfd_expert.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 expert SQL/Question pairs for my alloydb database financial named golden_bfd_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", - "expected_trajectory": [], - "kind": "agents", - "work_dir": "workspace_post_init/", - "max_turns": 15 - }, - { - "id": "autoctx:generate-dataset-app-property", - "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for property search application named golden_property_search_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the application at https://github.com/kupp0/multi-db-property-search-data-agents, save the generated pairs in output file named golden_property_search_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", - "expected_trajectory": [], - "kind": "agents", - "work_dir": "workspace_post_init_seed/case_property_search", - "max_turns": 15 - }, - { - "id": "autoctx:generate-dataset-app-flight", - "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for flight booking application named golden_flight_booking_expert.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 expert SQL/Question pairs in postgres dialect for the flight booking application at https://docs.cloud.google.com/alloydb/docs/ai/integrate-applications-data-agent, save the generated pairs in output file named golden_flight_booking_expert.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", - "expected_trajectory": [], - "kind": "agents", - "work_dir": "workspace_post_init_seed/case_flights", - "max_turns": 15 - }, - { - "id": "autoctx:generate-dataset-app-hr", - "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for hr application named golden_hr_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the hr application whose source codes can be found in the directory `./app_data`, save the generated pairs in output file named golden_hr_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", - "expected_trajectory": [], - "kind": "agents", - "work_dir": "workspace_post_init_seed/case_hr", - "max_turns": 15 - }, - { - "id": "autoctx:generate-dataset-ecommmerce-cryptic", - "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for ecommerce application named golden_ecomm_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the ecommerce application whose business context doc can be found in the directory `./app_data`, save the generated pairs in output file named golden_ecomm_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", - "expected_trajectory": [], - "kind": "agents", - "work_dir": "workspace_post_init_seed/case_ecommerce_cryptic", - "max_turns": 15 - }, - { - "id": "autoctx:generate-dataset-blog", - "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for blog application named golden_blog_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the blog application whose er diagram and design doc can be found in the directory `./app_data`, save the generated pairs in output file named golden_blog_app_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", - "expected_trajectory": [], - "kind": "agents", - "work_dir": "workspace_post_init_seed/case_blog", - "max_turns": 15 - }, - { - "id": "autoctx:generate-dataset-bdf-querylog", - "starting_prompt": "generate a seed evaluation dataset consisting of SQL/Question pairs for my alloydb database financials named golden_bdf_querylog_advanced.json", - "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 SQL/Question pairs in postgres dialect for my alloydb database financial whose query log can be found in the file `./app_data/querylog.txt`, save the generated pairs in output file named golden_hr_querylog_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", - "expected_trajectory": [], - "kind": "agents", - "work_dir": "workspace_post_init_seed/case_bdf_ql", - "max_turns": 15 - }, { "id": "autoctx:bootstrap", "starting_prompt": "create initial context set for my alloydb database", diff --git a/evals/core-cujs/workspace_post_init_seed/README.md b/evals/seed-eval-gen/README.md similarity index 99% rename from evals/core-cujs/workspace_post_init_seed/README.md rename to evals/seed-eval-gen/README.md index c5588a46..397fbbf3 100644 --- a/evals/core-cujs/workspace_post_init_seed/README.md +++ b/evals/seed-eval-gen/README.md @@ -32,7 +32,7 @@ **Expected Outcome**: The eval dataset generator needs to figure out how to generate NL which leverage domain knowledge from the business context doc which explains the cryptic column names, and its corresponding SQL only knows the cryptic column names. -## CUJ: case_bdf_ql +## CUJ: case_financials **Setup**: In this CUJ, the user provides the querylog file `querylog.txt` which simulates the query log for the `financials` database. diff --git a/evals/seed-eval-gen/dataset.json b/evals/seed-eval-gen/dataset.json new file mode 100644 index 00000000..ec24d97e --- /dev/null +++ b/evals/seed-eval-gen/dataset.json @@ -0,0 +1,94 @@ +{ + "scenarios": [ + { + "id": "autoctx:generate-dataset-financials-simple", + "starting_prompt": "generate a evaluation dataset consisting of simple SQL/Question pairs for my alloydb database financial named golden_simple.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 simple SQL/Question pairs for my alloydb database financial named golden_simple.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_financials/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-financials-intermediate", + "starting_prompt": "generate a evaluation dataset consisting of intermediate SQL/Question pairs for my alloydb database financial named golden_intermediate.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 intermediate SQL/Question pairs for my alloydb database financial named golden_intermediate.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_financials/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-financials-advanced", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for my alloydb database financial named golden_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs for my alloydb database financial named golden_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_financials/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-financials-expert", + "starting_prompt": "generate a seed evaluation dataset consisting of expert SQL/Question pairs for my alloydb database financial named golden_expert.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 expert SQL/Question pairs for my alloydb database financial named golden_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_financials/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-property-search", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for property search application named golden_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the application at https://github.com/kupp0/multi-db-property-search-data-agents, save the generated pairs in output file named golden_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_property_search/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-flights", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for flight booking application named golden_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 expert SQL/Question pairs in postgres dialect for the flight booking application at https://docs.cloud.google.com/alloydb/docs/ai/integrate-applications-data-agent, save the generated pairs in output file named golden_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_flights/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-app-hr", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for hr application named golden_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the hr application whose source codes can be found in the directory `./app_data`, save the generated pairs in output file named golden_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_hr/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-ecommmerce-cryptic", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for ecommerce application named golden_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the ecommerce application whose business context doc can be found in the directory `./app_data`, save the generated pairs in output file named golden_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_ecommerce_cryptic/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-blog", + "starting_prompt": "generate a seed evaluation dataset consisting of advanced SQL/Question pairs for blog application named golden_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 advanced SQL/Question pairs in postgres dialect for the blog application whose er diagram and design doc can be found in the directory `./app_data`, save the generated pairs in output file named golden_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_blog/", + "max_turns": 15 + }, + { + "id": "autoctx:generate-dataset-financials-querylog", + "starting_prompt": "generate a seed evaluation dataset consisting of SQL/Question pairs for my alloydb database financials named golden_advanced.json", + "conversation_plan": "Ask the agent to generate a evaluation dataset consisting of 5 SQL/Question pairs in postgres dialect for my alloydb database financial whose query log can be found in the file `./app_data/querylog.txt`, save the generated pairs in output file named golden_advanced.json. You should terminate the conversation immediately after the agent successfully generated the dataset, even if the agent prompts you to perform evaluation or take next steps.", + "expected_trajectory": [], + "kind": "agents", + "work_dir": "workspace_financials_ql/", + "max_turns": 15 + } + ] +} diff --git a/evals/seed-eval-gen/gemini_2.5_pro_model.yaml b/evals/seed-eval-gen/gemini_2.5_pro_model.yaml new file mode 100644 index 00000000..26eb0b7f --- /dev/null +++ b/evals/seed-eval-gen/gemini_2.5_pro_model.yaml @@ -0,0 +1,4 @@ +generator: gcp_vertex_gemini +vertex_model: gemini-2.5-pro +base_prompt: "" +execs_per_minute: 5 diff --git a/evals/seed-eval-gen/model.yaml b/evals/seed-eval-gen/model.yaml new file mode 100644 index 00000000..cdd19cc7 --- /dev/null +++ b/evals/seed-eval-gen/model.yaml @@ -0,0 +1,11 @@ +gemini_cli_version: "@google/gemini-cli@0.38.2" +generator: gemini_cli +env: + GOOGLE_CLOUD_PROJECT: "cloud-db-nl2sql" + GOOGLE_CLOUD_LOCATION: "us-central1" + GOOGLE_GENAI_USE_VERTEXAI: "true" + GEMINI_API_MODEL: "gemini-2.5-pro" + GEMINI_MODEL: "gemini-2.5-pro" +setup: + extensions: + "https://github.com/GoogleCloudPlatform/db-context-enrichment": {} diff --git a/evals/seed-eval-gen/run.yaml b/evals/seed-eval-gen/run.yaml new file mode 100644 index 00000000..c4421b9b --- /dev/null +++ b/evals/seed-eval-gen/run.yaml @@ -0,0 +1,34 @@ +############################################################ +### Dataset / Eval Items +############################################################ +dataset_config: seed-eval-gen/dataset.json +dataset_format: gemini-cli-format + +# Orchestrator Configuration +orchestrator: geminicli +model_config: seed-eval-gen/model.yaml +simulated_user_model_config: seed-eval-gen/gemini_2.5_pro_model.yaml + +############################################################ +### Scorer Related Configs +############################################################ +scorers: + # Checks if the agent used the expected tools in the correct order. + trajectory_matcher: {} + + # Uses an LLM to judge if the user's goal was met. + goal_completion: + model_config: seed-eval-gen/gemini_2.5_pro_model.yaml + + # Metrics for efficiency and performance. + turn_count: {} + end_to_end_latency: {} + tool_call_latency: {} + token_consumption: {} + +############################################################ +### Reporting Related Configs +############################################################ +reporting: + csv: + output_directory: 'results' diff --git a/evals/core-cujs/workspace_post_init_seed/case_blog/app_data/ER_Diagram.jpeg b/evals/seed-eval-gen/workspace_blog/app_data/ER_Diagram.jpeg similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_blog/app_data/ER_Diagram.jpeg rename to evals/seed-eval-gen/workspace_blog/app_data/ER_Diagram.jpeg diff --git a/evals/core-cujs/workspace_post_init_seed/case_blog/app_data/desgin_doc.pdf b/evals/seed-eval-gen/workspace_blog/app_data/desgin_doc.pdf similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_blog/app_data/desgin_doc.pdf rename to evals/seed-eval-gen/workspace_blog/app_data/desgin_doc.pdf diff --git a/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/experiments/.gitkeep b/evals/seed-eval-gen/workspace_blog/autoctx/experiments/.gitkeep similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/experiments/.gitkeep rename to evals/seed-eval-gen/workspace_blog/autoctx/experiments/.gitkeep diff --git a/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/state.md b/evals/seed-eval-gen/workspace_blog/autoctx/state.md similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/state.md rename to evals/seed-eval-gen/workspace_blog/autoctx/state.md diff --git a/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/tools.yaml b/evals/seed-eval-gen/workspace_blog/autoctx/tools.yaml similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/tools.yaml rename to evals/seed-eval-gen/workspace_blog/autoctx/tools.yaml diff --git a/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/business_blueprint.md b/evals/seed-eval-gen/workspace_ecommerce_cryptic/app_data/business_blueprint.md similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/business_blueprint.md rename to evals/seed-eval-gen/workspace_ecommerce_cryptic/app_data/business_blueprint.md diff --git a/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/db_schema.sql b/evals/seed-eval-gen/workspace_ecommerce_cryptic/app_data/db_schema.sql similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/app_data/db_schema.sql rename to evals/seed-eval-gen/workspace_ecommerce_cryptic/app_data/db_schema.sql diff --git a/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/experiments/.gitkeep b/evals/seed-eval-gen/workspace_ecommerce_cryptic/autoctx/experiments/.gitkeep similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/experiments/.gitkeep rename to evals/seed-eval-gen/workspace_ecommerce_cryptic/autoctx/experiments/.gitkeep diff --git a/evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/state.md b/evals/seed-eval-gen/workspace_ecommerce_cryptic/autoctx/state.md similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_blog/autoctx/state.md rename to evals/seed-eval-gen/workspace_ecommerce_cryptic/autoctx/state.md diff --git a/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/tools.yaml b/evals/seed-eval-gen/workspace_ecommerce_cryptic/autoctx/tools.yaml similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/tools.yaml rename to evals/seed-eval-gen/workspace_ecommerce_cryptic/autoctx/tools.yaml diff --git a/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/experiments/.gitkeep b/evals/seed-eval-gen/workspace_financials/autoctx/experiments/.gitkeep similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/experiments/.gitkeep rename to evals/seed-eval-gen/workspace_financials/autoctx/experiments/.gitkeep diff --git a/evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/state.md b/evals/seed-eval-gen/workspace_financials/autoctx/state.md similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_ecommerce_cryptic/autoctx/state.md rename to evals/seed-eval-gen/workspace_financials/autoctx/state.md diff --git a/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/tools.yaml b/evals/seed-eval-gen/workspace_financials/autoctx/tools.yaml similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_bdf_ql/autoctx/tools.yaml rename to evals/seed-eval-gen/workspace_financials/autoctx/tools.yaml diff --git a/evals/core-cujs/workspace_post_init_seed/case_bdf_ql/app_data/querylog.txt b/evals/seed-eval-gen/workspace_financials_ql/app_data/querylog.txt similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_bdf_ql/app_data/querylog.txt rename to evals/seed-eval-gen/workspace_financials_ql/app_data/querylog.txt diff --git a/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/experiments/.gitkeep b/evals/seed-eval-gen/workspace_financials_ql/autoctx/experiments/.gitkeep similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/experiments/.gitkeep rename to evals/seed-eval-gen/workspace_financials_ql/autoctx/experiments/.gitkeep diff --git a/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/state.md b/evals/seed-eval-gen/workspace_financials_ql/autoctx/state.md similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/state.md rename to evals/seed-eval-gen/workspace_financials_ql/autoctx/state.md diff --git a/evals/seed-eval-gen/workspace_financials_ql/autoctx/tools.yaml b/evals/seed-eval-gen/workspace_financials_ql/autoctx/tools.yaml new file mode 100644 index 00000000..2624bc1e --- /dev/null +++ b/evals/seed-eval-gen/workspace_financials_ql/autoctx/tools.yaml @@ -0,0 +1,33 @@ +kind: source +name: my-alloydb +type: alloydb-postgres +project: cloud-db-nl2sql +region: us-central1 +cluster: whaoyu-test +instance: whaoyu-test-primary +database: financial +--- +kind: tool +name: my-alloydb-list-schemas +type: postgres-list-tables +source: my-alloydb +description: | + Use this tool to list tables and their schemas in the my-alloydb database. + + Progressive Schema Discovery (Recommended): + 1) Fetch structure first (output_format='simple'), + 2) Go deep on specific parts if interested, + 3) Use batching if info is too large. + + Scope: + - The tool can fetch system/extension schemas. Agents should ignore them and focus on user data. + + Behavior: + - Omit 'table_names' to fetch all tables. + - Omit 'output_format' for detailed schema (default). +--- +kind: tool +name: my-alloydb-execute-sql +type: postgres-execute-sql +source: my-alloydb +description: Use this tool to execute SQL statements against the my-alloydb database. diff --git a/evals/core-cujs/workspace_post_init_seed/case_flights/app_data/db_schema.sql b/evals/seed-eval-gen/workspace_flights/app_data/db_schema.sql similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_flights/app_data/db_schema.sql rename to evals/seed-eval-gen/workspace_flights/app_data/db_schema.sql diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/experiments/.gitkeep b/evals/seed-eval-gen/workspace_flights/autoctx/experiments/.gitkeep similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/experiments/.gitkeep rename to evals/seed-eval-gen/workspace_flights/autoctx/experiments/.gitkeep diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/state.md b/evals/seed-eval-gen/workspace_flights/autoctx/state.md similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/state.md rename to evals/seed-eval-gen/workspace_flights/autoctx/state.md diff --git a/evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/tools.yaml b/evals/seed-eval-gen/workspace_flights/autoctx/tools.yaml similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_flights/autoctx/tools.yaml rename to evals/seed-eval-gen/workspace_flights/autoctx/tools.yaml diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/app_data/db_schema.sql b/evals/seed-eval-gen/workspace_hr/app_data/db_schema.sql similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_hr/app_data/db_schema.sql rename to evals/seed-eval-gen/workspace_hr/app_data/db_schema.sql diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/app_data/hr_agent.py b/evals/seed-eval-gen/workspace_hr/app_data/hr_agent.py similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_hr/app_data/hr_agent.py rename to evals/seed-eval-gen/workspace_hr/app_data/hr_agent.py diff --git a/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/experiments/.gitkeep b/evals/seed-eval-gen/workspace_hr/autoctx/experiments/.gitkeep similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/experiments/.gitkeep rename to evals/seed-eval-gen/workspace_hr/autoctx/experiments/.gitkeep diff --git a/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/state.md b/evals/seed-eval-gen/workspace_hr/autoctx/state.md similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/state.md rename to evals/seed-eval-gen/workspace_hr/autoctx/state.md diff --git a/evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/tools.yaml b/evals/seed-eval-gen/workspace_hr/autoctx/tools.yaml similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_hr/autoctx/tools.yaml rename to evals/seed-eval-gen/workspace_hr/autoctx/tools.yaml diff --git a/evals/core-cujs/workspace_post_init_seed/case_property_search/app_data/db_schema.sql b/evals/seed-eval-gen/workspace_property_search/app_data/db_schema.sql similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_property_search/app_data/db_schema.sql rename to evals/seed-eval-gen/workspace_property_search/app_data/db_schema.sql diff --git a/evals/seed-eval-gen/workspace_property_search/autoctx/experiments/.gitkeep b/evals/seed-eval-gen/workspace_property_search/autoctx/experiments/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/evals/seed-eval-gen/workspace_property_search/autoctx/state.md b/evals/seed-eval-gen/workspace_property_search/autoctx/state.md new file mode 100644 index 00000000..d5ca461c --- /dev/null +++ b/evals/seed-eval-gen/workspace_property_search/autoctx/state.md @@ -0,0 +1 @@ +# context authoring experiment state tracking diff --git a/evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/tools.yaml b/evals/seed-eval-gen/workspace_property_search/autoctx/tools.yaml similarity index 100% rename from evals/core-cujs/workspace_post_init_seed/case_property_search/autoctx/tools.yaml rename to evals/seed-eval-gen/workspace_property_search/autoctx/tools.yaml

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