From 70e65be8d8ee072da9fade86041df4a66df0b280 Mon Sep 17 00:00:00 2001 From: Lina Tang Date: Fri, 20 Oct 2023 16:06:32 +0800 Subject: [PATCH 01/11] Support including images in object type flow inputs (#833) # Description We only support including images in list type flow inputs before, in this PR, we support including images in object type flow inputs. # All Promptflow Contribution checklist: - [x] **The pull request does not introduce [breaking changes].** - [ ] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [x] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [ ] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [x] Title of the pull request is clear and informative. - [x] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [ ] Pull request includes test coverage for the included changes. Co-authored-by: Lina Tang --- .../_sdk/operations/_test_submitter.py | 4 +-- .../promptflow/_utils/multimedia_utils.py | 28 ++++++++++++++++-- .../promptflow/executor/flow_executor.py | 29 ++----------------- .../python_tool_with_image_list/flow.dag.yaml | 8 +++++ .../pick_images_from_list.py | 4 +-- 5 files changed, 40 insertions(+), 33 deletions(-) diff --git a/src/promptflow/promptflow/_sdk/operations/_test_submitter.py b/src/promptflow/promptflow/_sdk/operations/_test_submitter.py index 385547e8bd8..7396a95b3b9 100644 --- a/src/promptflow/promptflow/_sdk/operations/_test_submitter.py +++ b/src/promptflow/promptflow/_sdk/operations/_test_submitter.py @@ -19,7 +19,7 @@ from promptflow._utils.context_utils import _change_working_dir from promptflow._utils.dataclass_serializer import serialize from promptflow._utils.exception_utils import ErrorResponse -from promptflow._utils.multimedia_utils import persist_multimedia_date +from promptflow._utils.multimedia_utils import persist_multimedia_data from promptflow.contracts.flow import Flow as ExecutableFlow from promptflow.contracts.run_info import Status from promptflow.exceptions import UserErrorException @@ -163,7 +163,7 @@ def flow_test( ) flow_executor.enable_streaming_for_llm_flow(lambda: True) line_result = flow_executor.exec_line(inputs, index=0, allow_generator_output=allow_generator_output) - line_result.output = persist_multimedia_date( + line_result.output = persist_multimedia_data( line_result.output, base_dir=self.flow.code, sub_dir=Path(".promptflow/output") ) if line_result.aggregation_inputs: diff --git a/src/promptflow/promptflow/_utils/multimedia_utils.py b/src/promptflow/promptflow/_utils/multimedia_utils.py index 14d4ee52347..fd059b07345 100644 --- a/src/promptflow/promptflow/_utils/multimedia_utils.py +++ b/src/promptflow/promptflow/_utils/multimedia_utils.py @@ -11,7 +11,9 @@ from urllib.parse import urlparse from promptflow.contracts._errors import InvalidImageInput +from promptflow.contracts.flow import FlowInputDefinition from promptflow.contracts.multimedia import Image, PFBytes +from promptflow.contracts.tool import ValueType from promptflow.exceptions import ErrorTarget MIME_PATTERN = re.compile(r"^data:image/(.*);(path|base64|url)$") @@ -171,13 +173,13 @@ def default_json_encoder(obj): raise TypeError(f"Object of type {type(obj).__name__} is not JSON serializable") -def persist_multimedia_date(value: Any, base_dir: Path, sub_dir: Path = None): +def persist_multimedia_data(value: Any, base_dir: Path, sub_dir: Path = None): pfbytes_file_reference_encoder = get_file_reference_encoder(base_dir, sub_dir) serialization_funcs = {Image: partial(Image.serialize, **{"encoder": pfbytes_file_reference_encoder})} return recursive_process(value, process_funcs=serialization_funcs) -def convert_multimedia_date_to_base64(value: Any): +def convert_multimedia_data_to_base64(value: Any): to_base64_funcs = {PFBytes: PFBytes.to_base64} return recursive_process(value, process_funcs=to_base64_funcs) @@ -193,3 +195,25 @@ def recursive_process(value: Any, process_funcs: dict[type, Callable] = None) -> if isinstance(value, dict): return {k: recursive_process(v, process_funcs) for k, v in value.items()} return value + + +def load_multimedia_data(inputs: dict[str, FlowInputDefinition], line_inputs: dict, base_dir: Path): + updated_inputs = dict(line_inputs or {}) + for key, value in inputs.items(): + if value.type == ValueType.IMAGE: + updated_inputs[key] = create_image(updated_inputs[key], base_dir) + elif value.type == ValueType.LIST or value.type == ValueType.OBJECT: + updated_inputs[key] = load_multimedia_data_recursively(updated_inputs[key]) + return updated_inputs + + +def load_multimedia_data_recursively(value: Any): + if isinstance(value, list): + return [load_multimedia_data_recursively(item) for item in value] + elif isinstance(value, dict): + if is_multimedia_dict(value): + return create_image_from_dict(value) + else: + return {k: load_multimedia_data_recursively(v) for k, v in value.items()} + else: + return value diff --git a/src/promptflow/promptflow/executor/flow_executor.py b/src/promptflow/promptflow/executor/flow_executor.py index cffb43e844b..a4be3377b7a 100644 --- a/src/promptflow/promptflow/executor/flow_executor.py +++ b/src/promptflow/promptflow/executor/flow_executor.py @@ -24,12 +24,11 @@ from promptflow._core.tools_manager import ToolsManager from promptflow._utils.context_utils import _change_working_dir from promptflow._utils.logger_utils import logger -from promptflow._utils.multimedia_utils import create_image, create_image_from_dict, is_multimedia_dict +from promptflow._utils.multimedia_utils import load_multimedia_data from promptflow._utils.utils import transpose from promptflow.contracts.flow import Flow, FlowInputDefinition, InputAssignment, InputValueType, Node from promptflow.contracts.run_info import FlowRunInfo, Status from promptflow.contracts.run_mode import RunMode -from promptflow.contracts.tool import ValueType from promptflow.exceptions import PromptflowException from promptflow.executor import _input_assignment_parser from promptflow.executor._errors import ( @@ -697,7 +696,7 @@ def exec_line( """ self._node_concurrency = node_concurrency inputs_with_default_value = FlowExecutor._apply_default_value_for_input(self._flow.inputs, inputs) - inputs = self._process_images_from_inputs(self._flow.inputs, inputs_with_default_value) + inputs = load_multimedia_data(self._flow.inputs, inputs_with_default_value, self._working_dir) # For flow run, validate inputs as default with self._run_tracker.node_log_manager: # exec_line interface may be called by exec_bulk, so we only set run_mode as flow run when @@ -785,30 +784,6 @@ def _apply_default_value_for_input(inputs: Dict[str, FlowInputDefinition], line_ updated_inputs[key] = value.default return updated_inputs - def _process_images_from_inputs( - self, - inputs: Dict[str, FlowInputDefinition], - line_inputs: Mapping, - ) -> Dict[str, Any]: - updated_inputs = dict(line_inputs or {}) - for key, value in inputs.items(): - if value.type == ValueType.IMAGE: - updated_inputs[key] = create_image(updated_inputs[key], self._working_dir) - elif value.type == ValueType.LIST: - updated_inputs[key] = self._process_images_in_input_list(updated_inputs[key]) - return updated_inputs - - def _process_images_in_input_list(self, value): - if isinstance(value, list): - return [self._process_images_in_input_list(item) for item in value] - elif isinstance(value, dict): - if is_multimedia_dict(value): - return create_image_from_dict(value) - else: - return {k: self._process_images_in_input_list(v) for k, v in value.items()} - else: - return value - def validate_and_apply_inputs_mapping(self, inputs, inputs_mapping) -> List[Dict[str, Any]]: """Validate and apply inputs mapping for all lines in the flow. diff --git a/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/flow.dag.yaml b/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/flow.dag.yaml index 68bd25ba8d7..8bb57626e55 100644 --- a/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/flow.dag.yaml +++ b/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/flow.dag.yaml @@ -5,6 +5,13 @@ inputs: - data:image/jpg;path: logo.jpg - data:image/jpg;path: logo_2.jpg - data:image/jpg;path: logo_3.jpg + image_dict: + type: object + default: + - image_1: + data:image/jpg;path: logo.jpg + - image_2: + data:image/jpg;path: logo_2.jpg outputs: output: type: list @@ -17,5 +24,6 @@ nodes: path: pick_images_from_list.py inputs: image_list: ${inputs.image_list} + image_dict: ${inputs.image_dict} idx_l: 1 idx_r: 2 diff --git a/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/pick_images_from_list.py b/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/pick_images_from_list.py index 8f57a0eeb47..d6f03b39ed4 100644 --- a/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/pick_images_from_list.py +++ b/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/pick_images_from_list.py @@ -3,8 +3,8 @@ @tool -def pick_images_from_list(image_list: list[Image], idx_l: int, idx_r: int) -> list[Image]: +def pick_images_from_list(image_list: list[Image], image_dict: dict, idx_l: int, idx_r: int) -> list[Image]: if idx_l <= idx_r and idx_l >= 0 and idx_r < len(image_list): - return image_list[idx_l:idx_r + 1] + return {"Image list": image_list[idx_l:idx_r + 1], "Image dict": image_dict} else: raise Exception(f"Invalid index.") From bf779e2bc1a7f34ec34b5c741498d6c30f0cc34d Mon Sep 17 00:00:00 2001 From: zhen Date: Fri, 20 Oct 2023 16:16:02 +0800 Subject: [PATCH 02/11] [pf CLI] List all package tools in current environment (#798) # Description ![image](https://github.com/microsoft/promptflow/assets/17938940/728c8f76-7723-4c92-b2d9-d03ae6dd1ee7) List all package tools in current environment. ``` (prompt_github) D:\Project\github_promptflow\promptflow>pf tool list { "package": { "promptflow.tools.embedding.embedding": { "name": "Embedding", "description": "Use Open AI's embedding model to create an embedding vector representing the input text.", "type": "python", "module": "promptflow.tools.embedding", "function": "embedding", "inputs": { "connection": { "type": [ "AzureOpenAIConnection", "OpenAIConnection" ] }, "deployment_name": { "type": [ "string" ], "enabled_by": "connection", "enabled_by_type": [ "AzureOpenAIConnection" ], "capabilities": { "completion": false, "chat_completion": false, "embeddings": true }, "model_list": [ "text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001" ] }, "model": { "type": [ "string" ], "enabled_by": "connection", "enabled_by_type": [ "OpenAIConnection" ], "enum": [ "text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001" ] }, "input": { "type": [ "string" ] } }, "package": "promptflow-tools", "package_version": "0.1.0b6" }, "promptflow.tools.serpapi.SerpAPI.search": { "name": "Serp API", "description": "Use Serp API to obtain search results from a specific search engine.", "inputs": { "connection": { "type": [ "SerpConnection" ] }, "engine": { "default": "google", "enum": [ "google", "bing" ], "type": [ "string" ] }, "location": { "default": "", "type": [ "string" ] }, "num": { "default": "10", "type": [ "int" ] }, "query": { "type": [ "string" ] }, "safe": { "default": "off", "enum": [ "active", "off" ], "type": [ "string" ] } }, "type": "python", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "package": "promptflow-tools", "package_version": "0.1.0b6" } }, "code": {} } ``` List package tools and code tools in flow: ``` (prompt_github) D:\Project\github_promptflow\promptflow>pf tool list --flow chat_flow { "package": { "promptflow.tools.embedding.embedding": { "name": "Embedding", "description": "Use Open AI's embedding model to create an embedding vector representing the input text.", "type": "python", "module": "promptflow.tools.embedding", "function": "embedding", "inputs": { "connection": { "type": [ "AzureOpenAIConnection", "OpenAIConnection" ] }, "deployment_name": { "type": [ "string" ], "enabled_by": "connection", "enabled_by_type": [ "AzureOpenAIConnection" ], "capabilities": { "completion": false, "chat_completion": false, "embeddings": true }, "model_list": [ "text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001" ] }, "model": { "type": [ "string" ], "enabled_by": "connection", "enabled_by_type": [ "OpenAIConnection" ], "enum": [ "text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001" ] }, "input": { "type": [ "string" ] } }, "package": "promptflow-tools", "package_version": "0.1.0b6" }, "promptflow.tools.serpapi.SerpAPI.search": { "name": "Serp API", "description": "Use Serp API to obtain search results from a specific search engine.", "inputs": { "connection": { "type": [ "SerpConnection" ] }, "engine": { "default": "google", "enum": [ "google", "bing" ], "type": [ "string" ] }, "location": { "default": "", "type": [ "string" ] }, "num": { "default": "10", "type": [ "int" ] }, "query": { "type": [ "string" ] }, "safe": { "default": "off", "enum": [ "active", "off" ], "type": [ "string" ] } }, "type": "python", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "package": "promptflow-tools", "package_version": "0.1.0b6" } }, "code": { "chat.jinja2": { "type": "llm", "inputs": { "chat_history": { "type": [ "string" ] }, "question": { "type": [ "string" ] } }, "source": "chat.jinja2" }, "show_answer.py": { "type": "python", "inputs": { "chat_answer": { "type": [ "string" ] } }, "source": "show_answer.py", "function": "show_answer" } } } ``` # All Promptflow Contribution checklist: - [ ] **The pull request does not introduce [breaking changes].** - [ ] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [ ] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [ ] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [ ] Title of the pull request is clear and informative. - [ ] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [ ] Pull request includes test coverage for the included changes. --- docs/reference/pf-command-reference.md | 72 ++++++++++++++++++- src/promptflow/promptflow/_cli/_pf/_tool.py | 39 ++++++++++ .../_sdk/operations/_tool_operations.py | 26 +++++++ .../tests/sdk_cli_test/e2etests/test_cli.py | 34 +++++++++ 4 files changed, 170 insertions(+), 1 deletion(-) diff --git a/docs/reference/pf-command-reference.md b/docs/reference/pf-command-reference.md index 779adb18bb2..38a3e5cc624 100644 --- a/docs/reference/pf-command-reference.md +++ b/docs/reference/pf-command-reference.md @@ -654,4 +654,74 @@ pf run restore --name `--name -n` -Name of the run. \ No newline at end of file +Name of the run. + +## pf tool + +Manage promptflow tools. + +| Command | Description | +| --- | --- | +| [pf tool init](#pf-tool-init) | Initialize a tool directory. | +| [pf tool list](#pf-tool-list) | List all tools in the environment. | + +### pf tool init + +Initialize a tool directory. + +```bash +pf tool init [--package] + [--tool] +``` + +#### Examples + +Creating a package tool from scratch. + +```bash +pf tool init --package --tool +``` + +Creating a python tool from scratch. + +```bash +pf tool init --tool +``` + +#### Optional Parameters + +`--package` + +The package name to create. + +`--tool` + +The tool name to create. + +### pf tool list + +List all tools in the environment. + +```bash +pf tool list [--flow] +``` + +#### Examples + +List all package tool in the environment. + +```bash +pf tool list +``` + +List all package tool and code tool in the flow. + +```bash +pf tool list --flow +``` + +#### Optional Parameters + +`--flow` + +The flow directory. diff --git a/src/promptflow/promptflow/_cli/_pf/_tool.py b/src/promptflow/promptflow/_cli/_pf/_tool.py index 0be8e6fc179..02d72995699 100644 --- a/src/promptflow/promptflow/_cli/_pf/_tool.py +++ b/src/promptflow/promptflow/_cli/_pf/_tool.py @@ -5,6 +5,7 @@ import argparse import logging import re +import json from pathlib import Path from promptflow._cli._params import logging_params @@ -17,6 +18,7 @@ ) from promptflow._cli._utils import activate_action, exception_handler from promptflow._sdk._constants import LOGGER_NAME +from promptflow._sdk._pf_client import PFClient from promptflow.exceptions import UserErrorException logger = logging.getLogger(LOGGER_NAME) @@ -31,6 +33,7 @@ def add_tool_parser(subparsers): ) subparsers = tool_parser.add_subparsers() add_parser_init_tool(subparsers) + add_parser_list_tool(subparsers) tool_parser.set_defaults(action="tool") @@ -65,9 +68,38 @@ def add_parser_init_tool(subparsers): ) +def add_parser_list_tool(subparsers): + """Add tool list parser to the pf tool subparsers.""" + epilog = """ +Examples: + +# List all package tool in the environment: +pf tool list +# List all package tool and code tool in the flow: +pf tool list --flow flow-path +""" # noqa: E501 + add_param_flow = lambda parser: parser.add_argument( # noqa: E731 + "--flow", type=str, help="the flow directory" + ) + add_params = [ + add_param_flow, + ] + logging_params + return activate_action( + name="list", + description="List tools.", + epilog=epilog, + add_params=add_params, + subparsers=subparsers, + help_message="List all tools in the environment.", + action_param_name="sub_action", + ) + + def dispatch_tool_commands(args: argparse.Namespace): if args.sub_action == "init": init_tool(args) + elif args.sub_action == "list": + list_tool(args) @exception_handler("Tool init") @@ -95,3 +127,10 @@ def init_tool(args): ToolPackageGenerator(tool_name=args.tool).generate_to_file(script_code_path / f"{args.tool}.py") InitGenerator().generate_to_file(script_code_path / "__init__.py") print(f'Done. Created the tool "{args.tool}" in {script_code_path.resolve()}.') + + +@exception_handler("Tool list") +def list_tool(args): + pf_client = PFClient() + package_tools = pf_client._tools.list(args.flow) + print(json.dumps(package_tools, indent=4)) diff --git a/src/promptflow/promptflow/_sdk/operations/_tool_operations.py b/src/promptflow/promptflow/_sdk/operations/_tool_operations.py index 10b4ae30612..a4842b50db7 100644 --- a/src/promptflow/promptflow/_sdk/operations/_tool_operations.py +++ b/src/promptflow/promptflow/_sdk/operations/_tool_operations.py @@ -4,8 +4,11 @@ import inspect import json from dataclasses import asdict +from os import PathLike +from typing import Union from promptflow._core.tool_meta_generator import is_tool +from promptflow._core.tools_manager import collect_package_tools from promptflow._utils.tool_utils import function_to_interface from promptflow.contracts.tool import Tool, ToolType from promptflow.exceptions import UserErrorException @@ -80,3 +83,26 @@ def _parse_tool_from_function(f, initialize_inputs=None): function=f.__name__, module=f.__module__, ) + + def list( + self, + flow: Union[str, PathLike] = None, + ): + """ + List all package tools in the environment and code tools in the flow. + + :param flow: path to the flow directory + :type flow: Union[str, PathLike] + :return: Dict of package tools and code tools info. + :rtype: Dict[str, Dict] + """ + from promptflow._sdk._pf_client import PFClient + + local_client = PFClient() + package_tools = collect_package_tools() + if flow: + tools, _ = local_client.flows._generate_tools_meta(flow) + else: + tools = {"package": {}, "code": {}} + tools["package"].update(package_tools) + return tools diff --git a/src/promptflow/tests/sdk_cli_test/e2etests/test_cli.py b/src/promptflow/tests/sdk_cli_test/e2etests/test_cli.py index 0ce348e5920..deff57f410b 100644 --- a/src/promptflow/tests/sdk_cli_test/e2etests/test_cli.py +++ b/src/promptflow/tests/sdk_cli_test/e2etests/test_cli.py @@ -1,4 +1,5 @@ import contextlib +import importlib import importlib.util import io import json @@ -1344,6 +1345,39 @@ def test_tool_init(self, capsys): outerr = capsys.readouterr() assert f"The tool name {invalid_tool_name} is a invalid identifier." in outerr.out + def test_tool_list(self, capsys): + # List package tools in environment + run_pf_command("tool", "list") + outerr = capsys.readouterr() + tools_dict = json.loads(outerr.out) + package_tool_name = "promptflow.tools.embedding.embedding" + assert package_tool_name in tools_dict["package"] + + # List flow tools and package tools + run_pf_command("tool", "list", "--flow", f"{FLOWS_DIR}/chat_flow") + outerr = capsys.readouterr() + tools_dict = json.loads(outerr.out) + expect_flow_tools = { + "chat.jinja2": { + "type": "llm", + "inputs": {"chat_history": {"type": ["string"]}, "question": {"type": ["string"]}}, + "source": "chat.jinja2", + }, + "show_answer.py": { + "type": "python", + "inputs": {"chat_answer": {"type": ["string"]}}, + "source": "show_answer.py", + "function": "show_answer", + }, + } + assert tools_dict["code"] == expect_flow_tools + assert package_tool_name in tools_dict["package"] + + # Invalid flow parameter + with pytest.raises(Exception) as e: + run_pf_command("tool", "list", "--flow", "invalid_flow_folder") + assert "invalid_flow_folder does not exist" in e.value.args[0] + def test_chat_flow_with_conditional(self, monkeypatch, capsys): chat_list = ["1", "2"] From 7c19b265e6357f7079bf375f7fe19b120fd6e8b0 Mon Sep 17 00:00:00 2001 From: Robben Wang <350053002@qq.com> Date: Fri, 20 Oct 2023 16:25:35 +0800 Subject: [PATCH 03/11] Fix error message's link for column mapping usage (#830) # Description [PR](https://github.com/microsoft/promptflow/pull/651/files) changed link path, so the error message is not valid. Replace link by aka link. Then we only need to update redirection rule after changing link # All Promptflow Contribution checklist: - [X] **The pull request does not introduce [breaking changes].** - [ ] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [X] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [X] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [X] Title of the pull request is clear and informative. - [ ] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [ ] Pull request includes test coverage for the included changes. Co-authored-by: Robben Wang --- src/promptflow/promptflow/executor/flow_executor.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/src/promptflow/promptflow/executor/flow_executor.py b/src/promptflow/promptflow/executor/flow_executor.py index a4be3377b7a..89e5d1124a7 100644 --- a/src/promptflow/promptflow/executor/flow_executor.py +++ b/src/promptflow/promptflow/executor/flow_executor.py @@ -798,8 +798,7 @@ def validate_and_apply_inputs_mapping(self, inputs, inputs_mapping) -> List[Dict logger.warning( msg=( "Starting run without column mapping may lead to unexpected results. " - "Please consult the following documentation for more information: " - "https://microsoft.github.io/promptflow/how-to-guides/column-mapping.html." + "Please consult the following documentation for more information: https://aka.ms/pf/column-mapping" ) ) @@ -1054,8 +1053,7 @@ def apply_inputs_mapping( message_format=( "The input for batch run is incorrect. Couldn't find these mapping relations: {invalid_relations}. " "Please make sure your input mapping keys and values match your YAML input section and input data. " - "For more information, refer to the following documentation: " - "https://microsoft.github.io/promptflow/how-to-guides/column-mapping.html." + "For more information, refer to the following documentation: https://aka.ms/pf/column-mapping" ), invalid_relations=invalid_relations, ) From 7ba54bad644b8c0d8dc7af8ed37cfe85bf7c8a53 Mon Sep 17 00:00:00 2001 From: Han Wang Date: Fri, 20 Oct 2023 16:53:17 +0800 Subject: [PATCH 04/11] [SDK]Fix dict subscribing in py38 (#836) # Description Please add an informative description that covers that changes made by the pull request and link all relevant issues. `dict` subscribing is not supported in py38. This PR fixed it with `typing.Dict` ![image](https://github.com/microsoft/promptflow/assets/7776147/687e786c-052d-4c3c-a662-f75b89f39f2c) # All Promptflow Contribution checklist: - [ ] **The pull request does not introduce [breaking changes].** - [ ] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [ ] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [ ] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [ ] Title of the pull request is clear and informative. - [ ] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [ ] Pull request includes test coverage for the included changes. --- .../promptflow/_utils/multimedia_utils.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/src/promptflow/promptflow/_utils/multimedia_utils.py b/src/promptflow/promptflow/_utils/multimedia_utils.py index fd059b07345..b6bc68d8793 100644 --- a/src/promptflow/promptflow/_utils/multimedia_utils.py +++ b/src/promptflow/promptflow/_utils/multimedia_utils.py @@ -2,14 +2,14 @@ import imghdr import os import re -import requests import uuid - from functools import partial from pathlib import Path -from typing import Any, Callable +from typing import Any, Callable, Dict from urllib.parse import urlparse +import requests + from promptflow.contracts._errors import InvalidImageInput from promptflow.contracts.flow import FlowInputDefinition from promptflow.contracts.multimedia import Image, PFBytes @@ -146,12 +146,10 @@ def create_image(value: any, base_dir: Path = None): def save_image_to_file(image: Image, file_name: str, folder_path: Path, relative_path: Path = None): ext = get_extension_from_mime_type(image._mime_type) file_name = f"{file_name}.{ext}" if ext else file_name - image_reference = { - f"data:{image._mime_type};path": str(relative_path / file_name) if relative_path else file_name - } + image_reference = {f"data:{image._mime_type};path": str(relative_path / file_name) if relative_path else file_name} path = folder_path / relative_path if relative_path else folder_path os.makedirs(path, exist_ok=True) - with open(os.path.join(path, file_name), 'wb') as file: + with open(os.path.join(path, file_name), "wb") as file: file.write(image) return image_reference @@ -163,6 +161,7 @@ def pfbytes_file_reference_encoder(obj): file_name = str(uuid.uuid4()) return save_image_to_file(obj, file_name, folder_path, relative_path) raise TypeError(f"Not supported to dump type '{type(obj).__name__}'.") + return pfbytes_file_reference_encoder @@ -185,7 +184,7 @@ def convert_multimedia_data_to_base64(value: Any): # TODO: Move this function to a more general place and integrate serialization to this function. -def recursive_process(value: Any, process_funcs: dict[type, Callable] = None) -> dict: +def recursive_process(value: Any, process_funcs: Dict[type, Callable] = None) -> dict: if process_funcs: for cls, f in process_funcs.items(): if isinstance(value, cls): @@ -197,7 +196,7 @@ def recursive_process(value: Any, process_funcs: dict[type, Callable] = None) -> return value -def load_multimedia_data(inputs: dict[str, FlowInputDefinition], line_inputs: dict, base_dir: Path): +def load_multimedia_data(inputs: Dict[str, FlowInputDefinition], line_inputs: dict, base_dir: Path): updated_inputs = dict(line_inputs or {}) for key, value in inputs.items(): if value.type == ValueType.IMAGE: From 089f18de7a7fd7853e0b025b4e5ca6d74f263a1e Mon Sep 17 00:00:00 2001 From: Xingzhi Zhang <37076709+elliotzh@users.noreply.github.com> Date: Fri, 20 Oct 2023 17:45:42 +0800 Subject: [PATCH 05/11] refactor: remove load_as_component and update doc (#684) # Description Remove previous interface `load_as_component` and update corresponding tests and documents. # All Promptflow Contribution checklist: - [x] **The pull request does not introduce [breaking changes].** - [x] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [x] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [x] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [x] Title of the pull request is clear and informative. - [x] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [x] Pull request includes test coverage for the included changes. --- .../samples_flowinpipeline_pipeline.yml | 47 ++++ .../azureai/use-flow-in-azure-ml-pipeline.md | 129 +++++++++++ docs/cloud/index.md | 1 + docs/reference/pf-command-reference.md | 27 ++- examples/README.md | 2 + examples/configuration.ipynb | 34 +++ .../tutorials/flow-in-pipeline/pipeline.ipynb | 218 ++++++++++++++++++ src/promptflow/promptflow/azure/_pf_client.py | 60 +---- .../azure/operations/_artifact_utilities.py | 53 +---- .../azure/operations/_flow_operations.py | 151 +----------- src/promptflow/setup.py | 2 +- .../e2etests/test_flow_in_azure_ml.py | 91 ++------ .../flows/saved_component_spec/command.yaml | 49 ---- .../flows/saved_component_spec/parallel.yaml | 52 +++-- .../parallel_anonymous.yaml | 73 ------ .../flows/web_classification/flow.dag.yaml | 1 + 16 files changed, 518 insertions(+), 472 deletions(-) create mode 100644 .github/workflows/samples_flowinpipeline_pipeline.yml create mode 100644 docs/cloud/azureai/use-flow-in-azure-ml-pipeline.md create mode 100644 examples/tutorials/flow-in-pipeline/pipeline.ipynb delete mode 100644 src/promptflow/tests/test_configs/flows/saved_component_spec/command.yaml delete mode 100644 src/promptflow/tests/test_configs/flows/saved_component_spec/parallel_anonymous.yaml diff --git a/.github/workflows/samples_flowinpipeline_pipeline.yml b/.github/workflows/samples_flowinpipeline_pipeline.yml new file mode 100644 index 00000000000..4699878019b --- /dev/null +++ b/.github/workflows/samples_flowinpipeline_pipeline.yml @@ -0,0 +1,47 @@ +# This code is autogenerated. +# Code is generated by running custom script: python3 readme.py +# Any manual changes to this file may cause incorrect behavior. +# Any manual changes will be overwritten if the code is regenerated. + +name: samples_flowinpipeline_pipeline +on: + schedule: + - cron: "28 19 * * *" # Every day starting at 3:28 BJT + pull_request: + branches: [ main ] + paths: [ examples/**, .github/workflows/samples_flowinpipeline_pipeline.yml ] + workflow_dispatch: + +jobs: + samples_notebook_ci: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v3 + - name: Generate config.json + run: echo '${{ secrets.TEST_WORKSPACE_CONFIG_JSON_CANARY }}' > ${{ github.workspace }}/examples/config.json + - name: Azure Login + uses: azure/login@v1 + with: + creds: ${{ secrets.AZURE_CREDENTIALS }} + - name: Setup Python 3.9 environment + uses: actions/setup-python@v4 + with: + python-version: "3.9" + - name: Prepare requirements + run: | + python -m pip install --upgrade pip + pip install -r ${{ github.workspace }}/examples/requirements.txt + pip install -r ${{ github.workspace }}/examples/dev_requirements.txt + - name: Create Aoai Connection + run: pf connection create -f ${{ github.workspace }}/examples/connections/azure_openai.yml --set api_key="${{ secrets.AOAI_API_KEY_TEST }}" api_base="${{ secrets.AOAI_API_ENDPOINT_TEST }}" + - name: Test Notebook + working-directory: examples/tutorials/flow-in-pipeline + run: | + papermill -k python pipeline.ipynb pipeline.output.ipynb + - name: Upload artifact + if: ${{ always() }} + uses: actions/upload-artifact@v3 + with: + name: artifact + path: examples/tutorials/flow-in-pipeline diff --git a/docs/cloud/azureai/use-flow-in-azure-ml-pipeline.md b/docs/cloud/azureai/use-flow-in-azure-ml-pipeline.md new file mode 100644 index 00000000000..d4871b7110b --- /dev/null +++ b/docs/cloud/azureai/use-flow-in-azure-ml-pipeline.md @@ -0,0 +1,129 @@ +# Use flow in Azure ML pipeline job + +After you have developed and tested the flow in [init and test a flow](../../how-to-guides/init-and-test-a-flow.md), this guide will help you learn how to use a flow as a parallel component in a pipeline job on AzureML, so that you can integrate the created flow with existing pipelines and process a large amount of data. + +:::{admonition} Pre-requirements +- Customer need to install the extension `ml>=2.21.0` to enable this feature in CLI and package `azure-ai-ml>=1.11.0` to enable this feature in SDK; +- Customer need to put `$schema` in the target `flow.dag.yaml` to enable this feature; + - `flow.dag.yaml`: `$schema`: `https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json` + - `run.yaml`: `$schema`: `https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json` +- Customer need to generate `flow.tools.json` for the target flow before below usage. The generation can be done by `pf flow validate`. +::: + +For more information about AzureML and component: +- [Install and set up the CLI(v2)](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli?view=azureml-api-2&tabs=public) +- [Install and set up the SDK(v2)](https://learn.microsoft.com/en-us/python/api/overview/azure/ai-ml-readme?view=azure-python) +- [What is a pipeline](https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines?view=azureml-api-2) +- [What is a component](https://learn.microsoft.com/en-us/azure/machine-learning/concept-component?view=azureml-api-2) + +## Register a flow as a component + +Customer can register a flow as a component with either CLI or SDK. + +::::{tab-set} +:::{tab-item} CLI +:sync: CLI + +```bash +# Register flow as a component +# Default component name will be the name of flow folder, which is not a valid component name, so we override it here; default version will be "1" +az ml component create --file standard/web-classification/flow.dag.yaml --set name=web_classification + +# Register flow as a component with parameters override +az ml component create --file standard/web-classification/flow.dag.yaml --version 2 --set name=web_classification_updated +``` + +::: + +:::{tab-item} SDK +:sync: SDK + +```python +from azure.ai.ml import MLClient, load_component + +ml_client = MLClient() + +# Register flow as a component +flow_component = load_component("standard/web-classification/flow.dag.yaml") +# Default component name will be the name of flow folder, which is not a valid component name, so we override it here; default version will be "1" +flow_component.name = "web_classification" +ml_client.components.create_or_update(flow_component) + +# Register flow as a component with parameters override +ml_client.components.create_or_update( + "standard/web-classification/flow.dag.yaml", + version="2", + params_override=[ + {"name": "web_classification_updated"} + ] +) +``` + +::: + +:::: + +After registered a flow as a component, they can be referred in a pipeline job like [regular registered components](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components). + +## Directly use a flow in a pipeline job + +Besides explicitly registering a flow as a component, customer can also directly use flow in a pipeline job: +- [CLI example](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/pipelines-with-components/pipeline_job_with_flow_as_component) +- [SDK example](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1l_flow_in_pipeline) + +All connections and flow inputs will be exposed as input parameters of the component. Default value can be provided in flow/run definition; they can also be set/overwrite on job submission: + +::::{tab-set} +:::{tab-item} CLI +:sync: CLI + +```yaml +... +jobs: + flow_node: + type: parallel + component: standard/web-classification/flow.dag.yaml + inputs: + data: ${{parent.inputs.web_classification_input}} + url: "${data.url}" + connections.summarize_text_content.connection: azure_open_ai_connection + connections.summarize_text_content.deployment_name: text-davinci-003 +... +``` + +::: + +:::{tab-item} SDK +:sync: SDK + +```python +from azure.ai.ml import dsl + +ml_client = MLClient() + +# Register flow as a component +flow_component = load_component("standard/web-classification/flow.dag.yaml") +data_input = Input(path="standard/web-classification/data.jsonl", type=AssetTypes.URI_FILE) + +@dsl.pipeline +def pipeline_func_with_flow(data): + flow_node = flow_component( + data=data, + url="${data.url}", + connections={ + "summarize_text_content": { + "connection": "azure_open_ai_connection", + "deployment_name": "text-davinci-003", + }, + }, + ) + flow_node.compute = "cpu-cluster" + +pipeline_with_flow = pipeline_func_with_flow(data=data_input) + +ml_client.jobs.create_or_update(pipeline_with_flow) +``` + +::: + +:::: diff --git a/docs/cloud/index.md b/docs/cloud/index.md index f01219b0428..2c8aa2e2584 100644 --- a/docs/cloud/index.md +++ b/docs/cloud/index.md @@ -7,5 +7,6 @@ You can develope your flow locally and seamlessly move the experience to azure c :maxdepth: 1 azureai/quick-start azureai/deploy-to-azure-appservice +azureai/use-flow-in-azure-ml-pipeline.md azureai/faq ``` \ No newline at end of file diff --git a/docs/reference/pf-command-reference.md b/docs/reference/pf-command-reference.md index 38a3e5cc624..12dafbecbbc 100644 --- a/docs/reference/pf-command-reference.md +++ b/docs/reference/pf-command-reference.md @@ -20,8 +20,9 @@ Manage promptflow flow flows. | --- | --- | | [pf flow init](#pf-flow-init) | Initialize a prompt flow directory. | | [pf flow test](#pf-flow-test) | Test the prompt flow or flow node. | +| [pf flow validate](#pf-flow-validate) | Validate a flow and generate `flow.tools.json` for it. | | [pf flow build](#pf-flow-build) | Build a flow for further sharing or deployment. | -| [pf flow serve](#pf-flow-serve) | Serving a flow as an endpoint. | +| [pf flow serve](#pf-flow-serve) | Serve a flow as an endpoint. | ### pf flow init @@ -167,6 +168,30 @@ Start a interactive chat session for chat flow. Displays the output for each step in the chat flow. +### pf flow validate + +Validate the prompt flow and generate a `flow.tools.json` under `.promptflow`. This file is required when using flow as a component in a Azure ML pipeline. + +```bash +pf flow validate --source + [--debug] + [--verbose] +``` + +#### Examples + +Validate the flow. + +```bash +pf flow validate --source +``` + +#### Required Parameter + +`--source` + +The flow source to validate. + ### pf flow build Build a flow for further sharing or deployment. diff --git a/examples/README.md b/examples/README.md index 107c3094725..41617e99a4e 100644 --- a/examples/README.md +++ b/examples/README.md @@ -28,6 +28,7 @@ ------|--------|------------- | [chat-with-pdf](tutorials/e2e-development/chat-with-pdf.md) | [![samples_tutorials_e2e_development_chat_with_pdf](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_e2e_development_chat_with_pdf.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_e2e_development_chat_with_pdf.yml) | Retrieval Augmented Generation (or RAG) has become a prevalent pattern to build intelligent application with Large Language Models (or LLMs) since it can infuse external knowledge into the model, which is not trained with those up-to-date or proprietary information | | [azure-app-service](tutorials/flow-deploy/azure-app-service/README.md) | [![samples_tutorials_flow_deploy_azure_app_service](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_azure_app_service.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_azure_app_service.yml) | This example demos how to deploy a flow using Azure App Service | +| [distribute-flow-as-executable-app](tutorials/flow-deploy/distribute-flow-as-executable-app/README.md) | [![samples_tutorials_flow_deploy_distribute_flow_as_executable_app](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_distribute_flow_as_executable_app.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_distribute_flow_as_executable_app.yml) | This example demos how to package flow as a executable app | | [docker](tutorials/flow-deploy/docker/README.md) | [![samples_tutorials_flow_deploy_docker](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_docker.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_docker.yml) | This example demos how to deploy flow as a docker app | | [kubernetes](tutorials/flow-deploy/kubernetes/README.md) | [![samples_tutorials_flow_deploy_kubernetes](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_kubernetes.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_tutorials_flow_deploy_kubernetes.yml) | This example demos how to deploy flow as a Kubernetes app | @@ -87,6 +88,7 @@ ------|--------|------------- | [quickstart.ipynb](tutorials/get-started/quickstart.ipynb) | [![samples_getstarted_quickstart](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_quickstart.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_quickstart.yml) | A quickstart tutorial to run a flow and evaluate it. | | [quickstart-azure.ipynb](tutorials/get-started/quickstart-azure.ipynb) | [![samples_getstarted_quickstartazure](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_quickstartazure.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_getstarted_quickstartazure.yml) | A quickstart tutorial to run a flow in Azure AI and evaluate it. | +| [pipeline.ipynb](tutorials/flow-in-pipeline/pipeline.ipynb) | [![samples_flowinpipeline_pipeline](https://github.com/microsoft/promptflow/actions/workflows/samples_flowinpipeline_pipeline.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_flowinpipeline_pipeline.yml) | {'description': 'Create pipeline using components to run a distributed job with tensorflow'} | | [cloud-run-management.ipynb](tutorials/run-management/cloud-run-management.ipynb) | [![samples_runmanagement_cloudrunmanagement](https://github.com/microsoft/promptflow/actions/workflows/samples_runmanagement_cloudrunmanagement.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_runmanagement_cloudrunmanagement.yml) | Flow run management in Azure AI | | [connection.ipynb](connections/connection.ipynb) | [![samples_connections_connection](https://github.com/microsoft/promptflow/actions/workflows/samples_connections_connection.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_connections_connection.yml) | Manage various types of connections using sdk | | [chat-with-pdf-azure.ipynb](flows/chat/chat-with-pdf/chat-with-pdf-azure.ipynb) | [![samples_flows_chat_chatwithpdf_chatwithpdfazure](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chatwithpdf_chatwithpdfazure.yml/badge.svg?branch=main)](https://github.com/microsoft/promptflow/actions/workflows/samples_flows_chat_chatwithpdf_chatwithpdfazure.yml) | A tutorial of chat-with-pdf flow that executes in Azure AI | diff --git a/examples/configuration.ipynb b/examples/configuration.ipynb index c993738e4aa..158f571b06d 100644 --- a/examples/configuration.ipynb +++ b/examples/configuration.ipynb @@ -121,6 +121,40 @@ "print(pf)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Retrieve or create an Azure Machine Learning compute target\n", + "\n", + "To create a Azure Machine Learning job, you need a compute cluster as prerequisite. Below code ensures computes named `cpu-cluster` exists in your workspace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azure.ai.ml import MLClient\n", + "from azure.ai.ml.entities import AmlCompute\n", + "\n", + "# MLClient use the same configuration as PFClient\n", + "ml_client = MLClient.from_config(credential=credential)\n", + "\n", + "# specify aml compute name.\n", + "cpu_compute_target = \"cpu-cluster\"\n", + "\n", + "try:\n", + " ml_client.compute.get(cpu_compute_target)\n", + "except Exception:\n", + " print(\"Creating a new cpu compute target...\")\n", + " compute = AmlCompute(\n", + " name=cpu_compute_target, size=\"STANDARD_D2_V2\", min_instances=0, max_instances=4\n", + " )\n", + " ml_client.compute.begin_create_or_update(compute).result()" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/examples/tutorials/flow-in-pipeline/pipeline.ipynb b/examples/tutorials/flow-in-pipeline/pipeline.ipynb new file mode 100644 index 00000000000..604bd58cd82 --- /dev/null +++ b/examples/tutorials/flow-in-pipeline/pipeline.ipynb @@ -0,0 +1,218 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use Flow as Component in Pipeline\n", + "\n", + "**Requirements** - In order to benefit from this tutorial, you will need:\n", + "- A basic understanding of Machine Learning\n", + "- An Azure account with an active subscription - [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F)\n", + "- An Azure ML workspace with computer cluster - [Configure workspace](../../configuration.ipynb)\n", + "- A python environment\n", + "- Installed Azure Machine Learning Python SDK v2 - [install instructions](../../../README.md) - check the getting started section\n", + "\n", + "**Learning Objectives** - By the end of this tutorial, you should be able to:\n", + "- Connect to your AML workspace from the Python SDK\n", + "- Create `Pipeline` with a component loaded from `flow.dag.yaml`\n", + "\n", + "**Motivations** - This notebook explains how to run a pipeline with distributed training component." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. Connect to Azure Machine Learning Workspace\n", + "\n", + "The [workspace](https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace) is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. In this section we will connect to the workspace in which the job will be run.\n", + "\n", + "## 1.1 Import the required libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import required libraries\n", + "from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential\n", + "\n", + "from azure.ai.ml import MLClient, load_component, Input\n", + "from azure.ai.ml.constants import AssetTypes\n", + "from azure.ai.ml.dsl import pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 Configure credential\n", + "\n", + "We are using `DefaultAzureCredential` to get access to workspace. \n", + "`DefaultAzureCredential` should be capable of handling most Azure SDK authentication scenarios. \n", + "\n", + "Reference for more available credentials if it does not work for you: [configure credential example](../../configuration.ipynb), [azure-identity reference doc](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity?view=azure-python)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " credential = DefaultAzureCredential()\n", + " # Check if given credential can get token successfully.\n", + " credential.get_token(\"https://management.azure.com/.default\")\n", + "except Exception as ex:\n", + " # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work\n", + " credential = InteractiveBrowserCredential()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 Get a handle to the workspace\n", + "\n", + "We use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](../../configuration.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get a handle to workspace\n", + "ml_client = MLClient.from_config(credential=credential)\n", + "\n", + "# Retrieve an already attached Azure Machine Learning Compute.\n", + "cluster_name = \"cpu-cluster\"\n", + "print(ml_client.compute.get(cluster_name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Load flow as component\n", + "\n", + "We suppose that there has already been a flow authored with Promptflow SDK/CLI/portal. Then we can load its flow dag yaml as a component like regular component specs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flow_component = load_component(\"../../flows/standard/web-classification/flow.dag.yaml\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Pipeline job\n", + "## 3.1 Build pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_input = Input(path=\"../../flows/standard/web-classification/data.jsonl\", type=AssetTypes.URI_FILE)\n", + "\n", + "@pipeline()\n", + "def pipeline_func_with_flow(data):\n", + " flow_node = flow_component(\n", + " data=data,\n", + " url=\"${data.url}\",\n", + " connections={\n", + " \"summarize_text_content\": {\n", + " \"connection\": \"azure_open_ai_connection\",\n", + " \"deployment_name\": \"gpt-35-turbo\",\n", + " },\n", + " \"classify_with_llm\": {\n", + " \"connection\": \"azure_open_ai_connection\",\n", + " \"deployment_name\": \"gpt-35-turbo\",\n", + " },\n", + " },\n", + " )\n", + " flow_node.compute = \"cpu-cluster\"\n", + "\n", + "\n", + "# create pipeline instance\n", + "pipeline_job = pipeline_func_with_flow(data=data_input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 Submit pipeline job" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# submit job to workspace\n", + "pipeline_job = ml_client.jobs.create_or_update(\n", + " pipeline_job, experiment_name=\"pipeline_samples\"\n", + ")\n", + "pipeline_job" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Wait until the job completes\n", + "ml_client.jobs.stream(pipeline_job.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Next Steps\n", + "You can see further examples of running a pipeline job [here](../)" + ] + } + ], + "metadata": { + "description": { + "description": "Create pipeline using components to run a distributed job with tensorflow" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/promptflow/promptflow/azure/_pf_client.py b/src/promptflow/promptflow/azure/_pf_client.py index f82d8a2eac9..b2487033b10 100644 --- a/src/promptflow/promptflow/azure/_pf_client.py +++ b/src/promptflow/promptflow/azure/_pf_client.py @@ -4,7 +4,7 @@ import os from os import PathLike from pathlib import Path -from typing import IO, Any, AnyStr, Dict, List, Optional, Union +from typing import Dict, List, Optional, Union from azure.ai.ml import MLClient from azure.core.credentials import TokenCredential @@ -14,7 +14,6 @@ from promptflow._sdk._errors import RunOperationParameterError from promptflow._sdk._user_agent import USER_AGENT from promptflow._sdk.entities import Run -from promptflow.azure._load_functions import load_flow from promptflow.azure._restclient.service_caller_factory import _FlowServiceCallerFactory from promptflow.azure._utils.gerneral import is_remote_uri from promptflow.azure.operations import RunOperations @@ -300,63 +299,6 @@ def visualize(self, runs: Union[List[str], List[Run]]) -> None: """ self.runs.visualize(runs) - def load_as_component( - self, - source: Union[str, PathLike, IO[AnyStr]], - *, - component_type: str, - columns_mapping: Dict[str, Union[str, float, int, bool]] = None, - variant: str = None, - environment_variables: Dict[str, Any] = None, - is_deterministic: bool = True, - **kwargs, - ) -> "Component": - """ - Load a flow as a component. - - :param source: Source of the flow. Should be a path to a flow dag yaml file or a flow directory. - :type source: Union[str, PathLike, IO[AnyStr]] - :param component_type: Type of the loaded component, support parallel only for now. - :type component_type: str - :param variant: Node variant used for the flow. - :type variant: str - :param environment_variables: Environment variables to set for the flow. - :type environment_variables: dict - :param columns_mapping: Inputs mapping for the flow. - :type columns_mapping: dict - :param is_deterministic: Whether the loaded component is deterministic. - :type is_deterministic: bool - """ - name = kwargs.pop("name", None) - version = kwargs.pop("version", None) - description = kwargs.pop("description", None) - display_name = kwargs.pop("display_name", None) - tags = kwargs.pop("tags", None) - - flow = load_flow( - source=source, - relative_origin=kwargs.pop("relative_origin", None), - **kwargs, - ) - - if component_type != "parallel": - raise NotImplementedError(f"Component type {component_type} is not supported yet.") - - # TODO: confirm if we should keep flow operations - component = self._flows.load_as_component( - flow=flow, - columns_mapping=columns_mapping, - variant=variant, - environment_variables=environment_variables, - name=name, - version=version, - description=description, - is_deterministic=is_deterministic, - display_name=display_name, - tags=tags, - ) - return component - def _add_user_agent(self, kwargs) -> None: user_agent = kwargs.pop("user_agent", None) user_agent = f"{user_agent} {USER_AGENT}" if user_agent else USER_AGENT diff --git a/src/promptflow/promptflow/azure/operations/_artifact_utilities.py b/src/promptflow/promptflow/azure/operations/_artifact_utilities.py index 3edbbf48177..0a1cf2ec51a 100644 --- a/src/promptflow/promptflow/azure/operations/_artifact_utilities.py +++ b/src/promptflow/promptflow/azure/operations/_artifact_utilities.py @@ -9,7 +9,7 @@ import uuid from datetime import datetime, timedelta from pathlib import Path -from typing import Dict, Optional, Tuple, TypeVar, Union +from typing import Dict, Optional, TypeVar, Union from azure.ai.ml._artifacts._blob_storage_helper import BlobStorageClient from azure.ai.ml._artifacts._gen2_storage_helper import Gen2StorageClient @@ -34,7 +34,6 @@ get_artifact_path_from_storage_url, get_storage_client, ) -from azure.ai.ml._utils.utils import is_mlflow_uri, is_url from azure.ai.ml.constants._common import SHORT_URI_FORMAT, STORAGE_ACCOUNT_URLS from azure.ai.ml.entities import Environment from azure.ai.ml.entities._assets._artifacts.artifact import Artifact, ArtifactStorageInfo @@ -357,56 +356,6 @@ def _update_gen2_metadata(name, version, indicator_file, storage_client) -> None T = TypeVar("T", bound=Artifact) -def _check_and_upload_path( - artifact: T, - asset_operations: Union["DataOperations", "ModelOperations", "CodeOperations", "FeatureSetOperations"], - artifact_type: str, - datastore_name: Optional[str] = None, - sas_uri: Optional[str] = None, - show_progress: bool = True, -) -> Tuple[T, str]: - """Checks whether `artifact` is a path or a uri and uploads it to the datastore if necessary. - - param T artifact: artifact to check and upload param - Union["DataOperations", "ModelOperations", "CodeOperations"] - asset_operations: the asset operations to use for uploading - param str datastore_name: the name of the datastore to upload to - param str sas_uri: the sas uri to use for uploading - """ - - datastore_name = artifact.datastore - if ( - hasattr(artifact, "local_path") - and artifact.local_path is not None - or ( - hasattr(artifact, "path") - and artifact.path is not None - and not (is_url(artifact.path) or is_mlflow_uri(artifact.path)) - ) - ): - path = ( - Path(artifact.path) - if hasattr(artifact, "path") and artifact.path is not None - else Path(artifact.local_path) - ) - if not path.is_absolute(): - path = Path(artifact.base_path, path).resolve() - uploaded_artifact = _upload_to_datastore( - asset_operations._operation_scope, - asset_operations._datastore_operation, - path, - datastore_name=datastore_name, - asset_name=artifact.name, - asset_version=str(artifact.version), - asset_hash=artifact._upload_hash if hasattr(artifact, "_upload_hash") else None, - sas_uri=sas_uri, - artifact_type=artifact_type, - show_progress=show_progress, - ignore_file=getattr(artifact, "_ignore_file", None), - ) - return uploaded_artifact - - def _check_and_upload_env_build_context( environment: Environment, operations: "EnvironmentOperations", diff --git a/src/promptflow/promptflow/azure/operations/_flow_operations.py b/src/promptflow/promptflow/azure/operations/_flow_operations.py index cef2ef52a84..debb642fffb 100644 --- a/src/promptflow/promptflow/azure/operations/_flow_operations.py +++ b/src/promptflow/promptflow/azure/operations/_flow_operations.py @@ -5,9 +5,8 @@ import logging import os -import re from pathlib import Path -from typing import Any, Dict +from typing import Dict from azure.ai.ml._artifacts._artifact_utilities import _check_and_upload_path from azure.ai.ml._scope_dependent_operations import ( @@ -16,11 +15,7 @@ OperationScope, _ScopeDependentOperations, ) -from azure.ai.ml._utils._storage_utils import AzureMLDatastorePathUri -from azure.ai.ml._utils.utils import hash_dict -from azure.ai.ml.constants._common import SHORT_URI_FORMAT, AzureMLResourceType -from azure.ai.ml.operations import ComponentOperations -from azure.ai.ml.operations._code_operations import CodeOperations +from azure.ai.ml.constants._common import SHORT_URI_FORMAT from azure.ai.ml.operations._operation_orchestrator import OperationOrchestrator from azure.core.exceptions import HttpResponseError @@ -32,12 +27,8 @@ ) from promptflow._sdk._utils import PromptflowIgnoreFile, generate_flow_tools_json from promptflow._sdk._vendor._asset_utils import traverse_directory -from promptflow.azure._constants._flow import DEFAULT_STORAGE from promptflow.azure._entities._flow import Flow -from promptflow.azure._ml import Component -from promptflow.azure._restclient.flow.models import FlowRunMode, LoadFlowAsComponentRequest from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller -from promptflow.azure._utils import is_arm_id from promptflow.exceptions import SystemErrorException @@ -63,10 +54,6 @@ def __init__( self._service_caller = service_caller self._credential = credential - @property - def _code_operations(self) -> CodeOperations: - return self._all_operations.get_operation(AzureMLResourceType.CODE, lambda x: isinstance(x, CodeOperations)) - def _create_or_update(self, flow, **kwargs): # upload to file share self._resolve_arm_id_or_upload_dependencies(flow) @@ -102,140 +89,6 @@ def _download(self, source, dest): # TODO: support download flow raise NotImplementedError("Not implemented yet") - @classmethod - def _clear_empty_item(cls, obj): - if not isinstance(obj, dict): - return obj - return {k: cls._clear_empty_item(v) for k, v in obj.items() if v is not None} - - @classmethod - def _get_component_hash(cls, rest_object): - """this hash should include all the burn-in information: - - code - - keys of inputs_mapping - - environment_variables, it will be burned into something like component.task.environment_variables? - some other fields will be burned into component but will impact default value of inputs: - - variant - - connections - - values of inputs_mapping - Now we use all of them as hash key. - """ - obj = rest_object.as_dict() - - return hash_dict(cls._clear_empty_item(obj)) - - @classmethod - def _get_name_and_version(cls, *, rest_object, name: str = None, version: str = None): - if name and version: - return name, version - if name or version: - raise ValueError("name and version of the component must be provided together") - # the hash will be impacted by all editable fields, including default value of inputs_mapping - # so components with different default value of columns_mapping can't be reused from each other - return "azureml_anonymous_flow", cls._get_component_hash(rest_object) - - def load_as_component( - self, - flow, - name: str = None, - version: str = None, - display_name: str = None, - description: str = None, - tags: Dict[str, str] = None, - variant: str = None, - columns_mapping: Dict[str, str] = None, - environment_variables: Dict[str, Any] = None, - connections: Dict[str, Dict[str, str]] = None, - is_deterministic: bool = True, - **kwargs, - ) -> Component: - """Load a flow as a component.""" - rest_object = LoadFlowAsComponentRequest( - node_variant=variant, - inputs_mapping=columns_mapping, - environment_variables=environment_variables, - connections=connections, - display_name=display_name, - description=description, - tags=tags, - is_deterministic=is_deterministic, - # hack: MT support this only for now, will remove after MT release new version - run_mode=FlowRunMode.BULK_TEST, - ) - - if is_arm_id(flow): - rest_object.flow_definition_resource_id = flow.id - else: - # upload to file share - self._resolve_arm_id_or_upload_dependencies(flow) - if flow.path.startswith("azureml://"): - # upload via _check_and_upload_path - # submit with params FlowDefinitionDataStoreName and FlowDefinitionBlobPath - path_uri = AzureMLDatastorePathUri(flow.path) - rest_object.flow_definition_data_store_name = path_uri.datastore - rest_object.flow_definition_blob_path = path_uri.path - else: - # upload via CodeOperations.create_or_update - # submit with param FlowDefinitionDataUri - rest_object.flow_definition_data_uri = flow.path - - rest_object.component_name, rest_object.component_version = self._get_name_and_version( - rest_object=rest_object, name=name, version=version - ) - - component_id = self._service_caller.create_component_from_flow( - subscription_id=self._operation_scope.subscription_id, - resource_group_name=self._operation_scope.resource_group_name, - workspace_name=self._operation_scope.workspace_name, - body=rest_object, - ) - name, version = re.match(r".*/components/(.*)/versions/(.*)", component_id).groups() - return self._all_operations.get_operation( - AzureMLResourceType.COMPONENT, - lambda x: isinstance(x, ComponentOperations), - ).get(name, version) - - def _resolve_arm_id_or_upload_dependencies_to_file_share(self, flow: Flow) -> None: - ops = OperationOrchestrator(self._all_operations, self._operation_scope, self._operation_config) - # resolve flow's code - self._try_resolve_code_for_flow_to_file_share(flow=flow, ops=ops) - - @classmethod - def _try_resolve_code_for_flow_to_file_share(cls, flow: Flow, ops: OperationOrchestrator) -> None: - from ._artifact_utilities import _check_and_upload_path - - if flow.path: - if flow.path.startswith("azureml://datastores"): - # remote path - path_uri = AzureMLDatastorePathUri(flow.path) - if path_uri.datastore != DEFAULT_STORAGE: - raise ValueError(f"Only {DEFAULT_STORAGE} is supported as remote storage for now.") - flow.path = path_uri.path - flow._code_uploaded = True - return - else: - raise ValueError("Path is required for flow.") - - with flow._build_code() as code: - if code is None: - return - if flow._code_uploaded: - return - code.datastore = DEFAULT_STORAGE - uploaded_code_asset = _check_and_upload_path( - artifact=code, - asset_operations=ops._code_assets, - artifact_type="Code", - show_progress=False, - ) - if "remote_path" in uploaded_code_asset: - path = uploaded_code_asset["remote_path"] - elif "remote path" in uploaded_code_asset: - path = uploaded_code_asset["remote path"] - flow.code = path - flow.path = (Path(path) / flow.path).as_posix() - flow._code_uploaded = True - def _resolve_arm_id_or_upload_dependencies(self, flow: Flow, ignore_tools_json=False) -> None: ops = OperationOrchestrator(self._all_operations, self._operation_scope, self._operation_config) # resolve flow's code diff --git a/src/promptflow/setup.py b/src/promptflow/setup.py index bfc0e5e45f0..349856e47aa 100644 --- a/src/promptflow/setup.py +++ b/src/promptflow/setup.py @@ -77,7 +77,7 @@ "azure-core>=1.26.4,<2.0.0", "azure-storage-blob>=12.13.0,<13.0.0", "azure-identity>=1.12.0,<2.0.0", - "azure-ai-ml>=1.9.0,<2.0.0", + "azure-ai-ml>=1.11.0,<2.0.0", "pyjwt>=2.4.0,<3.0.0", # requirement of control plane SDK ], "executable": [ diff --git a/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_in_azure_ml.py b/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_in_azure_ml.py index aea913bbefd..bebdaea63bb 100644 --- a/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_in_azure_ml.py +++ b/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_in_azure_ml.py @@ -4,9 +4,8 @@ import pydash import pytest import yaml -from azure.ai.ml import Input, dsl -from azure.ai.ml.constants import AssetTypes -from azure.ai.ml.entities import Component, PipelineJob +from azure.ai.ml import load_component +from azure.ai.ml.entities import Component from promptflow.connections import AzureOpenAIConnection @@ -61,6 +60,7 @@ def update_saved_spec(component: Component, saved_spec_path: str): current_spec_text = saved_spec_path.read_text() if current_spec_text == yaml_text: return + saved_spec_path.parent.mkdir(parents=True, exist_ok=True) saved_spec_path.write_text(yaml_text) @@ -73,42 +73,19 @@ class TestFlowInAzureML: [ pytest.param( { - "component_type": "parallel", - "columns_mapping": { - "groundtruth": "1", - "prediction": "${{batch_run.outputs.category}}", - }, - "environment_variables": { - "verbose": "true", - }, - }, - { - "type": "parallel", - }, - id="parallel_anonymous", - ), - pytest.param( - { - "name": "web_classification_0", + "name": "web_classification_4", "version": "1.0.0", - "component_type": "parallel", "description": "Create flows that use large language models to " "classify URLs into multiple categories.", - "columns_mapping": { - "groundtruth": "1", - "prediction": "${{batch_run.outputs.category}}", - }, "environment_variables": { "verbose": "true", }, }, { - "name": "web_classification_0", + "name": "web_classification_4", "version": "1.0.0", "description": "Create flows that use large language models to " "classify URLs into multiple categories.", - "inputs.groundtruth.default": "1", - "inputs.prediction.default": "${{batch_run.outputs.category}}", "type": "parallel", }, id="parallel", @@ -119,62 +96,26 @@ def test_flow_as_component( self, azure_open_ai_connection: AzureOpenAIConnection, temp_output_dir, - pf, + ml_client, load_params: dict, expected_spec_attrs: dict, request, ) -> None: + # keep the simplest test here, more tests are in azure-ai-ml flows_dir = "./tests/test_configs/flows" - flow_func: Component = pf.load_as_component( - f"{flows_dir}/web_classification", - **load_params, - ) - - update_saved_spec(flow_func, f"./tests/test_configs/flows/saved_component_spec/{request.node.callspec.id}.yaml") - - component_dict = flow_func._to_dict() - slimmed_created_component_attrs = {key: pydash.get(component_dict, key) for key in expected_spec_attrs.keys()} - assert slimmed_created_component_attrs == expected_spec_attrs - - def test_flow_as_component_in_dsl_pipeline( - self, azure_open_ai_connection: AzureOpenAIConnection, temp_output_dir, pf - ) -> None: - - flows_dir = "./tests/test_configs/flows" - - flow_func: Component = pf.load_as_component( - f"{flows_dir}/web_classification", - component_type="parallel", - columns_mapping={ - "groundtruth": "${data.answer}", - "url": "${data.url}", - }, - environment_variables={ - "verbose": "true", - }, + flow_func: Component = load_component( + f"{flows_dir}/web_classification/flow.dag.yaml", params_override=[load_params] ) - @dsl.pipeline - def pipeline_with_flow(input_data): - flow_node = flow_func( - data=input_data, - connections={ - "summarize_text_content": { - "deployment_name": "test_deployment_name", - } - }, - ) - flow_node.logging_level = "DEBUG" - return flow_node.outputs + # TODO: snapshot of flow component changed every time? + created_component = ml_client.components.create_or_update(flow_func, is_anonymous=True) - pipeline: PipelineJob = pipeline_with_flow( - input_data=Input(path=f"{flows_dir}/web_classification_input_dir", type=AssetTypes.URI_FOLDER), + update_saved_spec( + created_component, f"./tests/test_configs/flows/saved_component_spec/{request.node.callspec.id}.yaml" ) - # compute cluster doesn't have access to azurecr for now, so the submitted job will fail in building image stage - pipeline.settings.default_compute = "cpu-cluster" - created_job: PipelineJob = pf.ml_client.jobs.create_or_update(pipeline) - assert created_job.id - assert created_job.jobs["flow_node"].logging_level == "DEBUG" + component_dict = created_component._to_dict() + slimmed_created_component_attrs = {key: pydash.get(component_dict, key) for key in expected_spec_attrs.keys()} + assert slimmed_created_component_attrs == expected_spec_attrs diff --git a/src/promptflow/tests/test_configs/flows/saved_component_spec/command.yaml b/src/promptflow/tests/test_configs/flows/saved_component_spec/command.yaml deleted file mode 100644 index fd9dbf0e52a..00000000000 --- a/src/promptflow/tests/test_configs/flows/saved_component_spec/command.yaml +++ /dev/null @@ -1,49 +0,0 @@ -$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json -name: web_classification_updated -version: version_updated -display_name: web_classification_display_name_updated -description: Create flows that use large language models to classify URLs into multiple - categories. -type: command -inputs: - data: - type: uri_folder - variant: - type: string - optional: true - default: variant_1 - groundtruth: - type: string - optional: true - default: '1' - prediction: - type: string - optional: true - default: ${{variant.outputs.category}} - connections.summarize_text_content.connection: - type: string - optional: true - default: azure_open_ai_connection - connections.summarize_text_content.deployment_name: - type: string - optional: true - connections.classify_with_llm.connection: - type: string - optional: true - default: azure_open_ai_connection - connections.classify_with_llm.deployment_name: - type: string - optional: true -outputs: - output: - type: uri_folder -command: pf run_bulk --input ${{inputs.data}} --output ${{outputs.output}} $[[--inputs-mapping - groundtruth=${{inputs.groundtruth}},prediction=${{inputs.prediction}}]] --connections - "$[[summarize_text_content.deployment_name=${{inputs.connections.summarize_text_content.deployment_name}},]]$[[summarize_text_content.connection=${{inputs.connections.summarize_text_content.connection}},]]$[[classify_with_llm.deployment_name=${{inputs.connections.classify_with_llm.deployment_name}},]]$[[classify_with_llm.connection=${{inputs.connections.classify_with_llm.connection}},]]" - $[[--variant ${{inputs.variant}}]] -environment: - name: CliV2AnonymousEnvironment - version: e2ebc71877ca60434ac0cb69936b523f - image: promptflow.azurecr.io/cli_test:latest -code: D:/PycharmProjects/PromptFlow/src/promptflow-sdk/tests/test_configs/flows/web_classification -is_deterministic: true diff --git a/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel.yaml b/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel.yaml index 74a0dff470b..cadaa6ec966 100644 --- a/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel.yaml +++ b/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel.yaml @@ -7,7 +7,7 @@ creation_context: last_modified_by_type: xxx description: Create flows that use large language models to classify URLs into multiple categories. -display_name: web_classification_0 +display_name: web_classification_4 error_threshold: -1 id: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/components/xxx/versions/xxx input_data: ${{inputs.data}} @@ -20,6 +20,18 @@ inputs: default: text-davinci-003 optional: true type: string + connections.classify_with_llm.model: + enum: + - text-davinci-001 + - text-davinci-002 + - text-davinci-003 + - text-curie-001 + - text-babbage-001 + - text-ada-001 + - code-cushman-001 + - code-davinci-002 + optional: true + type: string connections.summarize_text_content.connection: default: azure_open_ai_connection optional: true @@ -28,16 +40,26 @@ inputs: default: text-davinci-003 optional: true type: string + connections.summarize_text_content.model: + enum: + - text-davinci-001 + - text-davinci-002 + - text-davinci-003 + - text-curie-001 + - text-babbage-001 + - text-ada-001 + - code-cushman-001 + - code-davinci-002 + optional: true + type: string data: - description: Flow data input optional: false type: uri_folder - groundtruth: - default: '1' - optional: false - type: string - prediction: - default: ${{batch_run.outputs.category}} + run_outputs: + optional: true + type: uri_folder + url: + default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h optional: false type: string is_deterministic: true @@ -45,8 +67,10 @@ logging_level: INFO max_concurrency_per_instance: 1 mini_batch_error_threshold: 0 mini_batch_size: '1' -name: web_classification_0 +name: web_classification_4 outputs: + debug_info: + type: uri_folder flow_outputs: type: uri_folder retry_settings: @@ -54,13 +78,15 @@ retry_settings: timeout: 3600 task: append_row_to: ${{outputs.flow_outputs}} - code: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/codes/xxx/versions/xxx + code: /subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/codes/xxx/versions/xxx entry_script: driver/azureml_user/parallel_run/prompt_flow_entry.py environment: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/environments/xxx/versions/xxx program_arguments: --amlbi_pf_enabled True --amlbi_pf_run_mode component --amlbi_mini_batch_rows - 1 --amlbi_file_format jsonl --amlbi_pf_connections "$[[classify_with_llm.connection=${{inputs.connections.classify_with_llm.connection}},]]$[[classify_with_llm.deployment_name=${{inputs.connections.classify_with_llm.deployment_name}},]]$[[summarize_text_content.connection=${{inputs.connections.summarize_text_content.connection}},]]$[[summarize_text_content.deployment_name=${{inputs.connections.summarize_text_content.deployment_name}},]]" - --amlbi_pf_input_groundtruth ${{inputs.groundtruth}} --amlbi_pf_input_prediction - ${{inputs.prediction}} + 1 --amlbi_file_format jsonl $[[--amlbi_pf_run_outputs ${{inputs.run_outputs}}]] + --amlbi_pf_debug_info ${{outputs.debug_info}} --amlbi_pf_connections "$[[classify_with_llm.connection=${{inputs.connections.classify_with_llm.connection}},]]$[[summarize_text_content.connection=${{inputs.connections.summarize_text_content.connection}},]]" + --amlbi_pf_deployment_names "$[[classify_with_llm.deployment_name=${{inputs.connections.classify_with_llm.deployment_name}},]]$[[summarize_text_content.deployment_name=${{inputs.connections.summarize_text_content.deployment_name}},]]" + --amlbi_pf_model_names "$[[classify_with_llm.model=${{inputs.connections.classify_with_llm.model}},]]$[[summarize_text_content.model=${{inputs.connections.summarize_text_content.model}},]]" + --amlbi_pf_input_url ${{inputs.url}} type: run_function type: parallel version: 1.0.0 diff --git a/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel_anonymous.yaml b/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel_anonymous.yaml deleted file mode 100644 index f47c9c4d941..00000000000 --- a/src/promptflow/tests/test_configs/flows/saved_component_spec/parallel_anonymous.yaml +++ /dev/null @@ -1,73 +0,0 @@ -creation_context: - created_at: xxx - created_by: xxx - created_by_type: xxx - last_modified_at: xxx - last_modified_by: xxx - last_modified_by_type: xxx -display_name: azureml_anonymous_flow -error_threshold: -1 -id: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/components/xxx/versions/xxx -input_data: ${{inputs.data}} -inputs: - connections.classify_with_llm.connection: - default: azure_open_ai_connection - optional: true - type: string - connections.classify_with_llm.deployment_name: - default: text-davinci-003 - optional: true - type: string - connections.summarize_text_content.connection: - default: azure_open_ai_connection - optional: true - type: string - connections.summarize_text_content.deployment_name: - default: text-davinci-003 - optional: true - type: string - data: - optional: false - type: uri_folder - groundtruth: - default: '1' - optional: false - type: string - prediction: - default: ${{batch_run.outputs.category}} - optional: false - type: string - run_outputs: - optional: true - type: uri_folder - url: - default: https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h - optional: false - type: string -is_deterministic: true -logging_level: INFO -max_concurrency_per_instance: 1 -mini_batch_error_threshold: 0 -mini_batch_size: '1' -name: azureml_anonymous_flow -outputs: - debug_info: - type: uri_folder - flow_outputs: - type: uri_folder -retry_settings: - max_retries: 2 - timeout: 3600 -task: - append_row_to: ${{outputs.flow_outputs}} - code: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/codes/xxx/versions/xxx - entry_script: driver/azureml_user/parallel_run/prompt_flow_entry.py - environment: azureml:/subscriptions/xxx/resourceGroups/xxx/providers/Microsoft.MachineLearningServices/workspaces/xxx/environments/xxx/versions/xxx - program_arguments: --amlbi_pf_enabled True --amlbi_pf_run_mode component --amlbi_mini_batch_rows - 1 --amlbi_file_format jsonl $[[--amlbi_pf_run_outputs ${{inputs.run_outputs}}]] - --amlbi_pf_debug_info ${{outputs.debug_info}} --amlbi_pf_connections "$[[classify_with_llm.connection=${{inputs.connections.classify_with_llm.connection}},]]$[[classify_with_llm.deployment_name=${{inputs.connections.classify_with_llm.deployment_name}},]]$[[summarize_text_content.connection=${{inputs.connections.summarize_text_content.connection}},]]$[[summarize_text_content.deployment_name=${{inputs.connections.summarize_text_content.deployment_name}},]]" - --amlbi_pf_input_url ${{inputs.url}} --amlbi_pf_input_groundtruth ${{inputs.groundtruth}} - --amlbi_pf_input_prediction ${{inputs.prediction}} - type: run_function -type: parallel -version: c8160529-52a3-0626-39da-3040f0a43655 diff --git a/src/promptflow/tests/test_configs/flows/web_classification/flow.dag.yaml b/src/promptflow/tests/test_configs/flows/web_classification/flow.dag.yaml index ebcb2836255..e5c71f474be 100644 --- a/src/promptflow/tests/test_configs/flows/web_classification/flow.dag.yaml +++ b/src/promptflow/tests/test_configs/flows/web_classification/flow.dag.yaml @@ -1,3 +1,4 @@ +$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json inputs: url: type: string From cae6dfd8549349196b6ea9c0b3e3e8012c311994 Mon Sep 17 00:00:00 2001 From: Lina Tang Date: Fri, 20 Oct 2023 19:14:31 +0800 Subject: [PATCH 06/11] Support including images in complicated structure as node input (#837) # Description We only support single image as node input before, in this PR, we support including images in complicated structure as node input. # All Promptflow Contribution checklist: - [x] **The pull request does not introduce [breaking changes].** - [ ] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [x] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [ ] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [x] Title of the pull request is clear and informative. - [x] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [ ] Pull request includes test coverage for the included changes. Co-authored-by: Lina Tang --- .../promptflow/_utils/multimedia_utils.py | 67 +++++++++---------- .../promptflow/executor/_tool_resolver.py | 7 +- .../promptflow/executor/flow_executor.py | 2 +- .../python_tool_with_image_list/flow.dag.yaml | 10 ++- .../pick_images_from_list.py | 12 +++- 5 files changed, 53 insertions(+), 45 deletions(-) diff --git a/src/promptflow/promptflow/_utils/multimedia_utils.py b/src/promptflow/promptflow/_utils/multimedia_utils.py index b6bc68d8793..74644ef2f4e 100644 --- a/src/promptflow/promptflow/_utils/multimedia_utils.py +++ b/src/promptflow/promptflow/_utils/multimedia_utils.py @@ -19,19 +19,19 @@ MIME_PATTERN = re.compile(r"^data:image/(.*);(path|base64|url)$") -def get_mime_type_from_path(path: Path): +def _get_mime_type_from_path(path: Path): ext = path.suffix[1:] return f"image/{ext}" if ext else "image/*" -def get_extension_from_mime_type(mime_type: str): +def _get_extension_from_mime_type(mime_type: str): ext = mime_type.split("/")[-1] if ext == "*": return None return ext -def is_multimedia_dict(multimedia_dict: dict): +def _is_multimedia_dict(multimedia_dict: dict): if len(multimedia_dict) != 1: return False key = list(multimedia_dict.keys())[0] @@ -40,14 +40,14 @@ def is_multimedia_dict(multimedia_dict: dict): return False -def get_multimedia_info(key: str): +def _get_multimedia_info(key: str): match = re.match(MIME_PATTERN, key) if match: return match.group(1), match.group(2) return None, None -def is_url(value: str): +def _is_url(value: str): try: result = urlparse(value) return all([result.scheme, result.netloc]) @@ -55,21 +55,21 @@ def is_url(value: str): return False -def is_base64(value: str): +def _is_base64(value: str): base64_regex = re.compile(r"^([A-Za-z0-9+/]{4})*(([A-Za-z0-9+/]{2})*(==|[A-Za-z0-9+/]=)?)?$") if re.match(base64_regex, value): return True return False -def create_image_from_file(f: Path, mime_type: str = None): +def _create_image_from_file(f: Path, mime_type: str = None): if not mime_type: - mime_type = get_mime_type_from_path(f) + mime_type = _get_mime_type_from_path(f) with open(f, "rb") as fin: return Image(fin.read(), mime_type=mime_type) -def create_image_from_base64(base64_str: str, mime_type: str = None): +def _create_image_from_base64(base64_str: str, mime_type: str = None): image_bytes = base64.b64decode(base64_str) if not mime_type: format = imghdr.what(None, image_bytes) @@ -77,7 +77,7 @@ def create_image_from_base64(base64_str: str, mime_type: str = None): return Image(image_bytes, mime_type=mime_type) -def create_image_from_url(url: str, mime_type: str = None): +def _create_image_from_url(url: str, mime_type: str = None): response = requests.get(url) if response.status_code == 200: if not mime_type: @@ -92,15 +92,15 @@ def create_image_from_url(url: str, mime_type: str = None): ) -def create_image_from_dict(image_dict: dict): +def _create_image_from_dict(image_dict: dict): for k, v in image_dict.items(): - format, resource = get_multimedia_info(k) + format, resource = _get_multimedia_info(k) if resource == "path": - return create_image_from_file(v, mime_type=f"image/{format}") + return _create_image_from_file(v, mime_type=f"image/{format}") elif resource == "base64": - return create_image_from_base64(v, mime_type=f"image/{format}") + return _create_image_from_base64(v, mime_type=f"image/{format}") elif resource == "url": - return create_image_from_url(v, mime_type=f"image/{format}") + return _create_image_from_url(v, mime_type=f"image/{format}") else: raise InvalidImageInput( message_format=f"Unsupported image resource: {resource}. " @@ -109,24 +109,21 @@ def create_image_from_dict(image_dict: dict): ) -def create_image_from_string(value: str, base_dir: Path = None): - if is_base64(value): - return create_image_from_base64(value) - elif is_url(value): - return create_image_from_url(value) +def _create_image_from_string(value: str): + if _is_base64(value): + return _create_image_from_base64(value) + elif _is_url(value): + return _create_image_from_url(value) else: - path = Path(value) - if base_dir and not path.is_absolute(): - path = Path.joinpath(base_dir, path) - return create_image_from_file(path) + return _create_image_from_file(Path(value)) -def create_image(value: any, base_dir: Path = None): +def create_image(value: any): if isinstance(value, PFBytes): return value elif isinstance(value, dict): - if is_multimedia_dict(value): - return create_image_from_dict(value) + if _is_multimedia_dict(value): + return _create_image_from_dict(value) else: raise InvalidImageInput( message_format="Invalid image input format. The image input should be a dictionary like: " @@ -134,7 +131,7 @@ def create_image(value: any, base_dir: Path = None): target=ErrorTarget.EXECUTOR, ) elif isinstance(value, str): - return create_image_from_string(value, base_dir) + return _create_image_from_string(value) else: raise InvalidImageInput( message_format=f"Unsupported image input type: {type(value)}. " @@ -143,8 +140,8 @@ def create_image(value: any, base_dir: Path = None): ) -def save_image_to_file(image: Image, file_name: str, folder_path: Path, relative_path: Path = None): - ext = get_extension_from_mime_type(image._mime_type) +def _save_image_to_file(image: Image, file_name: str, folder_path: Path, relative_path: Path = None): + ext = _get_extension_from_mime_type(image._mime_type) file_name = f"{file_name}.{ext}" if ext else file_name image_reference = {f"data:{image._mime_type};path": str(relative_path / file_name) if relative_path else file_name} path = folder_path / relative_path if relative_path else folder_path @@ -159,7 +156,7 @@ def pfbytes_file_reference_encoder(obj): """Dumps PFBytes to a file and returns its reference.""" if isinstance(obj, PFBytes): file_name = str(uuid.uuid4()) - return save_image_to_file(obj, file_name, folder_path, relative_path) + return _save_image_to_file(obj, file_name, folder_path, relative_path) raise TypeError(f"Not supported to dump type '{type(obj).__name__}'.") return pfbytes_file_reference_encoder @@ -196,11 +193,11 @@ def recursive_process(value: Any, process_funcs: Dict[type, Callable] = None) -> return value -def load_multimedia_data(inputs: Dict[str, FlowInputDefinition], line_inputs: dict, base_dir: Path): +def load_multimedia_data(inputs: Dict[str, FlowInputDefinition], line_inputs: dict): updated_inputs = dict(line_inputs or {}) for key, value in inputs.items(): if value.type == ValueType.IMAGE: - updated_inputs[key] = create_image(updated_inputs[key], base_dir) + updated_inputs[key] = create_image(updated_inputs[key]) elif value.type == ValueType.LIST or value.type == ValueType.OBJECT: updated_inputs[key] = load_multimedia_data_recursively(updated_inputs[key]) return updated_inputs @@ -210,8 +207,8 @@ def load_multimedia_data_recursively(value: Any): if isinstance(value, list): return [load_multimedia_data_recursively(item) for item in value] elif isinstance(value, dict): - if is_multimedia_dict(value): - return create_image_from_dict(value) + if _is_multimedia_dict(value): + return _create_image_from_dict(value) else: return {k: load_multimedia_data_recursively(v) for k, v in value.items()} else: diff --git a/src/promptflow/promptflow/executor/_tool_resolver.py b/src/promptflow/promptflow/executor/_tool_resolver.py index 978589a0e02..0a360b02ac6 100644 --- a/src/promptflow/promptflow/executor/_tool_resolver.py +++ b/src/promptflow/promptflow/executor/_tool_resolver.py @@ -13,7 +13,7 @@ from promptflow._core.connection_manager import ConnectionManager from promptflow._core.tools_manager import BuiltinsManager, ToolLoader, connection_type_to_api_mapping from promptflow._utils.tool_utils import get_inputs_for_prompt_template, get_prompt_param_name_from_func -from promptflow._utils.multimedia_utils import create_image +from promptflow._utils.multimedia_utils import create_image, load_multimedia_data_recursively from promptflow.contracts.flow import InputAssignment, InputValueType, Node, ToolSourceType from promptflow.contracts.tool import ConnectionType, Tool, ToolType, ValueType from promptflow.contracts.types import PromptTemplate @@ -102,10 +102,11 @@ def _convert_node_literal_input_types(self, node: Node, tool: Tool, module: type else: updated_inputs[k].value = self._convert_to_connection_value(k, v, node, tool_input.type) elif value_type == ValueType.IMAGE: - updated_inputs[k].value = create_image(v.value, self._working_dir) + updated_inputs[k].value = create_image(v.value) elif isinstance(value_type, ValueType): try: updated_inputs[k].value = value_type.parse(v.value) + updated_inputs[k].value = load_multimedia_data_recursively(updated_inputs[k].value) except Exception as e: msg = f"Input '{k}' for node '{node.name}' of value {v.value} is not type {value_type}." raise NodeInputValidationError(message=msg) from e @@ -173,7 +174,7 @@ def _load_images_for_prompt_tpl(self, prompt_tpl_inputs_mapping: dict, node_inpu for input_name, input in prompt_tpl_inputs_mapping.items(): if ValueType.IMAGE in input.type and input_name in node_inputs: if node_inputs[input_name].value_type == InputValueType.LITERAL: - node_inputs[input_name].value = create_image(node_inputs[input_name].value, self._working_dir) + node_inputs[input_name].value = create_image(node_inputs[input_name].value) return node_inputs def _resolve_prompt_node(self, node: Node) -> ResolvedTool: diff --git a/src/promptflow/promptflow/executor/flow_executor.py b/src/promptflow/promptflow/executor/flow_executor.py index 89e5d1124a7..9f247e19342 100644 --- a/src/promptflow/promptflow/executor/flow_executor.py +++ b/src/promptflow/promptflow/executor/flow_executor.py @@ -696,7 +696,7 @@ def exec_line( """ self._node_concurrency = node_concurrency inputs_with_default_value = FlowExecutor._apply_default_value_for_input(self._flow.inputs, inputs) - inputs = load_multimedia_data(self._flow.inputs, inputs_with_default_value, self._working_dir) + inputs = load_multimedia_data(self._flow.inputs, inputs_with_default_value) # For flow run, validate inputs as default with self._run_tracker.node_log_manager: # exec_line interface may be called by exec_bulk, so we only set run_mode as flow run when diff --git a/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/flow.dag.yaml b/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/flow.dag.yaml index 8bb57626e55..5ab71cb5a9c 100644 --- a/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/flow.dag.yaml +++ b/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/flow.dag.yaml @@ -23,7 +23,11 @@ nodes: type: code path: pick_images_from_list.py inputs: - image_list: ${inputs.image_list} + image_list: + - data:image/jpg;path: logo.jpg + - data:image/jpg;path: logo_2.jpg + - data:image/jpg;path: logo_3.jpg + image_list_2: ${inputs.image_list} image_dict: ${inputs.image_dict} - idx_l: 1 - idx_r: 2 + idx_1: 1 + idx_2: 2 diff --git a/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/pick_images_from_list.py b/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/pick_images_from_list.py index d6f03b39ed4..1b439593974 100644 --- a/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/pick_images_from_list.py +++ b/src/promptflow/tests/test_configs/flows/python_tool_with_image_list/pick_images_from_list.py @@ -3,8 +3,14 @@ @tool -def pick_images_from_list(image_list: list[Image], image_dict: dict, idx_l: int, idx_r: int) -> list[Image]: - if idx_l <= idx_r and idx_l >= 0 and idx_r < len(image_list): - return {"Image list": image_list[idx_l:idx_r + 1], "Image dict": image_dict} +def pick_images_from_list( + image_list: list[Image], + image_list_2: list[Image], + image_dict: dict, + idx_1: int, + idx_2: int +) -> list[Image]: + if idx_1 >= 0 and idx_1 < len(image_list) and idx_2 >= 0 and idx_2 < len(image_list_2): + return {"Image list": [image_list[idx_1], image_list_2[idx_2]], "Image dict": image_dict} else: raise Exception(f"Invalid index.") From 0ec981aeb61c0cb40964582324f265ab32ca04dd Mon Sep 17 00:00:00 2001 From: Yao <46446115+16oeahr@users.noreply.github.com> Date: Mon, 23 Oct 2023 09:53:55 +0800 Subject: [PATCH 07/11] Update my tools package to 0.0.3 with custom strong type connection example (#839) # Description Update my tools package to 0.0.3 with custom strong type connection example --------- Co-authored-by: yalu4 --- ...tool_with_custom_strong_type_connection.py | 22 ++++++++++++++++ ...ol_with_custom_strong_type_connection.yaml | 15 +++++++++++ .../tools/tool-package-quickstart/setup.py | 2 +- ...tool_with_custom_strong_type_connection.py | 26 +++++++++++++++++++ scripts/tool/generate_package_tool_meta.py | 2 +- 5 files changed, 65 insertions(+), 2 deletions(-) create mode 100644 examples/tools/tool-package-quickstart/my_tool_package/tools/tool_with_custom_strong_type_connection.py create mode 100644 examples/tools/tool-package-quickstart/my_tool_package/yamls/tool_with_custom_strong_type_connection.yaml create mode 100644 examples/tools/tool-package-quickstart/tests/test_tool_with_custom_strong_type_connection.py diff --git a/examples/tools/tool-package-quickstart/my_tool_package/tools/tool_with_custom_strong_type_connection.py b/examples/tools/tool-package-quickstart/my_tool_package/tools/tool_with_custom_strong_type_connection.py new file mode 100644 index 00000000000..d7f502119a3 --- /dev/null +++ b/examples/tools/tool-package-quickstart/my_tool_package/tools/tool_with_custom_strong_type_connection.py @@ -0,0 +1,22 @@ +from promptflow import tool +from promptflow.connections import CustomStrongTypeConnection +from promptflow.contracts.types import Secret + + +class MyCustomConnection(CustomStrongTypeConnection): + """My custom strong type connection. + + :param api_key: The api key get from "https://xxx.com". + :type api_key: String + :param api_base: The api base. + :type api_base: String + """ + api_key: Secret + api_base: str = "This is a fake api base." + + +@tool +def my_tool(connection: MyCustomConnection, input_text: str) -> str: + # Replace with your tool code. + # Use custom strong type connection like: connection.api_key, connection.api_base + return "Hello " + input_text diff --git a/examples/tools/tool-package-quickstart/my_tool_package/yamls/tool_with_custom_strong_type_connection.yaml b/examples/tools/tool-package-quickstart/my_tool_package/yamls/tool_with_custom_strong_type_connection.yaml new file mode 100644 index 00000000000..888a5dbe6e1 --- /dev/null +++ b/examples/tools/tool-package-quickstart/my_tool_package/yamls/tool_with_custom_strong_type_connection.yaml @@ -0,0 +1,15 @@ +my_tool_package.tools.tool_with_custom_strong_type_connection.my_tool: + description: This is my tool with custom strong type connection. + function: my_tool + inputs: + connection: + custom_type: + - MyCustomConnection + type: + - CustomConnection + input_text: + type: + - string + module: my_tool_package.tools.tool_with_custom_strong_type_connection + name: Tool With Custom Strong Type Connection + type: python diff --git a/examples/tools/tool-package-quickstart/setup.py b/examples/tools/tool-package-quickstart/setup.py index f74ab4fce24..2c111fb8a8d 100644 --- a/examples/tools/tool-package-quickstart/setup.py +++ b/examples/tools/tool-package-quickstart/setup.py @@ -4,7 +4,7 @@ setup( name=PACKAGE_NAME, - version="0.0.2", + version="0.0.3", description="This is my tools package", packages=find_packages(), entry_points={ diff --git a/examples/tools/tool-package-quickstart/tests/test_tool_with_custom_strong_type_connection.py b/examples/tools/tool-package-quickstart/tests/test_tool_with_custom_strong_type_connection.py new file mode 100644 index 00000000000..ed84d02d6ac --- /dev/null +++ b/examples/tools/tool-package-quickstart/tests/test_tool_with_custom_strong_type_connection.py @@ -0,0 +1,26 @@ +import pytest +import unittest + +from my_tool_package.tools.tool_with_custom_strong_type_connection import MyCustomConnection, my_tool + + +@pytest.fixture +def my_custom_connection() -> MyCustomConnection: + my_custom_connection = MyCustomConnection( + { + "api_key" : "my-api-key", + "api_base" : "my-api-base" + } + ) + return my_custom_connection + + +class TestMyToolWithCustomStrongTypeConnection: + def test_my_tool(self, my_custom_connection): + result = my_tool(my_custom_connection, input_text="Microsoft") + assert result == "Hello Microsoft" + + +# Run the unit tests +if __name__ == "__main__": + unittest.main() diff --git a/scripts/tool/generate_package_tool_meta.py b/scripts/tool/generate_package_tool_meta.py index e83eaefa145..7d03211aa25 100644 --- a/scripts/tool/generate_package_tool_meta.py +++ b/scripts/tool/generate_package_tool_meta.py @@ -10,7 +10,6 @@ sys.path.append("src/promptflow-tools") sys.path.append(os.getcwd()) -from convert_image_to_data_url import check_image_type_and_generate_data_url # noqa: E402 from utils.generate_tool_meta_utils import generate_custom_llm_tools_in_module_as_dict, generate_python_tools_in_module_as_dict # noqa: E402, E501 @@ -55,6 +54,7 @@ icon = "" if args.icon: + from convert_image_to_data_url import check_image_type_and_generate_data_url # noqa: E402 icon = check_image_type_and_generate_data_url(args.icon) if args.tool_type == "custom_llm": From 42be77499b16ec51a603c3a84c60013b64981403 Mon Sep 17 00:00:00 2001 From: Ying Chen Date: Mon, 23 Oct 2023 10:05:08 +0800 Subject: [PATCH 08/11] Support list type flow input with image data (#786) # Description Please add an informative description that covers that changes made by the pull request and link all relevant issues. # All Promptflow Contribution checklist: - [ ] **The pull request does not introduce [breaking changes].** - [ ] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [ ] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [ ] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [ ] Title of the pull request is clear and informative. - [ ] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [ ] Pull request includes test coverage for the included changes. --------- Signed-off-by: Brynn Yin Co-authored-by: Ying Chen <2601502859@qq.com> Co-authored-by: zhen Co-authored-by: Brynn Yin <24237253+brynn-code@users.noreply.github.com> Co-authored-by: Brynn Yin --- src/promptflow/promptflow/_cli/_pf/_flow.py | 86 ++++--- .../_cli/_pf/_init_entry_generators.py | 33 +++ .../promptflow/_sdk/_serving/flow_invoker.py | 30 ++- src/promptflow/promptflow/_sdk/_utils.py | 38 +++ .../_sdk/data/executable/app.spec.jinja2 | 1 + .../_sdk/data/executable/main.py.jinja2 | 224 +++++++++++++++--- .../_sdk/operations/_flow_operations.py | 8 +- .../_sdk/operations/_test_submitter.py | 37 +-- .../promptflow/_utils/multimedia_utils.py | 7 +- src/promptflow/setup.py | 5 +- .../tests/sdk_cli_test/e2etests/test_cli.py | 12 + 11 files changed, 374 insertions(+), 107 deletions(-) diff --git a/src/promptflow/promptflow/_cli/_pf/_flow.py b/src/promptflow/promptflow/_cli/_pf/_flow.py index 8e277360a82..16be05896eb 100644 --- a/src/promptflow/promptflow/_cli/_pf/_flow.py +++ b/src/promptflow/promptflow/_cli/_pf/_flow.py @@ -7,6 +7,8 @@ import json import logging import os +import sys +import tempfile import webbrowser from pathlib import Path @@ -28,6 +30,7 @@ ChatFlowDAGGenerator, FlowDAGGenerator, OpenAIConnectionGenerator, + StreamlitFileGenerator, ToolMetaGenerator, ToolPyGenerator, copy_extra_files, @@ -36,6 +39,7 @@ from promptflow._cli._utils import _copy_to_flow, activate_action, confirm, inject_sys_path, list_of_dict_to_dict from promptflow._sdk._constants import LOGGER_NAME, PROMPT_FLOW_DIR_NAME, ConnectionProvider from promptflow._sdk._pf_client import PFClient +from promptflow._sdk._utils import dump_flow_result DEFAULT_CONNECTION = "open_ai_connection" DEFAULT_DEPLOYMENT = "gpt-35-turbo" @@ -210,6 +214,9 @@ def add_parser_test_flow(subparsers): add_param_interactive = lambda parser: parser.add_argument( # noqa: E731 "--interactive", action="store_true", help="start a interactive chat session for chat flow." ) + add_param_multi_modal = lambda parser: parser.add_argument( # noqa: E731 + "--multi-modal", action="store_true", help=argparse.SUPPRESS + ) add_param_input = lambda parser: parser.add_argument("--input", type=str, help=argparse.SUPPRESS) # noqa: E731 add_params = [ @@ -220,6 +227,7 @@ def add_parser_test_flow(subparsers): add_param_input, add_param_inputs, add_param_environment_variables, + add_param_multi_modal, add_param_config, ] + logging_params activate_action( @@ -373,43 +381,53 @@ def test_flow(args): if args.inputs: inputs.update(list_of_dict_to_dict(args.inputs)) - if args.interactive: - pf_client.flows._chat( - flow=args.flow, - inputs=inputs, - environment_variables=environment_variables, - variant=args.variant, - show_step_output=args.verbose, - config=config, - ) + if args.multi_modal: + with tempfile.TemporaryDirectory() as temp_dir: + from streamlit.web import cli as st_cli + + flow = load_flow(args.flow) + script_path = os.path.join(temp_dir, "main.py") + StreamlitFileGenerator(flow_name=flow.name, flow_dag_path=flow.flow_dag_path).generate_to_file(script_path) + sys.argv = ["streamlit", "run", script_path, "--global.developmentMode=false"] + st_cli.main() else: - result = pf_client.flows._test( - flow=args.flow, - inputs=inputs, - environment_variables=environment_variables, - variant=args.variant, - node=args.node, - allow_generator_output=False, - config=config, - ) - # Dump flow/node test info - flow = load_flow(args.flow) - if args.node: - TestSubmitter._dump_result(flow_folder=flow.code, node_result=result, prefix=f"flow-{args.node}.node") + if args.interactive: + pf_client.flows._chat( + flow=args.flow, + inputs=inputs, + environment_variables=environment_variables, + variant=args.variant, + show_step_output=args.verbose, + config=config, + ) else: - if args.variant: - tuning_node, node_variant = parse_variant(args.variant) - prefix = f"flow-{tuning_node}-{node_variant}" + result = pf_client.flows._test( + flow=args.flow, + inputs=inputs, + environment_variables=environment_variables, + variant=args.variant, + node=args.node, + allow_generator_output=False, + config=config, + ) + # Dump flow/node test info + flow = load_flow(args.flow) + if args.node: + dump_flow_result(flow_folder=flow.code, node_result=result, prefix=f"flow-{args.node}.node") else: - prefix = "flow" - TestSubmitter._dump_result(flow_folder=flow.code, flow_result=result, prefix=prefix) - - TestSubmitter._raise_error_when_test_failed(result, show_trace=args.node is not None) - # Print flow/node test result - if isinstance(result.output, dict): - print(json.dumps(result.output, indent=4)) - else: - print(result.output) + if args.variant: + tuning_node, node_variant = parse_variant(args.variant) + prefix = f"flow-{tuning_node}-{node_variant}" + else: + prefix = "flow" + dump_flow_result(flow_folder=flow.code, flow_result=result, prefix=prefix) + + TestSubmitter._raise_error_when_test_failed(result, show_trace=args.node is not None) + # Print flow/node test result + if isinstance(result.output, dict): + print(json.dumps(result.output, indent=4)) + else: + print(result.output) def serve_flow(args): diff --git a/src/promptflow/promptflow/_cli/_pf/_init_entry_generators.py b/src/promptflow/promptflow/_cli/_pf/_init_entry_generators.py index ea38a1acc10..dd0acdf159b 100644 --- a/src/promptflow/promptflow/_cli/_pf/_init_entry_generators.py +++ b/src/promptflow/promptflow/_cli/_pf/_init_entry_generators.py @@ -18,6 +18,7 @@ CHAT_FLOW_TEMPLATE_PATH = Path(__file__).parent.parent / "data" / "chat_flow" / "template" TOOL_TEMPLATE_PATH = Path(__file__).parent.parent / "data" / "package_tool" EXTRA_FILES_MAPPING = {"requirements.txt": "requirements_txt", ".gitignore": "gitignore"} +SERVE_TEMPLATE_PATH = Path(__file__).resolve().parent.parent.parent / "_sdk" / "data" / "executable" class BaseGenerator(ABC): @@ -218,6 +219,38 @@ def entry_template_keys(self): return ["flow_name"] +class StreamlitFileGenerator(BaseGenerator): + def __init__(self, flow_name, flow_dag_path): + self.flow_name = flow_name + self.flow_dag_path = Path(flow_dag_path) + + @property + def flow_inputs(self): + from promptflow.contracts.flow import Flow as ExecutableFlow + + executable = ExecutableFlow.from_yaml( + flow_file=Path(self.flow_dag_path.name), working_dir=self.flow_dag_path.parent + ) + return {flow_input: (value.default, value.type.value) for flow_input, value in executable.inputs.items()} + + @property + def flow_inputs_params(self): + flow_inputs_params = ["=".join([flow_input, flow_input]) for flow_input, _ in self.flow_inputs.items()] + return ",".join(flow_inputs_params) + + @property + def tpl_file(self): + return SERVE_TEMPLATE_PATH / "main.py.jinja2" + + @property + def flow_path(self): + return self.flow_dag_path.as_posix() + + @property + def entry_template_keys(self): + return ["flow_name", "flow_inputs", "flow_inputs_params", "flow_path"] + + class ChatFlowDAGGenerator(BaseGenerator): def __init__(self, connection, deployment): self.connection = connection diff --git a/src/promptflow/promptflow/_sdk/_serving/flow_invoker.py b/src/promptflow/promptflow/_sdk/_serving/flow_invoker.py index d753996d22d..e1b8c9b7936 100644 --- a/src/promptflow/promptflow/_sdk/_serving/flow_invoker.py +++ b/src/promptflow/promptflow/_sdk/_serving/flow_invoker.py @@ -2,6 +2,7 @@ # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- import logging +from pathlib import Path from typing import Callable, Union from promptflow import PFClient @@ -10,13 +11,17 @@ from promptflow._sdk._serving._errors import UnexpectedConnectionProviderReturn, UnsupportedConnectionProvider from promptflow._sdk._serving.utils import validate_request_data from promptflow._sdk._utils import ( + dump_flow_result, get_local_connections_from_executable, override_connection_config_with_environment_variable, resolve_connections_environment_variable_reference, update_environment_variables_with_connections, ) from promptflow._sdk.entities._connection import _Connection +from promptflow._sdk.operations._flow_operations import FlowOperations +from promptflow._utils.multimedia_utils import convert_multimedia_data_to_base64, persist_multimedia_data from promptflow.executor import FlowExecutor +from promptflow.storage._run_storage import DefaultRunStorage logger = logging.getLogger(LOGGER_NAME) @@ -34,16 +39,26 @@ class FlowInvoker: """ def __init__( - self, flow: str, connection_provider: [str, Callable] = None, streaming: Union[Callable[[], bool], bool] = False + self, + flow: str, + connection_provider: [str, Callable] = None, + streaming: Union[Callable[[], bool], bool] = False, + **kwargs, ): self.flow_dir = flow self.flow_entity = load_flow(self.flow_dir) self.streaming = streaming if isinstance(streaming, Callable) else lambda: streaming + # Pass dump_to path to dump flow result for extension. + self._dump_to = kwargs.get("dump_to", None) + self._init_connections(connection_provider) self._init_executor() self.flow = self.executor._flow + self._dump_file_prefix = "chat" if self._is_chat_flow else "flow" def _init_connections(self, connection_provider): + executable = self.flow_entity._init_executable() + self._is_chat_flow, _, _ = FlowOperations._is_chat_flow(executable) connection_provider = "local" if connection_provider is None else connection_provider if isinstance(connection_provider, str): logger.info(f"Getting connections from pf client with provider {connection_provider}...") @@ -74,11 +89,15 @@ def _init_connections(self, connection_provider): def _init_executor(self): logger.info("Promptflow executor starts initializing...") + storage = None + if self._dump_to: + storage = DefaultRunStorage(base_dir=self._dump_to, sub_dir=Path(".promptflow/intermediate")) self.executor = FlowExecutor.create( flow_file=self.flow_entity.path, working_dir=self.flow_entity.code, connections=self.connections, raise_ex=True, + storage=storage, ) self.executor.enable_streaming_for_llm_flow(self.streaming) logger.info("Promptflow executor initiated successfully.") @@ -98,4 +117,11 @@ def invoke(self, data: dict): validate_request_data(self.flow, data) logger.info(f"Execute flow with data {data!r}") result = self.executor.exec_line(data, allow_generator_output=self.streaming()) - return result.output + # Get base64 for multi modal object + resolved_outputs = {k: convert_multimedia_data_to_base64(v, with_type=True) for k, v in result.output.items()} + if self._dump_to: + result.output = persist_multimedia_data( + result.output, base_dir=self._dump_to, sub_dir=Path(".promptflow/output") + ) + dump_flow_result(flow_folder=self._dump_to, flow_result=result, prefix=self._dump_file_prefix) + return resolved_outputs diff --git a/src/promptflow/promptflow/_sdk/_utils.py b/src/promptflow/promptflow/_sdk/_utils.py index 71aa5fc38ce..60cd2b98fec 100644 --- a/src/promptflow/promptflow/_sdk/_utils.py +++ b/src/promptflow/promptflow/_sdk/_utils.py @@ -60,6 +60,7 @@ ) from promptflow._sdk._vendor import IgnoreFile, get_ignore_file, get_upload_files_from_folder from promptflow._utils.context_utils import _change_working_dir, inject_sys_path +from promptflow._utils.dataclass_serializer import serialize from promptflow.contracts.tool import ToolType @@ -836,3 +837,40 @@ def refresh_connections_dir(connection_spec_files, connection_template_yamls): file_name = connection_name + ".template.yaml" with open(connections_dir / file_name, "w", encoding=DEFAULT_ENCODING) as f: yaml.dump(yaml_data, f) + + +def dump_flow_result(flow_folder, prefix, flow_result=None, node_result=None): + """Dump flow result for extension. + + :param flow_folder: The flow folder. + :param prefix: The file prefix. + :param flow_result: The flow result returned by exec_line. + :param node_result: The node result when test node returned by load_and_exec_node. + """ + if flow_result: + flow_serialize_result = { + "flow_runs": [serialize(flow_result.run_info)], + "node_runs": [serialize(run) for run in flow_result.node_run_infos.values()], + } + else: + flow_serialize_result = { + "flow_runs": [], + "node_runs": [serialize(node_result)], + } + dump_folder = Path(flow_folder) / PROMPT_FLOW_DIR_NAME + dump_folder.mkdir(parents=True, exist_ok=True) + + with open(dump_folder / f"{prefix}.detail.json", "w") as f: + json.dump(flow_serialize_result, f, indent=2) + if node_result: + metrics = flow_serialize_result["node_runs"][0]["metrics"] + output = flow_serialize_result["node_runs"][0]["output"] + else: + metrics = flow_serialize_result["flow_runs"][0]["metrics"] + output = flow_serialize_result["flow_runs"][0]["output"] + if metrics: + with open(dump_folder / f"{prefix}.metrics.json", "w") as f: + json.dump(metrics, f, indent=2) + if output: + with open(dump_folder / f"{prefix}.output.json", "w") as f: + json.dump(output, f, indent=2) diff --git a/src/promptflow/promptflow/_sdk/data/executable/app.spec.jinja2 b/src/promptflow/promptflow/_sdk/data/executable/app.spec.jinja2 index 947cbdaf38f..ddb7de3a941 100644 --- a/src/promptflow/promptflow/_sdk/data/executable/app.spec.jinja2 +++ b/src/promptflow/promptflow/_sdk/data/executable/app.spec.jinja2 @@ -7,6 +7,7 @@ datas += collect_data_files('streamlit') datas += copy_metadata('streamlit') datas += collect_data_files('keyrings.alt', include_py_files=True) datas += copy_metadata('keyrings.alt') +datas += collect_data_files('streamlit_quill') block_cipher = None diff --git a/src/promptflow/promptflow/_sdk/data/executable/main.py.jinja2 b/src/promptflow/promptflow/_sdk/data/executable/main.py.jinja2 index 70ce2f25e17..ad006581f10 100644 --- a/src/promptflow/promptflow/_sdk/data/executable/main.py.jinja2 +++ b/src/promptflow/promptflow/_sdk/data/executable/main.py.jinja2 @@ -1,74 +1,242 @@ +import base64 import json import os +import re import streamlit as st from pathlib import Path +from streamlit_quill import st_quill +from bs4 import BeautifulSoup, NavigableString, Tag from promptflow._sdk._utils import print_yellow_warning from promptflow._sdk._serving.flow_invoker import FlowInvoker - +from promptflow._utils.multimedia_utils import is_multimedia_dict, MIME_PATTERN invoker = None - +{% set indent_level = 4 %} def start(): def clear_chat() -> None: st.session_state.messages = [] + def show_image(image, key=None): + if not image.startswith("data:image"): + st.image(key + ',' + image) + else: + st.image(image) + + def json_dumps(value): + try: + return json.dumps(value) + except Exception: + return value + + + def is_list_contains_rich_text(rich_text): + result = False + for item in rich_text: + if isinstance(item, list): + result |= is_list_contains_rich_text(item) + elif isinstance(item, dict): + result |= is_dict_contains_rich_text(item) + else: + if isinstance(item, str) and item.startswith("data:image"): + result = True + return result + + + def is_dict_contains_rich_text(rich_text): + result = False + for rich_text_key, rich_text_value in rich_text.items(): + if isinstance(rich_text_value, list): + result |= is_list_contains_rich_text(rich_text_value) + elif isinstance(rich_text_value, dict): + result |= is_dict_contains_rich_text(rich_text_value) + elif re.match(MIME_PATTERN, rich_text_key) or (isinstance(rich_text_value, str) and rich_text_value.startswith("data:image")): + result = True + return result + + + def render_message(role, message_items): + {% raw %} + def item_render_message(value, key=None): + if key and re.match(MIME_PATTERN, key): + show_image(value, key) + elif isinstance(value, str) and value.startswith("data:image"): + show_image(value) + else: + if key is None: + st.markdown(f"`{json_dumps(value)},`") + else: + st.markdown(f"`{key}: {json_dumps(value)},`") + + def list_iter_render_message(message_items): + if is_list_contains_rich_text(message_items): + st.markdown("`[ `") + for item in message_items: + if isinstance(item, list): + list_iter_render_message(item) + if isinstance(item, dict): + dict_iter_render_message(item) + else: + item_render_message(item) + st.markdown("`], `") + else: + st.markdown(f"`{json_dumps(message_items)},`") + + def dict_iter_render_message(message_items): + if is_multimedia_dict(message_items): + key = list(message_items.keys())[0] + value = message_items[key] + show_image(value, key) + elif is_dict_contains_rich_text(message_items): + st.markdown("`{ `") + for key, value in message_items.items(): + if re.match(MIME_PATTERN, key): + show_image(value, key) + else: + if isinstance(value, list): + st.markdown(f"`{key}: `") + list_iter_render_message(value) + elif isinstance(value, dict): + st.markdown(f"`{key}: `") + dict_iter_render_message(value) + else: + item_render_message(value, key) + st.markdown("`}, `") + else: + st.markdown(f"`{json_dumps(message_items)},`") + + with st.chat_message(role): + dict_iter_render_message(message_items) + {% endraw %} + def show_conversation() -> None: if "messages" not in st.session_state: st.session_state.messages = [] + st.session_state.history = [] if st.session_state.messages: - for role, message in st.session_state.messages: - st.chat_message(role).write(message) + for role, message_items in st.session_state.messages: + render_message(role, message_items) + + + def get_chat_history_from_session(): + if "history" in st.session_state: + return st.session_state.history + return [] def submit(**kwargs) -> None: - container.chat_message("user").write(json.dumps(kwargs)) - st.session_state.messages.append(("user", json.dumps(kwargs))) - response = run_flow(kwargs) - container.chat_message("assistant").write(response) + st.session_state.messages.append(("user", kwargs)) + st.session_state.history.append({"input": kwargs}) + with container: + render_message("user", kwargs) + # Force append chat history to kwargs + response = run_flow({"chat_history": get_chat_history_from_session(), **kwargs}) st.session_state.messages.append(("assistant", response)) + st.session_state.history.append({"output": kwargs}) + with container: + render_message("assistant", response) def run_flow(data: dict) -> dict: global invoker if not invoker: - flow = Path(__file__).parent / "flow" - os.chdir(flow) - invoker = FlowInvoker(flow, connection_provider="local") +{% if flow_path %} +{{ ' ' * indent_level * 3 }}flow = Path('{{flow_path}}') +{{ ' ' * indent_level * 3 }}dump_path = Path('{{flow_path}}').parent +{% else %} +{{ ' ' * indent_level * 3 }}flow = Path(__file__).parent / "flow" +{{ ' ' * indent_level * 3 }}dump_path = None +{% endif %} + if flow.is_dir(): + os.chdir(flow) + else: + os.chdir(flow.parent) + invoker = FlowInvoker(flow, connection_provider="local", dump_to=dump_path) result = invoker.invoke(data) print_yellow_warning(f"Result: {result}") return result + def extract_content(node): + if isinstance(node, NavigableString): + text = node.strip() + if text: + return [text] + elif isinstance(node, Tag): + if node.name == 'img': + prefix, base64_str = node['src'].split(',', 1) + return [{prefix: base64_str}] + else: + result = [] + for child in node.contents: + result.extend(extract_content(child)) + return result + return [] + + + def parse_html_content(html_content): + soup = BeautifulSoup(html_content, 'html.parser') + result = [] + for p in soup.find_all('p'): + result.extend(extract_content(p)) + return result + + + def parse_image_content(image_content, image_type): + if image_content is not None: + file_contents = image_content.read() + image_content = base64.b64encode(file_contents).decode('utf-8') + prefix = f"data:{image_type};base64" + return {prefix: image_content} + + st.title("{{flow_name}} APP") - st.chat_message("assistant").write("Hello, please input following flow inputs and connection keys.") + st.chat_message("assistant").write("Hello, please input following flow inputs.") container = st.container() with container: show_conversation() with st.form(key='input_form', clear_on_submit=True): - with open(os.path.join(os.path.dirname(__file__), "settings.json"), "r") as file: - json_data = json.load(file) - environment_variables = list(json_data.keys()) - for environment_variable in environment_variables: - secret_input = st.text_input(label=environment_variable, type="password", placeholder=f"Please input {environment_variable} here. If you input before, you can leave it blank.") - if secret_input != "": - os.environ[environment_variable] = secret_input - - {% for flow_input, default_value in flow_inputs.items() %} - {{flow_input}} = st.text_input(label='{{flow_input}}', placeholder='{{default_value}}') - {% endfor %} + settings_path = os.path.join(os.path.dirname(__file__), "settings.json") + if os.path.exists(settings_path): + with open(settings_path, "r") as file: + json_data = json.load(file) + environment_variables = list(json_data.keys()) + for environment_variable in environment_variables: + secret_input = st.text_input(label=environment_variable, type="password", placeholder=f"Please input {environment_variable} here. If you input before, you can leave it blank.") + if secret_input != "": + os.environ[environment_variable] = secret_input + +{% for flow_input, (default_value, value_type) in flow_inputs.items() %} +{% if flow_input != "chat_history" %} +{% if value_type == "list" %} +{{ ' ' * indent_level * 2 }}st.text('{{flow_input}}') +{{ ' ' * indent_level * 2 }}{{flow_input}} = st_quill(html=True, toolbar=["image"], key='{{flow_input}}') +{% elif value_type == "image" %} +{{ ' ' * indent_level * 2 }}{{flow_input}} = st.file_uploader(label='{{flow_input}}') +{% else %} +{{ ' ' * indent_level * 2 }}{{flow_input}} = st.text_input(label='{{flow_input}}', placeholder='{{default_value}}') +{% endif %} +{% endif %} +{% endfor %} cols = st.columns(7) submit_bt = cols[0].form_submit_button(label='Submit') clear_bt = cols[1].form_submit_button(label='Clear') - if submit_bt: - submit({{flow_inputs_params}}) + if submit_bt: +{% for flow_input, (default_value, value_type) in flow_inputs.items() %} +{% if value_type == "list" %} +{{ ' ' * indent_level * 3 }}{{flow_input}} = parse_html_content({{flow_input}}) +{% elif value_type == "image" %} +{{ ' ' * indent_level * 3 }}{{flow_input}} = parse_image_content({{flow_input}}, {{flow_input}}.type if {{flow_input}} else None) +{% endif %} +{% endfor %} - if clear_bt: - clear_chat() + submit({{flow_inputs_params}}) + if clear_bt: + clear_chat() if __name__ == "__main__": - start() \ No newline at end of file + start() diff --git a/src/promptflow/promptflow/_sdk/operations/_flow_operations.py b/src/promptflow/promptflow/_sdk/operations/_flow_operations.py index 2083c164a25..570d389c6c2 100644 --- a/src/promptflow/promptflow/_sdk/operations/_flow_operations.py +++ b/src/promptflow/promptflow/_sdk/operations/_flow_operations.py @@ -374,8 +374,11 @@ def _build_as_executable( try: import PyInstaller # noqa: F401 import streamlit + import streamlit_quill # noqa: F401 except ImportError as ex: - raise UserErrorException(f"Please install PyInstaller and streamlit for building executable, {ex.msg}.") + raise UserErrorException( + f"Please install PyInstaller, streamlit and streamlit_quill for building " f"executable, {ex.msg}." + ) from promptflow.contracts.flow import Flow as ExecutableFlow @@ -397,7 +400,7 @@ def _build_as_executable( runtime_interpreter_path = (Path(streamlit.__file__).parent / "runtime").as_posix() executable = ExecutableFlow.from_yaml(flow_file=Path(flow_dag_path.name), working_dir=flow_dag_path.parent) - flow_inputs = {flow_input: value.default for flow_input, value in executable.inputs.items()} + flow_inputs = {flow_input: (value.default, value.type.value) for flow_input, value in executable.inputs.items()} flow_inputs_params = ["=".join([flow_input, flow_input]) for flow_input, _ in flow_inputs.items()] flow_inputs_params = ",".join(flow_inputs_params) @@ -410,6 +413,7 @@ def _build_as_executable( "runtime_interpreter_path": runtime_interpreter_path, "flow_inputs": flow_inputs, "flow_inputs_params": flow_inputs_params, + "flow_path": None, }, ) try: diff --git a/src/promptflow/promptflow/_sdk/operations/_test_submitter.py b/src/promptflow/promptflow/_sdk/operations/_test_submitter.py index 7396a95b3b9..1042b8bde34 100644 --- a/src/promptflow/promptflow/_sdk/operations/_test_submitter.py +++ b/src/promptflow/promptflow/_sdk/operations/_test_submitter.py @@ -3,7 +3,6 @@ # --------------------------------------------------------- # this file is a middle layer between the local SDK and executor. import contextlib -import json import logging import re import time @@ -12,12 +11,11 @@ from typing import Any, Mapping from promptflow._sdk._constants import LOGGER_NAME, PROMPT_FLOW_DIR_NAME -from promptflow._sdk._utils import parse_variant +from promptflow._sdk._utils import dump_flow_result, parse_variant from promptflow._sdk.entities._flow import Flow from promptflow._sdk.operations._local_storage_operations import LoggerOperations from promptflow._sdk.operations._run_submitter import SubmitterHelper, variant_overwrite_context from promptflow._utils.context_utils import _change_working_dir -from promptflow._utils.dataclass_serializer import serialize from promptflow._utils.exception_utils import ErrorResponse from promptflow._utils.multimedia_utils import persist_multimedia_data from promptflow.contracts.flow import Flow as ExecutableFlow @@ -333,38 +331,7 @@ def resolve_generator(flow_result): flow_outputs = {k: v for k, v in flow_result.output.items()} history = {"inputs": {input_name: input_value}, "outputs": flow_outputs} chat_history.append(history) - self._dump_result(flow_folder=self._origin_flow.code, flow_result=flow_result, prefix="chat") - - @staticmethod - def _dump_result(flow_folder, prefix, flow_result=None, node_result=None): - - if flow_result: - flow_serialize_result = { - "flow_runs": [serialize(flow_result.run_info)], - "node_runs": [serialize(run) for run in flow_result.node_run_infos.values()], - } - else: - flow_serialize_result = { - "flow_runs": [], - "node_runs": [serialize(node_result)], - } - dump_folder = Path(flow_folder) / PROMPT_FLOW_DIR_NAME - dump_folder.mkdir(parents=True, exist_ok=True) - - with open(dump_folder / f"{prefix}.detail.json", "w") as f: - json.dump(flow_serialize_result, f, indent=2) - if node_result: - metrics = flow_serialize_result["node_runs"][0]["metrics"] - output = flow_serialize_result["node_runs"][0]["output"] - else: - metrics = flow_serialize_result["flow_runs"][0]["metrics"] - output = flow_serialize_result["flow_runs"][0]["output"] - if metrics: - with open(dump_folder / f"{prefix}.metrics.json", "w") as f: - json.dump(metrics, f, indent=2) - if output: - with open(dump_folder / f"{prefix}.output.json", "w") as f: - json.dump(output, f, indent=2) + dump_flow_result(flow_folder=self._origin_flow.code, flow_result=flow_result, prefix="chat") @staticmethod def _raise_error_when_test_failed(test_result, show_trace=False): diff --git a/src/promptflow/promptflow/_utils/multimedia_utils.py b/src/promptflow/promptflow/_utils/multimedia_utils.py index 74644ef2f4e..96821298630 100644 --- a/src/promptflow/promptflow/_utils/multimedia_utils.py +++ b/src/promptflow/promptflow/_utils/multimedia_utils.py @@ -175,8 +175,11 @@ def persist_multimedia_data(value: Any, base_dir: Path, sub_dir: Path = None): return recursive_process(value, process_funcs=serialization_funcs) -def convert_multimedia_data_to_base64(value: Any): - to_base64_funcs = {PFBytes: PFBytes.to_base64} +def convert_multimedia_data_to_base64(value: Any, with_type=False): + func = ( + lambda x: f"data:{x._mime_type};base64," + PFBytes.to_base64(x) if with_type else PFBytes.to_base64 + ) # noqa: E731 + to_base64_funcs = {PFBytes: func} return recursive_process(value, process_funcs=to_base64_funcs) diff --git a/src/promptflow/setup.py b/src/promptflow/setup.py index 349856e47aa..cb429033a4c 100644 --- a/src/promptflow/setup.py +++ b/src/promptflow/setup.py @@ -80,10 +80,7 @@ "azure-ai-ml>=1.11.0,<2.0.0", "pyjwt>=2.4.0,<3.0.0", # requirement of control plane SDK ], - "executable": [ - "pyinstaller", - "streamlit", - ], + "executable": ["pyinstaller", "streamlit>=1.26.0", "streamlit-quill<0.1.0"], }, packages=find_packages(), entry_points={ diff --git a/src/promptflow/tests/sdk_cli_test/e2etests/test_cli.py b/src/promptflow/tests/sdk_cli_test/e2etests/test_cli.py index deff57f410b..32a9565773a 100644 --- a/src/promptflow/tests/sdk_cli_test/e2etests/test_cli.py +++ b/src/promptflow/tests/sdk_cli_test/e2etests/test_cli.py @@ -1395,3 +1395,15 @@ def mock_input(*args, **kwargs): assert output_path.exists() detail_path = Path(FLOWS_DIR) / "conditional_chat_flow_with_skip" / ".promptflow" / "chat.detail.json" assert detail_path.exists() + + def test_flow_test_with_image_input_and_output(self): + run_pf_command( + "flow", + "test", + "--flow", + f"{FLOWS_DIR}/python_tool_with_image_input_and_output", + ) + output_path = Path(FLOWS_DIR) / "python_tool_with_image_input_and_output" / ".promptflow" / "output" + assert output_path.exists() + image_path = Path(FLOWS_DIR) / "python_tool_with_image_input_and_output" / ".promptflow" / "intermediate" + assert image_path.exists() From 0829d7cd906954263465f3d26ac44d0ddd8a1726 Mon Sep 17 00:00:00 2001 From: "Gerard@msft" <99283778+gjwoods@users.noreply.github.com> Date: Sun, 22 Oct 2023 19:53:07 -0700 Subject: [PATCH 09/11] [Open_Source_LLM] Add Icon + MIR Telemetry Attribution + Fix Tests (#825) # Description Add icon of open source llm, and attribute MIR telemetry, and fix unit tests. # All Promptflow Contribution checklist: - [x] **The pull request does not introduce [breaking changes].** - [x] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [x] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [x] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [x] Title of the pull request is clear and informative. - [x] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [x] Pull request includes test coverage for the included changes. --------- Co-authored-by: Gerard --- .../promptflow/tools/open_source_llm.py | 10 +++- .../tools/yamls/open_source_llm.yaml | 1 + src/promptflow-tools/tests/conftest.py | 30 +++++++++++- .../tests/test_open_source_llm.py | 49 ++++++------------- 4 files changed, 53 insertions(+), 37 deletions(-) diff --git a/src/promptflow-tools/promptflow/tools/open_source_llm.py b/src/promptflow-tools/promptflow/tools/open_source_llm.py index c05b7bf202c..a946f0de706 100644 --- a/src/promptflow-tools/promptflow/tools/open_source_llm.py +++ b/src/promptflow-tools/promptflow/tools/open_source_llm.py @@ -22,7 +22,7 @@ ChatAPIInvalidRole ) -VALID_LLAMA_ROLES = {"user", "assistant"} +VALID_LLAMA_ROLES = {"system", "user", "assistant"} REQUIRED_CONFIG_KEYS = ["endpoint_url", "model_family"] REQUIRED_SECRET_KEYS = ["endpoint_api_key"] DEFAULT_ENDPOINT_NAME = "-- please enter an endpoint name --" @@ -385,6 +385,7 @@ def __init__( endpoint_url: str, endpoint_api_key: str, content_formatter: ContentFormatterBase, + model_family: ModelFamily, deployment_name: Optional[str] = None, model_kwargs: Optional[Dict] = None, ): @@ -393,6 +394,7 @@ def __init__( self.deployment_name = deployment_name self.content_formatter = content_formatter self.model_kwargs = model_kwargs + self.model_family = model_family @property def _identifying_params(self) -> Mapping[str, Any]: @@ -410,7 +412,10 @@ def _llm_type(self) -> str: def _call_endpoint(self, body: bytes) -> bytes: """call.""" - headers = {"Content-Type": "application/json", "Authorization": ("Bearer " + self.endpoint_api_key)} + headers = { + "Content-Type": "application/json", + "Authorization": ("Bearer " + self.endpoint_api_key), + "x-ms-user-agent": "PromptFlow/OpenSourceLLM/" + self.model_family} # If this is not set it'll use the default deployment on the endpoint. if self.deployment_name is not None: @@ -489,6 +494,7 @@ def call( llm = AzureMLOnlineEndpoint( endpoint_url=self.endpoint_uri, endpoint_api_key=self.endpoint_key, + model_family=self.model_family, content_formatter=content_formatter, deployment_name=self.deployment_name, model_kwargs=model_kwargs diff --git a/src/promptflow-tools/promptflow/tools/yamls/open_source_llm.yaml b/src/promptflow-tools/promptflow/tools/yamls/open_source_llm.yaml index d5221244ae7..120aee91ca8 100644 --- a/src/promptflow-tools/promptflow/tools/yamls/open_source_llm.yaml +++ b/src/promptflow-tools/promptflow/tools/yamls/open_source_llm.yaml @@ -1,6 +1,7 @@ promptflow.tools.open_source_llm.OpenSourceLLM.call: name: Open Source LLM description: Use an Open Source model from the Azure Model catalog, deployed to an AzureML Online Endpoint for LLM Chat or Completion API calls. + icon: data:image/png;base64,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 type: custom_llm module: promptflow.tools.open_source_llm class_name: OpenSourceLLM diff --git a/src/promptflow-tools/tests/conftest.py b/src/promptflow-tools/tests/conftest.py index 0ec4f53865d..5ae09f25c73 100644 --- a/src/promptflow-tools/tests/conftest.py +++ b/src/promptflow-tools/tests/conftest.py @@ -1,9 +1,9 @@ import json import os +import pytest import sys -from pathlib import Path -import pytest +from pathlib import Path from pytest_mock import MockerFixture # noqa: E402 # Avoid circular dependencies: Use import 'from promptflow._internal' instead of 'from promptflow' # since the code here is in promptflow namespace as well @@ -45,6 +45,28 @@ def serp_connection(): return ConnectionManager().get("serp_connection") +def verify_oss_llm_custom_connection(connection: CustomConnection) -> bool: + '''Verify that there is a MIR endpoint up and available for the Custom Connection. + We explicitly do not pass the endpoint key to avoid the delay in generating a response. + ''' + + import urllib.request + from urllib.request import HTTPError + from urllib.error import URLError + + try: + urllib.request.urlopen( + urllib.request.Request(connection.configs['endpoint_url']), + timeout=50) + except HTTPError as e: + # verify that the connection is not authorized, anything else would mean the endpoint is failed + return e.code == 403 + except URLError: + # Endpoint does not exist - skip the test + return False + raise Exception("Task Succeeded unexpectedly.") + + @pytest.fixture def gpt2_custom_connection(): return ConnectionManager().get("gpt2_connection") @@ -68,6 +90,10 @@ def skip_if_no_key(request, mocker): elif isinstance(connection, CustomConnection): if "endpoint_api_key" not in connection.secrets or "-api-key" in connection.secrets["endpoint_api_key"]: pytest.skip('skipped because no key') + # Verify Custom Connections, but only those used by the Open_Source_LLM Tool + if "endpoint_url" in connection.configs and "-endpoint-url" not in connection.configs["endpoint_url"]: + if not verify_oss_llm_custom_connection(connection): + pytest.skip('skipped because the connection is not valid') # example prompts diff --git a/src/promptflow-tools/tests/test_open_source_llm.py b/src/promptflow-tools/tests/test_open_source_llm.py index 924329df4ab..2d7ef1fa4ea 100644 --- a/src/promptflow-tools/tests/test_open_source_llm.py +++ b/src/promptflow-tools/tests/test_open_source_llm.py @@ -22,13 +22,13 @@ def llama_chat_provider(llama_chat_custom_connection) -> OpenSourceLLM: class TestOpenSourceLLM: completion_prompt = "In the context of Azure ML, what does the ML stand for?" - gpt2_chat_prompt = """user: + gpt2_chat_prompt = """system: You are a AI which helps Customers answer questions. user: """ + completion_prompt - llama_chat_prompt = """user: + llama_chat_prompt = """system: You are a AI which helps Customers answer questions. """ + completion_prompt @@ -44,7 +44,7 @@ def test_open_source_llm_completion(self, gpt2_provider): def test_open_source_llm_completion_with_deploy(self, gpt2_custom_connection): os_tool = OpenSourceLLM( gpt2_custom_connection, - deployment_name="gpt2-8") + deployment_name="gpt2-9") response = os_tool.call( self.completion_prompt, API.COMPLETION) @@ -61,7 +61,7 @@ def test_open_source_llm_chat(self, gpt2_provider): def test_open_source_llm_chat_with_deploy(self, gpt2_custom_connection): os_tool = OpenSourceLLM( gpt2_custom_connection, - deployment_name="gpt2-8") + deployment_name="gpt2-9") response = os_tool.call( self.gpt2_chat_prompt, API.CHAT) @@ -130,23 +130,6 @@ def test_open_source_llm_llama_parse_multi_turn(self): """ LlamaContentFormatter.parse_chat(multi_turn_chat) - def test_open_source_llm_llama_parse_system_not_accepted(self): - bad_chat_prompt = """system: -You are a AI which helps Customers answer questions. - -user: -""" + self.completion_prompt - with pytest.raises(OpenSourceLLMUserError) as exc_info: - LlamaContentFormatter.parse_chat(bad_chat_prompt) - assert exc_info.value.message == ( - "The Chat API requires a specific format for prompt definition," - + " and the prompt should include separate lines as role delimiters: 'assistant:\\n','user:\\n'." - + " Current parsed role 'system' does not meet the requirement. If you intend to use the Completion " - + "API, please select the appropriate API type and deployment name. If you do intend to use the Chat " - + "API, please refer to the guideline at https://aka.ms/pfdoc/chat-prompt or view the samples in our " - + "gallery that contain 'Chat' in the name.") - assert exc_info.value.error_codes == "UserError/OpenSourceLLMUserError".split("/") - def test_open_source_llm_llama_parse_ignore_whitespace(self): bad_chat_prompt = f"""system: You are a AI which helps Customers answer questions. @@ -158,24 +141,24 @@ def test_open_source_llm_llama_parse_ignore_whitespace(self): with pytest.raises(OpenSourceLLMUserError) as exc_info: LlamaContentFormatter.parse_chat(bad_chat_prompt) assert exc_info.value.message == ( - "The Chat API requires a specific format for prompt definition, and " - + "the prompt should include separate lines as role delimiters: 'assistant:\\n','user:\\n'. Current parsed " - + "role 'system' does not meet the requirement. If you intend to use the Completion API, please select the " - + "appropriate API type and deployment name. If you do intend to use the Chat API, please refer to the " - + "guideline at https://aka.ms/pfdoc/chat-prompt or view the samples in our gallery that contain 'Chat' " - + "in the name.") + "The Chat API requires a specific format for prompt definition, and the prompt should include separate " + + "lines as role delimiters: 'assistant:\\n','system:\\n','user:\\n'. Current parsed role 'in the context " + + "of azure ml, what does the ml stand for?' does not meet the requirement. If you intend to use the " + + "Completion API, please select the appropriate API type and deployment name. If you do intend to use " + + "the Chat API, please refer to the guideline at https://aka.ms/pfdoc/chat-prompt or view the samples in " + + "our gallery that contain 'Chat' in the name.") assert exc_info.value.error_codes == "UserError/OpenSourceLLMUserError".split("/") def test_open_source_llm_llama_parse_chat_with_comp(self): with pytest.raises(OpenSourceLLMUserError) as exc_info: LlamaContentFormatter.parse_chat(self.completion_prompt) assert exc_info.value.message == ( - "The Chat API requires a specific format for prompt definition, and " - + "the prompt should include separate lines as role delimiters: 'assistant:\\n','user:\\n'. Current parsed " - + "role 'in the context of azure ml, what does the ml stand for?' does not meet the requirement. If you " - + "intend to use the Completion API, please select the appropriate API type and deployment name. If you do " - + "intend to use the Chat API, please refer to the guideline at https://aka.ms/pfdoc/chat-prompt or view " - + "the samples in our gallery that contain 'Chat' in the name.") + "The Chat API requires a specific format for prompt definition, and the prompt should include separate " + + "lines as role delimiters: 'assistant:\\n','system:\\n','user:\\n'. Current parsed role 'in the context " + + "of azure ml, what does the ml stand for?' does not meet the requirement. If you intend to use the " + + "Completion API, please select the appropriate API type and deployment name. If you do intend to use the " + + "Chat API, please refer to the guideline at https://aka.ms/pfdoc/chat-prompt or view the samples in our " + + "gallery that contain 'Chat' in the name.") assert exc_info.value.error_codes == "UserError/OpenSourceLLMUserError".split("/") @pytest.mark.skip_if_no_key("gpt2_custom_connection") From 48fa534f369550fe4b3d8fddd685d87880ae6e63 Mon Sep 17 00:00:00 2001 From: Han Wang Date: Mon, 23 Oct 2023 11:23:52 +0800 Subject: [PATCH 10/11] [Sample]Improve chat with pdf ci time by 2x (#840) # Description Please add an informative description that covers that changes made by the pull request and link all relevant issues. Cached predownloaded PDFs and prebuilt FAISS Index to avoid duplicated calculation. Used same config to avoid index building under different config. ![image](https://github.com/microsoft/promptflow/assets/7776147/eada118b-a6c8-4f8b-8a54-70b19ce9d8fe) # All Promptflow Contribution checklist: - [ ] **The pull request does not introduce [breaking changes].** - [ ] **CHANGELOG is updated for new features, bug fixes or other significant changes.** - [ ] **I have read the [contribution guidelines](../CONTRIBUTING.md).** - [ ] **Create an issue and link to the pull request to get dedicated review from promptflow team. Learn more: [suggested workflow](../CONTRIBUTING.md#suggested-workflow).** ## General Guidelines and Best Practices - [ ] Title of the pull request is clear and informative. - [ ] There are a small number of commits, each of which have an informative message. This means that previously merged commits do not appear in the history of the PR. For more information on cleaning up the commits in your PR, [see this page](https://github.com/Azure/azure-powershell/blob/master/documentation/development-docs/cleaning-up-commits.md). ### Testing Guidelines - [ ] Pull request includes test coverage for the included changes. --- .gitignore | 4 ++++ examples/flows/chat/chat-with-pdf/README.md | 3 +++ .../chat/chat-with-pdf/chat-with-pdf.ipynb | 11 +++++++---- .../index.faiss | Bin 0 -> 411693 bytes .../index.pkl | Bin 0 -> 69697 bytes .../index.faiss | Bin 0 -> 147501 bytes .../index.pkl | Bin 0 -> 53160 bytes ...n_docs_2018-03_20180301083100898652.pdf.pdf | Bin 0 -> 953185 bytes .../chat-with-pdf/chat_with_pdf/build_index.py | 7 ++++++- .../chat-with-pdf/chat_with_pdf/constants.py | 5 +++++ .../chat-with-pdf/chat_with_pdf/download.py | 3 ++- .../chat/chat-with-pdf/chat_with_pdf/main.py | 9 +++++---- .../flows/chat/chat-with-pdf/flow.dag.yaml | 2 +- examples/flows/chat/chat-with-pdf/setup_env.py | 12 +++++++----- .../chat/chat-with-pdf/tests/base_test.py | 4 ++-- 15 files changed, 42 insertions(+), 18 deletions(-) create mode 100644 examples/flows/chat/chat-with-pdf/chat_with_pdf/.index/.pdfs/https___arxiv.org_pdf_1810.04805.pdf.pdf.index_1024_64/index.faiss create mode 100644 examples/flows/chat/chat-with-pdf/chat_with_pdf/.index/.pdfs/https___arxiv.org_pdf_1810.04805.pdf.pdf.index_1024_64/index.pkl create mode 100644 examples/flows/chat/chat-with-pdf/chat_with_pdf/.index/.pdfs/https___grs.pku.edu.cn_docs_2018-03_20180301083100898652.pdf.pdf.index_1024_64/index.faiss create mode 100644 examples/flows/chat/chat-with-pdf/chat_with_pdf/.index/.pdfs/https___grs.pku.edu.cn_docs_2018-03_20180301083100898652.pdf.pdf.index_1024_64/index.pkl create mode 100644 examples/flows/chat/chat-with-pdf/chat_with_pdf/.pdfs/https___grs.pku.edu.cn_docs_2018-03_20180301083100898652.pdf.pdf create mode 100644 examples/flows/chat/chat-with-pdf/chat_with_pdf/constants.py diff --git a/.gitignore b/.gitignore index 5788b4be7e5..6236b9c9db3 100644 --- a/.gitignore +++ b/.gitignore @@ -182,3 +182,7 @@ scripts/docs/_build/ .pdfs config.json src/promptflow/promptflow/azure/_restclient/swagger.json + +# chat-with-pdf's prebuilt index +!.pdfs/ +!.index/ diff --git a/examples/flows/chat/chat-with-pdf/README.md b/examples/flows/chat/chat-with-pdf/README.md index ba1759e7219..2a36d62745d 100644 --- a/examples/flows/chat/chat-with-pdf/README.md +++ b/examples/flows/chat/chat-with-pdf/README.md @@ -33,6 +33,9 @@ fi #### Run flow +**Note**: this sample uses [predownloaded PDFs](./chat_with_pdf/.pdfs/) and [prebuilt FAISS Index](./chat_with_pdf/.index/) to speed up execution time. +You can remove the folders to start a fresh run. + ```bash # test with default input value in flow.dag.yaml pf flow test --flow . diff --git a/examples/flows/chat/chat-with-pdf/chat-with-pdf.ipynb b/examples/flows/chat/chat-with-pdf/chat-with-pdf.ipynb index 1f9692e57f4..6b9d57e6075 100644 --- a/examples/flows/chat/chat-with-pdf/chat-with-pdf.ipynb +++ b/examples/flows/chat/chat-with-pdf/chat-with-pdf.ipynb @@ -91,7 +91,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Test the flow" + "## 2. Test the flow\n", + "\n", + "**Note**: this sample uses [predownloaded PDFs](./chat_with_pdf/.pdfs/) and [prebuilt FAISS Index](./chat_with_pdf/.index/) to speed up execution time.\n", + "You can remove the folders to start a fresh run." ] }, { @@ -135,7 +138,7 @@ " \"MAX_COMPLETION_TOKENS\": 256,\n", " \"VERBOSE\": True,\n", " \"CHUNK_SIZE\": 256,\n", - " \"CHUNK_OVERLAP\": 32,\n", + " \"CHUNK_OVERLAP\": 64,\n", "}\n", "\n", "column_mapping = {\n", @@ -246,7 +249,7 @@ " \"MAX_COMPLETION_TOKENS\": 256,\n", " \"VERBOSE\": True,\n", " \"CHUNK_SIZE\": 256,\n", - " \"CHUNK_OVERLAP\": 32,\n", + " \"CHUNK_OVERLAP\": 64,\n", "}\n", "\n", "run_3k_context = pf.run(flow=flow_path, data=data_path, column_mapping=column_mapping)\n", @@ -312,7 +315,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.17" + "version": "3.9.12" } }, "nbformat": 4, diff --git 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