diff --git a/examples_notebooks/community_contrib/couchbase/.env.sample b/examples_notebooks/community_contrib/couchbase/.env.sample new file mode 100644 index 0000000000..4bafd451af --- /dev/null +++ b/examples_notebooks/community_contrib/couchbase/.env.sample @@ -0,0 +1,10 @@ +INPUT_DIR="" +COUCHBASE_CONNECTION_STRING="" +COUCHBASE_USERNAME="" +COUCHBASE_PASSWORD="" +COUCHBASE_BUCKET_NAME="" +COUCHBASE_SCOPE_NAME="" +COUCHBASE_VECTOR_INDEX_NAME="" +OPENAI_API_KEY="" +LLM_MODEL="" +EMBEDDING_MODEL="" \ No newline at end of file diff --git a/examples_notebooks/community_contrib/couchbase/GraphRAG_with_Couchbase.ipynb b/examples_notebooks/community_contrib/couchbase/GraphRAG_with_Couchbase.ipynb new file mode 100644 index 0000000000..6f5214fbd4 --- /dev/null +++ b/examples_notebooks/community_contrib/couchbase/GraphRAG_with_Couchbase.ipynb @@ -0,0 +1,634 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "fbw833Y2gckr" + }, + "source": [ + "# Tutorial on Graph RAG(Local Search) with Couchbase Vector Store\n", + "This notebook walks through the process of setting up a search engine that combines Couchbase for storing embeddings, OpenAI's models for generating embeddings, knowledge graph and communities from textual data.\n", + "\n", + "## Setting up Couchbase\n", + "\n", + "Before running this notebook, set up the following in Couchbase:\n", + "\n", + "1. Create a bucket named \"graphrag-demo\" (or as specified in COUCHBASE_BUCKET_NAME)\n", + "2. Within the bucket, create a scope named \"shared\" (or as specified in COUCHBASE_SCOPE_NAME)\n", + "3. Within the scope, create a collection named \"entity_description_embeddings\" (or as specified in COUCHBASE_COLLECTION_NAME)\n", + "\n", + "These settings should match the environment variables defined in your .env file or the default values in the code.\n", + "\n", + "4. In the Couchbase Full Text Search (FTS) index section, create a new index by importing the `graphrag_demo_index.json` file. This file contains the necessary configuration for the vector search index.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Local and Global Search in Graph RAG Systems\n", + "\n", + "Local and global search are two approaches used in Graph RAG (Retrieval-Augmented Generation) systems:\n", + "\n", + "### Local Search\n", + "Local search method generates answers by combining relevant data from the AI-extracted knowledge-graph with text chunks of the raw documents. This method is suitable for questions that require an understanding of specific entities mentioned in the documents.\n", + "\n", + "### Global Search\n", + "Global search method generates answers by searching over all AI-generated community reports in a map-reduce fashion. This is a resource-intensive method, but often gives good responses for questions that require an understanding of the dataset as a whole.\n", + "\n", + "## Couchbase as a Vector Store for Local Search\n", + "\n", + "Couchbase can be used as a vector store to support local search in Graph RAG systems. Its capabilities include:\n", + "\n", + "- **Vector Storage**: Couchbase can store vector embeddings alongside document data.\n", + "- **Vector Search**: It supports similarity search on vector fields using algorithms like cosine similarity.\n", + "- **Indexing**: Couchbase offers indexing options to optimize vector searches.\n", + "- **Scalability**: As a distributed database, it can handle large amounts of vector data.\n", + "\n", + "To implement local search, you would store node embeddings in Couchbase and use its vector search capabilities to find similar nodes efficiently within a local context.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PaN5siuBgctS" + }, + "source": [ + "# Importing Necessary Libraries\n", + "\n", + "In this section, we import all the essential Python libraries required to perform various tasks, \n", + "such as loading data, interacting with Couchbase, and using OpenAI models for generating text and embeddings.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "5tIHss5Rglye" + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "import pandas as pd\n", + "import tiktoken\n", + "from couchbase.auth import PasswordAuthenticator\n", + "from couchbase.options import ClusterOptions\n", + "from dotenv import load_dotenv\n", + "\n", + "from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey\n", + "from graphrag.query.indexer_adapters import (\n", + " read_indexer_covariates,\n", + " read_indexer_entities,\n", + " read_indexer_relationships,\n", + " read_indexer_reports,\n", + " read_indexer_text_units,\n", + ")\n", + "from graphrag.query.input.loaders.dfs import store_entity_semantic_embeddings\n", + "from graphrag.query.llm.oai.chat_openai import ChatOpenAI\n", + "from graphrag.query.llm.oai.embedding import OpenAIEmbedding\n", + "from graphrag.query.llm.oai.typing import OpenaiApiType\n", + "from graphrag.query.structured_search.local_search.mixed_context import (\n", + " LocalSearchMixedContext,\n", + ")\n", + "from graphrag.query.structured_search.local_search.search import LocalSearch\n", + "from graphrag.vector_stores.couchbasedb import CouchbaseVectorStore" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2efKWBqpgcw-" + }, + "source": [ + "# Configuring Environment Variables\n", + "Here, we configure various environment variables that define paths, API keys, and connection strings. These values are essential for connecting to Couchbase and OpenAI, loading data, and defining other constants.\n", + "\n", + "- INPUT_DIR: This specifies the directory path where the input data files are located. These files typically contain the raw data that will be processed and analyzed in the notebook.\n", + "- COUCHBASE_CONNECTION_STRING: This is the connection string used to establish a connection with the Couchbase database. It usually includes the protocol and host information (e.g., \"couchbase://localhost\").\n", + "- OPENAI_API_KEY: This is your personal API key for accessing OpenAI's services. It's required for authentication when making requests to OpenAI's API, allowing you to use their language models and other AI services.\n", + "- LLM_MODEL: This variable specifies which Large Language Model (LLM) from OpenAI to use for text generation tasks. For example, it could be set to \"gpt-4\" for using GPT-4, or \"gpt-3.5-turbo\" for using ChatGPT.\n", + "- EMBEDDING_MODEL: This defines the specific model used for generating text embeddings. Text embeddings are vector representations of text that capture semantic meaning. For OpenAI, a common choice is \"text-embedding-ada-002\".\n", + "\n", + "These environment variables are crucial for the notebook's functionality, as they provide necessary configuration details for data access, database connections, and AI model interactions." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "Cz1PfM7zgc39" + }, + "outputs": [], + "source": [ + "load_dotenv()\n", + "\n", + "INPUT_DIR = os.getenv(\"INPUT_DIR\")\n", + "COUCHBASE_CONNECTION_STRING = os.getenv(\n", + " \"COUCHBASE_CONNECTION_STRING\", \"couchbase://localhost\"\n", + ")\n", + "COUCHBASE_USERNAME = os.getenv(\"COUCHBASE_USERNAME\", \"Administrator\")\n", + "COUCHBASE_PASSWORD = os.getenv(\"COUCHBASE_PASSWORD\", \"password\")\n", + "COUCHBASE_BUCKET_NAME = os.getenv(\"COUCHBASE_BUCKET_NAME\", \"graphrag-demo\")\n", + "COUCHBASE_SCOPE_NAME = os.getenv(\"COUCHBASE_SCOPE_NAME\", \"shared\")\n", + "COUCHBASE_COLLECTION_NAME = os.getenv(\n", + " \"COUCHBASE_COLLECTION_NAME\", \"entity_description_embeddings\"\n", + ")\n", + "COUCHBASE_VECTOR_INDEX_NAME = os.getenv(\"COUCHBASE_VECTOR_INDEX_NAME\", \"graphrag_index\")\n", + "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n", + "LLM_MODEL = os.getenv(\"LLM_MODEL\", \"gpt-4o\")\n", + "EMBEDDING_MODEL = os.getenv(\"EMBEDDING_MODEL\", \"text-embedding-ada-002\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VlnoR17Tgc7b" + }, + "source": [ + "## Load text units and graph data tables as context for local search\n", + "In this part, we load data from Parquet files into a dictionary.We define functions that will handle the loading and processing of each paraquet." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data = {}\n", + "\n", + "# Constants\n", + "COMMUNITY_LEVEL = 2\n", + "\n", + "# Table names\n", + "TABLE_NAMES = {\n", + " \"COMMUNITY_REPORT_TABLE\": \"create_final_community_reports\",\n", + " \"ENTITY_TABLE\": \"create_final_nodes\",\n", + " \"ENTITY_EMBEDDING_TABLE\": \"create_final_entities\",\n", + " \"RELATIONSHIP_TABLE\": \"create_final_relationships\",\n", + " \"COVARIATE_TABLE\": \"create_final_covariates\",\n", + " \"TEXT_UNIT_TABLE\": \"create_final_text_units\",\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Entities:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " data[\"entities\"] = pd.read_parquet(\n", + " f\"{INPUT_DIR}/{TABLE_NAMES['ENTITY_TABLE']}.parquet\"\n", + " )\n", + " entity_embeddings = pd.read_parquet(\n", + " f\"{INPUT_DIR}/{TABLE_NAMES['ENTITY_EMBEDDING_TABLE']}.parquet\"\n", + " )\n", + " data[\"entities\"] = read_indexer_entities(\n", + " data[\"entities\"], entity_embeddings, COMMUNITY_LEVEL\n", + " )\n", + "except FileNotFoundError:\n", + " data[\"entities\"] = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Relationships:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " data[\"relationships\"] = pd.read_parquet(\n", + " f\"{INPUT_DIR}/{TABLE_NAMES['RELATIONSHIP_TABLE']}.parquet\"\n", + " )\n", + " data[\"relationships\"] = read_indexer_relationships(data[\"relationships\"])\n", + "except FileNotFoundError:\n", + " data[\"relationships\"] = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Covariates:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " data[\"covariates\"] = pd.read_parquet(\n", + " f\"{INPUT_DIR}/{TABLE_NAMES['COVARIATE_TABLE']}.parquet\"\n", + " )\n", + " data[\"covariates\"] = read_indexer_covariates(data[\"covariates\"])\n", + "except FileNotFoundError:\n", + " data[\"covariates\"] = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Reports:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " data[\"reports\"] = pd.read_parquet(\n", + " f\"{INPUT_DIR}/{TABLE_NAMES['COMMUNITY_REPORT_TABLE']}.parquet\"\n", + " )\n", + " entity_data = pd.read_parquet(f\"{INPUT_DIR}/{TABLE_NAMES['ENTITY_TABLE']}.parquet\")\n", + " data[\"reports\"] = read_indexer_reports(\n", + " data[\"reports\"], entity_data, COMMUNITY_LEVEL\n", + " )\n", + "except FileNotFoundError:\n", + " data[\"reports\"] = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read Text units:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data loading completed\n" + ] + } + ], + "source": [ + "try:\n", + " data[\"text_units\"] = pd.read_parquet(\n", + " f\"{INPUT_DIR}/{TABLE_NAMES['TEXT_UNIT_TABLE']}.parquet\"\n", + " )\n", + " data[\"text_units\"] = read_indexer_text_units(data[\"text_units\"])\n", + "except FileNotFoundError:\n", + " data[\"text_units\"] = None\n", + "\n", + "print(\"Data loading completed\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L8AOUrIAgc-8" + }, + "source": [ + "# Setting Up the Couchbase Vector Store\n", + "Couchbase is used here to store the semantic embeddings generated from entities. In this step, we define a method to connect to the Couchbase database using the provided credentials.\n", + "\n", + "The CouchbaseVectorStore allows you to store, retrieve, and manage vector embeddings in Couchbase.\n", + "The connect() method initializes the connection to Couchbase using the provided connection string, username, and password." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "kiYpzj7-gdC4" + }, + "outputs": [], + "source": [ + "couchbase_vector_store = CouchbaseVectorStore(\n", + " collection_name=COUCHBASE_COLLECTION_NAME,\n", + " bucket_name=COUCHBASE_BUCKET_NAME,\n", + " scope_name=COUCHBASE_SCOPE_NAME,\n", + " index_name=COUCHBASE_VECTOR_INDEX_NAME,\n", + ")\n", + "\n", + "auth = PasswordAuthenticator(str(COUCHBASE_USERNAME), str(COUCHBASE_PASSWORD))\n", + "cluster_options = ClusterOptions(auth)\n", + "\n", + "couchbase_vector_store.connect(\n", + " connection_string=COUCHBASE_CONNECTION_STRING,\n", + " cluster_options=cluster_options,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YJZhrj5egdGt" + }, + "source": [ + "# Setting Up Language Models\n", + "In this section, we configure the language models using OpenAI’s API. We initialize:\n", + "\n", + "ChatOpenAI: This is the language model used to generate responses to natural language queries.\n", + "OpenAIEmbedding: This is the model used to generate vector embeddings for text data.\n", + "tiktoken: This tokenizer is used to split text into tokens, which are essential for sending data to the language model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "japySJrUgdOG" + }, + "outputs": [], + "source": [ + "llm = ChatOpenAI(\n", + " api_key=OPENAI_API_KEY,\n", + " model=LLM_MODEL,\n", + " api_type=OpenaiApiType.OpenAI,\n", + " max_retries=20,\n", + ")\n", + "\n", + "token_encoder = tiktoken.get_encoding(\"cl100k_base\")\n", + "\n", + "text_embedder = OpenAIEmbedding(\n", + " api_key=OPENAI_API_KEY,\n", + " api_base=None,\n", + " api_type=OpenaiApiType.OpenAI,\n", + " model=EMBEDDING_MODEL,\n", + " deployment_name=EMBEDDING_MODEL,\n", + " max_retries=20,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r08V2NuugdSF" + }, + "source": [ + "# Storing Embeddings in Couchbase\n", + "After generating embeddings for the entities, we store them in Couchbase. We use the store_entity_semantic_embeddings function to store the embeddings.\n", + "\n", + "This method checks if the input is either a dictionary or a list and processes it accordingly.\n", + "It uses the Couchbase vector store to save the embeddings, ensuring that entities have the proper 'id' attribute for storage.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "C1wV0RqrgdVl" + }, + "outputs": [], + "source": [ + "\n", + "try:\n", + " if not isinstance(data[\"entities\"], list):\n", + " error_message = \"data['entities'] must be a list\"\n", + " raise TypeError(error_message)\n", + "\n", + " store_entity_semantic_embeddings(\n", + " entities=data[\"entities\"], vectorstore=couchbase_vector_store\n", + " )\n", + "except AttributeError as err:\n", + " error_message = \"Error storing entity semantic embeddings. Ensure all entities have an 'id' attribute\"\n", + " raise AttributeError(error_message) from err\n", + "except TypeError as err:\n", + " error_message = \"Error storing entity semantic embeddings. Ensure data['entities'] is a list\"\n", + " raise TypeError(error_message) from err\n", + "except Exception as err:\n", + " error_message = \"Error storing entity semantic embeddings\"\n", + " raise Exception(error_message) from err\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f0LSSSJlgdZd" + }, + "source": [ + "### **7. Building the Search Engine (In the Context of Graphrag)**\n", + "\n", + "Here, we explain the components of the search engine in detail and how they contribute to its functionality within Graphrag.\n", + "\n", + "#### **1. Context Builder (LocalSearchMixedContext)**\n", + "\n", + "The `LocalSearchMixedContext` class is the cornerstone of our search engine in Graphrag. It acts as a **contextual environment** for the search process by combining various types of data—such as **community reports, text units, entities, relationships, and covariates**—into a coherent structure that can be used by the search engine. In this context:\n", + "\n", + "- **Community Reports**: These are structured documents or insights generated at a community level, such as summaries or analytics reports, which are crucial when trying to query community-specific data.\n", + "- **Text Units**: Smaller pieces of text, such as paragraphs, sentences, or tokens that are stored in the system. These units help in understanding specific parts of the context when answering questions.\n", + "- **Entities**: These represent the core subjects (people, organizations, products, etc.) around which your queries are structured. Each entity has certain attributes and semantic embeddings stored in Couchbase, and these are used to enrich the search results.\n", + "- **Relationships**: The connections between entities, which can represent anything from business partnerships to familial ties or data dependencies. Understanding these relationships helps in contextualizing the search results more effectively.\n", + "- **Covariates**: Additional variables or metadata that provide more information about entities and relationships. These could include factors like location, time, or other metrics that affect the relevance of the search.\n", + "\n", + "All these elements work together to build the **context** that the search engine will use to find and rank results.\n", + "\n", + "- **entity_text_embeddings**: The entity descriptions are stored as vector embeddings (using the Couchbase vector store) to help in finding semantically similar entities.\n", + "- **text_embedder**: This is the **OpenAI embedding model** used to embed both the entities and user queries in a similar vector space, allowing for meaningful similarity comparisons.\n", + "- **token_encoder**: Tokenization splits the input text into tokens (smaller chunks), making it easier to process by the language models.\n", + "\n", + "#### **2. Local Search Parameters**\n", + "\n", + "Once the context is established, we define the parameters for the **search engine**. These parameters guide how the search engine processes the context to answer a query.\n", + "\n", + "- **text_unit_prop**: This sets the proportion of text units to be considered when building the context. In this case, 50% of the context comes from text units.\n", + "- **community_prop**: Similar to `text_unit_prop`, this defines how much weight to give community reports. Here, 10% of the context is derived from community reports.\n", + "- **conversation_history_max_turns**: This specifies how many conversation history turns are retained when building the context. It helps in multi-turn queries, where the context from previous queries may still be relevant.\n", + "- **top_k_mapped_entities**: Defines how many of the most relevant entities should be considered in each query. In this case, we are considering the top 10 entities.\n", + "- **top_k_relationships**: Similarly, we consider the top 10 relationships that are most relevant to the query.\n", + "- **include_entity_rank**: Whether to rank entities based on their relevance to the query.\n", + "- **include_relationship_weight**: Whether to include relationship weights in the ranking process. This is crucial because certain relationships may have higher importance based on the data being queried.\n", + "- **embedding_vectorstore_key**: Defines the **key** for accessing entity embeddings from Couchbase. Here, we use `EntityVectorStoreKey.ID` as the identifier for retrieving the correct embeddings.\n", + "- **max_tokens**: The maximum number of tokens to consider in the context.\n", + "\n", + "\n", + "#### **3. Language Model Parameters**\n", + "\n", + "For answering the query, we use the **language model** (LLM) to generate the response. The parameters for the LLM are configured here:\n", + "- **max_tokens**: Limits the number of tokens (words or sub-words) in the generated answer.\n", + "- **temperature**: Controls the randomness of the output. Setting it to `0.0` makes the model’s answers more deterministic.\n", + "\n", + "\n", + "#### **4. Integrating Everything: Creating the Search Engine**\n", + "\n", + "Finally, all components are integrated into the `LocalSearch` class, which serves as the main search engine. This class is responsible for:\n", + "- **Accepting queries** in natural language.\n", + "- **Using the context builder** to form a detailed context based on the available structured data (entities, relationships, text, reports).\n", + "- **Passing the query and context** to the language model (LLM), which generates the final answer.\n", + "\n", + "The search engine is now ready to process queries, using the underlying Graphrag system to provide context-aware and semantically rich answers.\n", + "\n", + "\n", + "### **Summary**\n", + "\n", + "This search engine leverages **structured data** (entities, relationships, reports, etc.) generated from the input files and integrates **semantic embeddings** stored in Couchbase. The search engine processes the query using OpenAI's language model, which uses the structured data context of the graph RAG to generate meaningful answers." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "8ZiML072gddQ" + }, + "outputs": [], + "source": [ + "context_builder = LocalSearchMixedContext(\n", + " community_reports=data[\"reports\"],\n", + " text_units=data[\"text_units\"],\n", + " entities=data[\"entities\"],\n", + " relationships=data[\"relationships\"],\n", + " covariates=data[\"covariates\"],\n", + " entity_text_embeddings=couchbase_vector_store,\n", + " embedding_vectorstore_key=EntityVectorStoreKey.ID,\n", + " text_embedder=text_embedder,\n", + " token_encoder=token_encoder,\n", + ")\n", + "\n", + "local_context_params = {\n", + " \"text_unit_prop\": 0.5,\n", + " \"community_prop\": 0.1,\n", + " \"conversation_history_max_turns\": 5,\n", + " \"conversation_history_user_turns_only\": True,\n", + " \"top_k_mapped_entities\": 10,\n", + " \"top_k_relationships\": 10,\n", + " \"include_entity_rank\": True,\n", + " \"include_relationship_weight\": True,\n", + " \"include_community_rank\": False,\n", + " \"return_candidate_context\": False,\n", + " \"embedding_vectorstore_key\": EntityVectorStoreKey.ID,\n", + " \"max_tokens\": 12_000,\n", + "}\n", + "\n", + "llm_params = {\n", + " \"max_tokens\": 2_000,\n", + " \"temperature\": 0.0,\n", + "}\n", + "\n", + "search_engine = LocalSearch(\n", + " llm=llm,\n", + " context_builder=context_builder,\n", + " token_encoder=token_encoder,\n", + " llm_params=llm_params,\n", + " context_builder_params=local_context_params,\n", + " response_type=\"multiple paragraphs\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "By8DVv-Igdg0" + }, + "source": [ + "# Running a Query\n", + "Finally, we run a query on the search engine. In this case, the query is \"Give me a summary about the story\". This simulates asking the search engine to summarize the entities and relationships stored in Couchbase." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "1SvUSrIbgdkh" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Question: 'Give me a summary about the story'\n", + "Answer: # Summary of the Story\n", + "\n", + "## Introduction to the Paranormal Military Squad\n", + "\n", + "The narrative centers around the Paranormal Military Squad, a secretive governmental faction tasked with investigating and engaging with extraterrestrial phenomena. This elite group operates primarily from the Dulce military base, where they are deeply involved in Operation: Dulce. The mission's primary objective is to mediate Earth's contact with alien intelligence, ensuring humanity's safety and preparing for potential first contact scenarios [Data: Paranormal Military Squad and Operation: Dulce (18)].\n", + "\n", + "## Key Figures and Their Roles\n", + "\n", + "Key figures within the squad include Alex Mercer, Dr. Jordan Hayes, Taylor Cruz, and Sam Rivera. Alex Mercer provides leadership and strategic insights, guiding the team through high-stakes operations. Dr. Jordan Hayes focuses on deciphering alien codes and understanding their intent, contributing significantly to the team's mission. Taylor Cruz oversees the team's efforts, providing strategic insights and emphasizing diligence. Sam Rivera brings youthful vigor and optimism, handling technical tasks, particularly in communications and signal interpretation [Data: Paranormal Military Squad and Operation: Dulce (18); Entities (30, 31, 38, 94)].\n", + "\n", + "## Operation: Dulce\n", + "\n", + "Operation: Dulce is a significant mission undertaken by the Paranormal Military Squad, focusing on mediating Earth's contact with alien intelligence. This operation involves investigating cosmic phenomena, decrypting alien communications, and preparing for potential first contact scenarios. The Dulce military base is equipped with high-tech equipment specifically designed for decoding alien communications, making it a strategic location for the squad's activities [Data: Paranormal Military Squad and Operation: Dulce (18); Relationships (142, 143, 144, 194, 196)].\n", + "\n", + "## Deciphering Alien Signals\n", + "\n", + "A primary focus of the Paranormal Military Squad is deciphering alien signals. This involves analyzing and decoding extraterrestrial communications to understand their intent and ensure humanity's safety. The squad's efforts in this area are critical for preparing for potential first contact scenarios and establishing effective communication with alien races. The use of advanced communications equipment and the strategic location at the Dulce military base are essential components of this mission [Data: Paranormal Military Squad and Operation: Dulce (18); Relationships (125, 126, 134, 108, 114, +more)].\n", + "\n", + "## Potential First Contact Scenarios\n", + "\n", + "The Paranormal Military Squad is preparing for potential first contact scenarios with extraterrestrial intelligence. This involves mediating Earth's bid for cosmic relevance through dialogue, ensuring effective communication and negotiation with alien beings. The squad's role in these scenarios is critical, as they represent humanity in these unprecedented encounters. The potential implications of first contact are monumental, making the squad's mission highly significant [Data: Paranormal Military Squad and Operation: Dulce (18); Relationships (119, 118, 109, 107, 127, +more)].\n", + "\n", + "## Global Implications of the Mission\n", + "\n", + "The activities of the Paranormal Military Squad have significant global implications. Their mission to engage with extraterrestrial intelligence and prepare for potential first contact scenarios could alter the course of human history. The squad's efforts in deciphering alien signals, ensuring humanity's safety, and establishing effective communication with alien races are critical for navigating the complexities of cosmic discovery. The potential for both positive and negative outcomes makes the squad's mission highly impactful [Data: Paranormal Military Squad and Operation: Dulce (18); Relationships (110, 111, 140, 123, 135, +more)].\n", + "\n", + "## Conclusion\n", + "\n", + "In summary, the story of the Paranormal Military Squad and Operation: Dulce is a gripping tale of humanity's quest to understand and engage with extraterrestrial intelligence. The squad's mission is fraught with challenges and potential dangers, but their work is crucial for the future of human civilization. Through their efforts, they aim to bridge the gap between Earth and the cosmos, ensuring that humanity is prepared for whatever lies beyond the stars.\n" + ] + } + ], + "source": [ + "question = \"Give me a summary about the story\"\n", + "\n", + "try:\n", + " result = await search_engine.asearch(question)\n", + " print(f\"Question: '{question}'\")\n", + " print(f\"Answer: {result.response}\")\n", + "except Exception as e:\n", + " print(f\"An error occurred while processing the query: {(e)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wb4weYVBgdn_" + }, + "source": [ + "With these steps, the entire process of loading data, setting up models, storing embeddings, and running a search engine query is written out in sequence without using functions. Let me know if any additional modifications are needed!" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples_notebooks/community_contrib/couchbase/couchbasedb_demo.py b/examples_notebooks/community_contrib/couchbase/couchbasedb_demo.py new file mode 100644 index 0000000000..44c67f9634 --- /dev/null +++ b/examples_notebooks/community_contrib/couchbase/couchbasedb_demo.py @@ -0,0 +1,293 @@ +""" +Couchbase Vector Store Demo for GraphRAG. + +This module demonstrates the usage of Couchbase as a vector store for GraphRAG, +including data loading, vector store setup, and query execution. +""" + +import asyncio +import logging +import os +from collections.abc import Callable +from typing import Any + +import pandas as pd +import tiktoken +from dotenv import load_dotenv + +from couchbase.auth import PasswordAuthenticator +from couchbase.options import ClusterOptions +from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey +from graphrag.query.indexer_adapters import ( + read_indexer_covariates, + read_indexer_entities, + read_indexer_relationships, + read_indexer_reports, + read_indexer_text_units, +) +from graphrag.query.input.loaders.dfs import store_entity_semantic_embeddings +from graphrag.query.llm.oai.chat_openai import ChatOpenAI +from graphrag.query.llm.oai.embedding import OpenAIEmbedding +from graphrag.query.llm.oai.typing import OpenaiApiType +from graphrag.query.structured_search.local_search.mixed_context import ( + LocalSearchMixedContext, +) +from graphrag.query.structured_search.local_search.search import LocalSearch +from graphrag.vector_stores.couchbasedb import CouchbaseVectorStore + +# Set up logging +logging.basicConfig( + level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) +logger = logging.getLogger(__name__) + +load_dotenv() + +INPUT_DIR = os.getenv("INPUT_DIR") +COUCHBASE_CONNECTION_STRING = os.getenv( + "COUCHBASE_CONNECTION_STRING", "couchbase://localhost" +) +COUCHBASE_USERNAME = os.getenv("COUCHBASE_USERNAME", "Administrator") +COUCHBASE_PASSWORD = os.getenv("COUCHBASE_PASSWORD", "password") +COUCHBASE_BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "graphrag-demo") +COUCHBASE_SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "shared") +COUCHBASE_COLLECTION_NAME = os.getenv( + "COUCHBASE_COLLECTION_NAME", "entity_description_embeddings" +) +COUCHBASE_VECTOR_INDEX_NAME = os.getenv("COUCHBASE_VECTOR_INDEX_NAME", "graphrag_index") +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") +LLM_MODEL = os.getenv("LLM_MODEL", "gpt-4o") +EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "text-embedding-ada-002") + +# Constants +COMMUNITY_LEVEL = 2 + +# Table names +TABLE_NAMES = { + "COMMUNITY_REPORT_TABLE": "create_final_community_reports", + "ENTITY_TABLE": "create_final_nodes", + "ENTITY_EMBEDDING_TABLE": "create_final_entities", + "RELATIONSHIP_TABLE": "create_final_relationships", + "COVARIATE_TABLE": "create_final_covariates", + "TEXT_UNIT_TABLE": "create_final_text_units", +} + + +def load_data() -> dict[str, Any]: + """Load data from parquet files.""" + logger.info("Loading data from parquet files") + data = {} + + def load_table(table_name: str, read_function: Callable, *args) -> Any: + try: + table_data = pd.read_parquet( + f"{INPUT_DIR}/{TABLE_NAMES[table_name]}.parquet" + ) + result = read_function(table_data, *args) + except FileNotFoundError: + logger.warning( + "%(table_name)s file not found. Setting %(table_name_lower)s to None.", + {"table_name": table_name, "table_name_lower": table_name.lower()}, + ) + result = None + except Exception: + logger.exception("Error loading %s", table_name) + result = None + return result + + data["entities"] = load_table( + "ENTITY_TABLE", + read_indexer_entities, + pd.read_parquet(f"{INPUT_DIR}/{TABLE_NAMES['ENTITY_EMBEDDING_TABLE']}.parquet"), + COMMUNITY_LEVEL, + ) + data["relationships"] = load_table("RELATIONSHIP_TABLE", read_indexer_relationships) + data["covariates"] = load_table("COVARIATE_TABLE", read_indexer_covariates) + data["reports"] = load_table( + "COMMUNITY_REPORT_TABLE", + read_indexer_reports, + pd.read_parquet(f"{INPUT_DIR}/{TABLE_NAMES['ENTITY_TABLE']}.parquet"), + COMMUNITY_LEVEL, + ) + data["text_units"] = load_table("TEXT_UNIT_TABLE", read_indexer_text_units) + + logger.info("Data loading completed") + return data + + +def setup_vector_store() -> CouchbaseVectorStore: + """Set up and connect to CouchbaseVectorStore.""" + logger.info("Setting up CouchbaseVectorStore") + try: + description_embedding_store = CouchbaseVectorStore( + collection_name=COUCHBASE_COLLECTION_NAME, + bucket_name=COUCHBASE_BUCKET_NAME, + scope_name=COUCHBASE_SCOPE_NAME, + index_name=COUCHBASE_VECTOR_INDEX_NAME, + ) + + auth = PasswordAuthenticator(COUCHBASE_USERNAME, COUCHBASE_PASSWORD) + cluster_options = ClusterOptions(auth) + + description_embedding_store.connect( + connection_string=COUCHBASE_CONNECTION_STRING, + cluster_options=cluster_options, + ) + logger.info("CouchbaseVectorStore setup completed") + except Exception: + logger.exception("Error setting up CouchbaseVectorStore") + raise + return description_embedding_store + + +def setup_models() -> dict[str, Any]: + """Set up LLM and embedding models.""" + logger.info("Setting up LLM and embedding models") + try: + llm = ChatOpenAI( + api_key=OPENAI_API_KEY, + model=LLM_MODEL, + api_type=OpenaiApiType.OpenAI, + max_retries=20, + ) + + token_encoder = tiktoken.get_encoding("cl100k_base") + + text_embedder = OpenAIEmbedding( + api_key=OPENAI_API_KEY, + api_base=None, + api_type=OpenaiApiType.OpenAI, + model=EMBEDDING_MODEL, + deployment_name=EMBEDDING_MODEL, + max_retries=20, + ) + + logger.info("LLM and embedding models setup completed") + except Exception: + logger.exception("Error setting up models") + raise + + return { + "llm": llm, + "token_encoder": token_encoder, + "text_embedder": text_embedder, + } + + +def store_embeddings(entities: list[Any], vector_store: CouchbaseVectorStore) -> None: + """Store entity semantic embeddings in Couchbase.""" + logger.info("Storing entity embeddings") + + try: + store_entity_semantic_embeddings(entities=entities, vectorstore=vector_store) + logger.info("Entity semantic embeddings stored successfully") + except AttributeError: + logger.exception( + "Error storing entity semantic embeddings. Ensure all entities have an 'id' attribute" + ) + raise + except Exception: + logger.exception("Error storing entity semantic embeddings") + raise + + +def create_search_engine( + data: dict[str, Any], models: dict[str, Any], vector_store: CouchbaseVectorStore +) -> LocalSearch: + """Create and configure the search engine.""" + logger.info("Creating search engine") + try: + context_builder = LocalSearchMixedContext( + community_reports=data["reports"], + text_units=data["text_units"], + entities=data["entities"], + relationships=data["relationships"], + covariates=data["covariates"], + entity_text_embeddings=vector_store, + embedding_vectorstore_key=EntityVectorStoreKey.ID, + text_embedder=models["text_embedder"], + token_encoder=models["token_encoder"], + ) + + local_context_params = { + "text_unit_prop": 0.5, + "community_prop": 0.1, + "conversation_history_max_turns": 5, + "conversation_history_user_turns_only": True, + "top_k_mapped_entities": 10, + "top_k_relationships": 10, + "include_entity_rank": True, + "include_relationship_weight": True, + "include_community_rank": False, + "return_candidate_context": False, + "embedding_vectorstore_key": EntityVectorStoreKey.ID, + "max_tokens": 12_000, + } + + llm_params = { + "max_tokens": 2_000, + "temperature": 0.0, + } + + search_engine = LocalSearch( + llm=models["llm"], + context_builder=context_builder, + token_encoder=models["token_encoder"], + llm_params=llm_params, + context_builder_params=local_context_params, + response_type="multiple paragraphs", + ) + logger.info("Search engine created") + except Exception: + logger.exception("Error creating search engine") + raise + return search_engine + + +async def run_query(search_engine: LocalSearch, question: str) -> None: + """Run a query using the search engine.""" + try: + logger.info("Running query: %s", question) + result = await search_engine.asearch(question) + logger.info("Question: %s", question) + logger.info("Answer: %s", result.response) + logger.info("Query completed successfully") + except Exception: + logger.exception("An error occurred while processing the query") + + +async def main() -> None: + """Orchestrate the demo.""" + try: + # Start the Couchbase demo + logger.info("Starting Couchbase demo") + + # Load data from parquet files + data = load_data() + + # Set up the Couchbase vector store + vector_store = setup_vector_store() + + # Set up the language models + models = setup_models() + + # Store entity embeddings if entities exist + if data["entities"]: + store_embeddings(data["entities"], vector_store) + else: + logger.warning("No entities found to store in Couchbase") + + # Create the search engine + search_engine = create_search_engine(data, models, vector_store) + + # Run a sample query + question = "Give me a summary about the story" + await run_query(search_engine, question) + + logger.info("Couchbase demo completed") + except Exception: + logger.exception("An error occurred in the main function") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples_notebooks/community_contrib/couchbase/graphrag_demo_index.json b/examples_notebooks/community_contrib/couchbase/graphrag_demo_index.json new file mode 100644 index 0000000000..b9d11c4589 --- /dev/null +++ b/examples_notebooks/community_contrib/couchbase/graphrag_demo_index.json @@ -0,0 +1,74 @@ +{ + "type": "fulltext-index", + "name": "graphrag_index", + "uuid": "1797bd6bd434cdd2", + "sourceType": "gocbcore", + "sourceName": "graphrag-demo", + "sourceUUID": "4bbae514001eaf402caed43976ec4120", + "planParams": { + "maxPartitionsPerPIndex": 64, + "indexPartitions": 16 + }, + "params": { + "doc_config": { + "docid_prefix_delim": "", + "docid_regexp": "", + "mode": "scope.collection.type_field", + "type_field": "type" + }, + "mapping": { + "analysis": {}, + "default_analyzer": "standard", + "default_datetime_parser": "dateTimeOptional", + "default_field": "_all", + "default_mapping": { + "dynamic": true, + "enabled": false + }, + "default_type": "_default", + "docvalues_dynamic": false, + "index_dynamic": true, + "store_dynamic": false, + "type_field": "_type", + "types": { + "shared.entity_description_embeddings": { + "dynamic": true, + "enabled": true, + "properties": { + "embedding": { + "dynamic": false, + "enabled": true, + "fields": [ + { + "dims": 1536, + "index": true, + "name": "embedding", + "similarity": "dot_product", + "type": "vector", + "vector_index_optimized_for": "recall" + } + ] + }, + "text": { + "dynamic": false, + "enabled": true, + "fields": [ + { + "index": true, + "name": "text", + "store": true, + "type": "text" + } + ] + } + } + } + } + }, + "store": { + "indexType": "scorch", + "segmentVersion": 16 + } + }, + "sourceParams": {} +} \ No newline at end of file diff --git a/graphrag/index/operations/embed_text/embed_text.py b/graphrag/index/operations/embed_text/embed_text.py index f335802c5f..83064e84d9 100644 --- a/graphrag/index/operations/embed_text/embed_text.py +++ b/graphrag/index/operations/embed_text/embed_text.py @@ -72,7 +72,7 @@ async def embed_text( max_tokens: !ENV ${GRAPHRAG_MAX_TOKENS:6000} # The max tokens to use for openai organization: !ENV ${GRAPHRAG_OPENAI_ORGANIZATION} # The organization to use for openai vector_store: # The optional configuration for the vector store - type: lancedb # The type of vector store to use, available options are: azure_ai_search, lancedb + type: lancedb # The type of vector store to use, available options are: azure_ai_search, lancedb, couchbase <...> ``` """ diff --git a/graphrag/vector_stores/couchbasedb.py b/graphrag/vector_stores/couchbasedb.py new file mode 100644 index 0000000000..2b861af888 --- /dev/null +++ b/graphrag/vector_stores/couchbasedb.py @@ -0,0 +1,221 @@ +"""Couchbase vector store implementation for GraphRAG.""" + +import json +import logging +from typing import Any + +from couchbase.cluster import Cluster +from couchbase.exceptions import CouchbaseException, DocumentExistsException +from couchbase.options import SearchOptions +from couchbase.result import MultiMutationResult +from couchbase.search import SearchRequest +from couchbase.vector_search import VectorQuery, VectorSearch + +from graphrag.model.types import TextEmbedder + +from .base import ( + DEFAULT_VECTOR_SIZE, + BaseVectorStore, + VectorStoreDocument, + VectorStoreSearchResult, +) + +# Set up logger +logger = logging.getLogger(__name__) + + +class CouchbaseVectorStore(BaseVectorStore): + """The Couchbase vector storage implementation.""" + + def __init__( + self, + collection_name: str, + bucket_name: str, + scope_name: str = "_default", + index_name: str = "graphrag_index", + text_key: str = "text", + embedding_key: str = "embedding", + scoped_index: bool = True, + **kwargs: Any, + ): + super().__init__(collection_name, **kwargs) + self.bucket_name = bucket_name + self.scope_name = scope_name + self.index_name = index_name + self.text_key = text_key + self.embedding_key = embedding_key + self.scoped_index = scoped_index + self.vector_size = kwargs.get("vector_size", DEFAULT_VECTOR_SIZE) + self._cluster = None + logger.debug( + "Initialized CouchbaseVectorStore with collection: %s, bucket: %s, scope: %s, index: %s", + collection_name, + bucket_name, + scope_name, + index_name, + ) + + def connect(self, **kwargs: Any) -> None: + """Connect to the Couchbase vector store.""" + connection_string = kwargs.get("connection_string") + cluster_options = kwargs.get("cluster_options") + + if not isinstance(connection_string, str): + error_msg = "Connection string must be a string" + logger.error(error_msg) + raise TypeError(error_msg) + + try: + logger.info("Connecting to Couchbase at %s", connection_string) + self._cluster = Cluster(connection_string, cluster_options) + self.db_connection = self._cluster + self.bucket = self._cluster.bucket(self.bucket_name) + + if self.scoped_index and self.scope_name: + self.scope = self.bucket.scope(self.scope_name) + else: + self.scope = self.bucket.default_scope() + + self.document_collection = self.scope.collection(self.collection_name) + logger.info("Successfully connected to Couchbase") + except Exception as e: + error_msg = f"Failed to connect to Couchbase: {e}" + logger.exception(error_msg) + raise ConnectionError(error_msg) from e + + def load_documents(self, documents: list[VectorStoreDocument]) -> int: + """ + Load documents into vector storage. + + :param documents: A list of VectorStoreDocuments to load into the vector store. + :raises DuplicateDocumentError: If a document with the same ID already exists in the vector store. + :raises DocumentStoreError: If there's an error writing documents to Couchbase. + :returns: The number of documents loaded into the vector store. + """ + logger.info("Loading %d documents into vector storage", len(documents)) + batch = { + doc.id: { + self.text_key: doc.text, + self.embedding_key: doc.vector, + "attributes": json.dumps(doc.attributes), + } + for doc in documents + if doc.vector is not None + } + + if not batch: + logger.warning("No valid documents to load") + return 0 + + try: + result: MultiMutationResult = self.document_collection.upsert_multi(batch) + + if not result.all_ok and result.exceptions: + duplicate_ids = [] + other_errors = [] + for doc_id, ex in result.exceptions.items(): + if isinstance(ex, DocumentExistsException): + duplicate_ids.append(doc_id) + else: + other_errors.append({"id": doc_id, "exception": str(ex)}) + + if duplicate_ids: + msg = f"IDs '{', '.join(duplicate_ids)}' already exist in the vector store." + raise DocumentExistsException(msg) + + if other_errors: + msg = f"Failed to load documents into Couchbase. Errors:\n{other_errors}" + raise CouchbaseException(msg) + + logger.info("Successfully loaded %d documents", len(batch)) + return len(batch) + + except Exception as e: + logger.exception("Error occurred while loading documents: %s", e) + msg = f"Failed to load documents into Couchbase. Error: {e}" + raise CouchbaseException(msg) from e + + def similarity_search_by_text( + self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any + ) -> list[VectorStoreSearchResult]: + """Perform KNN search by text.""" + logger.info("Performing similarity search by text with k=%d", k) + query_embedding = text_embedder(text) + if query_embedding: + return self.similarity_search_by_vector(query_embedding, k) + logger.warning("Failed to generate embedding for the query text") + return [] + + def similarity_search_by_vector( + self, query_embedding: list[float], k: int = 10, **kwargs: Any + ) -> list[VectorStoreSearchResult]: + """Perform KNN search by vector.""" + logger.info("Performing similarity search by vector with k=%d", k) + + search_req = SearchRequest.create( + VectorSearch.from_vector_query( + VectorQuery( + self.embedding_key, + query_embedding, + k, + ) + ) + ) + + fields = kwargs.get("fields", ["*"]) + + if self.scoped_index: + search_iter = self.scope.search( + self.index_name, + search_req, + SearchOptions( + limit=k, + fields=fields, + ), + ) + else: + search_iter = self.db_connection.search( + index=self.index_name, + request=search_req, + options=SearchOptions(limit=k, fields=fields), + ) + + results = [] + for row in search_iter.rows(): + text = row.fields.pop(self.text_key, "") + metadata = self._format_metadata(row.fields) + score = row.score + doc = VectorStoreDocument( + id=row.id, + text=text, + vector=row.fields.get(self.embedding_key), + attributes=metadata, + ) + results.append(VectorStoreSearchResult(document=doc, score=score)) + + logger.info("Found %d results in similarity search by vector", len(results)) + return results + + def filter_by_id(self, include_ids: list[str] | list[int]) -> Any: + """Build a query filter to filter documents by id.""" + # id_filter = ",".join([f"{id!s}" for id in include_ids]) + # logger.debug("Created filter by ID: %s", id_filter) + # return f"search.in(id, '{id_filter}', ',')" + + raise NotImplementedError( + "filter_by_id method is not implemented for CouchbaseVectorStore" + ) + + def _format_metadata(self, row_fields: dict[str, Any]) -> dict[str, Any]: + """Format the metadata from the Couchbase Search API. + + Extract and reorganize metadata fields from the Couchbase Search API response. + """ + metadata = {} + for key, value in row_fields.items(): + if key.startswith("attributes."): + new_key = key.split("attributes.")[-1] + metadata[new_key] = value + else: + metadata[key] = value + return metadata diff --git a/graphrag/vector_stores/factory.py b/graphrag/vector_stores/factory.py index eedbefab13..44e4f30be6 100644 --- a/graphrag/vector_stores/factory.py +++ b/graphrag/vector_stores/factory.py @@ -9,6 +9,7 @@ from graphrag.vector_stores.azure_ai_search import AzureAISearch from graphrag.vector_stores.base import BaseVectorStore from graphrag.vector_stores.lancedb import LanceDBVectorStore +from graphrag.vector_stores.couchbasedb import CouchbaseVectorStore class VectorStoreType(str, Enum): @@ -16,6 +17,7 @@ class VectorStoreType(str, Enum): LanceDB = "lancedb" AzureAISearch = "azure_ai_search" + Couchbase = "couchbase" class VectorStoreFactory: @@ -38,6 +40,8 @@ def create_vector_store( return LanceDBVectorStore(**kwargs) case VectorStoreType.AzureAISearch: return AzureAISearch(**kwargs) + case VectorStoreType.Couchbase: + return CouchbaseVectorStore(**kwargs) case _: if vector_store_type in cls.vector_store_types: return cls.vector_store_types[vector_store_type](**kwargs) diff --git a/poetry.lock b/poetry.lock index 841b2c8c39..584064904a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. 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b/tests/unit/vector_stores/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/tests/unit/vector_stores/test_couchbasedb.py b/tests/unit/vector_stores/test_couchbasedb.py new file mode 100644 index 0000000000..a0b4aa2d80 --- /dev/null +++ b/tests/unit/vector_stores/test_couchbasedb.py @@ -0,0 +1,117 @@ +# Constants for Couchbase connection +import os +import time +import unittest + +from couchbase.auth import PasswordAuthenticator +from couchbase.options import ClusterOptions +from dotenv import load_dotenv + +from graphrag.vector_stores.base import VectorStoreDocument, VectorStoreSearchResult +from graphrag.vector_stores.couchbasedb import CouchbaseVectorStore + +load_dotenv() + +COUCHBASE_CONNECTION_STRING = os.getenv( + "COUCHBASE_CONNECTION_STRING", "couchbase://localhost" +) +COUCHBASE_USERNAME = os.getenv("COUCHBASE_USERNAME", "Administrator") +COUCHBASE_PASSWORD = os.getenv("COUCHBASE_PASSWORD", "password") +BUCKET_NAME = os.getenv("COUCHBASE_BUCKET_NAME", "graphrag-demo") +SCOPE_NAME = os.getenv("COUCHBASE_SCOPE_NAME", "shared") +COLLECTION_NAME = os.getenv( + "COUCHBASE_COLLECTION_NAME", "entity_description_embeddings" +) +INDEX_NAME = os.getenv("COUCHBASE_INDEX_NAME", "graphrag_index") +VECTOR_SIZE = int(os.getenv("VECTOR_SIZE", 1536)) + + +class TestCouchbaseVectorStore(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.vector_store = CouchbaseVectorStore( + collection_name=COLLECTION_NAME, + bucket_name=BUCKET_NAME, + scope_name=SCOPE_NAME, + index_name=INDEX_NAME, + vector_size=VECTOR_SIZE, + ) + auth = PasswordAuthenticator(COUCHBASE_USERNAME, COUCHBASE_PASSWORD) + cluster_options = ClusterOptions(auth) + + cls.vector_store.connect( + connection_string=COUCHBASE_CONNECTION_STRING, + cluster_options=cluster_options, + ) + + @classmethod + def tearDownClass(cls): + # Clean up the test collection + query = f"DELETE FROM `{BUCKET_NAME}`.`{SCOPE_NAME}`.`{COLLECTION_NAME}`" + cls.vector_store.db_connection.query(query).execute() + + def test_load_documents(self): + documents = [ + VectorStoreDocument( + id="1", + text="Test 1", + vector=[0.1] * VECTOR_SIZE, + attributes={"attr": "value1"}, + ), + VectorStoreDocument( + id="2", + text="Test 2", + vector=[0.2] * VECTOR_SIZE, + attributes={"attr": "value2"}, + ), + ] + self.vector_store.load_documents(documents) + + # Add a sleep to allow time for indexing + time.sleep(2) + + # Verify documents were loaded + for doc in documents: + result = self.vector_store.document_collection.get(doc.id) + assert result.content_as[dict] is not None + assert result.content_as[dict]["text"] == doc.text + + def test_similarity_search_by_vector(self): + # Ensure we have some documents in the store + self.test_load_documents() + + # Add a sleep to allow time for indexing + time.sleep(2) + + results = self.vector_store.similarity_search_by_vector( + [0.1] * VECTOR_SIZE, k=2 + ) + assert len(results) == 2 + assert isinstance(results[0], VectorStoreSearchResult) + assert isinstance(results[0].document, VectorStoreDocument) + + def test_similarity_search_by_text(self): + # Mock text embedder function + def mock_text_embedder(text): + return [0.1] * VECTOR_SIZE + + # Ensure we have some documents in the store + self.test_load_documents() + + # Add a sleep to allow time for indexing + time.sleep(2) + + results = self.vector_store.similarity_search_by_text( + "test query", mock_text_embedder, k=2 + ) + assert len(results) == 2 + assert isinstance(results[0], VectorStoreSearchResult) + assert isinstance(results[0].document, VectorStoreDocument) + + # def test_filter_by_id(self): + # filter_query = self.vector_store.filter_by_id(["1", "2", "3"]) + # assert filter_query == "search.in(id, '1,2,3', ',')" + + +if __name__ == "__main__": + unittest.main()