> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # PGVectorStore integration > Integrate with the PGVectorStore using LangChain Python. `PGVectorStore` is an implementation of a LangChain vectorstore using `postgres` as the backend. This notebook goes over how to use the `PGVectorStore` API. The code lives in an integration package called: [langchain-postgres](https://github.com/langchain-ai/langchain-postgres/). ## Setup This package requires a PostgreSQL database with the `pgvector` extension. You can run the following command to spin up a container for a `pgvector` enabled Postgres instance: ```shell theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} docker run --name pgvector-container -e POSTGRES_USER=langchain -e POSTGRES_PASSWORD=langchain -e POSTGRES_DB=langchain -p 6024:5432 -d pgvector/pgvector:pg16 ``` ### Install Install the integration library, `langchain-postgres`. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -qU langchain-postgres # This notebook also requires the following dependencies pip install -qU langchain-core langchain-cohere sqlalchemy ``` ### Set your Postgres values Set your Postgres values to test the functionality in this notebook against a Postgres instance. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # @title Set your values or use the defaults to connect to Docker { display-mode: "form" } POSTGRES_USER = "langchain" # @param {type: "string"} POSTGRES_PASSWORD = "langchain" # @param {type: "string"} POSTGRES_HOST = "localhost" # @param {type: "string"} POSTGRES_PORT = "6024" # @param {type: "string"} POSTGRES_DB = "langchain" # @param {type: "string"} TABLE_NAME = "vectorstore" # @param {type: "string"} VECTOR_SIZE = 1024 # @param {type: "int"} ``` ## Initialization ### PGEngine connection pool One of the requirements and arguments to establish PostgreSQL as a vector store is a `PGEngine` object. The `PGEngine` configures a shared connection pool to your Postgres database. This is an industry best practice to manage number of connections and to reduce latency through cached database connections. `PGVectorStore` can be used with the `asyncpg` and `psycopg3` drivers. To create a `PGEngine` using `PGEngine.from_connection_string()` you need to provide: 1. `url` : Connection string using the `postgresql+asyncpg` driver. **Note:** This tutorial demonstrates the async interface. All async methods have corresponding sync methods. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # See docker command above to launch a Postgres instance with pgvector enabled. CONNECTION_STRING = ( f"postgresql+asyncpg://{POSTGRES_USER}:{POSTGRES_PASSWORD}@{POSTGRES_HOST}" f":{POSTGRES_PORT}/{POSTGRES_DB}" ) # To use psycopg3 driver, set your connection string to `postgresql+psycopg://` ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_postgres import PGEngine pg_engine = PGEngine.from_connection_string(url=CONNECTION_STRING) ``` To create a `PGEngine` using `PGEngine.from_engine()` you need to provide: 1. `engine` : An object of `AsyncEngine` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from sqlalchemy.ext.asyncio import create_async_engine # Create an SQLAlchemy Async Engine engine = create_async_engine( CONNECTION_STRING, ) pg_engine = PGEngine.from_engine(engine=engine) ``` ### Initialize a table The `PGVectorStore` class requires a database table. The `PGEngine` engine has a helper method `ainit_vectorstore_table()` that can be used to create a table with the proper schema for you. See [Create a custom Vector Store](#create-a-custom-vector-store) or [Create a Vector Store using existing table](#create-a-vector-store-using-existing-table) for customizing the schema. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await pg_engine.ainit_vectorstore_table( table_name=TABLE_NAME, vector_size=VECTOR_SIZE, ) ``` #### Optional tip: 💡 You can also specify a schema name by passing `schema_name` wherever you pass `table_name`. Eg: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} SCHEMA_NAME="my_schema" await pg_engine.ainit_vectorstore_table( table_name=TABLE_NAME, vector_size=768, schema_name=SCHEMA_NAME, # Default: "public" ) ``` ### Create an embedding class instance You can use any [LangChain embeddings model](https://python.langchain.com/docs/integrations/embeddings/). ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_cohere import CohereEmbeddings embedding = CohereEmbeddings(model="embed-english-v3.0") ``` ### Initialize a default PGVectorStore Use the default table schema to connect to the vectorstore. See [Create a custom Vector Store](#create-a-custom-vector-store) or [Create a Vector Store using existing table](#create-a-vector-store-using-existing-table) for customizing the schema. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_postgres import PGVectorStore store = await PGVectorStore.create( engine=pg_engine, table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, embedding_service=embedding, ) ``` ## Manage vector store ### Add documents Add documents to the vector store. Metadata is stored in a JSON column, see "Create a custom Vector Store" to store metadata to be used for filters. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import uuid from langchain_core.documents import Document docs = [ Document( id=str(uuid.uuid4()), page_content="Red Apple", metadata={"description": "red", "content": "1", "category": "fruit"}, ), Document( id=str(uuid.uuid4()), page_content="Banana Cavendish", metadata={"description": "yellow", "content": "2", "category": "fruit"}, ), Document( id=str(uuid.uuid4()), page_content="Orange Navel", metadata={"description": "orange", "content": "3", "category": "fruit"}, ), ] await store.aadd_documents(docs) ``` ### Add texts Add text directly to the vectorstore, if not structured as a Document. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import uuid all_texts = ["Apples and oranges", "Cars and airplanes", "Pineapple", "Train", "Banana"] metadatas = [{"len": len(t)} for t in all_texts] ids = [str(uuid.uuid4()) for _ in all_texts] await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids) ``` ### Delete documents Documents can be deleted using ids. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await store.adelete([ids[1]]) ``` ## Query vector store ### Search for documents Use a natural language query to search for similar documents. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} query = "I'd like a fruit." docs = await store.asimilarity_search(query) print(docs) ``` ### Search for documents by vector Search for similar documents using a vector embedding. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} query_vector = embedding.embed_query(query) docs = await store.asimilarity_search_by_vector(query_vector, k=2) print(docs) ``` ## Add a index Speed up vector search queries by applying a vector index. Learn more about [vector indexes](https://cloud.google.com/blog/products/databases/faster-similarity-search-performance-with-pgvector-indexes). Indexes will use a default index name if a name is not provided. To add multiple indexes, different index names are required. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_postgres.v2.indexes import HNSWIndex, IVFFlatIndex index = IVFFlatIndex() await store.aapply_vector_index(index) index = HNSWIndex(name="my-hnsw-index") await store.aapply_vector_index(index) ``` Set index parameters to tune the index for optimal balance between recall and QPS. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} index = IVFFlatIndex(name="my-ivfflat", lists=120) await store.aapply_vector_index(index) ``` ### Re-index Rebuild an index using the data stored in the index's table, replacing the old copy of the index. Some index types may require re-indexing after a considerable amount of new data is added. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await store.areindex() # Re-index using the default index name await store.areindex("my-hnsw-index") # Re-index using the index name ``` ### Drop an index Remove a vector index. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await store.adrop_vector_index() # Delete index using the default name await store.adrop_vector_index("my-hnsw-index") # Delete index using the index name ``` ### Create a custom vector store Customize the vectorstore with special column names or with custom metadata columns. `ainit_vectorstore_table` * Use fields `content_column`, `embedding_column`,`metadata_columns`, `metadata_json_column`, `id_column` to rename the columns. * Use the `Column` class to create custom id or metadata columns. A Column is defined by a name and data type. Any Postgres [data type](https://www.postgresql.org/docs/current/datatype.html) can be used. * Use `store_metadata` to create a JSON column to store extra metadata. #### Optional tip: 💡 To use non-uuid ids, you must customize the id column: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await pg_engine.ainit_vectorstore_table( ..., id_column=Column(name="langchain_id", data_type="INTEGER") ) ``` `PGVectorStore` * Use fields `content_column`, `embedding_column`,`metadata_columns`, `metadata_json_column`, `id_column` to rename the columns. * `ignore_metadata_columns` to ignore columns that should not be used for Document metadata. This is helpful when using a preexisting table, where all data columns are not necessary. * Use a different `distance_strategy` for the similarity calculation during vector search. * Use `index_query_options` to tune local index parameters during vector search. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_postgres import Column # Set table name TABLE_NAME = "vectorstore_custom" # SCHEMA_NAME = "my_schema" await pg_engine.ainit_vectorstore_table( table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, vector_size=VECTOR_SIZE, metadata_columns=[Column("len", "INTEGER")], ) # Initialize PGVectorStore custom_store = await PGVectorStore.create( engine=pg_engine, table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, embedding_service=embedding, metadata_columns=["len"], ) ``` ### Search for documents with metadata filter A Vector Store can take advantage of relational data to filter similarity searches. The vectorstore supports a set of filters that can be applied against the metadata fields of the documents. See the [migration guide](https://github.com/langchain-ai/langchain-postgres/blob/main/examples/migrate_pgvector_to_pgvectorstore.ipynb) for details on how to migrate to use metadata columns. `PGVectorStore` currently supports the following operators and all Postgres data types. | Operator | Meaning/Category | | --------- | ---------------------------- | | \$eq | Equality (==) | | \$ne | Inequality (!=) | | \$lt | Less than (\<) | | \$lte | Less than or equal (\<=) | | \$gt | Greater than (>) | | \$gte | Greater than or equal (>=) | | \$in | Special Cased (in) | | \$nin | Special Cased (not in) | | \$between | Special Cased (between) | | \$exists | Special Cased (is null) | | \$like | Text (like) | | \$ilike | Text (case-insensitive like) | | \$and | Logical (and) | | \$or | Logical (or) | ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import uuid docs = [ Document( id=str(uuid.uuid4()), page_content="Red Apple", metadata={"description": "red", "content": "1", "category": "fruit"}, ), Document( id=str(uuid.uuid4()), page_content="Banana Cavendish", metadata={"description": "yellow", "content": "2", "category": "fruit"}, ), Document( id=str(uuid.uuid4()), page_content="Orange Navel", metadata={"description": "orange", "content": "3", "category": "fruit"}, ), ] await custom_store.aadd_documents(docs) # Use a dictionary filter on search docs = await custom_store.asimilarity_search(query, filter={"content": {"$gte": 1}}) print(docs) ``` ### Create a vector store using existing table A Vector Store can be built up on an existing table. Assuming there's a pre-existing table in PG DB: `products`, which stores product details for an eComm venture.
Click for Table Schema Details ### SQL query for table creation ``` CREATE TABLE products ( product_id SERIAL PRIMARY KEY, name VARCHAR(255) NOT NULL, description TEXT, price_usd DECIMAL(10, 2) NOT NULL, category VARCHAR(255), quantity INT DEFAULT 0, sku VARCHAR(255) UNIQUE NOT NULL, image_url VARCHAR(255), metadata JSON, embed vector(768) DEFAULT NULL -- vector dimensions depends on the embedding model ); ``` ### Insertion of records ``` INSERT INTO products (name, description, price_usd, category, quantity, sku, image_url, METADATA, embed) VALUES ('Laptop', 'High-performance gaming laptop', 1200.00, 'Electronics', 10, 'SKU12345', 'https://example.com/laptop.jpg', '{"category" : "Electronics", "name" : "Laptop", "description" : "High-performance gaming laptop"}', 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('Smartphone', 'Latest model with high-resolution camera', 800.00, 'Electronics', 15, 'SKU12346', 'https://example.com/smartphone.jpg', '{"category" : "Electronics", "name" : "Smartphone", "description" : "Latest model with high-resolution camera"}', 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('Coffee Maker', 'Brews coffee in under 5 minutes', 99.99, 'Kitchen Appliances', 20, 'SKU12347', 'https://example.com/coffeemaker.jpg', '{"category" : "Kitchen Appliances", "name" : "Coffee Maker", "description" : "Brews coffee in under 5 minutes"}', 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('Bluetooth Headphones', 'Noise cancelling, over the ear headphones', 250.00, 'Accessories', 5, 'SKU12348', 'https://example.com/headphones.jpg', '{"category" : "Accessories", "name" : "Bluetooth Headphones", "description" : "Noise cancelling, over the ear headphones"}', 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('Backpack', 'Waterproof backpack with laptop compartment', 59.99, 'Accessories', 30, 'SKU12349', 'https://example.com/backpack.jpg', '{"category" : "Accessories", "name" : "Backpack", "description" : "Waterproof backpack with laptop compartment"}', 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```
Here is how this table mapped to `PGVectorStore`: * **`id_column="product_id"`**: ID column uniquely identifies each row in the products table. * **`content_column="description"`**: The `description` column contains text descriptions of each product. This text is used by the `embedding_service` to create vectors that go in embedding\_column and represent the semantic meaning of each description. * **`embedding_column="embed"`**: The `embed` column stores the vectors created from the product descriptions. These vectors are used to find products with similar descriptions. * **`metadata_columns=["name", "category", "price_usd", "quantity", "sku", "image_url"]`**: These columns are treated as metadata for each product. Metadata provides additional information about a product, such as its name, category, price, quantity available, SKU (Stock Keeping Unit), and an image URL. This information is useful for displaying product details in search results or for filtering and categorization. * **`metadata_json_column="metadata"`**: The `metadata` column can store any additional information about the products in a flexible JSON format. This allows for storing varied and complex data that doesn't fit into the standard columns. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # Set an existing table name TABLE_NAME = "products" # SCHEMA_NAME = "my_schema" # Initialize PGVectorStore custom_store = await PGVectorStore.create( engine=pg_engine, table_name=TABLE_NAME, # schema_name=SCHEMA_NAME, embedding_service=embedding, # Connect to existing VectorStore by customizing below column names id_column="product_id", content_column="description", embedding_column="embed", metadata_columns=["name", "category", "price_usd", "quantity", "sku", "image_url"], metadata_json_column="metadata", ) ``` Note: 1. Optional: If the `embed` column is newly created or has different dimensions than supported by embedding model, it is required to one-time add the embeddings for the old records, like this: `ALTER TABLE products ADD COLUMN embed vector(768) DEFAULT NULL` 2. For new records, added via [`VectorStore`](https://reference.langchain.com/python/langchain-core/vectorstores/base/VectorStore) embeddings are automatically generated. ## Clean up **⚠️ WARNING: this can not be undone** Drop the vector store table. ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} await pg_engine.adrop_table(TABLE_NAME) ``` ## Usage for retrieval-augmented generation For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections: * [Retrieval docs](/oss/python/langchain/retrieval) * [Build a RAG app with LangChain](/oss/python/langchain/rag) * [Agentic RAG](/oss/python/langgraph/agentic-rag) *** ## API reference For detailed documentation of all `PGVectorStore` features and configurations head to the [API reference](https://reference.langchain.com/python/langchain-postgres/v2/vectorstores/PGVectorStore) ***
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