diff --git a/examples/pg_vectorstore.ipynb b/examples/pg_vectorstore.ipynb index 4d25d410..317a988f 100644 --- a/examples/pg_vectorstore.ipynb +++ b/examples/pg_vectorstore.ipynb @@ -4,11 +4,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# PGVectorStore & PGEngine\n", + "# PGVectorStore\n", "\n", - "This is an implementation of a LangChain vectorstore using `postgres` as the backend.\n", + "`PGVectorStore` is a an implementation of the the LangChain vectorstore abstraction using `postgres` as the backend.\n", "\n", - "This notebook goes over how to use `PGVectorStore` to store vector embeddings." + "## Requirements\n", + "\n", + "You'll need a PostgreSQL database with the `pgvector` extension enabled.\n", + "\n", + "\n", + "For local development, you can use the following docker command to spin up the database:\n", + "\n", + "```shell\n", + "docker run --name pgvector-container -e POSTGRES_USER=langchain -e POSTGRES_PASSWORD=langchain -e POSTGRES_DB=langchain -p 6024:5432 -d pgvector/pgvector:pg16\n", + "```" ] }, { @@ -17,8 +26,9 @@ "id": "IR54BmgvdHT_" }, "source": [ - "### 🦜🔗 Library Installation\n", - "Install the integration library, `langchain-postgres`." + "## Install\n", + "\n", + "Install the `langchain-postgres` package." ] }, { @@ -30,208 +40,158 @@ "height": 1000 }, "id": "0ZITIDE160OD", - "outputId": "e184bc0d-6541-4e0a-82d2-1e216db00a2d" + "outputId": "e184bc0d-6541-4e0a-82d2-1e216db00a2d", + "tags": [] }, "outputs": [], "source": [ - "%pip install --upgrade --quiet langchain-postgres\n", - "# This tutorial also requires the following dependencies\n", - "%pip install --upgrade --quiet langchain-core langchain-cohere SQLAlchemy" + "%pip install --upgrade --quiet langchain-postgres" ] }, { "cell_type": "markdown", - "id": "f8f2830ee9ca1e01", "metadata": { - "id": "f8f2830ee9ca1e01" + "id": "QuQigs4UoFQ2" }, "source": [ - "## Basic Usage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This package requires a PostgreSQL database with the `pgvector` extension.\n", + "## Create an engine\n", "\n", - "You can run the following command to spin up a container for a `pgvector` enabled Postgres instance:\n", + "The first step is to create a `PGEngine` instance, which does the following:\n", "\n", - "```shell\n", - "docker run --name pgvector-container -e POSTGRES_USER=langchain -e POSTGRES_PASSWORD=langchain -e POSTGRES_DB=langchain -p 6024:5432 -d pgvector/pgvector:pg16\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "OMvzMWRrR6n7", - "metadata": { - "id": "OMvzMWRrR6n7" - }, - "source": [ - "### Set the postgres connection url\n", - "\n", - "`PGVectorStore` can be used with the `asyncpg` and `psycopg3` drivers." + "1. Allows you to create tables for storing documents and embeddings.\n", + "2. Maintains a connection pool that manages connections to the database. This allows sharing of the connection pool and helps to reduce latency for database calls." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "irl7eMFnSPZr", + "execution_count": 1, "metadata": { - "id": "irl7eMFnSPZr" + "tags": [] }, "outputs": [], "source": [ - "# @title Set your values or use the defaults to connect to Docker { display-mode: \"form\" }\n", - "POSTGRES_USER = \"langchain\" # @param {type: \"string\"}\n", - "POSTGRES_PASSWORD = \"langchain\" # @param {type: \"string\"}\n", - "POSTGRES_HOST = \"localhost\" # @param {type: \"string\"}\n", - "POSTGRES_PORT = \"6024\" # @param {type: \"string\"}\n", - "POSTGRES_DB = \"langchain\" # @param {type: \"string\"}\n", - "TABLE_NAME = \"vectorstore\" # @param {type: \"string\"}\n", - "VECTOR_SIZE = 1024 # @param {type: \"int\"}" - ] - }, - { - "cell_type": "markdown", - "id": "QuQigs4UoFQ2", - "metadata": { - "id": "QuQigs4UoFQ2" - }, - "source": [ - "### PGEngine Connection Pool\n", - "\n", - "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.\n", - "\n", - "To create a `PGEngine` using `PGEngine.from_connection_string()` you need to provide:\n", + "from langchain_postgres import PGEngine\n", "\n", - "1. `url` : Connection string using the `postgresql+asyncpg` driver.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note:** This tutorial demonstrates the async interface. All async methods have corresponding sync methods." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ "# See docker command above to launch a Postgres instance with pgvector enabled.\n", + "# Replace these values with your own configuration.\n", + "POSTGRES_USER = \"langchain\"\n", + "POSTGRES_PASSWORD = \"langchain\"\n", + "POSTGRES_HOST = \"localhost\"\n", + "POSTGRES_PORT = \"6024\"\n", + "POSTGRES_DB = \"langchain\"\n", + "\n", "CONNECTION_STRING = (\n", " f\"postgresql+asyncpg://{POSTGRES_USER}:{POSTGRES_PASSWORD}@{POSTGRES_HOST}\"\n", " f\":{POSTGRES_PORT}/{POSTGRES_DB}\"\n", ")\n", - "# To use psycopg3 driver, set your connection string to `postgresql+psycopg://`" + "\n", + "pg_engine = PGEngine.from_connection_string(url=CONNECTION_STRING)" ] }, { - "cell_type": "code", - "execution_count": 4, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from langchain_postgres import PGEngine\n", - "\n", - "pg_engine = PGEngine.from_connection_string(url=CONNECTION_STRING)" + "To use psycopg3 driver, set your connection string to `postgresql+psycopg://`" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "D9Xs2qhm6X56" + }, "source": [ - "To create a `PGEngine` using `PGEngine.from_engine()` you need to provide:\n", + "## Create a document collection\n", "\n", - "1. `engine` : An object of `AsyncEngine`" + "Use the `PGEngine.ainit_vectorstore_table()` method to create a database table to store the documents and embeddings. This table will be created with appropriate schema." ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 2, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "from sqlalchemy.ext.asyncio import create_async_engine\n", + "TABLE_NAME = \"vectorstore\"\n", "\n", - "# Create an SQLAlchemy Async Engine\n", - "engine = create_async_engine(\n", - " CONNECTION_STRING,\n", - ")\n", - "\n", - "pg_engine = PGEngine.from_engine(engine=engine)" + "# The vector size (also called embedding size) is determined by the embedding model you use!\n", + "VECTOR_SIZE = 1536" ] }, { "cell_type": "markdown", - "metadata": { - "id": "D9Xs2qhm6X56" - }, + "metadata": {}, "source": [ - "### Initialize a table\n", - "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." + "Use the `Column` class to customize the table schema. 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." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { - "id": "avlyHEMn6gzU" + "id": "avlyHEMn6gzU", + "tags": [] }, "outputs": [], "source": [ - "await pg_engine.ainit_vectorstore_table(\n", - " table_name=TABLE_NAME,\n", - " vector_size=VECTOR_SIZE,\n", - ")" + "from langchain_postgres import Column\n", + "from sqlalchemy.exc import ProgrammingError\n", + "\n", + "try:\n", + " await pg_engine.ainit_vectorstore_table(\n", + " table_name=TABLE_NAME,\n", + " vector_size=VECTOR_SIZE,\n", + " id_column=Column(name=\"id\", data_type=\"INTEGER\", nullable=False),\n", + " metadata_columns=[\n", + " Column(\"location\", \"TEXT\"),\n", + " Column(\"topic\", \"TEXT\"),\n", + " ],\n", + " )\n", + "except ProgrammingError as e:\n", + " # Catching the exception here\n", + " print(\"Table already exists. Skipping creation.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Optional Tip: 💡\n", - "You can also specify a schema name by passing `schema_name` wherever you pass `table_name`. Eg:\n", + "### Configure an embeddings model\n", "\n", - "```python\n", - "SCHEMA_NAME=\"my_schema\"\n", + "You need to configure a vectorstore with an embedding model. The embedding model will be used automatically when adding documents and when searching.\n", "\n", - "await pg_engine.ainit_vectorstore_table(\n", - " table_name=TABLE_NAME,\n", - " vector_size=768,\n", - " schema_name=SCHEMA_NAME, # Default: \"public\"\n", - ")\n", - "```" + "We'll use `langchain-openai` as the embedding more here, but you can use any [LangChain embeddings model](https://python.langchain.com/docs/integrations/text_embedding/)." ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "### Create an embedding class instance\n", - "\n", - "You can use any [LangChain embeddings model](https://python.langchain.com/docs/integrations/text_embedding/)." + "%pip install --upgrade --quiet langchain-openai" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Vb2RJocV9_LQ", - "outputId": "37f5dc74-2512-47b2-c135-f34c10afdcf4" + "outputId": "37f5dc74-2512-47b2-c135-f34c10afdcf4", + "tags": [] }, "outputs": [], "source": [ - "from langchain_cohere import CohereEmbeddings\n", + "from langchain_openai import OpenAIEmbeddings\n", "\n", - "embedding = CohereEmbeddings(model=\"embed-english-v3.0\")" + "embedding = OpenAIEmbeddings(model=\"text-embedding-3-small\")" ] }, { @@ -240,24 +200,34 @@ "id": "e1tl0aNx7SWy" }, "source": [ - "### Initialize a default PGVectorStore" + "## Initialize the vectorstore\n", + "\n", + "Once the schema for the document collection exists, you can initialize a vectorstore that uses the schema.\n", + "\n", + "You can use the vectorstore to do basic operations; including:\n", + "\n", + "1. Add documents\n", + "2. Delete documents\n", + "3. Search through the documents" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { - "id": "z-AZyzAQ7bsf" + "id": "z-AZyzAQ7bsf", + "tags": [] }, "outputs": [], "source": [ "from langchain_postgres import PGVectorStore\n", "\n", - "store = await PGVectorStore.create(\n", + "vectorstore = await PGVectorStore.create(\n", " engine=pg_engine,\n", " table_name=TABLE_NAME,\n", - " # schema_name=SCHEMA_NAME,\n", " embedding_service=embedding,\n", + " id_column=\"id\",\n", + " metadata_columns=[\"location\", \"topic\"]\n", ")" ] }, @@ -265,72 +235,61 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Initialize Vector Store with documents\n", + "## Add documents\n", + "\n", + "\n", + "You can add documents using the `aadd_documents` method. \n", "\n", - "This is a great way to get started quickly. However, the default method is recommended for most applications to avoid accidentally adding duplicate documents." + "* Assign unique IDs to documents to avoid duplicated content in your database.\n", + "* Adding a document by ID implements has `upsert` semantics (i.e., create if does not exist, update if exists)." ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": null, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "from langchain_core.documents import Document\n", "import uuid\n", + "from langchain_core.documents import Document\n", + "\n", "\n", "docs = [\n", " Document(\n", - " page_content=\"Red Apple\",\n", - " metadata={\"description\": \"red\", \"content\": \"1\", \"category\": \"fruit\"},\n", + " id=uuid.uuid4(),\n", + " page_content=\"there are cats in the pond\",\n", + " metadata={\"id\": 1, \"location\": \"pond\", \"topic\": \"animals\"},\n", " ),\n", " Document(\n", - " page_content=\"Banana Cavendish\",\n", - " metadata={\"description\": \"yellow\", \"content\": \"2\", \"category\": \"fruit\"},\n", + " id=uuid.uuid4(),\n", + " page_content=\"ducks are also found in the pond\",\n", + " metadata={\"id\": 2, \"location\": \"pond\", \"topic\": \"animals\"},\n", " ),\n", " Document(\n", - " page_content=\"Orange Navel\",\n", - " metadata={\"description\": \"orange\", \"content\": \"3\", \"category\": \"fruit\"},\n", + " id=uuid.uuid4(),\n", + " page_content=\"fresh apples are available at the market\",\n", + " metadata={\"id\": 3, \"location\": \"market\", \"topic\": \"food\"},\n", + " ),\n", + " Document(\n", + " id=uuid.uuid4(),\n", + " page_content=\"the market also sells fresh oranges\",\n", + " metadata={\"id\": 4, \"location\": \"market\", \"topic\": \"food\"},\n", " ),\n", "]\n", - "ids = [str(uuid.uuid4()) for i in range(len(docs))]\n", "\n", - "store_with_documents = await PGVectorStore.afrom_documents(\n", - " documents=docs,\n", - " ids=ids,\n", - " engine=pg_engine,\n", - " table_name=TABLE_NAME,\n", - " embedding=embedding,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add texts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", "\n", - "all_texts = [\"Apples and oranges\", \"Cars and airplanes\", \"Pineapple\", \"Train\", \"Banana\"]\n", - "metadatas = [{\"len\": len(t)} for t in all_texts]\n", - "ids = [str(uuid.uuid4()) for _ in all_texts]\n", - "\n", - "await store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)" + "await vectorstore.aadd_documents(documents=docs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Delete texts" + "## Delete Documents\n", + "\n", + "Documents can be deleted by ID." ] }, { @@ -339,14 +298,18 @@ "metadata": {}, "outputs": [], "source": [ - "await store.adelete([ids[1]])" + "# We'll use the ID of the first doc to delete it\n", + "ids = [docs[0].id]\n", + "await vectorstore.adelete(ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents" + "## Search\n", + "\n", + "Search for similar documents using a natural language query." ] }, { @@ -356,15 +319,16 @@ "outputs": [], "source": [ "query = \"I'd like a fruit.\"\n", - "docs = await store.asimilarity_search(query)\n", - "print(docs)" + "docs = await vectorstore.asimilarity_search(query)\n", + "for doc in docs:\n", + " print(repr(doc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents by vector" + "### Search by vector" ] }, { @@ -374,7 +338,7 @@ "outputs": [], "source": [ "query_vector = embedding.embed_query(query)\n", - "docs = await store.asimilarity_search_by_vector(query_vector, k=2)\n", + "docs = await vectorstore.asimilarity_search_by_vector(query_vector, k=2)\n", "print(docs)" ] }, @@ -382,233 +346,63 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Add a Index\n", - "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)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_postgres.v2.indexes import IVFFlatIndex\n", - "\n", - "index = IVFFlatIndex()\n", - "await store.aapply_vector_index(index)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Re-index" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "await store.areindex() # Re-index using default index name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Remove an index" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "await store.adrop_vector_index() # Delete index using default name" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a custom Vector Store\n", - "A Vector Store can take advantage of relational data to filter similarity searches.\n", - "\n", - "Create a new table with custom metadata columns.\n", - "You can also re-use an existing table which already has custom columns for a Document's id, content, embedding, and/or metadata." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_postgres import Column\n", - "\n", - "# Set table name\n", - "TABLE_NAME = \"vectorstore_custom\"\n", - "# SCHEMA_NAME = \"my_schema\"\n", - "\n", - "await pg_engine.ainit_vectorstore_table(\n", - " table_name=TABLE_NAME,\n", - " # schema_name=SCHEMA_NAME,\n", - " vector_size=VECTOR_SIZE,\n", - " metadata_columns=[Column(\"len\", \"INTEGER\")],\n", - ")\n", - "\n", - "\n", - "# Initialize PGVectorStore\n", - "custom_store = await PGVectorStore.create(\n", - " engine=pg_engine,\n", - " table_name=TABLE_NAME,\n", - " # schema_name=SCHEMA_NAME,\n", - " embedding_service=embedding,\n", - " metadata_columns=[\"len\"],\n", - ")" + "## Filtering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents with metadata filter" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "\n", - "# Add texts to the Vector Store\n", - "all_texts = [\"Apples and oranges\", \"Cars and airplanes\", \"Pineapple\", \"Train\", \"Banana\"]\n", - "metadatas = [{\"len\": len(t)} for t in all_texts]\n", - "ids = [str(uuid.uuid4()) for _ in all_texts]\n", - "await custom_store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)\n", - "\n", - "# Use string filter on search\n", - "docs = await custom_store.asimilarity_search(query, filter=\"len >= 6\")\n", - "\n", - "print(docs)\n", + "To enable search with filters, it is necessary to declare the columns that you want to filter on when creating the table. The vectorstore supports a set of filters that can be applied against the metadata fields of the documents.\n", "\n", - "# Use a dictionary filter on search\n", - "docs = await custom_store.asimilarity_search(query, filter={\"len\": {\"$gte\": 6}})\n", + "`PGVectorStore` currently supports the following operators.\n", "\n", - "print(docs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create a Vector Store using existing table\n", - "\n", - "A Vector Store can be built up on an existing table.\n", - "\n", - "Assuming there's a pre-existing table in PG DB: `products`, which stores product details for an eComm venture.\n", - "\n", - "
\n", - " Click for Table Schema Details\n", - "\n", - " ### SQL query for table creation\n", - " ```\n", - " CREATE TABLE products (\n", - " product_id SERIAL PRIMARY KEY,\n", - " name VARCHAR(255) NOT NULL,\n", - " description TEXT,\n", - " price_usd DECIMAL(10, 2) NOT NULL,\n", - " category VARCHAR(255),\n", - " quantity INT DEFAULT 0,\n", - " sku VARCHAR(255) UNIQUE NOT NULL,\n", - " image_url VARCHAR(255),\n", - " metadata JSON,\n", - " embed vector(768) DEFAULT NULL --> vector dimensions depends on the embedding model\n", - " );\n", - " ```\n", - " ### Insertion of records\n", - " ```\n", - "INSERT INTO\n", - " products (name,\n", - " description,\n", - " price_usd,\n", - " category,\n", - " quantity,\n", - " sku,\n", - " image_url,\n", - " METADATA,\n", - " embed)\n", - "VALUES\n", - " ('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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- " ('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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- " ```\n", - "
\n", - "\n", - "Here is how this table mapped to `PGVectorStore`:\n", - "\n", - "- **`id_column=\"product_id\"`**: ID column uniquely identifies each row in the products table.\n", - "\n", - "- **`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.\n", - "\n", - "- **`embedding_column=\"embed\"`**: The `embed` column stores the vectors created from the product descriptions. These vectors are used to find products with similar descriptions.\n", - "\n", - "- **`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.\n", - "\n", - "- **`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.\n" + "| Operator | Meaning/Category |\n", + "|-----------|-------------------------|\n", + "| $eq | Equality (==) |\n", + "| $ne | Inequality (!=) |\n", + "| $lt | Less than (<) |\n", + "| $lte | Less than or equal (<=) |\n", + "| $gt | Greater than (>) |\n", + "| $gte | Greater than or equal (>=) |\n", + "| $in | Special Cased (in) |\n", + "| $nin | Special Cased (not in) |\n", + "| $between | Special Cased (between) |\n", + "| $exists | Special Cased (is null) |\n", + "| $like | Text (like) |\n", + "| $ilike | Text (case-insensitive like) |\n", + "| $and | Logical (and) |\n", + "| $or | Logical (or) |" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "# Set an existing table name\n", - "TABLE_NAME = \"products\"\n", - "# SCHEMA_NAME = \"my_schema\"\n", - "\n", - "# Initialize PGVectorStore\n", - "custom_store = await PGVectorStore.create(\n", - " engine=pg_engine,\n", - " table_name=TABLE_NAME,\n", - " # schema_name=SCHEMA_NAME,\n", - " embedding_service=embedding,\n", - " # Connect to existing VectorStore by customizing below column names\n", - " id_column=\"product_id\",\n", - " content_column=\"description\",\n", - " embedding_column=\"embed\",\n", - " metadata_columns=[\"name\", \"category\", \"price_usd\", \"quantity\", \"sku\", \"image_url\"],\n", - " metadata_json_column=\"metadata\",\n", - ")" + "query = \"meow\"\n", + "docs = await vectorstore.asimilarity_search(query, filter={\"topic\": \"food\"})\n", + "for doc in docs:\n", + " print(repr(doc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note: \n", - "\n", - "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: \n", + "## Optimization\n", "\n", - " `ALTER TABLE products ADD COLUMN embed vector(768) DEFAULT NULL`\n", - "\n", - "1. For new records, added via `VectorStore` embeddings are automatically generated." + "Speed up vector search queries by adding appropriate indexes. Learn more about [vector indexes](https://cloud.google.com/blog/products/databases/faster-similarity-search-performance-with-pgvector-indexes)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents with metadata filter\n", - "Since price_usd is one of the metadata_columns, we can use price filter while searching" + "### Add an Index" ] }, { @@ -617,19 +411,19 @@ "metadata": {}, "outputs": [], "source": [ - "import uuid\n", - "\n", - "docs = await custom_store.asimilarity_search(query, filter=\"price_usd > 100\")\n", + "from langchain_postgres.v2.indexes import IVFFlatIndex\n", "\n", - "print(docs)" + "index = IVFFlatIndex() # Add an index using a default index name\n", + "await vectorstore.aapply_vector_index(index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents with json filter\n", - "Since category is added in json metadata, we can use filter on JSON fields while searching\n" + "### Re-index\n", + "\n", + "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." ] }, { @@ -638,19 +432,16 @@ "metadata": {}, "outputs": [], "source": [ - "docs = await custom_store.asimilarity_search(\n", - " query, filter=\"metadata->>'category' = 'Electronics'\"\n", - ")\n", - "\n", - "print(docs)" + "await vectorstore.areindex() # Re-index using default index name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents with dictionary filter\n", - "Use a dictionary filter to make your filter DB Agnostic.\n" + "### Drop an index\n", + "\n", + "You can delete indexes" ] }, { @@ -659,9 +450,7 @@ "metadata": {}, "outputs": [], "source": [ - "docs = await custom_store.asimilarity_search(query, filter={\"category\": \"Electronics\"})\n", - "\n", - "print(docs)" + "await vectorstore.adrop_vector_index() # Drop index using default name" ] } ], @@ -671,7 +460,8 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -684,9 +474,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.11.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 }