diff --git a/examples/pg_vectorstore.ipynb b/examples/pg_vectorstore.ipynb index 4d25d41..7bf10bd 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", + "```" ] }, { @@ -17,126 +26,75 @@ "id": "IR54BmgvdHT_" }, "source": [ - "### 🦜🔗 Library Installation\n", - "Install the integration library, `langchain-postgres`." + "## Install\n", + "\n", + "Install the langchain-postgres package" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "0ZITIDE160OD", - "outputId": "e184bc0d-6541-4e0a-82d2-1e216db00a2d" + "outputId": "e184bc0d-6541-4e0a-82d2-1e216db00a2d", + "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "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", - "\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", + "The first step is to create a `PGEngine` instance, which does the following:\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 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://`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_postgres import PGEngine\n", "\n", "pg_engine = PGEngine.from_connection_string(url=CONNECTION_STRING)" ] @@ -145,65 +103,74 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To create a `PGEngine` using `PGEngine.from_engine()` you need to provide:\n", - "\n", - "1. `engine` : An object of `AsyncEngine`" + "To use psycopg3 driver, set your connection string to `postgresql+psycopg://`" ] }, { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "metadata": { + "id": "D9Xs2qhm6X56" + }, "source": [ - "from sqlalchemy.ext.asyncio import create_async_engine\n", - "\n", - "# Create an SQLAlchemy Async Engine\n", - "engine = create_async_engine(\n", - " CONNECTION_STRING,\n", - ")\n", + "## Create a document collection\n", "\n", - "pg_engine = PGEngine.from_engine(engine=engine)" + "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": "markdown", + "cell_type": "code", + "execution_count": 2, "metadata": { - "id": "D9Xs2qhm6X56" + "tags": [] }, + "outputs": [], "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." + "TABLE_NAME = \"vectorstore\"\n", + "\n", + "# The vector size (also called embedding size) is determined by the embedding model you use!\n", + "VECTOR_SIZE = 1536" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": { - "id": "avlyHEMn6gzU" + "id": "avlyHEMn6gzU", + "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table already exists. Skipping creation.\n" + ] + } + ], "source": [ - "await pg_engine.ainit_vectorstore_table(\n", - " table_name=TABLE_NAME,\n", - " vector_size=VECTOR_SIZE,\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", + " )\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", + "You can specify a schema name by passing `schema_name`. It defaults to \"public\" if not specified.\n", "\n", "```python\n", - "SCHEMA_NAME=\"my_schema\"\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", + " schema_name=\"my_schema\",\n", ")\n", "```" ] @@ -212,26 +179,51 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Create an embedding class instance\n", + "### Configure an embeddings model\n", "\n", - "You can use any [LangChain embeddings model](https://python.langchain.com/docs/integrations/text_embedding/)." + "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", + "We'll use langchain-cohere as the embedding more here, but you can use any [LangChain embeddings model](https://python.langchain.com/docs/integrations/text_embedding/)." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install --upgrade --quiet langchain-openai" + ] + }, + { + "cell_type": "code", + "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\")" ] }, { @@ -240,23 +232,31 @@ "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": 6, "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", ")" ] @@ -265,98 +265,151 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Initialize Vector Store with documents\n", + "## Add documents\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." + "\n", + "You can add documents using the `aadd_documents` method. \n", + "\n", + "* 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": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['1d4eaabd-eb02-42c9-8630-48a0364414a6',\n", + " '0d083f18-eb74-4796-ab1a-d906ecc9e05e',\n", + " '5b73bb7c-9a9a-476b-a219-d2e65ad98b25',\n", + " '3411f088-5a6c-4c17-b25f-a1b02cd8c679']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "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", + " 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=\"Banana Cavendish\",\n", - " metadata={\"description\": \"yellow\", \"content\": \"2\", \"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", - " page_content=\"Orange Navel\",\n", - " metadata={\"description\": \"orange\", \"content\": \"3\", \"category\": \"fruit\"},\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", - ")" + "\n", + "await vectorstore.aadd_documents(documents=docs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Add texts" + "## Delete Documents\n", + "\n", + "Documents can be deleted by ID." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "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)" + "# 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": [ - "### Delete texts" + "## Search" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Document(id='5b73bb7c-9a9a-476b-a219-d2e65ad98b25', metadata={'id': 3, 'location': 'market', 'topic': 'food'}, page_content='fresh apples are available at the market')\n", + "Document(id='3411f088-5a6c-4c17-b25f-a1b02cd8c679', metadata={'id': 4, 'location': 'market', 'topic': 'food'}, page_content='the market also sells fresh oranges')\n", + "Document(id='0d083f18-eb74-4796-ab1a-d906ecc9e05e', metadata={'id': 2, 'location': 'pond', 'topic': 'animals'}, page_content='ducks are also found in the pond')\n" + ] + } + ], "source": [ - "await store.adelete([ids[1]])" + "query = \"I'd like a fruit.\"\n", + "docs = await vectorstore.asimilarity_search(query)\n", + "for doc in docs:\n", + " print(repr(doc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents" + "### Search by vector" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Document(id='5b73bb7c-9a9a-476b-a219-d2e65ad98b25', metadata={'id': 3, 'location': 'market', 'topic': 'food'}, page_content='fresh apples are available at the market'), Document(id='3411f088-5a6c-4c17-b25f-a1b02cd8c679', metadata={'id': 4, 'location': 'market', 'topic': 'food'}, page_content='the market also sells fresh oranges')]\n" + ] + } + ], "source": [ - "query = \"I'd like a fruit.\"\n", - "docs = await store.asimilarity_search(query)\n", + "query_vector = embedding.embed_query(query)\n", + "docs = await vectorstore.asimilarity_search_by_vector(query_vector, k=2)\n", "print(docs)" ] }, @@ -364,118 +417,190 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents by vector" + "## Filtering" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To enable search with filters, you will need to declare the columns that you want to filter on when creating the table." ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "query_vector = embedding.embed_query(query)\n", - "docs = await store.asimilarity_search_by_vector(query_vector, k=2)\n", - "print(docs)" + "from langchain_postgres import Column\n", + "from langchain_postgres import PGVectorStore\n", + "\n", + "\n", + "TABLE_NAME = \"my_vectorstore_2\"\n", + "\n", + "await pg_engine.ainit_vectorstore_table(\n", + " table_name=TABLE_NAME,\n", + " vector_size=VECTOR_SIZE,\n", + " metadata_columns=[\n", + " Column(\"location\", \"TEXT\"), \n", + " Column(\"topic\", \"TEXT\"), \n", + " ],\n", + ")" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 20, + "metadata": { + "tags": [] + }, + "outputs": [], "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)." + "vectorstore = await PGVectorStore.create(\n", + " engine=pg_engine,\n", + " table_name=TABLE_NAME,\n", + " embedding_service=embedding,\n", + " metadata_columns=[\"location\", \"topic\"],\n", + ")" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "execution_count": 24, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "from langchain_postgres.v2.indexes import IVFFlatIndex\n", "\n", - "index = IVFFlatIndex()\n", - "await store.aapply_vector_index(index)" + "vectorstore = await PGVectorStore.create(\n", + " engine=pg_engine,\n", + " table_name=TABLE_NAME,\n", + " embedding_service=embedding,\n", + ")\n" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['eb49c185-c14f-4496-a5ea-7730f56cccf3',\n", + " '6b7fe5e1-cbc8-489f-a50c-a76912682654',\n", + " '914aa95e-56e4-4e33-9d45-d9e8e8d99091',\n", + " 'f3620c05-4f85-4e01-8802-5c84012ca752']" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "### Re-index" + "import uuid\n", + "from langchain_core.documents import Document\n", + "\n", + "\n", + "docs = [\n", + " Document(\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", + " 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", + " 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", + "\n", + "\n", + "await vectorstore.aadd_documents(documents=docs)" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": 23, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ - "await store.areindex() # Re-index using default index name" + "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": [ + "## Optimization\n", + "\n", + "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": [ - "### Remove an index" + "### Add an Index" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "await store.adrop_vector_index() # Delete index using default name" + "from langchain_postgres.v2.indexes import IVFFlatIndex\n", + "\n", + "index = IVFFlatIndex()\n", + "await vectorstore.aapply_vector_index(index)" ] }, { "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." + "### Re-index" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "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", - ")" + "await store.areindex() # Re-index using default index name" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Search for documents with metadata filter" + "### Drop an index\n", + "\n", + "You can delete indexes" ] }, { @@ -484,30 +609,21 @@ "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", - "\n", - "# Use a dictionary filter on search\n", - "docs = await custom_store.asimilarity_search(query, filter={\"len\": {\"$gte\": 6}})\n", - "\n", - "print(docs)" + "await store.adrop_vector_index() # Drop index using default name" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Create a Vector Store using existing table\n", + "### Create a Vector Store using existing table\n", "\n", "A Vector Store can be built up on an existing table.\n", "\n", @@ -568,7 +684,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], "source": [ "# Set an existing table name\n", @@ -602,67 +720,6 @@ "\n", "1. For new records, added via `VectorStore` embeddings are automatically generated." ] - }, - { - "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" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import uuid\n", - "\n", - "docs = await custom_store.asimilarity_search(query, filter=\"price_usd > 100\")\n", - "\n", - "print(docs)" - ] - }, - { - "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" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "docs = await custom_store.asimilarity_search(\n", - " query, filter=\"metadata->>'category' = 'Electronics'\"\n", - ")\n", - "\n", - "print(docs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Search for documents with dictionary filter\n", - "Use a dictionary filter to make your filter DB Agnostic.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "docs = await custom_store.asimilarity_search(query, filter={\"category\": \"Electronics\"})\n", - "\n", - "print(docs)" - ] } ], "metadata": { @@ -671,7 +728,8 @@ "toc_visible": true }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { @@ -684,9 +742,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.8" + "version": "3.11.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/langchain_postgres/vectorstores.py b/langchain_postgres/vectorstores.py index 3ca9950..a3743e4 100644 --- a/langchain_postgres/vectorstores.py +++ b/langchain_postgres/vectorstores.py @@ -429,13 +429,6 @@ def __init__( self._async_engine: Optional[AsyncEngine] = None self._async_init = False - warnings.warn( - "PGVector is being deprecated and will be removed in the future. " - "Please migrate to PGVectorStore. " - "Refer to the migration guide at [https://github.com/langchain-ai/langchain-postgres/blob/main/examples/migrate_pgvector_to_pgvectorstore.md] for details.", - PendingDeprecationWarning, - ) - if isinstance(connection, str): if async_mode: self._async_engine = create_async_engine( diff --git a/poetry.lock b/poetry.lock index e4510fa..50ffee8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1023,6 +1023,7 @@ description = "IPython: Productive Interactive Computing" optional = false python-versions = ">=3.9" groups = ["dev"] +markers = "python_version < \"3.12\"" files = [ {file = "ipython-8.18.1-py3-none-any.whl", hash = "sha256:e8267419d72d81955ec1177f8a29aaa90ac80ad647499201119e2f05e99aa397"}, {file = "ipython-8.18.1.tar.gz", hash = "sha256:ca6f079bb33457c66e233e4580ebfc4128855b4cf6370dddd73842a9563e8a27"}, @@ -1054,6 +1055,39 @@ qtconsole = ["qtconsole"] test = ["pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath"] test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.22)", "pandas", "pickleshare", "pytest (<7.1)", "pytest-asyncio (<0.22)", "testpath", "trio"] +[[package]] +name = "ipython" +version = "9.1.0" +description = "IPython: Productive Interactive Computing" +optional = false +python-versions = ">=3.11" +groups = ["dev"] +markers = "python_version >= \"3.12\"" +files = [ + {file = "ipython-9.1.0-py3-none-any.whl", hash = "sha256:2df07257ec2f84a6b346b8d83100bcf8fa501c6e01ab75cd3799b0bb253b3d2a"}, + {file = "ipython-9.1.0.tar.gz", hash = "sha256:a47e13a5e05e02f3b8e1e7a0f9db372199fe8c3763532fe7a1e0379e4e135f16"}, +] + +[package.dependencies] +colorama = {version = "*", markers = "sys_platform == \"win32\""} +decorator = "*" +ipython-pygments-lexers = "*" +jedi = ">=0.16" +matplotlib-inline = "*" +pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""} +prompt_toolkit = ">=3.0.41,<3.1.0" +pygments = ">=2.4.0" +stack_data = "*" +traitlets = ">=5.13.0" + +[package.extras] +all 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