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https://github.com/hwchase17/langchain
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feat: Supprt pgvecto.rs as a VectorStore (#12718)
Supprt [pgvecto.rs](https://github.com/tensorchord/pgvecto.rs) as a new VectorStore type. This introduces a new dependency [pgvecto_rs](https://pypi.org/project/pgvecto_rs/) and upgrade SQLAlchemy to ^2. Relate to https://github.com/tensorchord/pgvecto.rs/issues/11 --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
盐粒 Yanli
GitHub
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commit
1b233798a0
214
docs/docs/integrations/vectorstores/pgvecto_rs.ipynb
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214
docs/docs/integrations/vectorstores/pgvecto_rs.ipynb
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@ -0,0 +1,214 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# PGVecto.rs\n",
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"\n",
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"This notebook shows how to use functionality related to the Postgres vector database ([pgvecto.rs](https://github.com/tensorchord/pgvecto.rs)). You need to install SQLAlchemy >= 2 manually."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"## Loading Environment Variables\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import List\n",
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"from langchain.embeddings.openai import OpenAIEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores.pgvecto_rs import PGVecto_rs\n",
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"from langchain.document_loaders import TextLoader\n",
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"from langchain.docstore.document import Document"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = TextLoader(\"../../../state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Start the database with the [official demo docker image](https://github.com/tensorchord/pgvecto.rs#installation)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
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"docker run --name pgvecto-rs-demo -e POSTGRES_PASSWORD=mysecretpassword -p 5432:5432 -d tensorchord/pgvecto-rs:latest"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Then contruct the db URL"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"## PGVecto.rs needs the connection string to the database.\n",
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"## We will load it from the environment variables.\n",
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"import os\n",
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"\n",
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"PORT = os.getenv(\"DB_PORT\", 5432)\n",
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"HOST = os.getenv(\"DB_HOST\", \"localhost\")\n",
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"USER = os.getenv(\"DB_USER\", \"postgres\")\n",
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"PASS = os.getenv(\"DB_PASS\", \"mysecretpassword\")\n",
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"DB_NAME = os.getenv(\"DB_NAME\", \"postgres\")\n",
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"\n",
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"# Run tests with shell:\n",
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"URL = \"postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}\".format(\n",
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" port=PORT,\n",
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" host=HOST,\n",
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" username=USER,\n",
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" password=PASS,\n",
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" db_name=DB_NAME,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Finally, create the VectorStore from the documents:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"db1 = PGVecto_rs.from_documents(\n",
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" documents=docs,\n",
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" embedding=embeddings,\n",
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" db_url=URL,\n",
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" # The table name is f\"collection_{collection_name}\", so that it should be unique.\n",
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" collection_name=\"state_of_the_union\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can connect to the table laterly with:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create new empty vectorstore with collection_name.\n",
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"# Or connect to an existing vectorstore in database if exists.\n",
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"# Arguments should be the same as when the vectorstore was created.\n",
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"db1 = PGVecto_rs.from_collection_name(\n",
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" embedding=embeddings,\n",
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" db_url=URL,\n",
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" collection_name=\"state_of_the_union\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Make sure that the user is permitted to create a table."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Similarity search with score"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Similarity Search with Euclidean Distance (Default)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs: List[Document] = db1.similarity_search(query, k=4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"for doc in docs:\n",
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" print(doc.page_content)\n",
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" print(\"======================\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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249
libs/langchain/langchain/vectorstores/pgvecto_rs.py
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249
libs/langchain/langchain/vectorstores/pgvecto_rs.py
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from __future__ import annotations
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import uuid
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from typing import Any, Iterable, List, Literal, Optional, Tuple, Type
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import numpy as np
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import sqlalchemy
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from sqlalchemy import insert, select
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from sqlalchemy.dialects import postgresql
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from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column
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from sqlalchemy.orm.session import Session
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from langchain.schema import Document
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from langchain.schema.embeddings import Embeddings
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from langchain.schema.vectorstore import VectorStore
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class _ORMBase(DeclarativeBase):
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__tablename__: str
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id: Mapped[uuid.UUID]
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text: Mapped[str]
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meta: Mapped[dict]
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embedding: Mapped[np.ndarray]
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class PGVecto_rs(VectorStore):
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_engine: sqlalchemy.engine.Engine
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_table: Type[_ORMBase]
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_embedding: Embeddings
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def __init__(
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self,
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embedding: Embeddings,
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dimension: int,
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db_url: str,
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collection_name: str,
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new_table: bool = False,
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) -> None:
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try:
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from pgvecto_rs.sqlalchemy import Vector
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except ImportError as e:
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raise ImportError(
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"Unable to import pgvector_rs, please install with "
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"`pip install pgvector_rs`."
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) from e
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class _Table(_ORMBase):
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__tablename__ = f"collection_{collection_name}"
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id: Mapped[uuid.UUID] = mapped_column(
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postgresql.UUID(as_uuid=True), primary_key=True, default=uuid.uuid4
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)
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text: Mapped[str] = mapped_column(sqlalchemy.String)
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meta: Mapped[dict] = mapped_column(postgresql.JSONB)
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embedding: Mapped[np.ndarray] = mapped_column(Vector(dimension))
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self._engine = sqlalchemy.create_engine(db_url)
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self._table = _Table
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self._table.__table__.create(self._engine, checkfirst=not new_table) # type: ignore
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self._embedding = embedding
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# ================ Create interface =================
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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db_url: str = "",
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collection_name: str = str(uuid.uuid4().hex),
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**kwargs: Any,
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) -> PGVecto_rs:
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"""Return VectorStore initialized from texts and optional metadatas."""
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sample_embedding = embedding.embed_query("Hello pgvecto_rs!")
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dimension = len(sample_embedding)
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if db_url is None:
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raise ValueError("db_url must be provided")
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_self: PGVecto_rs = cls(
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embedding=embedding,
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dimension=dimension,
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db_url=db_url,
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collection_name=collection_name,
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new_table=True,
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)
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_self.add_texts(texts, metadatas, **kwargs)
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return _self
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@classmethod
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def from_documents(
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cls,
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documents: List[Document],
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embedding: Embeddings,
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db_url: str = "",
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collection_name: str = str(uuid.uuid4().hex),
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**kwargs: Any,
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) -> PGVecto_rs:
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"""Return VectorStore initialized from documents."""
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texts = [document.page_content for document in documents]
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metadatas = [document.metadata for document in documents]
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return cls.from_texts(
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texts, embedding, metadatas, db_url, collection_name, **kwargs
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)
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@classmethod
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def from_collection_name(
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cls,
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embedding: Embeddings,
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db_url: str,
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collection_name: str,
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) -> PGVecto_rs:
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"""Create new empty vectorstore with collection_name.
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Or connect to an existing vectorstore in database if exists.
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Arguments should be the same as when the vectorstore was created."""
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sample_embedding = embedding.embed_query("Hello pgvecto_rs!")
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return cls(
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embedding=embedding,
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dimension=len(sample_embedding),
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db_url=db_url,
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collection_name=collection_name,
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)
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# ================ Insert interface =================
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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kwargs: vectorstore specific parameters
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Returns:
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List of ids of the added texts.
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"""
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embeddings = self._embedding.embed_documents(list(texts))
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with Session(self._engine) as _session:
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results: List[str] = []
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for text, embedding, metadata in zip(
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texts, embeddings, metadatas or [dict()] * len(list(texts))
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):
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t = insert(self._table).values(
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text=text, meta=metadata, embedding=embedding
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)
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id = _session.execute(t).inserted_primary_key[0] # type: ignore
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results.append(str(id))
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_session.commit()
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return results
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def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
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"""Run more documents through the embeddings and add to the vectorstore.
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Args:
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documents (List[Document]): List of documents to add to the vectorstore.
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Returns:
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List of ids of the added documents.
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"""
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return self.add_texts(
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[document.page_content for document in documents],
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[document.metadata for document in documents],
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**kwargs,
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)
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# ================ Query interface =================
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def similarity_search_with_score_by_vector(
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self,
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query_vector: List[float],
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k: int = 4,
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distance_func: Literal[
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"sqrt_euclid", "neg_dot_prod", "ned_cos"
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] = "sqrt_euclid",
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query vector, with its score."""
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with Session(self._engine) as _session:
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real_distance_func = (
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self._table.embedding.squared_euclidean_distance
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if distance_func == "sqrt_euclid"
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else self._table.embedding.negative_dot_product_distance
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if distance_func == "neg_dot_prod"
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else self._table.embedding.negative_cosine_distance
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if distance_func == "ned_cos"
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else None
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)
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if real_distance_func is None:
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raise ValueError("Invalid distance function")
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t = (
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select(self._table, real_distance_func(query_vector).label("score"))
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.order_by("score")
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.limit(k) # type: ignore
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)
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return [
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(Document(page_content=row[0].text, metadata=row[0].meta), row[1])
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for row in _session.execute(t)
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]
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def similarity_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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distance_func: Literal[
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"sqrt_euclid", "neg_dot_prod", "ned_cos"
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] = "sqrt_euclid",
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**kwargs: Any,
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) -> List[Document]:
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return [
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doc
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for doc, score in self.similarity_search_with_score_by_vector(
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embedding, k, distance_func, **kwargs
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)
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]
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def similarity_search_with_score(
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self,
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query: str,
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k: int = 4,
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distance_func: Literal[
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"sqrt_euclid", "neg_dot_prod", "ned_cos"
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] = "sqrt_euclid",
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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query_vector = self._embedding.embed_query(query)
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return self.similarity_search_with_score_by_vector(
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query_vector, k, distance_func, **kwargs
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)
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def similarity_search(
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self,
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query: str,
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k: int = 4,
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distance_func: Literal[
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"sqrt_euclid", "neg_dot_prod", "ned_cos"
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] = "sqrt_euclid",
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs most similar to query."""
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query_vector = self._embedding.embed_query(query)
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return [
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doc
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for doc, score in self.similarity_search_with_score_by_vector(
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query_vector, k, distance_func, **kwargs
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)
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]
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