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3a2855945b
This PR adds in documentation on querying an existing vectorstore in PG Fixes 3191 (issue)
306 lines
11 KiB
Plaintext
306 lines
11 KiB
Plaintext
{
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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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"# PGVector\n",
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"\n",
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">[PGVector](https://github.com/pgvector/pgvector) is an open-source vector similarity search for `Postgres`\n",
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"\n",
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"It supports:\n",
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"- exact and approximate nearest neighbor search\n",
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"- L2 distance, inner product, and cosine distance\n",
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"\n",
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"This notebook shows how to use the Postgres vector database (`PGVector`)."
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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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"See the [installation instruction](https://github.com/pgvector/pgvector)."
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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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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install pgvector"
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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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"We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key."
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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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"import os\n",
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"import getpass\n",
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"\n",
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"os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')"
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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": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"False"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"## Loading Environment Variables\n",
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"from typing import List, Tuple\n",
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"from dotenv import load_dotenv\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": 4,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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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.pgvector import PGVector\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": 3,
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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": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"## PGVector 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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"CONNECTION_STRING = PGVector.connection_string_from_db_params(\n",
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" driver=os.environ.get(\"PGVECTOR_DRIVER\", \"psycopg2\"),\n",
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" host=os.environ.get(\"PGVECTOR_HOST\", \"localhost\"),\n",
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" port=int(os.environ.get(\"PGVECTOR_PORT\", \"5432\")),\n",
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" database=os.environ.get(\"PGVECTOR_DATABASE\", \"postgres\"),\n",
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" user=os.environ.get(\"PGVECTOR_USER\", \"postgres\"),\n",
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" password=os.environ.get(\"PGVECTOR_PASSWORD\", \"postgres\"),\n",
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")\n",
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"\n",
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"\n",
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"## Example\n",
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"# postgresql+psycopg2://username:password@localhost:5432/database_name"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# The PGVector Module will try to create a table with the name of the collection. So, make sure that the collection name is unique and the user has the \n",
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"# permission to create a table.\n",
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"\n",
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"db = PGVector.from_documents(\n",
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" embedding=embeddings,\n",
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" documents=docs,\n",
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" collection_name=\"state_of_the_union\",\n",
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" connection_string=CONNECTION_STRING,\n",
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")\n",
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"\n",
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query)"
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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": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"--------------------------------------------------------------------------------\n",
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"Score: 0.6076628081132506\n",
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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
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"--------------------------------------------------------------------------------\n",
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"--------------------------------------------------------------------------------\n",
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"Score: 0.6076628081132506\n",
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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
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"--------------------------------------------------------------------------------\n",
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"--------------------------------------------------------------------------------\n",
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"Score: 0.6076804780049968\n",
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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
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"--------------------------------------------------------------------------------\n",
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"--------------------------------------------------------------------------------\n",
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"Score: 0.6076804780049968\n",
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"Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n",
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"\n",
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"Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n",
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"\n",
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"One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n",
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"\n",
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n",
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"--------------------------------------------------------------------------------\n"
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]
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}
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],
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"source": [
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"for doc, score in docs_with_score:\n",
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" print(\"-\" * 80)\n",
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" print(\"Score: \", score)\n",
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" print(doc.page_content)\n",
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" print(\"-\" * 80)\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Working with vectorstore in PG"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Uploading a vectorstore in PG "
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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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"db = PGVector.from_documents(\n",
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" documents=data,\n",
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" embedding=embeddings,\n",
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" collection_name=collection_name,\n",
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" connection_string=connection_string,\n",
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" distance_strategy=DistanceStrategy.COSINE,\n",
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" openai_api_key=api_key,\n",
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" pre_delete_collection=False \n",
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")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Retrieving a vectorstore in PG"
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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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"store = PGVector(\n",
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" connection_string=connection_string, \n",
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" embedding_function=embedding, \n",
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" collection_name=collection_name,\n",
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" distance_strategy=DistanceStrategy.COSINE\n",
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")\n",
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"\n",
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"retriever = store.as_retriever()"
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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": "3.10.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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