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550 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "13afcae7",
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"metadata": {},
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"source": [
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"# Timescale Vector (Postgres) \n",
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"\n",
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">[Timescale Vector](https://www.timescale.com/ai) is `PostgreSQL++` for AI applications. It enables you to efficiently store and query billions of vector embeddings in `PostgreSQL`.\n",
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">\n",
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">[PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL) also known as `Postgres`,\n",
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"> is a free and open-source relational database management system (RDBMS) \n",
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"> emphasizing extensibility and `SQL` compliance.\n",
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"\n",
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"This notebook shows how to use the Postgres vector database (`TimescaleVector`) to perform self-querying. In the notebook we'll demo the `SelfQueryRetriever` wrapped around a TimescaleVector vector store. \n",
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"\n",
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"## What is Timescale Vector?\n",
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"**[Timescale Vector](https://www.timescale.com/ai) is PostgreSQL++ for AI applications.**\n",
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"\n",
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"Timescale Vector enables you to efficiently store and query millions of vector embeddings in `PostgreSQL`.\n",
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"- Enhances `pgvector` with faster and more accurate similarity search on 1B+ vectors via DiskANN inspired indexing algorithm.\n",
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"- Enables fast time-based vector search via automatic time-based partitioning and indexing.\n",
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"- Provides a familiar SQL interface for querying vector embeddings and relational data.\n",
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"\n",
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"Timescale Vector is cloud PostgreSQL for AI that scales with you from POC to production:\n",
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"- Simplifies operations by enabling you to store relational metadata, vector embeddings, and time-series data in a single database.\n",
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"- Benefits from rock-solid PostgreSQL foundation with enterprise-grade feature liked streaming backups and replication, high-availability and row-level security.\n",
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"- Enables a worry-free experience with enterprise-grade security and compliance.\n",
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"\n",
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"## How to access Timescale Vector\n",
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"Timescale Vector is available on [Timescale](https://www.timescale.com/ai), the cloud PostgreSQL platform. (There is no self-hosted version at this time.)\n",
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"\n",
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"LangChain users get a 90-day free trial for Timescale Vector.\n",
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"- To get started, [signup](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) to Timescale, create a new database and follow this notebook!\n",
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"- See the [Timescale Vector explainer blog](https://www.timescale.com/blog/how-we-made-postgresql-the-best-vector-database/?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral) for more details and performance benchmarks.\n",
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"- See the [installation instructions](https://github.com/timescale/python-vector) for more details on using Timescale Vector in python.\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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"id": "68e75fb9",
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"metadata": {},
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"source": [
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"## Creating a TimescaleVector vectorstore\n",
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"First we'll want to create a Timescale Vector vectorstore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
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"\n",
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"NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `timescale-vector` package."
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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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"id": "63a8af5b",
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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 --upgrade --quiet lark"
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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": 2,
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"id": "22431060-52c4-48a7-a97b-9f542b8b0928",
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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 --upgrade --quiet timescale-vector"
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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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"id": "83811610-7df3-4ede-b268-68a6a83ba9e2",
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"metadata": {},
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"source": [
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"In this example, we'll use `OpenAIEmbeddings`, so let's load your 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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"id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840",
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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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"# Get openAI api key by reading local .env file\n",
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"# The .env file should contain a line starting with `OPENAI_API_KEY=sk-`\n",
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"import os\n",
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"\n",
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"from dotenv import find_dotenv, load_dotenv\n",
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"\n",
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"_ = load_dotenv(find_dotenv())\n",
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"\n",
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"OPENAI_API_KEY = os.environ[\"OPENAI_API_KEY\"]\n",
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"# Alternatively, use getpass to enter the key in a prompt\n",
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"# import os\n",
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"# import getpass\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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"attachments": {},
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"cell_type": "markdown",
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"id": "766e9c4b",
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"metadata": {},
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"source": [
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"To connect to your PostgreSQL database, you'll need your service URI, which can be found in the cheatsheet or `.env` file you downloaded after creating a new database. \n",
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"\n",
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"If you haven't already, [signup for Timescale](https://console.cloud.timescale.com/signup?utm_campaign=vectorlaunch&utm_source=langchain&utm_medium=referral), and create a new database.\n",
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"\n",
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"The URI will look something like this: `postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require`"
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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": 2,
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"id": "6bd6877e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the service url by reading local .env file\n",
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"# The .env file should contain a line starting with `TIMESCALE_SERVICE_URL=postgresql://`\n",
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"_ = load_dotenv(find_dotenv())\n",
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"TIMESCALE_SERVICE_URL = os.environ[\"TIMESCALE_SERVICE_URL\"]\n",
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"\n",
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"# Alternatively, use getpass to enter the key in a prompt\n",
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"# import os\n",
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"# import getpass\n",
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"# TIMESCALE_SERVICE_URL = getpass.getpass(\"Timescale Service 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": 3,
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"id": "cb4a5787",
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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_community.vectorstores.timescalevector import TimescaleVector\n",
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"from langchain_core.documents import Document\n",
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"from langchain_openai import OpenAIEmbeddings\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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"attachments": {},
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"cell_type": "markdown",
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"id": "a4f863f5",
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"metadata": {},
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"source": [
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"Here's the sample documents we'll use for this demo. The data is about movies, and has both content and metadata fields with information about particular movie."
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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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"id": "bcbe04d9",
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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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"docs = [\n",
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" Document(\n",
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" page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
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" metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
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" metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
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" metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
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" metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Toys come alive and have a blast doing so\",\n",
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" metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
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" ),\n",
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" Document(\n",
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" page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
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" metadata={\n",
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" \"year\": 1979,\n",
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" \"director\": \"Andrei Tarkovsky\",\n",
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" \"genre\": \"science fiction\",\n",
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" \"rating\": 9.9,\n",
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" },\n",
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" ),\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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"id": "7d0d771e",
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"metadata": {},
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"source": [
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"Finally, we'll create our Timescale Vector vectorstore. Note that the collection name will be the name of the PostgreSQL table in which the documents are stored in."
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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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"id": "2428d1ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"COLLECTION_NAME = \"langchain_self_query_demo\"\n",
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"vectorstore = TimescaleVector.from_documents(\n",
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" embedding=embeddings,\n",
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" documents=docs,\n",
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" collection_name=COLLECTION_NAME,\n",
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" service_url=TIMESCALE_SERVICE_URL,\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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"id": "5ecaab6d",
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"metadata": {},
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"source": [
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"## Creating our self-querying retriever\n",
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"Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
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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": 14,
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"id": "86e34dbf",
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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.chains.query_constructor.base import AttributeInfo\n",
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"from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
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"from langchain_openai import OpenAI\n",
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"\n",
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"# Give LLM info about the metadata fields\n",
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"metadata_field_info = [\n",
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" AttributeInfo(\n",
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" name=\"genre\",\n",
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" description=\"The genre of the movie\",\n",
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" type=\"string or list[string]\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"year\",\n",
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" description=\"The year the movie was released\",\n",
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" type=\"integer\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"director\",\n",
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" description=\"The name of the movie director\",\n",
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" type=\"string\",\n",
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" ),\n",
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" AttributeInfo(\n",
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" name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
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" ),\n",
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"]\n",
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"document_content_description = \"Brief summary of a movie\"\n",
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"\n",
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"# Instantiate the self-query retriever from an LLM\n",
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"llm = OpenAI(temperature=0)\n",
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"retriever = SelfQueryRetriever.from_llm(\n",
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" llm, vectorstore, document_content_description, metadata_field_info, verbose=True\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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"id": "ea9df8d4",
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"metadata": {},
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"source": [
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"## Self Querying Retrieval with Timescale Vector\n",
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"And now we can try actually using our retriever!\n",
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"\n",
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"Run the queries below and note how you can specify a query, filter, composite filter (filters with AND, OR) in natural language and the self-query retriever will translate that query into SQL and perform the search on the Timescale Vector (Postgres) vectorstore.\n",
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"\n",
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"This illustrates the power of the self-query retriever. You can use it to perform complex searches over your vectorstore without you or your users having to write any SQL directly!"
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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": 15,
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"id": "38a126e9",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/libs/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
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" warnings.warn(\n"
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]
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},
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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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"query='dinosaur' filter=None limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}),\n",
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" Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n",
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" Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
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]
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},
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"execution_count": 15,
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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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"# This example only specifies a relevant query\n",
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"retriever.invoke(\"What are some movies about dinosaurs\")"
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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": 16,
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"id": "fc3f1e6e",
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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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"query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'}),\n",
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" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'}),\n",
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" Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'rating': 8.6, 'director': 'Satoshi Kon'}),\n",
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" Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'rating': 8.6, 'director': 'Satoshi Kon'})]"
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]
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},
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"execution_count": 16,
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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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"# This example only specifies a filter\n",
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"retriever.invoke(\"I want to watch a movie rated higher than 8.5\")"
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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": 17,
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"id": "b19d4da0",
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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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"query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'rating': 8.3, 'director': 'Greta Gerwig'}),\n",
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" Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'rating': 8.3, 'director': 'Greta Gerwig'})]"
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]
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},
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"execution_count": 17,
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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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"# This example specifies a query and a filter\n",
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"retriever.invoke(\"Has Greta Gerwig directed any movies about women\")"
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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": 18,
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"id": "f900e40e",
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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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"query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='science fiction')]) limit=None\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'}),\n",
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" Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'genre': 'science fiction', 'rating': 9.9, 'director': 'Andrei Tarkovsky'})]"
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]
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},
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"execution_count": 18,
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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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"# This example specifies a composite filter\n",
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"retriever.invoke(\"What's a highly rated (above 8.5) science fiction film?\")"
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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": 11,
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"id": "12a51522",
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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": [
|
|
"query='toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GT: 'gt'>, attribute='year', value=1990), Comparison(comparator=<Comparator.LT: 'lt'>, attribute='year', value=2005), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated')]) limit=None\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# This example specifies a query and composite filter\n",
|
|
"retriever.invoke(\n",
|
|
" \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Filter k\n",
|
|
"\n",
|
|
"We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
|
|
"\n",
|
|
"We can do this by passing `enable_limit=True` to the constructor."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"retriever = SelfQueryRetriever.from_llm(\n",
|
|
" llm,\n",
|
|
" vectorstore,\n",
|
|
" document_content_description,\n",
|
|
" metadata_field_info,\n",
|
|
" enable_limit=True,\n",
|
|
" verbose=True,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "2758d229-4f97-499c-819f-888acaf8ee10",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"query='dinosaur' filter=None limit=2\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}),\n",
|
|
" Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7})]"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# This example specifies a query with a LIMIT value\n",
|
|
"retriever.invoke(\"what are two movies about dinosaurs\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.12"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|