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Timescale Vector (Postgres) self-querying

Timescale Vector is PostgreSQL++ for AI applications. It enables you to efficiently store and query billions of vector embeddings in PostgreSQL.

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.

What is Timescale Vector?

Timescale Vector is PostgreSQL++ for AI applications.

Timescale Vector enables you to efficiently store and query millions of vector embeddings in PostgreSQL. - Enhances pgvector with faster and more accurate similarity search on 1B+ vectors via DiskANN inspired indexing algorithm. - Enables fast time-based vector search via automatic time-based partitioning and indexing. - Provides a familiar SQL interface for querying vector embeddings and relational data.

Timescale Vector is cloud PostgreSQL for AI that scales with you from POC to production: - Simplifies operations by enabling you to store relational metadata, vector embeddings, and time-series data in a single database. - Benefits from rock-solid PostgreSQL foundation with enterprise-grade feature liked streaming backups and replication, high-availability and row-level security. - Enables a worry-free experience with enterprise-grade security and compliance.

How to access Timescale Vector

Timescale Vector is available on Timescale, the cloud PostgreSQL platform. (There is no self-hosted version at this time.)

LangChain users get a 90-day free trial for Timescale Vector. - To get started, signup to Timescale, create a new database and follow this notebook! - See the Timescale Vector explainer blog for more details and performance benchmarks. - See the installation instructions for more details on using Timescale Vector in python.

Creating a TimescaleVector vectorstore

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.

NOTE: The self-query retriever requires you to have lark installed (pip install lark). We also need the timescale-vector package.

%pip install --upgrade --quiet  lark
%pip install --upgrade --quiet  timescale-vector

In this example, we’ll use OpenAIEmbeddings, so let’s load your OpenAI API key.

# Get openAI api key by reading local .env file
# The .env file should contain a line starting with `OPENAI_API_KEY=sk-`
import os

from dotenv import find_dotenv, load_dotenv

_ = load_dotenv(find_dotenv())

OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
# Alternatively, use getpass to enter the key in a prompt
# import os
# import getpass
# os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

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.

If you haven’t already, signup for Timescale, and create a new database.

The URI will look something like this: postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require

# Get the service url by reading local .env file
# The .env file should contain a line starting with `TIMESCALE_SERVICE_URL=postgresql://`
_ = load_dotenv(find_dotenv())
TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]

# Alternatively, use getpass to enter the key in a prompt
# import os
# import getpass
# TIMESCALE_SERVICE_URL = getpass.getpass("Timescale Service URL:")
from langchain_community.vectorstores.timescalevector import TimescaleVector
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

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.

docs = [
Document(
page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
),
Document(
page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},
),
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, "director": "Satoshi Kon", "rating": 8.6},
),
Document(
page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",
metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},
),
Document(
page_content="Toys come alive and have a blast doing so",
metadata={"year": 1995, "genre": "animated"},
),
Document(
page_content="Three men walk into the Zone, three men walk out of the Zone",
metadata={
"year": 1979,
"director": "Andrei Tarkovsky",
"genre": "science fiction",
"rating": 9.9,
},
),
]

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.

COLLECTION_NAME = "langchain_self_query_demo"
vectorstore = TimescaleVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name=COLLECTION_NAME,
service_url=TIMESCALE_SERVICE_URL,
)

Creating our self-querying retriever

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.

from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import OpenAI

# Give LLM info about the metadata fields
metadata_field_info = [
AttributeInfo(
name="genre",
description="The genre of the movie",
type="string or list[string]",
),
AttributeInfo(
name="year",
description="The year the movie was released",
type="integer",
),
AttributeInfo(
name="director",
description="The name of the movie director",
type="string",
),
AttributeInfo(
name="rating", description="A 1-10 rating for the movie", type="float"
),
]
document_content_description = "Brief summary of a movie"

# Instantiate the self-query retriever from an LLM
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)

Self Querying Retrieval with Timescale Vector

And now we can try actually using our retriever!

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.

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!

# This example only specifies a relevant query
retriever.get_relevant_documents("What are some movies about dinosaurs")
/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.
warnings.warn(
query='dinosaur' filter=None limit=None
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}),
Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),
Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]
# This example only specifies a filter
retriever.get_relevant_documents("I want to watch a movie rated higher than 8.5")
query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None
[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'}),
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'}),
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'}),
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'})]
# This example specifies a query and a filter
retriever.get_relevant_documents("Has Greta Gerwig directed any movies about women")
query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None
[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'}),
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'})]
# This example specifies a composite filter
retriever.get_relevant_documents(
"What's a highly rated (above 8.5) science fiction film?"
)
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
[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'}),
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'})]
# This example specifies a query and composite filter
retriever.get_relevant_documents(
"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated"
)
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
[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]

Filter k

We can also use the self query retriever to specify k: the number of documents to fetch.

We can do this by passing enable_limit=True to the constructor.

retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
enable_limit=True,
verbose=True,
)
# This example specifies a query with a LIMIT value
retriever.get_relevant_documents("what are two movies about dinosaurs")
query='dinosaur' filter=None limit=2
[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7}),
Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'genre': 'science fiction', 'rating': 7.7})]