langchain/templates/rag-timescale-hybrid-search-time

RAG with Timescale Vector using hybrid search

This template shows how to use timescale-vector with the self-query retriver to perform hybrid search on similarity and time. This is useful any time your data has a strong time-based component. Some examples of such data are:

• News articles (politics, business, etc)
• Blog posts, documentation or other published material (public or private).
• Social media posts
• Changelogs of any kind
• Messages

Such items are often searched by both similarity and time. For example: Show me all news about Toyota trucks from 2022.

Timescale Vector provides superior performance when searching for embeddings within a particular timeframe by leveraging automatic table partitioning to isolate data for particular time-ranges.

Langchain's self-query retriever allows deducing time-ranges (as well as other search criteria) from the text of user queries.

What is Timescale Vector?

Timescale Vector is PostgreSQL++ for AI applications.

Timescale Vector enables you to efficiently store and query billions 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 installation instructions for more details on using Timescale Vector in python.

Using Timescale Vector with this template

This template uses TimescaleVector as a vectorstore and requires that TIMESCALES_SERVICE_URL is set.

LLM

Be sure that OPENAI_API_KEY is set in order to the OpenAI models.

Loading sample data

We have provided a sample dataset you can use for demoing this template. It consists of the git history of the timescale project.

To load this dataset, set the LOAD_SAMPLE_DATA environmental variable.

Loading your own dataset.

To load your own dataset you will have to modify the code in the DATASET SPECIFIC CODE section of chain.py. This code defines the name of the collection, how to load the data, and the human-language description of both the contents of the collection and all of the metadata. The human-language descriptions are used by the self-query retriever to help the LLM convert the question into filters on the metadata when searching the data in Timescale-vector.

Using in your own applications

This is a standard LangServe template. Instructions on how to use it with your LangServe applications are here.