Integration on Vchord

LangChain

Mon, 01 Jan 0001 00:00:00 +0000

This document describes the LangChain integration.

LangChain is a framework for developing applications powered by language models. It is a set of tools that allows you to fine-tune, deploy, and use language models in production.

pgvecto.rs provides a LangChain integration that allows you to retrieve the most similar vectors in LangChain.

Install dependencies

Some dependencies are required to use the LangChain integration:

pip install langchain langchain-openai pgvecto-rs

You could start the postgres instance with pgvecto.rs extension in a docker container:

LlamaIndex

Mon, 01 Jan 0001 00:00:00 +0000

LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models (LLMs).

pgvecto.rs provides a LlamaIndex integration that allows you to retrieve the most similar vectors.

Install dependencies

Some dependencies are required to use the LlamaIndex integration:

pip install llama-index "pgvecto_rs[sdk]"

You could start the postgres instance with pgvecto.rs extension in a docker container:

docker run \
  --name pgvecto-rs-demo \
  -e POSTGRES_PASSWORD=mysecretpassword \
  -p 5432:5432 \
  -d tensorchord/pgvecto-rs:pg16-v0.2.0

Then you can connect to the database using the psql command line tool. The default username is postgres, and the default password is mysecretpassword.

Citus

Mon, 01 Jan 0001 00:00:00 +0000

citus is a PostgreSQL extension that turns Postgres into a distributed database. pgvecto.rs works well with citus Here are the steps to enable vector search in a distributed PostgreSQL instance.

citus can works on Single-Node and Multi-Node, both of which are compatible with pgvecto.rs natively.

Single-Node Citus

First, prepare the dockerfile with pgvecto.rs and citus installed and attach them to PostgreSQL.

Details

citus must be placed first in shared_preload_libraries due to its restrictions.