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Building LLM Applications in LangChain using pgvecto.rs

Fri, 08 Mar 2024 00:00:00 +0000

LangChain is a framework for developing applications powered by language models. It enables applications that:

Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc.)
Reason: rely on a language model to reason (about how to answer based on provided context, what actions to take, etc.)

pgvecto.rs is a vector search extension in Postgres. In this blog post, we’ll show you how to build LLM applications with LangChain and pgvecto.rs.

Building the RAG pipeline with LlamaIndex and pgvecto.rs

Fri, 08 Mar 2024 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. In this blog post, we will show you how to build a RAG pipeline with pgvecto.rs and LlamaIndex.

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:

Do we really need a specialized vector database?

Fri, 08 Mar 2024 00:00:00 +0000

With the popularity of LLM (Large Language Model), vector databases have also become a hot topic. With just a few lines of simple Python code, a vector database can act as a cheap but highly effective “external brain” for your LLM. But do we really need a specialized vector database?

Why does LLM need vector search?

First, let me briefly introduce why LLM needs to use vector search technology. Vector search is a problem that has been around for a long time. The process of finding the most similar object in a collection given an object is vector search. Text/images, etc. can be converted into a vector representation, and the similarity problem of text/images can be transformed into a vector similarity problem.