What is FAISS and how is it used in a LangChain to create a RAG (retrieval-augmented generation) chain?

19 September 2023
FAISS

Summary #

FAISS (Facebook AI Similarity Search) is a library developed by Facebook AI Research that enables efficient similarity search and clustering of dense vectors. In a LangChain, FAISS is used to create a RAG (retrieval-augmented generation) chain by indexing and retrieving relevant context from a large corpus of text, which is then used to generate more accurate and context-aware responses.

In Depth #

What is FAISS? #

FAISS is an open-source library developed by Facebook AI Research that specializes in efficient similarity search and clustering of dense vectors. It is particularly useful for large-scale applications, as it can handle billions of vectors and perform searches in milliseconds. FAISS is written in C++ and has Python bindings, making it accessible for a wide range of applications.

How is FAISS used in a LangChain? #

A LangChain, or Language Model Chain, is a sequence of language models that work together to generate more accurate and context-aware responses. In a LangChain, FAISS is used to index and retrieve relevant context from a large corpus of text. This is done by representing the text as dense vectors and using FAISS to perform efficient similarity search.

The retrieved context is then fed into the next language model in the chain, which generates a response based on the input and the retrieved context. This process can be repeated with multiple language models in the chain, each refining the response based on the context provided by the previous models.

RAG: Retrieval-Augmented Generation #

RAG is a method that combines the power of pre-trained language models with the ability to retrieve relevant context from a large corpus of text. In a RAG chain, a language model generates a query based on the input, and FAISS is used to retrieve relevant context from the indexed text. The retrieved context is then used to condition the generation of the response, making it more accurate and context-aware.

RAG has been shown to improve the performance of language models in tasks such as question-answering, summarization, and dialogue systems. By leveraging the power of FAISS for efficient similarity search, RAG chains can scale to handle large corpora and generate high-quality responses in real-time.