AI’nt That Easy #13: Advanced RAG -The Power of Re-Ranking

Implementation with FAISS, Groq, Ollama, LangChain, Mistral and Cohere

Retrieval-Augmented Generation (RAG) has revolutionized how AI systems access and utilize information. However, the effectiveness of RAG heavily depends on the quality of retrieved information. This is where re-ranking comes into play. In this blog post, we’ll dive deep into the importance of re-ranking in RAG systems and demonstrate an advanced implementation using FAISS, Groq, Ollama, LangChain, and Cohere.

Why Re-Ranking is Crucial

Imagine you’re building a movie recommendation chatbot. A user asks, “What are some good comedy movies to watch this weekend?” Your initial retrieval system might return the following results:

• A list of all-time top-rated movies (including various genres)
• A blog post on the history of comedy in cinema
• A review of the latest action movies
• A guide to creating your own movie marathon
• A list of popular comedy movies from the last year (the most relevant document)

Without re-ranking, your RAG system might generate a response based on these results in order, potentially focusing on irrelevant information about top-rated movies or action films. This is where re-ranking becomes crucial.