Advanced RAG 03 - Hybrid Search BM25 & Ensembles

🔎 Hybrid search combines keyword and vector style search for improved retrieval.

🔍 BM25 is a powerful algorithm for creating sparse vectors in hybrid search.

⚡ BM25 has been effective even against newer techniques using deep learning.

🔑 BM 25 is a search algorithm that uses term frequency and inverse document frequency.

⚡ BM 25 is fast and efficient compared to other methods like using vectors or embeddings.

💡 LangChain allows for easy implementation of a BM 25 sparse retriever for efficient lookups.

🔍 Combining keyword lookup and semantic lookup using an ensemble retriever.

💡 Utilizing vector stores and BM 25 for retrieval and calculations.

🎯 Demonstrating the ambiguity of the word 'apple' and its multiple meanings.

🔎 Keyword retrieval in BM25 and its use in search algorithms

🍎 Using the word 'apple' as a query in BM25 retrieval

🔍 The behavior of BM25 retrieval with non-direct matches

🔍 Using OpenAI embeddings for semantic lookup in a faiss vector store.

🔄 Creating an ensemble retriever by combining different retrievers.

⚖️ Setting up a weighting system to determine result ranking in the ensemble retriever.

🍎 Hybrid search combines keyword search and semantic search.

💡 Hybrid search is beneficial for cases where specific words or names need to be found.

📚 Hybrid search can be useful in certain projects and provides advantages over purely semantic search.

🔍 Hybrid Search combines BM25 and Ensembles for improved retrieval.

💡 Try out Hybrid Search for your own use cases to see its effectiveness.

📺 Check out more videos in the series for different techniques in retrieval augmented generation.