🔎 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.
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