Advanced RAG 03 - Hybrid Search BM25 & Ensembles

The video discusses the concept of hybrid search, combining keyword-style retrieval with semantic lookup using the BM25 algorithm and embedding retriever in LangChain. By blending the strengths of both methods, users can benefit from a more comprehensive and accurate search experience, catering to a wider range of search queries and requirements.

“Hybrid search” is a combination of keyword-style and vector-style searches, offering the benefits of both. The BM25 algorithm is a well-established tool that enables users to create sparse vectors by counting words or N-grams and applying TFIDF principles. BM25 is known for its quick computational speed compared to dense methods like embeddings. In LangChain, a BM25 sparse retriever can be easily implemented by importing the BM25 retriever.

To enhance search capabilities, BM25 can be combined with an embedding retriever in an ensemble retriever. The embedding retriever utilizes semantic lookup to provide results based on meanings and relationships between words. By merging the keyword lookup of BM25 with the semantic lookup of the embedding retriever, users can benefit from a hybrid search approach that leverages both methods’ strengths.

In an example scenario within LangChain, documents containing varying mentions of the word “apple” illustrate the differences between keyword and semantic retrieval. The BM25 retriever excels at keyword retrieval by returning documents with direct matches, while the embedding retriever performs semantic retrieval by considering the contextual meaning of words. The ensemble retriever blends the outputs from both retrievers, utilizing a weighting system to rank and present the combined results effectively.

The advantages of hybrid search become apparent as it offers a balance between finding exact words through keyword search and determining contextual relevance through semantic search. This approach can be beneficial for use cases where precise keyword matches are required or when searching for specific terms related to names or other structured content. Ultimately, users are encouraged to experiment with hybrid search techniques in their projects to evaluate its effectiveness based on their specific needs and contexts.

In conclusion, the integration of BM25 and embedding retrievers in an ensemble retriever within LangChain showcases the potential of hybrid search in information retrieval applications. By combining keyword and semantic search capabilities, users can achieve a more comprehensive and accurate search experience, catering to a wider range of search queries and requirements. Experimenting with hybrid search methods allows individuals to explore the nuances of different retrieval techniques and optimize search functionalities for various use cases in data retrieval and generation tasks.