This project demonstrates the creation of a hybrid search Retrieval-Augmented Generation (RAG) pipeline using the LangChain library. The pipeline integrates BM25 and vector search techniques to enhance the accuracy and relevance of results in question-answering tasks.
The hybrid search RAG pipeline leverages the strengths of both BM25, a traditional term-based search method, and vector search, which uses embeddings to capture semantic meaning. By combining these approaches, the pipeline is designed to improve the retrieval of contextually relevant documents, leading to more precise and informative answers.
- BM25 Search: Utilizes a classic retrieval method based on term frequency and document relevance.
- Vector Search: Employs embeddings to find semantically similar documents, enhancing the search process.
- LangChain Integration: Demonstrates the use of LangChain to build and manage the RAG pipeline.
- Efficient Question Answering: The pipeline supports advanced question-answering by combining search methods for superior results.