Hybrid Search: Combining BM25 and Semantic Search for Better Results with Langchain

Hybrid search combines keyword search and vector search to enhance the retrieval of relevant documents by understanding both specific words and their contextual meanings. BM25, a ranking algorithm, plays a key role in keyword search by evaluating term frequency and document length, making it ideal for large document collections. On the other hand, vector search focuses on semantic meaning, aiming to comprehend the underlying context of queries. In a practical application, a hybrid search system begins with BM25 retrieving documents based on keywords, followed by a vector database (VectorDB) delving deeper into context, and finally, an Ensemble Retriever integrating both methods to refine the results. This approach is particularly useful for large digital libraries, ensuring comprehensive and nuanced document retrieval. Additionally, the use of tools like LanceDB and Langchain demonstrates the practical implementation of such a system, allowing for efficient information retrieval and improved search quality.