An Efficient Retrieval-Augmented Hybrid Search Framework with LLM-Based Re-ranking for Enterprise Knowledge Systems

The enterprise knowledge systems should have effective and smart search capabilities to handle the increasing amount of diverse information. The classical keyword-based retrieval systems can hardly retrieve the semantic relationships, whereas pure vector-based systems can be inaccurate and lack contextual usability. To overcome these shortcomings, this paper suggests a Retrieval-Augmented Hybrid Search Framework which combines semantic vector search, keyword-based indexing and large language model (LLM) facilitated response generation. The framework builds on embedding models to encode documents in a high-dimensional vector space and couples them with standard models of inverted indexing to facilitate hybrid retrieval. Ranking fusion approach is utilized in order to enhance the relevance of retrieved results, which are then narrowed down using LLM-based contextual understanding and summarization. The implemented system is developed with the help of tools such as FAISS, Elasticsearch, and LangChain. Experimental analysis shows that the hybrid methodology is far more successful compared to pure retrieval techniques with regard to Precision@K, Recall and quality of response. Moreover, the framework improves user experience by providing succinct and context-sensitive responses. The findings suggest that the suggested method gives a scalable and efficient knowledge retrieval solution at the enterprise level.