Enterprise RAG 2.0: Knowledge Graphs, Hybrid Vector Lexical Search & Self Reflective Agentic Retrieval

Dense vector embeddings excel at semantic similarity but frequently fail at exact entity lookups (e.g., part numbers, legal clauses, specific dates). Moreover, breaking long documents into isolated 500-token chunks shatters global context and relationships spanning multiple chapters or contracts.

We implement reciprocal rank fusion (RRF) combining dense vector embeddings (OpenAI / Cohere) with sparse lexical search (BM25 or SPLADE). Candidates retrieved from both indexes pass through a cross encoder reranker model (such as Cohere Rerank 3 or BGE Reranker), reordering results based on true conversational relevance.

Knowledge Graphs model entities (people, organizations, systems) and directed relationships (owns, operates, depends on) extracted from enterprise corpora using LLMs. By combining community clustering algorithms (Leiden) with vector search, GraphRAG answers high level holistic questions across thousands of documents that naive chunking cannot resolve.

Rather than naively generating an answer from retrieved documents, the agent evaluates the retrieved context for relevancy, completeness, and contradictions. If the context is insufficient, the agent autonomously rewrites the search query, adjusts retrieval parameters, or initiates secondary queries before synthesizing the final response.