Enterprise Knowledge Assistant
Hybrid dense-sparse vector RAG architecture with contextual chunk re-ranking and factual grounding verification.
A prototype retrieval engine that indexes enterprise documentation and returns cited, factual responses without model hallucinations.
The Research Challenge
Naive vector search often misses exact keyword nuances (part numbers, legal IDs) or retrieves out-of-context text chunks.
Engineering Approach
We combine BM25 sparse keyword search with Qdrant dense vector embeddings, passed through a cross-encoder re-ranker stage.
Hybrid Reciprocal Rank Fusion (RRF)
Contextual Document Chunking
Cross-Encoder Re-Ranking Pipeline
Citation Anchor Verification
Evaluating retrieval recall metrics on 500,000 internal engineering documents.
Developing streaming multi-modal PDF table extraction modules.
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