Unorganized documentation makes finding reliable information difficult. Contextual AI is a retrieval-first workspace built to turn unorganized knowledge into clear, verifiable answers.
System Architecture
The architecture prioritizes provenance and precision over raw generative speed:
- Document Ingestion & Chunking: Recursive semantic chunking with metadata preservation.
- Hybrid Search Index: Combines dense vector embeddings with BM25 sparse keyword retrieval.
- Re-Ranking & Citation: Reranks top candidates and forces model output to explicitly cite line-level sources.
# Hybrid Search Retriever
def retrieve_context(query: str, top_k: int = 5):
vector_candidates = vector_store.similarity_search(query, k=top_k * 2)
sparse_candidates = bm25_index.search(query, k=top_k * 2)
reciprocal_ranks = reciprocal_rank_fusion(vector_candidates, sparse_candidates)
return reranker.rank(reciprocal_ranks)[:top_k]
The goal is to build answers users can inspect and trust, rather than black-box responses.