Hybrid RAG
Document intelligence at the intersection of semantic search, knowledge graphs, and retrieval routing.
DOCUMENT INTELLIGENCEHybrid RAGPython / FastAPI / FAISS / LangGraphProject case study ↗
01 / CONTEXT
The problem and the approach.
The problem
A document answer is only as useful as the context behind it. Semantic similarity and explicit relationships capture different parts of that context.
The approach
A hybrid retrieval design combines vector search and graph traversal, routes the question, and assembles context before answer generation.
02 / CAPABILITIES
What the project does.
- 01Document ingestion and chunking
- 02Semantic vector retrieval
- 03Knowledge graph exploration
- 04Retrieval routing
- 05Answers with supporting context
03 / ARCHITECTURE
Under the surface.
Select a component to understand its role.
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Engineering decisions.
- 01Keep ingestion separate from query execution so prepared sample data remains available without a live model.
- 02Use vector retrieval for semantic similarity and a graph for explicit relationships.
- 03Label the portfolio demonstration as illustrative; live generation requires a separately configured backend.
SOURCE
Browse source code ↗