Gen-AI
AI/ML
Building a Hybrid Search Knowledge Base Service
Developing a high-performance document retrieval system for AI Agents using FastAPI, Elasticsearch, and Qdrant.
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9 min read
Beyond Simple Vector Search
Standard vector search often misses exact keyword matches. A robust Knowledge Base for RAG (Retrieval-Augmented Generation) requires Hybrid Search, combining BM25 full-text search with Dense Vector KNN search.
Reciprocal Rank Fusion (RRF)Using Elasticsearch, we can execute both semantic and keyword queries simultaneously, fusing the results using RRF for maximum relevance. Inspired by advanced chunking strategies, we can also retrieve surrounding context (parent-child chunks) to provide LLMs with complete information.
Hybrid Retrieval Flow
sequenceDiagram
Client->>API: Hybrid Query
API->>VertexAI: Generate Embedding
API->>ES: RRF Search (KNN + BM25)
ES-->>API: Fused Results
API->>DB: Fetch Neighbor Chunks
API-->>Client: Final Context
python
@app.post("/retrieve")
async def retrieve_documents(query: str):
embedding = await generate_embedding(query)
results = await es_client.hybrid_search(query, embedding)
return enrich_with_context(results)