Document Chunking & Embedding Strategies.
Successful Retrieval-Augmented Generation (RAG) relies on smart text chunking. Overlapping semantic paragraphs preserves context when generating vector embeddings.
We utilize high-performance vector databases (Pinecone / Qdrant) with HNSW indexing for sub-20ms vector similarity lookup.
Latency Optimization & Streaming Responses.
LLM API calls can introduce latency. Utilizing Server-Sent Events (SSE) to stream token responses instantly keeps the user interface responsive and interactive.
Implementing hybrid semantic + keyword search guarantees fallback precision when vector similarity alone falls short.
Implement semantic paragraph chunking for vector embeddings.
Stream token responses via Server-Sent Events (SSE) to eliminate UI lag.
Combine vector search with keyword fallbacks for hybrid precision.
