Rust + Python RAG Chunking Pipeline

Problem

Naive text chunking can exceed embedding model token limits, cause truncation, and degrade retrieval quality.

Solution

Built a token-aware RAG ingestion pipeline where Rust handles performance-critical chunking, Python orchestrates embeddings and retrieval, and Qdrant stores searchable vectors.

Deliverables

• Rust tokenizer/chunker exposed to Python through PyO3
• Python orchestration package
• Qdrant vector store integration
• CLI commands for ingest/search/ask
• Local and OpenAI embedding paths
• Hallucination guard
• Benchmark results

Why it matters

• Rust chunking eliminates token overflow failures that silently degrade retrieval quality in pure-Python stacks
• PyO3 bridge gives you Rust performance without rewriting your existing Python workflow
• Hallucination guard reduces the risk of confident wrong answers from the LLM layer
• Pluggable embeddings: OpenAI for quality, local sentence-transformers to cut API costs