rag-architect

For each step, validate before moving on (see checkpoints below). Load detailed guidance based on context: Checkpoint: assert all(c.metadata.get("source") for c in chunks), "Missing source metadata" Checkpoint: assert qdrant.count("knowledge_base").count == len(set(p.id for p in points)), "Deduplication failed" Checkpoint: assert len(hybrid_search("test query", tenant_id="demo")) > 0, "Hybrid search returned no results" Checkpoint: Target context_precision >= 0.7 and context_recall >= 0.6 before moving to LLM integration. When designing RAG architecture, deliver: - Requirements Analysis — Identify retrieval needs, latency constraints, accuracy requirements, and scale - Vector Store Design — Select database, schema design, indexing strategy, sharding approach - Chunking Strategy — Document splitting, overlap, semantic boundaries, metadata enrichment - Retrieval Pipeline — Embedding selection, query transformation, hybrid search, reranking - Evaluation & Iteration — Metrics tracking, retrieval debugging, continuous optimization - Evaluate multiple embedding models on your domain data before committing - Implement hybrid search (vector + keyword) for production systems - Add metadata filters for multi-tenant or domain-specific retrieval - Measure retrieval metrics (precision@k, recall@k, MRR, NDCG) - Use reranking for top-k results before passing context to LLM - Implement...