rag-architect

# RAG Architect ## Core Workflow 1. **Requirements Analysis** — Identify retrieval needs, latency constraints, accuracy requirements, and scale 2. **Vector Store Design** — Select database, schema design, indexing strategy, sharding approach 3. **Chunking Strategy** — Document splitting, overlap, semantic boundaries, metadata enrichment 4. **Retrieval Pipeline** — Embedding selection, query transformation, hybrid search, reranking 5. **Evaluation & Iteration** — Metrics tracking, retrieval debugging, continuous optimization For each step, validate before moving on (see checkpoints below). ## Reference Guide Load detailed guidance based on context: | Topic | Reference | Load When | |-------|-----------|-----------| | Vector Databases | `references/vector-databases.md` | Comparing Pinecone, Weaviate, Chroma, pgvector, Qdrant | | Embedding Models | `references/embedding-models.md` | Selecting embeddings, fine-tuning, dimension trade-offs | | Chunking Strategies | `references/chunking-strategies.md` | Document splitting, overlap, semantic chunking | | Retrieval Optimization | `references/retrieval-optimization.md` | Hybrid search, reranking, query expansion, filtering | | RAG Evaluation | `references/rag-evaluation.md` | Metrics, evaluation frameworks, debugging retrieval | ## Implementation Examples ### 1. Chunking Documents ```python from langchain.text_splitter import RecursiveCharacterTextSplitter # Evaluate chunk_size on your domain data — never use 512 blindly spl...