rag-architect — Инструмент для проектирования RAG-архитектур

--- name: rag-architect description: Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing. license: MIT metadata: author: https://github.com/Jeffallan version: "1.1.0" domain: data-ml triggers: RAG, retrieval-augmented generation, vector search, embeddings, semantic search, vector database, document retrieval, knowledge base, context retrieval, similarity search role: architect scope: system-design output-format: architecture related-skills: python-pro, database-optimizer, monitoring-expert, api-designer --- # 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 splitter = RecursiveCharacterTextSplitter( chunk_size=800, chunk_overlap=100, separators=["nn", "n", ". ", " "], ) chunks = splitter.create_documents( texts=[doc.page_content for doc in raw_docs], metadatas=[{"source": doc.metadata["source"], "timestamp": doc.metadata.get("timestamp")} for doc in raw_docs], ) ``` **Checkpoint:** `assert all(c.metadata.get("source") for c in chunks), "Missing source metadata"` ### 2. Generating Embeddings & Indexing ```python from openai import OpenAI import qdrant_client from qdrant_client.models import VectorParams, Distance, PointStruct client = OpenAI() qdrant = qdrant_client.QdrantClient("localhost", port=6333) # Create collection qdrant.recreate_collection( collection_name="knowledge_base", vectors_config=VectorParams(size=1536, distance=Distance.COSINE), ) def embed_chunks(chunks: list[str], model: str = "text-embedding-3-small") -> list[list[float]]: response = client.embeddings.create(input=chunks, model=model) return [r.embedding for r in response.data] # Idempotent upsert with deduplication via deterministic IDs import hashlib, uuid points = [] for i, chunk in enumerate(chunks): doc_id = str(uuid.UUID(hashlib.md5(chunk.page_content.encode()).hexdigest())) embedding = embed_chunks([chunk.page_content])[0] points.append(PointStruct(id=doc_id, vector=embedding, payload=chunk.metadata)) qdrant.upsert(collection_name="knowledge_base", points=points) ``` **Checkpoint:** `assert qdrant.count("knowledge_base").count == len(set(p.id for p in points)), "Deduplication failed"` ### 3. Hybrid Search (Vector + BM25) ```python from qdrant_client.models import Filter, FieldCondition, MatchValue, SparseVector from rank_bm25 import BM25Okapi def hybrid_search(query: str, tenant_id: str, top_k: int = 20) -> list: # Dense retrieval query_embedding = embed_chunks([query])[0] tenant_filter = Filter(must=[FieldCondition(key="tenant_id", match=MatchValue(value=tenant_id))]) dense_results = qdrant.search( collection_name="knowledge_base", query_vector=query_embedding, query_filter=tenant_filter, limit=top_k, ) # Sparse retrieval (BM25) corpus = [r.payload.get("text", "") for r in dense_results] bm25 = BM25Okapi([doc.split() for doc in corpus]) bm25_scores = bm25.get_scores(query.split()) # Reciprocal Rank Fusion ranked = sorted( zip(dense_results, bm25_scores), key=lambda x: 0.6 * x[0].score + 0.4 * x[1], reverse=True, ) return [r for r, _ in ranked[:top_k]] ``` **Checkpoint:** `assert len(hybrid_search("test query", tenant_id="demo")) > 0, "Hybrid search returned no results"` ### 4. Reranking Top-K Results ```python import cohere co = cohere.Client("YOUR_API_KEY") def rerank(query: str, results: list, top_n: int = 5) -> list: docs = [r.payload.get("text", "") for r in results] reranked = co.rerank(query=query, documents=docs, top_n=top_n, model="rerank-english-v3.0") return [results[r.index] for r in reranked.results] ``` ### 5. Retrieval Evaluation ```python # Run precision@k and recall@k against a labeled evaluation set # python evaluate.py --metrics precision@10 recall@10 mrr --collection knowledge_base from ragas import evaluate from ragas.metrics import context_precision, context_recall, faithfulness, answer_relevancy from datasets import Dataset eval_dataset = Dataset.from_dict({ "question": questions, "contexts": retrieved_contexts, "answer": generated_answers, "ground_truth": ground_truth_answers, }) results = evaluate(eval_dataset, metrics=[context_precision, context_recall, faithfulness, answer_relevancy]) print(results) ``` **Checkpoint:** Target `context_precision >= 0.7` and `context_recall >= 0.6` before moving to LLM integration. ## Constraints ### MUST DO - 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 idempotent ingestion with deduplication (deterministic IDs) - Monitor retrieval latency and quality over time - Version embeddings and plan for model migration ### MUST NOT DO - Use default chunk size (512) without evaluation on your domain data - Skip metadata enrichment (source, timestamp, section) - Ignore retrieval quality metrics in favor of only LLM output quality - Store raw documents without preprocessing/cleaning - Use cosine similarity alone for complex multi-domain retrieval - Deploy without testing on production-like data volumes - Forget to handle edge cases (empty results, malformed docs) - Couple the embedding model tightly to application code ## Output Templates When designing RAG architecture, deliver: 1. System architecture diagram (ingestion + retrieval pipelines) 2. Vector database selection with trade-off analysis 3. Chunking strategy with examples and rationale 4. Retrieval pipeline design (query → results flow) 5. Evaluation plan with metrics, benchmarks, and pass/fail thresholds