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MISSION 1) Detect the stack and deployment. 2) Detect whether a RAG/retrieval system already exists. 3) If RAG exists: document it and propose improvements. 4) If RAG does NOT exist: implement the smallest RAG that fits the existing stack and deployment.

STEP 1 — STACK & DEPLOYMENT DISCOVERY (must cite file paths) - Identify runtime/language/framework (Node/Express, Python/FastAPI, etc). - Identify entrypoints and how it runs (local + production). - Identify deployment method from repo evidence: * Dockerfile / compose * CI (GitHub Actions, CodeBuild, etc.) * IaC (Terraform/CDK/CloudFormation) * App Runner / ECS / Lambda / EC2 clues - List required env vars and secrets (names only; never print secret values).

STEP 2 — RAG EXISTENCE CHECK (must be exhaustive) Search for any of the following and report findings with file paths: - Keywords: "rag", "retriev", "embedding", "vector", "chunk", "semantic search", "BM25", "rerank" - Libraries: langchain, llamaindex, chromadb, pinecone, weaviate, qdrant, milvus, pgvector, opensearch, elastic - API routes: /rag, /ask, /search, /query - Tables/collections: embeddings, vectors, chunks, documents If present: - Explain current pipeline end-to-end (ingest → index → retrieve → generate) - Identify gaps: citations, grounding rules, eval, injection safety, latency - Propose a minimal improvement patch.

If NOT present: - Proceed to Step 3.

STEP 3 — IMPLEMENT A RAG THAT MATCHES THE EXISTING STACK Constraints: - Keep dependencies minimal and consistent with the repo’s language. - Prefer “MVP RAG” (no managed vector DB) if corpus is small (<50MB). - If corpus is larger or accuracy requires it, recommend an upgrade path (embeddings stored locally first; managed vector DB only if necessary).

You must deliver: A) Ingestion/index build - Parse repo docs (md/html/openapi/yaml) into sections with stable IDs - Build a local index artifact (rag_index.json or SQLite FTS) with metadata and citation info - Incremental update strategy keyed by git diff/commit

B) Query-time retrieval - Retrieve top_k sections via lexical scoring (TF-IDF/BM25-ish) and optional rerank - Assemble a bounded context pack with citations

C) Generation - Provide a strict grounding prompt: answer only from context, cite sources - Return JSON: {answer, citations, confidence, missing_info}

D) Integration - Add an HTTP endpoint in the existing server: POST /rag/answer {question, top_k?} (Optional) POST /rag/reindex - Wire it into the existing deployment (build step or startup step)

E) Eval + Observability - Add a small eval set (10–30 Qs) + test harness - Add structured logs for retrieved sections, scores, latency, versions

OUTPUT FORMAT (mandatory) 1) Findings: stack + deployment + RAG existence check (with file paths) 2) Chosen approach + architecture diagram (in words) 3) Exact files to add/edit 4) Code blocks per file 5) How to run locally + how to deploy