Python Developer for Backend Task

need a single, production-quality retrieval-augmented (RAG) API endpoint built in Python. This is a one-time task, not an ongoing role. You will deliver one FastAPI service that ingests a set of documents into a Postgres + pgvector store, then answers questions over them through a single /chat endpoint backed by an LLM. What you will build 1. Ingestion script - Load a folder of provided documents (PDF and .txt). - Chunk the text, generate embeddings, and upsert them into PostgreSQL using pgvector. - Idempotent: re-running should not create duplicates. 2. /chat endpoint (FastAPI) - Runs cosine similarity search over pgvector to retrieve the top-k chunks. - Passes the retrieved context to an LLM via LangChain and returns a structured JSON response: the answer plus the source chunks used. 3. Basics done right - Pydantic request/response models. - Config via environment variables (DB URL, model/API key). - A README with setup and run instructions. - A docker-compose that spins up Postgres + pgvector so I can run it locally in one command. Tech stack (required) - Python 3.11+, FastAPI - PostgreSQL + pgvector (HNSW or IVFFlat index) - LangChain for the retrieval + LLM call - Embeddings: OpenAI text-embedding-3-small (or an open model such as bge-m3, your choice) - Docker / docker-compose Acceptance criteria - I can clone, run docker-compose up, run the ingestion script, and hit /chat successfully within 15 minutes using the README. - /chat returns a relevant answer plus the source chunks for a question about the sample documents. - No hardcoded secrets; keys read from env. To apply - Share one relevant RAG or FastAPI sample you have built. - Confirm the scope and $150 fixed price. - Give a realistic delivery time (I expect 2 to 4 days).