I am looking for someone who can take me from zero to a fully working Retrieval-Augmented Generation setup. The heart of the build is a robust vector database because the system must excel at high-dimensional data analysis: documents or embeddings come in, relevant context is pulled in milliseconds, and the language model produces grounded answers. Here is what I need the finished solution to do: data ingestion, embedding creation, storage inside the chosen vector store, fast similarity search, and seamless hand-off of results to the LLM for final generation. I am open on stack (Python-based pipelines, LangChain, FAISS, Milvus, Pinecone, or another tool you are comfortable with), as long as it remains well-documented and reproducible on a standard cloud VM or container. Deliverables 1. Clean, modular source code for the ingestion–retrieval–generation pipeline 2. A configured vector database optimised for high-dimensional queries 3. REST or GraphQL endpoint (or CLI) that exposes a simple ask/answer interface 4. Setup notes and a short video or written walk-through that lets me redeploy the system from scratch 5. A quick performance report: latency numbers on a sample dataset and any tuning recommendations Acceptance will be based on the system returning accurate grounded answers against an agreed sample corpus, plus the ability for me to rebuild it by following your documentation.