About this service
RAG (Retrieval-Augmented Generation) lets you connect an LLM to your own data — so instead of getting generic answers, users get answers pulled directly from your documents, manuals, databases, or knowledge base.
I've built this for a Career Bot that ingests resumes, indexes them in ChromaDB, and answers specific questions about them while generating personalized cover letters automatically.
WHAT I DELIVER:
✔ Document ingestion pipeline (PDF, TXT, DOCX, HTML)
✔ Embedding and vector storage with ChromaDB or FAISS
✔ LangChain RAG chain connected to OpenAI or Gemini
✔ FastAPI chat endpoint with source references in responses
✔ Clean, reusable, well-documented code
✔ Setup guide so your team can run and extend it
WHAT I NEED FROM YOU:
— Your documents or data source
— Preferred LLM (OpenAI or Gemini — you provide the API key)
— Description of what questions users should be able to ask
NOT INCLUDED:
— LLM API costs
— Model fine-tuning
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