Built around your outcome
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
Clear scope
Know the deliverables before work starts.
Direct collaboration
Stay aligned throughout the project.
Verified seller
Identity and payout details are checked.
From brief to launch
01
Choose
Pick the package matching your goal.
02
Collaborate
Share requirements and review progress.
03
Launch
Receive the final delivery and approve it.