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Fiverr Pro
English
$
USD
I will build a production rag pipeline with vector database, langchain, and fastapi
Muhammad Afzal
Level 2
About this gig
Most RAG systems fail in production they hallucinate, lose context, and surface irrelevant chunks. I build RAG that doesn't.
AI engineer, 5+ years, 125+ projects delivered. I build retrieval systems that answer accurately, cite sources, and hold up under real usage not a demo dataset.
WHAT I BUILD
- Multi-source ingestion - PDFs, websites, databases, APIs
- Smart chunking matched to your data
- Hybrid search - vector + BM25 for higher accuracy
- Re-ranking to surface the most relevant chunks
- Citations - every answer traces to its exact source
- Agentic RAG - agent decides what to retrieve and when
- Evaluation report faithfulness & relevance benchmarks
- FastAPI backend, not a Streamlit demo
MY STACK
- LangChain
- LlamaIndex
- LangGraph
- pgvector
- Pinecone
- Qdrant
- OpenAI
- Claude
- LangSmith
- RAGAS
- FastAPI
- Next.js
WHAT SETS ME APART I don't just build a retriever I measure it. You get an evaluation report on how accurately your RAG answers your real questions, optimized before delivery.
TELL ME
- What data sources?
- What questions must it answer?
- Chat UI or backend API only?
Let's build RAG you can trust.
Get to know Muhammad Afzal
Muhammad Afzal
AI engineer building AI agents, chatbots, and full stack web apps that convert
Level 2
- FromPakistan
- Avg. response time1 hour
- Last delivery3 weeks
Languages
English, French, German, Spanish
I'm an AI engineer and full-stack developer with 5+ years and 125+ delivered projects for international clients. I build production AI: RAG chatbots with citations, tool-calling agents, and workflow automation, on top of solid full-stack foundations in Next.js, TypeScript, FastAPI, and PostgreSQL. I'm currently completing an M.Sc. in AI and Data Analytics in Germany. I care about systems that can be trusted, measured, and operated, not just demoed. Tell me what you're building and I'll tell you the fastest reliable way to ship it.
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FAQ
What's the difference between your RAG and a basic chatbot with file upload?
Basic RAG tools chunk naively, do a single vector lookup, and return whatever comes back. I add hybrid search (vector + keyword), re-ranking, hallucination reduction, and citation tracking. You get measurable accuracy, not hope.
What vector database should I use - Pinecone or pgvector?
If you already use PostgreSQL, pgvector is simpler to operate and often sufficient. Pinecone is better for very large document sets needing managed scaling. I'll recommend based on your scale and infra.
Can it handle scanned PDFs?
Yes, with OCR preprocessing. Add it to your requirements when messaging me.
What does the evaluation report include?
Context precision, context recall, faithfulness (does the answer contradict the source?), and answer relevance - measured on a test set of your real questions.
Can you add a chat interface?
Yes - that's included in the Premium package. Next.js frontend with conversation history, source citation display, and document upload.
