I will develop a rag pipeline using langchain, llamaindex, and vector databases


About this gig
Want your AI to answer questions using YOUR documents? I build custom RAG (Retrieval-Augmented Generation) pipelines that connect your private data (PDFs, websites, databases) to LLMs like GPT-4 or Claude.
No hallucinationsjust accurate, source-backed answers from your own content.
PERFECT FOR: Internal team knowledge bases Customer support & FAQ chatbots Legal, medical, or research document Q&A
WHAT I DELIVER:
- Data Ingestion: Seamless integration with PDFs, Word, CSV, Notion, or URLs.
- Vector DB Setup: Pinecone, ChromaDB, FAISS, or Weaviate.
- LLM Integration: GPT-4, Claude, Gemini, or LLaMA.
- Advanced Features: Text chunking, embeddings, reranking, and chat memory.
- Interface: A clean Streamlit UI or a FastAPI backend.
️MY TECH STACK: LangChain, LlamaIndex, OpenAI, HuggingFace, Docker, and Cloud Deployment (AWS/Render).
WHY CHOOSE ME? You get clean, production-ready code. I don't just deliver the project; I explain how your system works so you are fully in control.
Kindly message me before placing order
Get to know Muhammad Hammad
Turning Raw Data into Intelligent AI Solutions
- FromPakistan
- Member sinceFeb 2025
- Avg. response time1 hour
Languages
Urdu, English
FAQ
What data sources can you work with?
PDF, Word, CSV, Excel, websites, Notion, YouTube transcripts, SQL databases and more.
Which LLM will you use?
I work with GPT-4, Claude, Gemini, LLaMA, and DeepSeek — your choice or I recommend the best fit.
Will I own the source code?
Yes — full source code with detailed comments is delivered in every package.
Can you deploy the RAG system for me?
Yes — cloud deployment on AWS, Railway, or Render is included in the Premium package.
What if my use case is complex?
Message me before ordering — I will assess your needs and suggest the right package for free.
