I will build a robust rag pipeline with langchain, langgraph


About this gig
I design and build robust Retrieval-Augmented Generation (RAG) pipelines that deliver accurate, context-aware answers from your own data sources.
No hallucinations. No brittle scripts. Just production-grade architectures clean, modular, and fully documented.
️ What You Get
- End-to-End RAG Architecture: Retriever, chunker, embedder, generator, evaluator
- Framework Options: LangChain, LlamaIndex, or custom lightweight implementation
- LLM Flexibility: OpenAI, Anthropic, or open models (Llama 3, Mistral, Falcon)
- Vector Database Integration: FAISS, Chroma, Pinecone, or Qdrant
- Optimized Prompting: Context-aware, dynamically constructed queries
- Deployment Ready: Streamlit, FastAPI, or Hugging Face Spaces
- Clear Code + Docs: Production-quality, modular, reproducible setup
Why Work With Me
- Engineering-first approach built for performance, not just demos
- Deep understanding of embeddings, retrieval, and context optimization
- End-to-end testing for retrieval accuracy and latency
Tech Stack: Python · LangChain · LlamaIndex · Hugging Face · FAIS· Chroma · OpenAI API · Streamlit · FastAPI
Lets discuss your data sources and desired deployment stack
Get to know Sayem
Machine Learning, Deep learning, Gen AI and Agentic AI
- FromBangladesh
- Member sinceDec 2024
- Last delivery1 year
Languages
Bengali, English, Italian, Hindi
My Portfolio
FAQ
Can I use my own data (PDFs, Notion, Google Drive)?
Absolutely. I can set up connectors for your local or cloud-based data sources.
Will I get the full source code?
es. All code and environment files are included and documented.
Can you integrate with my existing app or API?
Yes — I can wrap the RAG pipeline with FastAPI endpoints or embed it in your frontend.

