I will build a robust rag pipeline with langchain, langgraph

S
sayem_1995
S
sayem_1995
Sayem

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

Sayem

Machine Learning, Deep learning, Gen AI and Agentic AI

5.0(1)
  • FromBangladesh
  • Member sinceDec 2024
  • Last delivery1 year
  • Languages

    Bengali, English, Italian, Hindi
I’m Salman, a Machine Learning & Deep Learning engineer building intelligent AI solutions. I specialize in predictive models, Generative AI and Agentic AI agents capable of reasoning and autonomous actions. I also design RAG pipelines and LangChain,LangGraph-based chatbots for context-aware applications. I deliver end-to-end, scalable, and business-ready AI solutions that turn data and ideas into actionable intelligence.

My Portfolio