I will build a custom ai chatbot and rag application using python

R
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R
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Muhammad Rizwan
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build a custom ai chatbot and rag application using python
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build a custom ai chatbot and rag application using python
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build a custom ai chatbot and rag application using pythonbuild a custom ai chatbot and rag application using pythonbuild a custom ai chatbot and rag application using pythonbuild a custom ai chatbot and rag application using pythonbuild a custom ai chatbot and rag application using python

Over deze dienst

Are you looking for a custom AI chatbot that can answer questions using your own documents, website content, or business data?

I will develop a professional AI chatbot and RAG application using Python, OpenAI API, LangChain, and a suitable vector database.

My services include:

  • Custom AI chatbot development
  • RAG chatbot using your documents or PDFs
  • Website knowledge-base chatbot
  • OpenAI API and LLM integration
  • PDF, CSV, Word, or text document processing
  • Conversation memory and custom prompts
  • Vector database integration
  • Flask, FastAPI, Streamlit, or web API deployment
  • Clean and documented Python code
  • Bug fixing and chatbot improvement


The chatbot can be developed for customer support, education, internal company documents, product information, FAQs, or personal knowledge bases.

Please contact me before ordering so I can review your requirements and recommend the right solution.

  • AI engine

    • GPT
    • Gemini
    • Langchain

  • Programmeertaal

    • Python
    • R
    • PyTorch
    • Tensorflow

Maak kennis met Muhammad Rizwan

Muhammad Rizwan

AI , ML Engineer

  • Afkomstig uitPakistan
  • Lid sindsjul 2026

  • Talen

    Engels
AI/ML Engineer with 5+ years of industry experience building and deploying production-grade intelligent systems across Generative AI, Computer Vision, and NLP. Specialized in Retrieval-Augmented Generation (RAG), Large Language Models, LangChain, LLaMA, and Hugging Face Transformers, with hands-on strength in OpenCV and YOLO. Experienced across the full ML lifecycle: data ingestion, model training, finetuning, MLOps, and deployment with FastAPI and Docker.

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