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I will build a rag chatbot or llm app with langchain and fastapi
Abbas Mustafa
About this gig
Your data is locked in PDFs, databases, and documents your LLM can't see. Let's fix that.
Building a RAG application from scratch is 80% plumbing and 20% actual intelligence. Wrong chunking strategy, broken retrieval, hallucinating answers. If you want a system that actually retrieves the right context and generates accurate responses, the architecture has to be right from the start.
What you get:
- RAG pipeline built with LangChain, connected to your data sources
- Vector store setup using FAISS or ChromaDB based on your use case
- FastAPI backend with clean REST endpoints your frontend can call
- Prompt engineering tuned for your specific domain
- Chunking and embedding strategy optimized for retrieval accuracy
- Source citation in responses so users know where answers come from
- Full code, documented and ready to extend
How it works:
You share your data sources (PDFs, URLs, databases, text files) and describe what your users need to ask. I design the retrieval and generation pipeline around your actual documents. You get working code, not a demo notebook.
This is for you if:
- You have internal docs, manuals, or knowledge bases users need to query
- You want a chatbot that uses your own content
Get to know Abbas Mustafa
Abbas Mustafa
AI Engineer for LLM, RAG, Chatbot and ML Pipeline Solutions
- FromFrance
- Member sinceDec 2018
- Avg. response time1 hour
- Last delivery1 year
Languages
English
I'm an AI Systems Engineer with 5+ years of experience building production-grade AI solutions — RAG pipelines, fine-tuned LLMs (LoRA/PEFT), multi-agent systems, and computer vision applications. I've deployed ML systems for medical imaging, trading, and security, and built full-stack platforms serving 10,000+ users with FastAPI, Docker, and Kubernetes. Currently pursuing an MSc in Cybersecurity in France, I bring a security-first mindset to every build. Whether you need a chatbot, an automation agent, or a complete ML pipeline, I deliver clean, scalable, well-documented work.
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FAQ
What do I need to provide before you start?
Your data sources (PDF files, URLs, database access, or text exports), a description of the questions your users will ask, and your preferred LLM provider (OpenAI, Anthropic, or open-source). API keys are your responsibility and stay private.
Which LLMs and vector stores do you support?
I work with OpenAI (GPT-4, GPT-3.5), Anthropic (Claude), and open-source models via HuggingFace. For vector stores: FAISS, ChromaDB, and Pinecone. I will recommend the best fit for your use case.
Will the code work on my server, not just locally?
Standard and Premium packages include Docker containerization so the app runs consistently anywhere. Premium includes full deployment configuration.
How accurate will the retrieval be?
Accuracy depends on document quality and query type. I optimize chunking, embedding, and retrieval parameters for your specific dataset. Most clients see significantly better results than off-the-shelf setups.
Can I extend the code after delivery?
Yes. All code is clean, documented, and structured so you or your team can build on it. I can also scope follow-on work if you need new features added
Do you sign NDAs?
Yes, for Standard and Premium orders on request.
What if I need something between the Basic and Standard scope?
Message me with your requirements. I will put together a custom offer that fits exactly what you need.
