I will build automated ai rag pipelines and python automation workflows


Over deze dienst
Need an AI system that works on YOUR data - not generic ChatGPT answers?
I build production-grade RAG pipelines and Python automation that turn your documents, databases and APIs into a reliable AI assistant.
WHAT I BUILD
- RAG chatbots over PDFs, docs, websites and SQL data
- Automated ingestion pipelines: chunking, embeddings, vector DB
- Hybrid search, reranking and source citations to cut hallucinations
- AI agents and multi-step workflows (LangGraph, CrewAI, n8n)
- FastAPI / Streamlit apps, Docker and cloud deployment
TECH STACK
Python, LangChain, LangGraph, LlamaIndex, Haystack, DSPy, OpenAI, Claude, Gemini, Llama, Ollama, Hugging Face, ChromaDB, Pinecone, Qdrant, FAISS, FastAPI, Docker, AWS / Azure / GCP.
WHY WORK WITH ME
- Real shipped systems: advanced RAG with evaluation, multimodal document intelligence, QLoRA fine-tuning
- Groundedness and citation scoring so you can trust every answer
- Clean, documented, tested code that you own 100 percent
- Clear updates and on-time delivery
WHAT YOU GET
Full source code, setup documentation and a walkthrough of how it works.
Send me your use case and I will tell you exactly what is possible and what it costs - free.
Maak kennis met Divyam J
Make agentic frameworks
- Afkomstig uitIndia
- Lid sindsmrt 2023
- Gem. reactietijd1 uur
Talen
Hindi, Engels
Mijn portfolio
Veelgestelde vragen
What exactly is a RAG pipeline and why do I need one?
RAG connects an LLM to your own documents and data, so answers come from your content with citations instead of generic or made-up information.
What do I need to provide to get started?
Your documents or data source, the questions your users will ask, and an API key for your chosen model. I handle everything else.
Which models and vector databases do you support?
OpenAI, Claude, Gemini, Llama and Ollama for local or private setups, with ChromaDB, Pinecone, Qdrant or FAISS for storage.
Can you deploy it and connect it to my existing app?
Yes. I deliver a FastAPI or Streamlit service, Dockerised, and can deploy to AWS, Azure, GCP or your own server.
Is my data kept private?
Yes. Your data stays in your own accounts and infrastructure, and I can build a fully local pipeline with open-source models if required.
Do I own the code?
You own 100 percent of the source code, plus setup documentation so your team can run and extend it.

