I will build a rag ai chatbot with vector database, langchain and openai


About this gig
Want an AI chatbot that answers from YOUR documents and data - accurately, with no made-up answers?
I build custom RAG (Retrieval-Augmented Generation) AI chatbots and assistants that are grounded in your own knowledge base, so every answer is based on your real content - not generic guesses. Perfect for customer support, internal knowledge, documentation, and research.
WHAT YOU GET
- RAG pipeline with a vector database (Pinecone, Weaviate, Chroma or pgvector)
- Ingestion of your PDFs, docs, websites and databases
- LangChain or LlamaIndex orchestration
- OpenAI GPT-4, Claude or open-source LLMs
- Accurate, source-cited answers that reduce hallucination
- API or chat widget you can embed anywhere
- Full, documented source code you own
WHY WORK WITH ME
- Specialised in RAG and production AI systems
- Clear communication and on-time delivery
- Post-delivery support and guidance
Tell me about your data and use case, and I will reply with the best RAG architecture and package for your project. Let's build an AI that actually knows your business.
Get to know Eduard M
Senior Full Stack and AI Engineer, React, Node, Python, React Native, RAG, Agent
- FromRomania
- Member sinceMar 2017
- Avg. response time1 hour
Languages
English, German
My Portfolio
FAQ
What LLMs and vector databases do you support?
I support OpenAI GPT-4, Claude, Gemini, and open-source models like Llama. For vector databases, I work with Pinecone, ChromaDB, Weaviate, and FAISS.
Do I need to provide API keys for the LLM?
Yes, you will need to provide your own API key for the LLM provider (e.g., OpenAI, Anthropic). I will guide you through the setup and the keys will only be used for your project.
Will I receive the source code?
Yes, you will receive the complete, well-documented source code along with setup instructions. The code is clean, modular, and easy to extend or maintain.

