I will build a rag chatbot using python, langchain and a vector database


About this gig
Want an AI chatbot that answers questions using your own documents and business data?
I will build a custom RAG chatbot using Python, LangChain, and vector databases to help users find accurate, relevant information from your knowledge base.
What I can build:
- Chat with PDFs, Word files, and text documents
- RAG chatbots for business knowledge bases
- Website content question-answering systems
- Document search using semantic embeddings
- AI assistants with context-aware responses
- Vector database integration and retrieval pipelines
- Custom chatbot APIs using FastAPI
- Source-grounded answers to reduce unsupported responses
Technologies: Python, LangChain, OpenAI API, embeddings, FAISS, ChromaDB, Pinecone, PostgreSQL with pgvector, and FastAPI.
I focus on building practical RAG solutions with relevant document retrieval, clear response generation, and maintainable code.
Whether you need a PDF chatbot, an internal company assistant, or a searchable knowledge base, I can help build a solution tailored to your requirements.
Please message me before ordering so we can discuss your documents, data sources, and required features.
Get to know Muhammd Bilal
AI Engineer Python Developer LLM AI Agent Expert
- FromPakistan
- Member sinceAug 2026
- Avg. response time3 hours
Languages
English, Hindi, Urdu
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FAQ
What is a RAG chatbot?
RAG stands for Retrieval-Augmented Generation. It retrieves relevant information from your documents and uses that context to generate answers.
Can you build a chatbot for my PDFs?
Yes. I can extract text from supported PDFs, create embeddings, index the content, and build a chatbot that answers questions using the retrieved information.
Can the chatbot use my website content?
Yes, provided the website content is accessible and you have the necessary permissions. The ingestion method depends on the website structure.
Which vector databases do you support?
I can work with options such as FAISS, ChromaDB, Pinecone, and PostgreSQL with pgvector, depending on the project's needs.
Will the chatbot always give correct answers?
No AI system can guarantee perfect accuracy. I can improve reliability with relevant retrieval, source references, testing, and instructions to acknowledge missing information.
Can I integrate the chatbot into my application?
Yes. I can expose chatbot functionality through an API and help integrate it with compatible applications.
Do you provide the source code?
Yes. Source code is included in all packages, according to the agreed scope.

