I will develop a custom ai chatbot for your PDF documents


About this gig
I will build a custom AI-powered RAG chatbot that allows users to ask questions and get accurate, context-aware answers from their documents.
Using Python, LLMs, LangChain, embeddings, and vector databases, I can develop a reliable document-based AI assistant for PDFs, business documents, knowledge bases, reports, and other text-based data.
What I can provide:
- Custom RAG (Retrieval-Augmented Generation) chatbot
- PDF and document question-answering
- LLM integration
- Document chunking and embeddings
- Vector database integration
- Context-aware AI responses
- Simple and user-friendly chatbot interface
- Source-based responses
- API integration and deployment support based on the selected package
I focus on building clean, practical, and customized solutions based on your requirements.
Please message me before ordering if you have a complex or custom requirement so we can discuss the project scope.
Get to know Ashutosh Dubey
Senior Data Scientist
- FromIndia
- Member sinceAug 2026
Languages
Hindi, English
FAQ
What is a RAG AI chatbot?
A RAG chatbot retrieves relevant information from your documents or knowledge base and uses an LLM to generate context-aware answers based on that information.
What types of documents can I use?
I can work with PDFs, TXT files, CSV files, and other text-based documents. Please contact me before ordering if you have a different or complex file format.
Can users upload their own documents?
Yes. I can build a document-upload feature where users upload supported files and ask questions based on their content.
Can you deploy the chatbot?
Deployment assistance can be included in the Premium package. Please contact me beforehand to discuss your preferred hosting environment.
Will I need to provide an API key?
If your application uses a paid third-party LLM or service, you may need to provide your own API credentials.
Should I contact you before ordering?
Yes, especially for custom or complex projects. This helps confirm your documents, features, integrations, deployment requirements, and expected deliverables before starting.
