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I will build a custom rag ai chatbot with fastapi and vector database
Ayush Bishnoi
About this gig
Want an AI chatbot that can actually understand and answer questions from your own data?
I will build a custom RAG AI chatbot that connects your documents or knowledge base with modern LLMs to provide relevant, context-aware answers.
What I can build:
- Custom RAG pipeline for PDFs, TXT, DOCX and other data sources
- Vector database and embedding integration
- OpenAI, Gemini and other LLM integrations
- FastAPI backend and REST APIs
- User authentication with JWT
- Chat sessions and conversation history
- PostgreSQL or MongoDB integration
- Context-aware conversational AI
- Web search integration for current information
- Multimodal/image-based AI capabilities
- API documentation and clean source code
- Docker and deployment assistance
Your AI assistant can be built for:
- Business knowledge bases
- Customer support
- PDF/document Q&A
- SaaS applications
- Internal company assistants
- Research assistants
- Website knowledge assistants
I focus on building custom and scalable AI applications, not just a simple ChatGPT wrapper.
Please contact me before ordering so I can understand your use case, data sources and required features and recommend the right package
Get to know Ayush Bishnoi
Ayush Bishnoi
AI ML Developer RAG Developer
- FromIndia
- Member sinceSep 2026
- Avg. response time1 hour
Languages
Hindi, English
Hi, I'm Ayush, an AI/ML Developer specializing in Python, Generative AI, RAG, and LLM-powered applications. I build AI chatbots, RAG systems, AI agents, document intelligence, and machine learning solutions.
I have hands-on experience with LangChain, LangGraph, FastAPI, vector databases, Google Vertex AI, vision models, and Python ML workflows. I focus on practical, scalable AI solutions that solve real business problems.
Need a custom AI chatbot, RAG pipeline, AI agent, document processing system, or Python AI solution? I can turn your idea into a working application.
FAQ
What is a RAG chatbot?
RAG (Retrieval-Augmented Generation) allows an AI chatbot to retrieve relevant information from your documents or knowledge base before generating an answer. This helps the chatbot provide responses based on your data instead of relying only on the LLM's general knowledge.
Can you build a chatbot using my PDF or documents?
Yes. I can build a RAG system that processes documents such as PDF, TXT and DOCX, creates embeddings, stores them in a vector database and retrieves relevant information when users ask questions.
Which AI models can you integrate?
I can integrate LLM providers such as OpenAI and Google Gemini. The appropriate model will depend on your use case, performance requirements and budget.
Can you add user authentication and chat history?
Yes. I can implement user authentication, protected APIs, chat sessions and persistent conversation history so each user can maintain their own conversations.
Can you integrate a vector database?
Yes. I can implement vector search using a suitable vector database and embedding model based on your project's requirements.
Can you add web search to the chatbot?
Yes. Web search can be added when the chatbot needs information that may not exist in the knowledge base or requires more current information. This feature is included in the appropriate package or can be discussed as an additional requirement.
Can you integrate the chatbot with my existing application?
Yes. I can provide FastAPI REST APIs that can be integrated with an existing web, mobile or SaaS application. Please share your existing architecture and requirements before ordering.
an you build a multimodal AI chatbot?
Yes. I can integrate multimodal AI capabilities for use cases involving images and other supported content. The exact implementation depends on your model, data and requirements.
Do you provide the source code?
Yes. Source code is included according to the selected package. I provide clean, organized code for the implemented functionality.
