I build RAG-powered AI chatbots and knowledge assistants that let users ask questions and receive context-aware answers from your own documents, FAQs, website content, or knowledge base.

Document and knowledge-base Q&A assistants

PDF, text, FAQ, and website-content retrieval

Retrieval-Augmented Generation (RAG) pipelines

LLM-powered question answering

Vector search and semantic retrieval

AI chat interfaces with Streamlit

Context-aware responses grounded in your provided data

Basic conversation and retrieval testing

Typical workflow Your content → preprocessing → chunking → embeddings/retrieval → relevant context → LLM → grounded response → user-facing chatbot

RAG pipeline connected to your knowledge source

Document ingestion and preprocessing

Retrieval/search component

LLM response generation

Interactive chatbot interface where required

Source code and project structure

Basic testing and documentation

Technologies Python, Gemini/LLMs, ChromaDB, vector search, NLP, Streamlit, and related AI tools.

I have hands-on experience building AgriSense AI, a RAG-based agronomy advisor using Gemini, ChromaDB, TF-IDF retrieval, and Streamlit. This project involved combining retrieval with generative AI to produce responses based on a defined knowledge source.

Suitable for Internal knowledge assistants, document Q&A, FAQ bots, research assistants, support knowledge bases, educational assistants, and other applications where an LLM needs to work with your own information.

I focus on grounded responses, clear retrieval workflows, maintainable code, and practical implementation rather than simply connecting an LLM to a chat interface.