I built a Multiple PDF LLM Chatbot that allows users to upload multiple PDF documents and ask questions in natural language. The system reads all the PDFs, extracts the text, and splits it into smaller chunks. These chunks are converted into embeddings and stored in a vector database. When a user asks a question, the system uses a retrieval technique (RAG) to find the most relevant parts from the PDFs and then passes that information to a Large Language Model to generate a clear and accurate answer. This helps users quickly get insights from large documents without reading them manually.

SQL + LLM project is an AI-based system that allows users to interact with a database using simple English instead of writing SQL queries. When a user asks a question, the LLM (Large Language Model) understands the request and converts it into a SQL query, which is then executed on the database to fetch the required data. The result is displayed back to the user in an easy-to-understand format. In the advanced version of the project, you added features like chat memory so the system can remember previous questions, RAG to improve understanding of the database structure, automatic error handling to fix incorrect queries, and data visualization to present results clearly. Overall, it acts like a smart AI assistant that makes working with databases faster and easier.