AI / LLM

PDF Chatbot with LLaMA-2 & RAG

An AI assistant that answers questions grounded in your PDF content.

2025

OVERVIEW

The Project

An intelligent chatbot that answers user questions about an uploaded PDF using semantic search and language understanding. It applies a Retrieval-Augmented Generation (RAG) pipeline — retrieving relevant document chunks and providing them to LLaMA-2 — so answers stay grounded in the original content.

Objectives

Build a domain-agnostic chatbot for querying PDFs.

Use RAG to ground responses and minimize hallucination.

Provide relevant context and accurate, grounded answers.

Tools & Technologies

LLaMA-2 (7B)LangChainChromaDBHugging FaceSentence-Transformer embeddings

METHODOLOGY

The Approach

Parse and chunk the uploaded PDF; embed chunks and store in ChromaDB.

On a question, embed the query and retrieve the most relevant chunks (semantic search).

Feed retrieved context + question to LLaMA-2 to generate a grounded answer.

Wrap in an upload → ask → history UI with secure accounts.

OUTCOME

Results & Learnings

Accurate, context-grounded answers drawn directly from the source PDF.

Clean account, upload, chat and history interface.

Key Learnings

Chunking strategy and embeddings drive retrieval quality.
Prompt templates and grounded context are key to reducing hallucination.

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