https://github.com/skrishna-7/conversational-chatbot-with-pdf
A Conversational RAG (Retrieval-Augmented Generation) chatbot that allows users to upload PDF documents, extract relevant information, and engage in a dynamic conversation with historical context.
https://github.com/skrishna-7/conversational-chatbot-with-pdf
Last synced: 2 months ago
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A Conversational RAG (Retrieval-Augmented Generation) chatbot that allows users to upload PDF documents, extract relevant information, and engage in a dynamic conversation with historical context.
- Host: GitHub
- URL: https://github.com/skrishna-7/conversational-chatbot-with-pdf
- Owner: SKrishna-7
- Created: 2025-03-08T12:49:53.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-08T13:34:32.000Z (3 months ago)
- Last Synced: 2025-03-08T13:34:55.612Z (3 months ago)
- Language: Python
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Conversational RAG with PDF Uploads & Chat History
## 📌 Project Overview
**This project is a Conversational RAG (Retrieval-Augmented Generation) chatbot that allows users to upload PDF documents, extract relevant information, and engage in a dynamic conversation with historical context. The chatbot utilizes a retrieval-based approach to fetch relevant document segments before generating responses, making it highly effective for question-answering over custom documents.**
## 🚀 Features
- 📂 Upload multiple PDF documents
- 🔍 Extracts and indexes document content for efficient retrieval
- 💬 Conversational memory to maintain chat history
- 🤖 Uses a powerful LLM (Gemma-2-9b-It via Groq API) for response generation
- ⚡ Efficient vector search with ChromaDB
- 🏗 Streamlit-based interactive UI
## 🛠️ Tech Stack
- Python 🐍
- LangChain 🔗 (for retrieval-augmented generation)
- ChromaDB 📚 (for vector storage & retrieval)
- Streamlit 🎨 (for building the UI)
- Hugging Face Embeddings 🧠 (for text embedding)
- Groq API ⚡ (for running the LLM)
- PyPDFLoader 📄 (for processing PDFs)