GroqLLM_DocChat

Overview

GroqLLM_DocChat is an AI-powered document chat system that extracts text from PDFs, generates vector embeddings, stores them in a vector database, and allows users to query information using natural language. It leverages LangChain, FAIS and google's embeddings to provide an efficient document retrieval and question-answering system.

Features

• 📄 Extracts text from PDFs, PPTs, etc.
• 🔍 Stores embeddings in a vector database (FAISS)
• 🤖 Uses AI embeddings for semantic search
• 💬 Allows users to ask natural language questions about the document
• 🚀 Fast and scalable retrieval-augmented generation (RAG)
• ⚡ Uses Groq API for fast inferencing

Tech Stack

• Python (Core language)
• LangChain (Document processing and retrieval)
• FAISS (Vector database)
• google/models/text-embedding-004 (Embeddings model)
• Docling (Document text extraction)
• Streamlit (API or UI for interaction)

Installation

Clone the repository:

git clone https://github.com/SPYLoveC2/GroqLLM_DocChat.git 
cd GroqLLM_DocChat

Create a virtual environment and install dependencies:

conda env create -f environment.yml

Usage

Start the chatbot interface:

streamlit run app.py  # If using Streamlit UI

Ask questions about the document in the UI or API.

Configuration

Set your API keys (if using OpenAI embeddings) in an .env file:

GOOGLE_API_KEY=your_api_key
GROQ=your_api_key

Roadmap

• ✅ Basic Doc processing & embedding storage
• ✅ Q&A system with retrieval
• 🔜 Multi-document support
• 🔜 Fine-tuned LLM integration

Contributing

Feel free to submit issues or pull requests to improve the project.