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This is a RAG (Retrieval-Augmented Generation) system for document querying, allowing users to upload documents and interact with them using natural language queries powered by various AI models.

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rahulsamant37/document_portal

 
 

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Project Setup Guide

Create Project Folder and Environment Setup

# Create a new project folder
mkdir <project_folder_name>

# Move into the project folder
cd <project_folder_name>

# Open the folder in VS Code
code .

# Create a new Conda environment with Python 3.10
conda create -p <env_name> python=3.10 -y

# Activate the environment (use full path to the environment)
conda activate <path_of_the_env>

# Install dependencies from requirements.txt
pip install -r requirements.txt

# Initialize Git
git init

# Stage all files
git add .

# Commit changes
git commit -m "<write your commit message>"

# Push to remote (after adding remote origin)
git push

# Cloning the repository
git clone https://github.com/sunnysavita10/document_portal.git

Minimum Requirements for the Project

LLM Models

  • Groq (Free)
  • OpenAI (Paid)
  • Gemini (15 Days Free Access)
  • Claude (Paid)
  • Hugging Face (Free)
  • Ollama (Local Setup)

Embedding Models

  • OpenAI
  • Hugging Face
  • Gemini

Vector Databases

  • In-Memory
  • On-Disk
  • Cloud-Based

API Keys

GROQ API Key

Gemini API Key

About

This is a RAG (Retrieval-Augmented Generation) system for document querying, allowing users to upload documents and interact with them using natural language queries powered by various AI models.

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