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# Document-Based RAG AI
This project implements a Retrieval-Augmented Generation (RAG) architecture to extract and retrieve information from uploaded documents and answer user queries using a chat interface. The application uses a Flask-based web interface and a Chroma vector database for document indexing and retrieval.
---
## Problem Statement
Organizations often struggle to manage and query unstructured textual data spread across various documents. This application provides an efficient solution by creating a searchable vector database of document contents, enabling precise query-based retrieval and response generation.
---
## Setup
### 1. Virtual Environment
Set up a Python virtual environment to manage dependencies:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
### 2. Install Dependencies
Install required packages from the `requirements.txt` file:
```bash
pip install -r requirements.txt
```
### 3. Running the Application
Start the Flask application:
```bash
python app.py
```
---
## Components Overview
### 1. [LangChain](https://github.com/hwchase17/langchain)
LangChain enables seamless integration of LLMs with retrieval systems like vector databases.
### 2. [Flask](https://flask.palletsprojects.com/)
Flask provides the web framework to build the user interface and RESTful APIs.
### 3. [Chroma Vector Database](https://docs.trychroma.com/)
Chroma is used to store and retrieve document embeddings for similarity-based querying.
### 4. RAG Architecture
Combines retrieval of relevant document chunks with LLMs to generate precise responses based on context.
### 5. Models Used
- **Embedding Model:** `all-MiniLM-L6-v2` (via HuggingFace)
- **Chat Model:** `Mistral-7B-Instruct-v0.3`
---
## Application Workflow
### Overview
A typical RAG application has two components:
1. **Indexing**: Processes and indexes documents for searchability.
2. **Retrieval and Generation**: Retrieves relevant document chunks and generates a context-based response.
#### Indexing
1. **Load**: Upload documents using the web interface.
2. **Split**: Break documents into smaller chunks for indexing.
3. **Store**: Save embeddings in a Chroma vector database.
#### Retrieval and Generation
1. **Retrieve**: Search for relevant chunks based on user queries.
2. **Generate**: Produce context-aware answers using the Chat Model.
---
## Application Features
1. **Create Database**
- Upload documents and generate a searchable vector database.
2. **Update Database**
- Update the vector database by adding new document.
3. **Remove Database**
- Remove the vector database.
4. **Delete Documents in Database**
- Delete any specific document in the vector database.
5. **List Databases**
- View all available vector databases.
6. **Chat Interface**
- Select a vector database and interact via queries.
---
## App Tree
```
.
├── app.py # Flask application
├── retrival.py # Data retrieval and vector database management
├── templates/
│ ├── home.html # Home page template
│ ├── chat.html # Chat interface template
│ ├── create_db.html # Upload documents for database creation
│ ├── list_dbs.html # List available vector databases
├── uploads/ # Uploaded document storage
├── VectorDB/ # Vector database storage
├── TableDB/ # Table database storage
├── ImageDB/ # Image database storage
├── requirements.txt # Python dependencies
├── .env # Environment variables (e.g., HuggingFace API key)
└── README.md # Documentation
```
---
## Example Use Using the flask
1. **Navigate to `/create-db`** to upload documents and generate a vector database (via Flask)
2. **Navigate to `/list-db`** to view all available databases.
3. **Select a database** using `/select-db/<db_name>` (Flask).
4. **Query a database** using `/chat` (Flask) to retrieve relevant information and generate a response.
5. **Update a database** using `/update-dbs/<db_name>` (Flask) to update db by adding the files or even whole folder containing the files.
6. **Remove a database** using `/remove-dbs/<db_name>` (Flask) to remove or delete entire database.
7. **Delete document in database** using `/delete-doc/<db_name>` (Flask) to delete a specific document in the database.
---
Happy experimenting! 🚀
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