Offline-RAG-Model

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Offline-RAG-Model

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# Offline RAG (Retrieval-Augmented Generation)

An offline Retrieval-Augmented Generation (RAG) application that allows users to upload documents, generate embeddings locally, store them in a Chroma vector database, and ask questions without relying on cloud-based AI services.

## Features

* Fully offline document question answering
* Local document ingestion and indexing
* Chroma vector database for semantic search
* Embedding-based document retrieval
* React-based user interface
* FastAPI backend
* Supports document uploads for knowledge extraction
* No internet connection required after setup

## Tech Stack

### Frontend

* React
* JavaScript
* Tailwind CSS

### Backend

* FastAPI
* Python

### AI Components

* Local Embedding Model
* Chroma Vector Database
* Retrieval-Augmented Generation (RAG)

## Project Structure
```
Offline-RAG/
│
├── backend/
│   ├── app/
│   │   ├── __init__.py
│   │   ├── embed.py              # Generates document embeddings
│   │   ├── ingest.py             # Processes and indexes uploaded documents
│   │   ├── main.py               # FastAPI application entry point
│   │   ├── rag.py                # RAG pipeline
│   │   ├── retriever.py          # Retrieves relevant document chunks
│   │   └── vectorstore.py        # Chroma vector database operations
│   │
│   ├── data/
│   │   └── chroma/               # Local Chroma vector database
│   │
│   ├── requirements.txt          # Python dependencies
│   └── run_backend.sh            # Backend startup script
│
├── data/                         # Source documents for ingestion
│
├── frontend/
│   ├── public/
│   │   └── index.html
│   │
│   ├── src/
│   │   ├── components/
│   │   │   ├── Chat.js           # Chat interface
│   │   │   └── Upload.js         # Document upload component
│   │   │
│   │   ├── App.css
│   │   ├── App.js
│   │   └── index.js
│   │
│   ├── package.json
│   ├── package-lock.json
│   ├── postcss.config.js
│   └── tailwind.config.js
│
├── .gitignore
└── README.md
```
## Installation

### 1. Clone the Repository

```bash
git clone https://github.com/your-username/offline-rag.git
cd offline-rag
```

### 2. Backend Setup

```bash
cd backend

python -m venv .venv

# Windows
.venv\Scripts\activate

# Linux / macOS
source .venv/bin/activate

pip install -r requirements.txt
```

### 3. Frontend Setup

```bash
cd frontend

npm install
npm start
```

### 4. Start the Backend

```bash
uvicorn app.main:app --reload
```

The backend will run on:

```
http://127.0.0.1:8000
```

The frontend will run on:

```
http://localhost:3000
```

## Usage

1. Start the backend server.
2. Start the React frontend.
3. Upload your documents.
4. Documents are converted into embeddings and stored locally.
5. Ask questions through the chat interface.
6. The application retrieves relevant document chunks and generates context-aware responses.

## Workflow

```
Upload Document
        │
        ▼
Document Processing
        │
        ▼
Embedding Generation
        │
        ▼
Store in Chroma Database
        │
        ▼
User Question
        │
        ▼
Semantic Retrieval
        │
        ▼
Generate Response
```

## Future Improvements

* Support for PDF, DOCX, TXT, and PPT files
* Multi-document conversations
* Hybrid search (keyword + semantic)
* Chat history
* Citation support
* Multimodal document understanding
* GPU acceleration

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