Instructions to use Navaneeth-14/rag-hackathon-app with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Navaneeth-14/rag-hackathon-app with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Navaneeth-14/rag-hackathon-app:Q4_K_M # Run inference directly in the terminal: llama cli -hf Navaneeth-14/rag-hackathon-app:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Navaneeth-14/rag-hackathon-app:Q4_K_M # Run inference directly in the terminal: llama cli -hf Navaneeth-14/rag-hackathon-app:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Navaneeth-14/rag-hackathon-app:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Navaneeth-14/rag-hackathon-app:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Navaneeth-14/rag-hackathon-app:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Navaneeth-14/rag-hackathon-app:Q4_K_M
Use Docker
docker model run hf.co/Navaneeth-14/rag-hackathon-app:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Navaneeth-14/rag-hackathon-app with Ollama:
ollama run hf.co/Navaneeth-14/rag-hackathon-app:Q4_K_M
- Unsloth Studio
How to use Navaneeth-14/rag-hackathon-app with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Navaneeth-14/rag-hackathon-app to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Navaneeth-14/rag-hackathon-app to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Navaneeth-14/rag-hackathon-app to start chatting
- Docker Model Runner
How to use Navaneeth-14/rag-hackathon-app with Docker Model Runner:
docker model run hf.co/Navaneeth-14/rag-hackathon-app:Q4_K_M
- Lemonade
How to use Navaneeth-14/rag-hackathon-app with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Navaneeth-14/rag-hackathon-app:Q4_K_M
Run and chat with the model
lemonade run user.rag-hackathon-app-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 8,027 Bytes
09281fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 | # Flask API Server for Advanced RAG System
This Flask API server provides REST endpoints for the Advanced RAG System, allowing you to upload documents and process queries programmatically.
## Features
- **Document Upload**: Upload and process various document formats (PDF, TXT, DOCX, HTML, etc.)
- **Query Processing**: Ask questions about uploaded documents
- **OCR Support**: Process scanned documents with OCR
- **System Management**: Check status, validate components, clear data
- **Authentication**: Bearer token authentication
- **Error Handling**: Comprehensive error handling and fallback mechanisms
## API Endpoints
### 1. Health Check
**GET** `/health`
Check if the server is running and healthy.
**Response:**
```json
{
"status": "healthy",
"rag_system_initialized": true,
"ocr_available": true
}
```
### 2. System Status
**GET** `/hackrx/status`
Get detailed system status and statistics.
**Headers:**
```
Authorization: Bearer your_api_key_here
```
**Response:**
```json
{
"status": "ready",
"statistics": {
"vector_database": {
"total_chunks": 150,
"unique_sources": 3,
"file_types": ["pdf", "txt"]
},
"audit_trail": {
"total_entries": 25,
"successful_queries": 20,
"failed_queries": 5
}
},
"ocr_available": true
}
```
### 3. Document Upload
**POST** `/hackrx/upload`
Upload and process a document.
**Headers:**
```
Authorization: Bearer your_api_key_here
```
**Form Data:**
- `file`: The document file to upload
- `use_ocr`: (optional) "true" or "false" to enable OCR for PDFs
**Supported File Types:**
- PDF (.pdf)
- Text (.txt)
- Word (.docx)
- HTML (.html, .htm)
- Email (.eml, .msg)
- CSV (.csv)
- JSON (.json)
**Response:**
```json
{
"success": true,
"message": "Document processed successfully",
"chunks_processed": 45,
"processing_time": 2.34,
"filename": "document.pdf"
}
```
### 4. Query Processing
**POST** `/hackrx/run`
Process questions about uploaded documents.
**Headers:**
```
Authorization: Bearer your_api_key_here
Content-Type: application/json
```
**Request Body:**
```json
{
"questions": [
"What is covered under this policy?",
"What is the maximum coverage amount?",
"What documents are required for claims?"
]
}
```
**Response:**
```json
{
"answers": [
{
"question": "What is covered under this policy?",
"answer": "Based on the policy document, the following are covered...",
"decision": "COVERED",
"confidence": 0.85,
"processing_time": 1.23,
"amount": 50000.0,
"waiting_period": "30 days",
"relevant_clauses": ["Section 3.1", "Section 4.2"],
"conditions": ["Must be hospitalized", "Pre-authorization required"],
"exclusions": ["Cosmetic procedures", "Experimental treatments"],
"required_documents": ["Hospital bills", "Medical reports"]
}
]
}
```
### 5. System Validation
**GET** `/hackrx/validate`
Validate all system components.
**Headers:**
```
Authorization: Bearer your_api_key_here
```
**Response:**
```json
{
"document_processor": true,
"vector_database": true,
"query_parser": true,
"reasoning_engine": true,
"all_valid": true,
"errors": []
}
```
### 6. Clear System
**POST** `/hackrx/clear`
Clear all system data and reset the RAG system.
**Headers:**
```
Authorization: Bearer your_api_key_here
```
**Response:**
```json
{
"success": true,
"message": "System cleared successfully"
}
```
## Authentication
All endpoints (except `/health`) require Bearer token authentication:
```
Authorization: Bearer your_api_key_here
```
**Default API Key:** `your_api_key_here`
**Note:** Change this in production for security.
## Error Responses
All endpoints return appropriate HTTP status codes:
- `200`: Success
- `400`: Bad Request (missing parameters, invalid data)
- `401`: Unauthorized (missing or invalid Authorization header)
- `403`: Forbidden (invalid API key)
- `500`: Internal Server Error
Error response format:
```json
{
"error": "Error description"
}
```
## Usage Examples
### Python Example
```python
import requests
import json
# Configuration
BASE_URL = "http://localhost:5000"
API_KEY = "your_api_key_here"
HEADERS = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
# 1. Upload a document
with open("document.pdf", "rb") as f:
files = {"file": f}
data = {"use_ocr": "false"}
upload_headers = {"Authorization": f"Bearer {API_KEY}"}
response = requests.post(
f"{BASE_URL}/hackrx/upload",
files=files,
data=data,
headers=upload_headers
)
print("Upload response:", response.json())
# 2. Process queries
questions = [
"What is covered under this policy?",
"What is the maximum coverage amount?"
]
payload = {"questions": questions}
response = requests.post(
f"{BASE_URL}/hackrx/run",
json=payload,
headers=HEADERS
)
answers = response.json()["answers"]
for answer in answers:
print(f"Q: {answer['question']}")
print(f"A: {answer['answer']}")
print(f"Decision: {answer['decision']}")
print(f"Confidence: {answer['confidence']}")
print("---")
```
### cURL Examples
**Health Check:**
```bash
curl http://localhost:5000/health
```
**System Status:**
```bash
curl -H "Authorization: Bearer your_api_key_here" \
http://localhost:5000/hackrx/status
```
**Upload Document:**
```bash
curl -X POST \
-H "Authorization: Bearer your_api_key_here" \
-F "file=@document.pdf" \
-F "use_ocr=false" \
http://localhost:5000/hackrx/upload
```
**Process Queries:**
```bash
curl -X POST \
-H "Authorization: Bearer your_api_key_here" \
-H "Content-Type: application/json" \
-d '{"questions": ["What is covered under this policy?"]}' \
http://localhost:5000/hackrx/run
```
## Running the Server
1. **Install Dependencies:**
```bash
pip install flask requests
```
2. **Start the Server:**
```bash
python app.py
```
3. **Test the API:**
```bash
python test_api.py
```
## Configuration
### Environment Variables
You can set these environment variables:
- `FLASK_ENV`: Set to `production` for production deployment
- `API_KEY`: Override the default API key
- `PORT`: Override the default port (5000)
### Production Deployment
For production deployment:
1. Change the API key in `app.py`
2. Set `debug=False` in `app.run()`
3. Use a production WSGI server like Gunicorn:
```bash
pip install gunicorn
gunicorn -w 4 -b 0.0.0.0:5000 app:app
```
## Troubleshooting
### Common Issues
1. **RAG System Initialization Failed**
- Check if all required dependencies are installed
- Ensure model files are available
- Check system memory and resources
2. **Document Upload Fails**
- Verify file format is supported
- Check file size limits
- Ensure proper file permissions
3. **Query Processing Errors**
- Make sure documents are uploaded first
- Check if the RAG system is properly initialized
- Verify the question format
4. **Authentication Errors**
- Ensure the Authorization header is present
- Verify the API key is correct
- Check the Bearer token format
### Logs
The server provides detailed logging. Check the console output for:
- RAG system initialization status
- Document processing progress
- Query processing results
- Error messages and stack traces
## Security Considerations
1. **Change the Default API Key**: Update `your_api_key_here` in production
2. **Use HTTPS**: Always use HTTPS in production
3. **Rate Limiting**: Consider implementing rate limiting for production use
4. **Input Validation**: The API includes basic validation, but add more as needed
5. **File Upload Security**: Implement additional file validation for production
## Support
For issues and questions:
1. Check the console logs for error messages
2. Verify all dependencies are installed
3. Test with the provided `test_api.py` script
4. Check the system validation endpoint for component status |