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: 5,085 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 | # Flask API Server for main.py
A simple Flask API server that provides REST endpoints for the main.py RAG system without duplicating model loading or initialization.
## How It Works
The `app.py` server acts as a lightweight wrapper around `main.py`:
- **No Model Loading**: The server doesn't load models, parsers, or RAG components
- **Subprocess Calls**: Uses `subprocess` to call `main.py` with command line arguments
- **Localhost Only**: Runs only on 127.0.0.1 for security
- **Simple Interface**: Provides basic GET/POST endpoints
## API Endpoints
### 1. Health Check
**GET** `http://127.0.0.1:5000/health`
Check if the Flask server is running.
**Response:**
```json
{
"status": "healthy",
"message": "Flask server is running"
}
```
### 2. System Status
**GET** `http://127.0.0.1:5000/hackrx/status`
Check if main.py is ready and working.
**Response:**
```json
{
"status": "ready",
"message": "main.py is ready"
}
```
### 3. Document Upload
**POST** `http://127.0.0.1:5000/hackrx/upload`
Upload and process a document using main.py.
**Form Data:**
- `file`: The document file to upload
**Response:**
```json
{
"success": true,
"message": "Document processed successfully",
"chunks_processed": 45,
"processing_time": 2.34,
"filename": "document.pdf"
}
```
### 4. Query Processing
**POST** `http://127.0.0.1:5000/hackrx/run`
Process questions about uploaded documents using main.py.
**Request Body:**
```json
{
"questions": [
"What is covered under this policy?",
"What is the maximum coverage amount?"
]
}
```
**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
}
]
}
```
## How the Server Works
### Document Upload Process:
1. Flask receives uploaded file
2. Saves file to `uploads/` directory
3. Calls: `python main.py --upload /path/to/file`
4. Parses output from main.py
5. Returns JSON response
### Query Processing Process:
1. Flask receives JSON with questions
2. For each question:
- Creates temporary file with question
- Calls: `python main.py --query /path/to/question.txt`
- Parses structured output from main.py
- Extracts decision, confidence, justification
3. Returns JSON with all answers
### Status Check Process:
1. Calls: `python main.py --status`
2. Checks if main.py responds successfully
3. Returns status JSON
## Usage Examples
### Python Example
```python
import requests
BASE_URL = "http://127.0.0.1:5000"
# 1. Upload a document
with open("document.pdf", "rb") as f:
files = {"file": f}
response = requests.post(f"{BASE_URL}/hackrx/upload", files=files)
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={"Content-Type": "application/json"}
)
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://127.0.0.1:5000/health
```
**System Status:**
```bash
curl http://127.0.0.1:5000/hackrx/status
```
**Upload Document:**
```bash
curl -X POST -F "file=@document.pdf" http://127.0.0.1:5000/hackrx/upload
```
**Process Queries:**
```bash
curl -X POST \
-H "Content-Type: application/json" \
-d '{"questions": ["What is covered under this policy?"]}' \
http://127.0.0.1:5000/hackrx/run
```
## Running the Server
1. **Start the server:**
```bash
python app.py
```
2. **Test the API:**
```bash
python test_api.py
```
## Command Line Interface
The main.py now supports command line arguments:
```bash
# Process a single query
python main.py --query question.txt
# Upload and process a document
python main.py --upload document.pdf
# Check system status
python main.py --status
# Interactive mode (default)
python main.py
```
## Advantages
1. **No Duplication**: Doesn't load models or initialize RAG system
2. **Lightweight**: Minimal memory footprint
3. **Simple**: Easy to understand and maintain
4. **Secure**: Localhost only
5. **Reliable**: Uses existing main.py functionality
## Error Handling
- **File Not Found**: Returns 400 if file doesn't exist
- **Unsupported Format**: Returns 400 for unsupported file types
- **Processing Errors**: Returns 500 with error details
- **Timeouts**: 60 seconds for queries, 120 seconds for uploads
## Notes
- **main.py Required**: The server requires main.py to be in the same directory
- **Python Path**: Assumes `python` command is available
- **File Cleanup**: Temporary files are automatically cleaned up
- **Upload Directory**: Creates `uploads/` directory if it doesn't exist |