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
| # Simple Flask API for RAG System | |
| A simplified Flask API server that provides localhost access to the main.py RAG system functionality. | |
| ## Features | |
| - **Localhost Only**: Runs only on 127.0.0.1 (localhost) | |
| - **Simple Authentication**: No authentication required for localhost use | |
| - **Core Operations**: Document upload and query processing | |
| - **GET/POST Operations**: Simple REST endpoints | |
| ## API Endpoints | |
| ### 1. Health Check | |
| **GET** `http://127.0.0.1:5000/health` | |
| Check if the server is running. | |
| **Response:** | |
| ```json | |
| { | |
| "status": "healthy", | |
| "rag_system_initialized": true | |
| } | |
| ``` | |
| ### 2. System Status | |
| **GET** `http://127.0.0.1:5000/hackrx/status` | |
| Check if the RAG system is ready. | |
| **Response:** | |
| ```json | |
| { | |
| "status": "ready", | |
| "message": "RAG system is ready" | |
| } | |
| ``` | |
| ### 3. Document Upload | |
| **POST** `http://127.0.0.1:5000/hackrx/upload` | |
| Upload and process a document. | |
| **Form Data:** | |
| - `file`: The document file to upload | |
| **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** `http://127.0.0.1:5000/hackrx/run` | |
| Process questions about uploaded documents. | |
| **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 | |
| } | |
| ] | |
| } | |
| ``` | |
| ## Usage Examples | |
| ### Python Example | |
| ```python | |
| import requests | |
| import json | |
| 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 | |
| ``` | |
| ## Error Responses | |
| - `200`: Success | |
| - `400`: Bad Request (missing parameters, invalid data) | |
| - `500`: Internal Server Error | |
| Error response format: | |
| ```json | |
| { | |
| "error": "Error description" | |
| } | |
| ``` | |
| ## Notes | |
| - **Localhost Only**: The server only accepts connections from localhost (127.0.0.1) | |
| - **No Authentication**: No API keys or authentication required for localhost use | |
| - **Simple Interface**: Focused on core document upload and query processing | |
| - **Automatic Cleanup**: Uploaded files are automatically cleaned up after processing |