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: 12,738 Bytes
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Test RAG System Integration
Tests the complete flow: Document Processor β Vector Database β Query Parser β LLM Reasoning β RAG System
"""
import os
import tempfile
import shutil
from pathlib import Path
def test_rag_system():
"""Test the complete RAG system workflow"""
print("π RAG System Integration Test")
print("="*50)
print("Testing: Document Processor β Vector Database β Query Parser β LLM Reasoning β RAG System")
print("="*50)
try:
# Import RAG system
print("π Importing RAG system...")
from rag_system import AdvancedRAGSystem
print("β
RAG system imported successfully")
# Initialize RAG system
print("π Initializing RAG system...")
rag_system = AdvancedRAGSystem(
use_gpu=False, # Use CPU for testing
vector_db_path="./test_rag_db"
)
print("β
RAG system initialized")
# Validate system
print("π Validating system components...")
validation = rag_system.validate_system()
if validation['overall_status']:
print("β
All components validated successfully")
else:
print("β οΈ Some components have issues:")
for error in validation['errors']:
print(f" - {error}")
# Create test documents
print("\nπ Creating test documents...")
test_docs = create_test_documents()
# Ingest documents
print("π Ingesting documents...")
total_chunks = 0
for doc_info in test_docs:
try:
chunks = rag_system.ingest_document(doc_info['file_path'])
total_chunks += len(chunks)
print(f" β
Ingested {len(chunks)} chunks from {doc_info['name']}")
except Exception as e:
print(f" β Failed to ingest {doc_info['name']}: {e}")
print(f"β
Total chunks ingested: {total_chunks}")
# Test queries
test_queries = [
"Is heart surgery covered under this policy?",
"What's the waiting period for dental procedures?",
"How do I file a claim?",
"What documents are needed for medical claims?",
"Are pre-existing conditions covered?"
]
print(f"\nπ Testing {len(test_queries)} queries...")
results = []
for i, query in enumerate(test_queries, 1):
print(f"\n--- Query {i}: {query} ---")
try:
# Process query through RAG system
result = rag_system.process_query(query, n_results=3)
print(f" Processing Time: {result.processing_time:.2f}s")
print(f" Query Type: {result.parsed_query.query_type}")
print(f" Intent: {result.parsed_query.intent}")
print(f" Confidence: {result.parsed_query.confidence:.2f}")
print(f" Search Results: {len(result.search_results)}")
print(f" Decision: {result.reasoning_result.decision}")
print(f" Reasoning Confidence: {result.reasoning_result.confidence_score:.2f}")
# Show top search result
if result.search_results:
top_result = result.search_results[0]
print(f" Top Result: {top_result.content[:100]}...")
print(f" Source: {top_result.source_file}")
print(f" Similarity: {top_result.similarity_score:.3f}")
# Show reasoning justification
if result.reasoning_result.justification:
print(f" Justification: {result.reasoning_result.justification[:150]}...")
results.append({
'query': query,
'result': result,
'success': True
})
except Exception as e:
print(f" β Query processing failed: {e}")
results.append({
'query': query,
'result': None,
'success': False,
'error': str(e)
})
# Generate summary report
print(f"\n{'='*50}")
print("π RAG SYSTEM TEST RESULTS")
print(f"{'='*50}")
successful_queries = sum(1 for r in results if r['success'])
total_queries = len(results)
print(f"Total Queries Tested: {total_queries}")
print(f"Successful Queries: {successful_queries}")
print(f"Success Rate: {successful_queries/total_queries*100:.1f}%")
# Detailed results
print(f"\nπ DETAILED RESULTS:")
for i, result in enumerate(results, 1):
if result['success']:
rag_result = result['result']
status = "β
"
decision = rag_result.reasoning_result.decision
confidence = rag_result.reasoning_result.confidence_score
print(f"{i}. {status} {result['query']}")
print(f" Decision: {decision}")
print(f" Confidence: {confidence:.2f}")
print(f" Search Results: {len(rag_result.search_results)}")
else:
print(f"{i}. β {result['query']}")
print(f" Error: {result['error']}")
# Test system statistics
print(f"\nπ SYSTEM STATISTICS:")
stats = rag_system.get_system_statistics()
print(f" Vector Database: {stats.get('vector_database', {}).get('total_chunks', 0)} chunks")
print(f" Audit Trail: {stats.get('audit_trail', {}).get('total_entries', 0)} entries")
print(f" Successful Queries: {stats.get('audit_trail', {}).get('successful_queries', 0)}")
# Test audit trail
print(f"\nπ AUDIT TRAIL SAMPLE:")
audit_trail = rag_system.get_audit_trail()
if audit_trail:
latest_entry = audit_trail[-1]
print(f" Latest Action: {latest_entry.get('action', 'unknown')}")
print(f" Status: {latest_entry.get('status', 'unknown')}")
print(f" Timestamp: {latest_entry.get('timestamp', 'unknown')}")
# Cleanup
print(f"\nπ§Ή Cleaning up...")
cleanup_test_data()
print(f"\nπ RAG system test completed!")
if successful_queries == total_queries:
print("β
All queries processed successfully!")
print("π― RAG System is working perfectly!")
else:
print("β οΈ Some queries failed. Check the detailed results above.")
return successful_queries == total_queries
except Exception as e:
print(f"β RAG system test failed: {e}")
import traceback
traceback.print_exc()
return False
def create_test_documents():
"""Create test documents for RAG system"""
test_dir = tempfile.mkdtemp()
print(f"π Created test directory: {test_dir}")
docs = []
# Create policy document
policy_content = """
MEDICAL INSURANCE POLICY
COVERAGE DETAILS:
- Heart surgery: Covered up to $50,000
- Dental procedures: Covered up to $2,000 annually
- Prescription medications: 80% coverage
- Hospital stays: Up to $1,000 per day
- Specialist consultations: $100 per visit
WAITING PERIODS:
- General medical: 30 days
- Pre-existing conditions: 12 months
- Dental procedures: 6 months
- Major surgeries: 90 days
CLAIM PROCEDURES:
- Submit claim form within 30 days
- Include medical certificate
- Provide original receipts and bills
- Processing time: 10-15 business days
EXCLUSIONS:
- Cosmetic procedures
- Experimental treatments
- Injuries from dangerous activities
- Pre-existing conditions (first 12 months)
"""
policy_path = os.path.join(test_dir, "medical_policy.txt")
with open(policy_path, 'w', encoding='utf-8') as f:
f.write(policy_content)
docs.append({
'name': 'Medical Policy',
'file_path': policy_path,
'type': 'policy'
})
# Create claims guide
claims_content = """
CLAIMS PROCESSING GUIDE
REQUIRED DOCUMENTS:
1. Completed claim form
2. Medical certificate from doctor
3. Original receipts and bills
4. Prescription details (if applicable)
5. Hospital discharge summary (if hospitalized)
PROCESSING TIMES:
- Standard claims: 10-15 business days
- Urgent claims: 3-5 business days
- Complex cases: 20-30 business days
CLAIM LIMITS:
- Maximum annual benefit: $100,000
- Maximum per claim: $25,000
- Deductible: $500 per year
SUBMISSION METHODS:
- Online portal
- Mobile app
- Mail to claims department
- In-person at service centers
"""
claims_path = os.path.join(test_dir, "claims_guide.txt")
with open(claims_path, 'w', encoding='utf-8') as f:
f.write(claims_content)
docs.append({
'name': 'Claims Guide',
'file_path': claims_path,
'type': 'guide'
})
return docs
def cleanup_test_data():
"""Clean up test data"""
try:
import time
import gc
# Force garbage collection
gc.collect()
time.sleep(2)
# Remove test directories
test_dirs = ["./test_rag_db", "./test_vector_db", "./temp_test_db"]
for dir_path in test_dirs:
if os.path.exists(dir_path):
try:
shutil.rmtree(dir_path, ignore_errors=True)
print(f" β
Cleaned {dir_path}")
except Exception as e:
print(f" β οΈ Could not clean {dir_path}: {e}")
# Remove temporary files
temp_files = [f for f in os.listdir('.') if f.startswith('temp_')]
for file in temp_files:
try:
os.remove(file)
print(f" β
Removed {file}")
except Exception as e:
print(f" β οΈ Could not remove {file}: {e}")
except Exception as e:
print(f" β οΈ Cleanup warning: {e}")
def test_individual_components():
"""Test individual components before RAG system"""
print("\nπ§ͺ TESTING INDIVIDUAL COMPONENTS")
print("="*40)
components = {
'Document Processor': 'document_processer',
'Vector Database': 'vector_database',
'Query Parser': 'query_parser',
'LLM Reasoning': 'llm_reasoning'
}
results = {}
for name, module in components.items():
print(f"\nπ Testing {name}...")
try:
__import__(module)
print(f" β
{name} imported successfully")
results[name] = True
except Exception as e:
print(f" β {name} import failed: {e}")
results[name] = False
# Summary
print(f"\nπ COMPONENT TEST RESULTS:")
passed = sum(results.values())
total = len(results)
for name, result in results.items():
status = "β
PASS" if result else "β FAIL"
print(f" {name}: {status}")
print(f"\nOverall: {passed}/{total} components ready")
return passed == total
def main():
"""Main test runner"""
print("π RAG System Test Suite")
print("="*50)
# Test individual components first
components_ready = test_individual_components()
if not components_ready:
print("\nβ Some components are not ready. Please fix the issues above.")
return False
print(f"\n{'='*50}")
print("π RUNNING RAG SYSTEM INTEGRATION TEST")
print(f"{'='*50}")
# Test RAG system
success = test_rag_system()
if success:
print(f"\nπ RAG System Integration Test PASSED!")
print("β
All components working together successfully")
print("π― Your RAG system is ready for production use!")
else:
print(f"\nβ οΈ RAG System Integration Test FAILED!")
print("β Some issues need to be resolved")
print(f"\nπ‘ Next steps:")
print(" 1. Add your actual documents")
print(" 2. Customize the query processing")
print(" 3. Fine-tune the reasoning engine")
print(" 4. Deploy to production")
if __name__ == "__main__":
main() |