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# Core dependencies
python>=3.9
docker>=20.10
docker-compose>=1.29
kubernetes>=1.21 (optional)

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# Clone the AIMatrix repository
git clone https://github.com/aimatrix/platform.git
cd platform/data-knowledge

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

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# docker-compose.yml
version: '3.8'
services:
  # Vector Database (Qdrant)
  qdrant:
    image: qdrant/qdrant
    ports:
      - "6333:6333"
    volumes:
      - ./data/qdrant:/qdrant/storage

  # Knowledge Graph (Neo4j)
  neo4j:
    image: neo4j:latest
    ports:
      - "7474:7474"
      - "7687:7687"
    environment:
      NEO4J_AUTH: neo4j/aimatrix123
    volumes:
      - ./data/neo4j:/data

  # Document Search (Elasticsearch)
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.8.0
    ports:
      - "9200:9200"
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
    volumes:
      - ./data/elasticsearch:/usr/share/elasticsearch/data

  # Message Queue (Redis)
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - ./data/redis:/data

  # MLflow Tracking
  mlflow:
    image: ghcr.io/mlflow/mlflow
    ports:
      - "5000:5000"
    environment:
      BACKEND_STORE_URI: sqlite:///mlflow.db
      DEFAULT_ARTIFACT_ROOT: ./artifacts
    volumes:
      - ./data/mlflow:/mlflow

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# Deploy infrastructure
docker-compose up -d

# Verify deployment
docker-compose ps

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# quick_setup.py
import asyncio
from aimatrix.data_knowledge import (
    DataRepositoryManager, 
    KnowledgeExtractor, 
    RAGEngine,
    FeatureStore
)

async def quick_setup():
    # Initialize core components
    repo_manager = DataRepositoryManager({
        'vector_db': {'type': 'qdrant', 'host': 'localhost', 'port': 6333},
        'graph_db': {'type': 'neo4j', 'uri': 'bolt://localhost:7687'},
        'search_db': {'type': 'elasticsearch', 'host': 'localhost:9200'}
    })
    
    # Create sample knowledge base
    await repo_manager.create_knowledge_base('aimatrix_kb')
    
    # Initialize knowledge extractor
    extractor = KnowledgeExtractor()
    
    # Setup RAG engine
    rag_engine = RAGEngine(repo_manager)
    
    print("✅ AIMatrix Data & Knowledge Layer initialized successfully!")
    
    # Test with sample data
    sample_text = """
    AIMatrix is an intelligent business automation platform that uses 
    artificial intelligence to streamline operations and improve decision-making.
    """
    
    # Extract knowledge
    knowledge = await extractor.extract_knowledge({'text': sample_text})
    print(f"📊 Extracted {len(knowledge['entities'])} entities and {len(knowledge['relations'])} relations")
    
    # Test RAG retrieval
    results = await rag_engine.search("What is AIMatrix?", top_k=5)
    print(f"🔍 RAG search returned {len(results)} results")

if __name__ == "__main__":
    asyncio.run(quick_setup())

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# Run setup
python quick_setup.py

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# document_processor.py
import asyncio
from pathlib import Path
from aimatrix.data_knowledge import DocumentProcessor, KnowledgeValidator

class SimpleDocumentPipeline:
    def __init__(self):
        self.processor = DocumentProcessor()
        self.validator = KnowledgeValidator()
        
    async def process_documents(self, documents_path: str):
        """Process documents and extract knowledge"""
        
        documents = list(Path(documents_path).glob("**/*.{txt,pdf,docx}"))
        print(f"📁 Processing {len(documents)} documents...")
        
        all_knowledge = []
        
        for doc_path in documents:
            try:
                # Extract knowledge from document
                knowledge = await self.processor.process_file(doc_path)
                
                # Validate extracted knowledge
                validation = await self.validator.validate_knowledge(knowledge)
                
                if validation['overall_success']:
                    all_knowledge.append(knowledge)
                    print(f"✅ Processed: {doc_path.name}")
                else:
                    print(f"❌ Failed validation: {doc_path.name}")
                    
            except Exception as e:
                print(f"⚠️  Error processing {doc_path.name}: {e}")
                
        print(f"🎉 Successfully processed {len(all_knowledge)} documents")
        return all_knowledge

# Usage
async def main():
    pipeline = SimpleDocumentPipeline()
    knowledge = await pipeline.process_documents("./sample_documents/")

if __name__ == "__main__":
    asyncio.run(main())

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# simple_rag.py
from aimatrix.data_knowledge import HybridSearchEngine, ContextOptimizer

class SimpleRAG:
    def __init__(self):
        self.search_engine = HybridSearchEngine()
        self.context_optimizer = ContextOptimizer()
        
    async def answer_question(self, question: str) -> str:
        """Answer question using RAG approach"""
        
        # Search for relevant information
        search_results = await self.search_engine.search(
            query=question,
            top_k=5,
            search_strategies=['semantic', 'keyword']
        )
        
        # Optimize context for LLM
        context, selected_results = self.context_optimizer.optimize_context(
            query=question,
            search_results=search_results
        )
        
        # Generate response (placeholder - integrate with your LLM)
        response = await self._generate_response(question, context)
        
        return {
            'answer': response,
            'sources': [r.source for r in selected_results],
            'confidence': sum(r.score for r in selected_results) / len(selected_results)
        }
        
    async def _generate_response(self, question: str, context: str) -> str:
        # Placeholder for LLM integration
        # In practice, integrate with OpenAI, Anthropic, or local models
        return f"Based on the available information: {context[:200]}..."

# Test the RAG system
async def test_rag():
    rag = SimpleRAG()
    
    questions = [
        "What is artificial intelligence?",
        "How does machine learning work?",
        "What are the benefits of automation?"
    ]
    
    for question in questions:
        result = await rag.answer_question(question)
        print(f"❓ Question: {question}")
        print(f"💡 Answer: {result['answer']}")
        print(f"📊 Confidence: {result['confidence']:.2f}")
        print(f"📚 Sources: {result['sources']}")
        print("-" * 50)

if __name__ == "__main__":
    asyncio.run(test_rag())

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# real_time_updates.py
from kafka import KafkaConsumer
import json
import asyncio

class RealTimeKnowledgeUpdater:
    def __init__(self, knowledge_manager):
        self.knowledge_manager = knowledge_manager
        self.consumer = KafkaConsumer(
            'knowledge_updates',
            bootstrap_servers=['localhost:9092'],
            value_deserializer=lambda m: json.loads(m.decode('utf-8'))
        )
        
    async def start_listening(self):
        """Listen for real-time knowledge updates"""
        
        for message in self.consumer:
            try:
                update_data = message.value
                
                # Process knowledge update
                await self._process_update(update_data)
                
                print(f"✅ Processed update: {update_data.get('type', 'unknown')}")
                
            except Exception as e:
                print(f"❌ Error processing update: {e}")
                
    async def _process_update(self, update_data):
        """Process individual knowledge update"""
        
        update_type = update_data.get('type')
        
        if update_type == 'new_document':
            await self._process_new_document(update_data['content'])
        elif update_type == 'entity_update':
            await self._update_entity(update_data['entity'])
        elif update_type == 'relation_update':
            await self._update_relation(update_data['relation'])

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# multimodal_extraction.py
from aimatrix.data_knowledge import ImageExtractor, AudioExtractor, TextExtractor

class MultiModalProcessor:
    def __init__(self):
        self.extractors = {
            'text': TextExtractor(),
            'image': ImageExtractor(), 
            'audio': AudioExtractor()
        }
        
    async def process_multimodal_content(self, content_items):
        """Process multiple content types simultaneously"""
        
        extraction_tasks = []
        
        for item in content_items:
            content_type = item['type']
            if content_type in self.extractors:
                task = self.extractors[content_type].extract(item['data'])
                extraction_tasks.append(task)
                
        # Extract knowledge from all modalities
        results = await asyncio.gather(*extraction_tasks)
        
        # Merge multimodal knowledge
        merged_knowledge = self._merge_multimodal_knowledge(results)
        
        return merged_knowledge
        
    def _merge_multimodal_knowledge(self, results):
        """Merge knowledge from different modalities"""
        
        merged = {
            'entities': [],
            'relations': [],
            'facts': [],
            'multimodal_links': []
        }
        
        # Combine entities from all modalities
        for result in results:
            merged['entities'].extend(result.get('entities', []))
            merged['relations'].extend(result.get('relations', []))
            merged['facts'].extend(result.get('facts', []))
            
        # Find cross-modal connections
        cross_modal_links = self._find_cross_modal_connections(results)
        merged['multimodal_links'] = cross_modal_links
        
        return merged

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# auto_deployment.py
from aimatrix.data_knowledge import ModelRegistry, DeploymentManager

class AutoDeployment:
    def __init__(self):
        self.registry = ModelRegistry()
        self.deployer = DeploymentManager()
        
    async def setup_auto_deployment(self, model_name: str):
        """Setup automated model deployment pipeline"""
        
        # Monitor for new model versions
        await self.registry.watch_model(
            model_name, 
            callback=self._on_model_update
        )
        
    async def _on_model_update(self, model_info):
        """Handle new model version"""
        
        # Validate model performance
        validation_passed = await self._validate_model_performance(model_info)
        
        if validation_passed:
            # Deploy to staging
            await self.deployer.deploy_to_staging(model_info)
            
            # Run integration tests
            tests_passed = await self._run_integration_tests(model_info)
            
            if tests_passed:
                # Promote to production with canary deployment
                await self.deployer.canary_deployment(
                    model_info, 
                    traffic_split=0.1  # Start with 10% traffic
                )
                
                print(f"🚀 Model {model_info['name']} deployed to production")

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# Check Qdrant status
curl http://localhost:6333/health

# Solution: Restart with proper configuration
docker-compose restart qdrant

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# Debug knowledge extraction
async def debug_extraction(text):
    try:
        extractor = KnowledgeExtractor()
        result = await extractor.extract_knowledge({'text': text})
        print("✅ Extraction successful")
        return result
    except Exception as e:
        print(f"❌ Extraction failed: {e}")
        # Enable detailed logging
        import logging
        logging.basicConfig(level=logging.DEBUG)
        return None

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# Optimize RAG performance
class OptimizedRAG:
    def __init__(self):
        self.cache = {}  # Add caching
        self.batch_size = 32  # Batch processing
        
    async def optimized_search(self, query):
        # Check cache first
        if query in self.cache:
            return self.cache[query]
            
        # Perform search with optimizations
        results = await self.search_with_optimizations(query)
        
        # Cache results
        self.cache[query] = results
        return results