src/core/examples.py

"""
LlamaIndex Integration Examples

Demonstrates usage patterns for the knowledge base
"""

import os
from typing import List, Dict, Any

from .llama_integration import EcoMCPKnowledgeBase, IndexConfig
from .knowledge_base import KnowledgeBase
from .document_loader import DocumentLoader
from .vector_search import VectorSearchEngine


def example_basic_indexing():
    """Example: Basic document indexing"""
    print("=== Basic Indexing Example ===")
    
    # Initialize knowledge base
    kb = EcoMCPKnowledgeBase()
    
    # Index documents from a directory
    docs_path = "./docs"
    if os.path.exists(docs_path):
        kb.initialize(docs_path)
        print(f"Indexed documents from {docs_path}")
    else:
        print(f"Directory {docs_path} not found")


def example_product_search():
    """Example: Search for products"""
    print("\n=== Product Search Example ===")
    
    kb = EcoMCPKnowledgeBase()
    
    # Add sample products
    products = [
        {
            "id": "prod_001",
            "name": "Wireless Headphones",
            "description": "High-quality noise-canceling wireless headphones",
            "price": "$299",
            "category": "Electronics",
            "features": ["Noise Canceling", "30h Battery", "Bluetooth 5.0"],
            "tags": ["audio", "wireless", "premium"]
        },
        {
            "id": "prod_002",
            "name": "Laptop Stand",
            "description": "Adjustable aluminum laptop stand",
            "price": "$49",
            "category": "Accessories",
            "features": ["Adjustable", "Aluminum", "Portable"],
            "tags": ["ergonomic", "desk"]
        },
    ]
    
    kb.add_products(products)
    
    # Search
    query = "noise canceling audio equipment"
    results = kb.search_products(query, top_k=3)
    
    print(f"\nSearch query: '{query}'")
    print(f"Found {len(results)} results:")
    for i, result in enumerate(results, 1):
        print(f"\n{i}. Score: {result.score:.2f}")
        print(f"   Content: {result.content[:200]}...")


def example_documentation_search():
    """Example: Search documentation"""
    print("\n=== Documentation Search Example ===")
    
    kb = EcoMCPKnowledgeBase()
    
    # Index docs directory
    docs_path = "./docs"
    if os.path.exists(docs_path):
        kb.initialize(docs_path)
        
        # Search
        query = "how to deploy"
        results = kb.search_documentation(query, top_k=3)
        
        print(f"\nSearch query: '{query}'")
        print(f"Found {len(results)} results:")
        for i, result in enumerate(results, 1):
            print(f"\n{i}. Source: {result.source}")
            print(f"   Score: {result.score:.2f}")
            print(f"   Preview: {result.content[:200]}...")


def example_semantic_search():
    """Example: Semantic similarity search"""
    print("\n=== Semantic Search Example ===")
    
    kb = EcoMCPKnowledgeBase()
    docs_path = "./docs"
    
    if os.path.exists(docs_path):
        kb.initialize(docs_path)
        
        # Semantic search with threshold
        query = "installation and setup"
        results = kb.search_engine.semantic_search(query, top_k=5, similarity_threshold=0.5)
        
        print(f"\nSemantic search for: '{query}'")
        print(f"Results with similarity >= 0.5:")
        for i, result in enumerate(results, 1):
            print(f"{i}. Score: {result.score:.2f} - {result.content[:100]}...")


def example_recommendations():
    """Example: Get recommendations"""
    print("\n=== Recommendations Example ===")
    
    kb = EcoMCPKnowledgeBase()
    
    # Add products
    products = [
        {
            "id": "prod_001",
            "name": "Wireless Mouse",
            "description": "Ergonomic wireless mouse with precision tracking",
            "price": "$29",
            "category": "Accessories",
            "tags": ["mouse", "wireless", "ergonomic"]
        },
        {
            "id": "prod_002",
            "name": "Keyboard",
            "description": "Mechanical keyboard with RGB lighting",
            "price": "$129",
            "category": "Accessories",
            "tags": ["keyboard", "mechanical", "gaming"]
        },
    ]
    
    kb.add_products(products)
    
    # Get recommendations
    query = "I need a wireless input device for programming"
    recommendations = kb.get_recommendations(query, recommendation_type="products", limit=3)
    
    print(f"\nUser query: '{query}'")
    print("Recommendations:")
    for rec in recommendations:
        print(f"\n#{rec['rank']}")
        print(f"Confidence: {rec['confidence']:.2f}")
        print(f"Product: {rec['content'][:150]}...")


def example_hierarchical_search():
    """Example: Multi-level search across types"""
    print("\n=== Hierarchical Search Example ===")
    
    kb = EcoMCPKnowledgeBase()
    docs_path = "./docs"
    
    # Setup with both docs and products
    if os.path.exists(docs_path):
        products = [
            {
                "id": "prod_001",
                "name": "E-commerce Platform",
                "description": "Complete e-commerce solution",
                "category": "Software",
                "tags": ["ecommerce", "platform"]
            }
        ]
        
        kb.initialize(docs_path, products=products)
        
        # Hierarchical search
        query = "e-commerce"
        results = kb.search_engine.hierarchical_search(query, levels=["product", "documentation"])
        
        print(f"\nHierarchical search for: '{query}'")
        for level, items in results.items():
            print(f"\n{level.upper()}: {len(items)} results")
            for item in items[:2]:
                print(f"  - {item.content[:80]}...")


def example_custom_config():
    """Example: Custom configuration"""
    print("\n=== Custom Configuration Example ===")
    
    config = IndexConfig(
        embedding_model="text-embedding-3-large",
        chunk_size=2048,
        chunk_overlap=128,
        use_pinecone=False,  # Set to True if using Pinecone
    )
    
    kb = EcoMCPKnowledgeBase(config=config)
    print(f"Knowledge base created with custom config:")
    print(f"  - Embedding model: {config.embedding_model}")
    print(f"  - Chunk size: {config.chunk_size}")
    print(f"  - Vector store: {'Pinecone' if config.use_pinecone else 'In-memory'}")


def example_persistence():
    """Example: Save and load knowledge base"""
    print("\n=== Persistence Example ===")
    
    kb = EcoMCPKnowledgeBase()
    
    # Initialize with documents
    docs_path = "./docs"
    if os.path.exists(docs_path):
        kb.initialize(docs_path)
        
        # Save
        save_path = "./kb_index"
        kb.save(save_path)
        print(f"Knowledge base saved to {save_path}")
        
        # Create new instance and load
        kb2 = EcoMCPKnowledgeBase()
        if kb2.load(save_path):
            print("Knowledge base loaded successfully")
            
            # Verify with search
            results = kb2.search("test query", top_k=1)
            print(f"Loaded index contains {len(results)} search results for test query")


def example_query_engine():
    """Example: Natural language query"""
    print("\n=== Query Engine Example ===")
    
    kb = EcoMCPKnowledgeBase()
    
    docs_path = "./docs"
    if os.path.exists(docs_path):
        kb.initialize(docs_path)
        
        # Natural language query
        question = "What are the main features of the platform?"
        response = kb.query(question)
        
        print(f"\nQuestion: {question}")
        print(f"Response: {response}")


if __name__ == "__main__":
    print("LlamaIndex Integration Examples\n")
    
    # Run examples
    example_basic_indexing()
    example_custom_config()
    example_product_search()
    example_documentation_search()
    example_semantic_search()
    example_recommendations()
    example_hierarchical_search()
    example_persistence()
    example_query_engine()
    
    print("\n✓ All examples completed")