#!/usr/bin/env python3
"""
Analyze the current chunking strategy by examining actual chunks created from English articles.
"""

import os
import sys

# Add the parent directory to Python path
current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, current_dir)

def analyze_chunking_strategy():
    """Analyze how articles are being chunked."""
    print("🔍 Chunking Strategy Analysis")
    print("=" * 60)
    
    try:
        from cve_factchecker.firebase_loader import FirebaseNewsLoader
        from cve_factchecker.retriever import VectorNewsRetriever
        from langchain.text_splitter import RecursiveCharacterTextSplitter
        
        # 1. Fetch a few English articles
        loader = FirebaseNewsLoader()
        print("📊 Fetching sample English articles...")
        articles = loader.fetch_english_articles(limit=3)
        
        if not articles:
            print("❌ No articles found")
            return
        
        print(f"✅ Got {len(articles)} articles for analysis")
        
        # 2. Show article content before chunking
        print(f"\n📄 Article Content Analysis:")
        for i, article in enumerate(articles, 1):
            print(f"\n   Article {i}: {article.title[:80]}...")
            print(f"   Content Length: {len(article.content)} characters")
            print(f"   URL: {article.url}")
            print(f"   Content Preview: {article.content[:200]}...")
        
        # 3. Demonstrate chunking process
        print(f"\n🔪 Chunking Process Analysis:")
        splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
        
        for i, article in enumerate(articles, 1):
            print(f"\n--- Article {i} Chunking ---")
            print(f"Title: {article.title}")
            print(f"Original Length: {len(article.content)} chars")
            
            # Create chunks
            chunks = splitter.split_text(article.content)
            print(f"Number of Chunks: {len(chunks)}")
            
            # Analyze each chunk
            for j, chunk in enumerate(chunks):
                print(f"\n  Chunk {j+1}:")
                print(f"    Length: {len(chunk)} characters")
                print(f"    Content: {chunk[:150]}...")
                if j < len(chunks) - 1:
                    # Check overlap with next chunk
                    next_chunk = chunks[j+1]
                    overlap = find_overlap(chunk, next_chunk)
                    print(f"    Overlap with next: {len(overlap)} chars")
                    if overlap:
                        print(f"    Overlap text: '{overlap[:50]}...'")
        
        # 4. Test the complete vector storage process
        print(f"\n🗄️ Vector Storage Process:")
        retriever = VectorNewsRetriever()
        
        # Process one article to see the complete document creation
        test_article = articles[0]
        print(f"\nProcessing: {test_article.title[:50]}...")
        
        # Simulate the document creation process
        chunks = splitter.split_text(test_article.content)
        documents = []
        
        for i, chunk in enumerate(chunks):
            if len(chunk.strip()) < 30:
                continue
            
            # Show how page_content is constructed
            page_content = f"Title: {test_article.title}\n\n{chunk}"
            
            if test_article.source and test_article.source not in chunk:
                page_content += f"\n\nSource: {test_article.source}"
            
            metadata = {
                "url": test_article.url,
                "source": test_article.source,
                "published_date": test_article.published_date,
                "scraped_date": test_article.scraped_date,
                "id": test_article.article_id,
                "chunk_id": f"{test_article.article_id}_{i}",
                "title": test_article.title
            }
            
            documents.append({
                "page_content": page_content,
                "metadata": metadata
            })
        
        print(f"Created {len(documents)} document objects")
        
        # Show sample document structure
        if documents:
            print(f"\n📋 Sample Document Structure:")
            sample_doc = documents[0]
            print(f"Page Content Length: {len(sample_doc['page_content'])} chars")
            print(f"Page Content Preview:")
            print(f"   {sample_doc['page_content'][:300]}...")
            print(f"\nMetadata:")
            for key, value in sample_doc['metadata'].items():
                print(f"   {key}: {value}")
        
        return True
        
    except Exception as e:
        print(f"❌ Analysis failed: {e}")
        import traceback
        traceback.print_exc()
        return False

def find_overlap(text1, text2):
    """Find overlapping text between two chunks."""
    # Look for overlap from the end of text1 to the beginning of text2
    max_overlap = min(200, len(text1), len(text2))  # Match chunk_overlap=200
    
    for i in range(max_overlap, 0, -1):
        if text1[-i:] == text2[:i]:
            return text1[-i:]
    return ""

def test_chunking_parameters():
    """Test different chunking parameters to understand the strategy."""
    print(f"\n🧪 Chunking Parameters Test")
    print("=" * 60)
    
    try:
        from langchain.text_splitter import RecursiveCharacterTextSplitter
        from cve_factchecker.firebase_loader import FirebaseNewsLoader
        
        # Get a test article
        loader = FirebaseNewsLoader()
        articles = loader.fetch_english_articles(limit=1)
        
        if not articles:
            print("❌ No test article available")
            return
        
        test_content = articles[0].content
        print(f"Test Content Length: {len(test_content)} characters")
        
        # Test different chunk sizes
        test_configs = [
            {"chunk_size": 500, "chunk_overlap": 100},
            {"chunk_size": 1000, "chunk_overlap": 200},   # Current setting
            {"chunk_size": 1500, "chunk_overlap": 300},
            {"chunk_size": 2000, "chunk_overlap": 400},
        ]
        
        for config in test_configs:
            splitter = RecursiveCharacterTextSplitter(
                chunk_size=config["chunk_size"],
                chunk_overlap=config["chunk_overlap"]
            )
            
            chunks = splitter.split_text(test_content)
            
            print(f"\n📊 Config: chunk_size={config['chunk_size']}, overlap={config['chunk_overlap']}")
            print(f"   Chunks created: {len(chunks)}")
            
            if chunks:
                chunk_lengths = [len(chunk) for chunk in chunks]
                avg_length = sum(chunk_lengths) / len(chunk_lengths)
                print(f"   Average chunk length: {avg_length:.0f} chars")
                print(f"   Chunk length range: {min(chunk_lengths)} - {max(chunk_lengths)} chars")
                
                # Show first chunk
                print(f"   First chunk preview: {chunks[0][:100]}...")
                
                # Test overlap
                if len(chunks) > 1:
                    overlap = find_overlap(chunks[0], chunks[1])
                    print(f"   Actual overlap: {len(overlap)} chars")
        
        return True
        
    except Exception as e:
        print(f"❌ Parameter test failed: {e}")
        return False

def analyze_current_vector_db():
    """Analyze what's currently in the vector database."""
    print(f"\n🗄️ Current Vector Database Analysis")
    print("=" * 60)
    
    try:
        from cve_factchecker.retriever import VectorNewsRetriever
        
        retriever = VectorNewsRetriever()
        
        # Try a few different search queries to see what chunks look like
        test_queries = [
            "security vulnerability",
            "cyberattack",
            "data breach",
            "malware",
            "terrorism"
        ]
        
        for query in test_queries:
            print(f"\n🔍 Search: '{query}'")
            results = retriever.semantic_search(query, k=2)
            
            if results:
                for i, result in enumerate(results, 1):
                    print(f"\n   Result {i}:")
                    print(f"   Title: {result['title'][:60]}...")
                    print(f"   Content Length: {len(result['content'])} chars")
                    print(f"   Content Preview: {result['content'][:200]}...")
                    print(f"   URL: {result['url']}")
                    print(f"   Source: {result['source']}")
                    
                    # Check chunk metadata
                    metadata = result.get('metadata', {})
                    if 'chunk_id' in metadata:
                        print(f"   Chunk ID: {metadata['chunk_id']}")
            else:
                print(f"   No results found")
            
            if results:
                break  # Stop after first successful query
        
        return True
        
    except Exception as e:
        print(f"❌ Vector DB analysis failed: {e}")
        return False

def main():
    """Main analysis function."""
    print("📊 CVE Fact Checker - Chunking Strategy Analysis")
    print("=" * 80)
    
    # Run all analyses
    success1 = analyze_chunking_strategy()
    success2 = test_chunking_parameters() if success1 else False
    success3 = analyze_current_vector_db() if success1 else False
    
    print(f"\n📋 Analysis Summary:")
    print(f"   Chunking Process: {'✅ Analyzed' if success1 else '❌ Failed'}")
    print(f"   Parameter Testing: {'✅ Completed' if success2 else '❌ Failed'}")
    print(f"   Vector DB Content: {'✅ Analyzed' if success3 else '❌ Failed'}")
    
    if success1:
        print(f"\n💡 Current Chunking Strategy:")
        print(f"   📏 Chunk Size: 1000 characters")
        print(f"   🔄 Overlap: 200 characters")
        print(f"   🔪 Splitter: RecursiveCharacterTextSplitter")
        print(f"   📝 Format: Title + Content + Source")
        print(f"   🏷️ Metadata: URL, source, dates, chunk_id")
    
    return success1

if __name__ == "__main__":
    success = main()
    sys.exit(0 if success else 1)