The Future of AI is Relationship-Aware

db = rudradb.RudraDB()  # 🎯 Auto-detects ANY dimension!

# OpenAI Ada-002 (1536D) → Auto-detected ✓
db.add_vector("doc1", openai_embedding)
print(f"Detected: {db.dimension()}D")  # 1536

# Switch to Sentence Transformers (384D) → New detection ✓
db2 = rudradb.RudraDB()
db2.add_vector("doc2", sentence_transformer_emb)
print(f"Detected: {db2.dimension()}D")  # 384

# 🚀 Traditional databases would throw errors!

# Just add documents with rich metadata
db.add_vector("ai_intro", embedding, {
    "category": "AI", 
    "difficulty": "beginner", 
    "tags": ["intro", "basics"]
})

db.add_vector("ml_advanced", embedding, {
    "category": "AI", 
    "difficulty": "advanced", 
    "tags": ["ml", "complex"]
})

# 🧠 Automatically creates:
# - Semantic relationship (same category)
# - Temporal relationship (beginner → advanced)
# - Associative relationship (shared tags)
print(f"Auto-relationships: {db.relationship_count()}")

# Traditional search: only similar documents
basic_results = traditional_db.search(query)  # 3 results

# 🔍 RudraDB-Opin auto-enhanced search
enhanced_results = db.search(query, SearchParams(
    include_relationships=True,  # 🧠 Auto-detected relationships
    max_hops=2                  # Multi-hop traversal
))  # 7 results!

# Discovers: A → (semantic) → B → (causal) → C
for result in enhanced_results:
    connection = "Direct" if result.hop_count == 0 else f"{result.hop_count}-hop auto-connection"
    print(f"{result.vector_id}: {connection}")

# 🚀 133% more relevant results through auto-intelligence!

# ⚡ Auto-optimization in action
params = SearchParams(
    auto_enhance=True,                    # Enable all auto-optimizations
    auto_balance_weights=True,            # Auto-balance similarity vs relationships
    auto_select_relationship_types=True,  # Auto-choose relevant types
    auto_optimize_hops=True,             # Auto-optimize traversal depth
    auto_calibrate_threshold=True        # Auto-adjust similarity threshold
)

results = db.search(query, params)

# 📊 Check what auto-optimizations were applied
stats = db.get_last_search_enhancement_stats()
print(f"Auto-optimizations applied:")
print(f"  Weight balanced: {stats['weight_balanced']}")
print(f"  Performance gain: {stats['performance_gain']:.1%}")

# 🚀 System learns and optimizes automatically!