#!/usr/bin/env python3
"""
TokioAI Engine -- Basic Usage Example

Prerequisites:
  pip install tokioai-engine
  ollama serve &
  ollama pull qwen3-coder
"""

from engine.pipeline import Pipeline, EngineConfig

# Create engine with default config (connects to local Ollama)
config = EngineConfig(
    model_name="qwen3-coder",          # Any Ollama model
    ollama_host="http://localhost:11434",
    max_retries=2,                      # Retry on bad output
    use_guided_gen=True,                # Force valid JSON for tool calls
    use_memory=True,                    # Learn from successful interactions
    verbose=True,                       # Show engine internals
)

engine = Pipeline(config)

# --- Tool Call Example ---
print("=" * 60)
print("TOOL CALL: 'check disk space'")
print("=" * 60)

result = engine.process("check disk space")
print(f"Intent:  {result.intent}")
print(f"Type:    {result.output_type}")
print(f"Calls:   {result.tool_calls}")
print(f"Latency: {result.latency_ms}ms")
print(f"Retries: {result.attempts}")
print()

# --- Text Response Example ---
print("=" * 60)
print("TEXT: 'what is SQL injection'")
print("=" * 60)

engine.reset()
result = engine.process("what is SQL injection")
print(f"Intent:  {result.intent}")
print(f"Type:    {result.output_type}")
print(f"Text:    {result.text[:200]}")
print(f"Latency: {result.latency_ms}ms")
print()

# --- Decision Example ---
print("=" * 60)
print("DECISION: 'is docker running'")
print("=" * 60)

engine.reset()
result = engine.process("is docker running")
print(f"Intent:  {result.intent}")
print(f"Type:    {result.output_type}")
if result.tool_calls:
    print(f"Calls:   {result.tool_calls}")
else:
    print(f"Text:    {result.text[:200]}")
print(f"Latency: {result.latency_ms}ms")
print()

# --- Stats ---
print("=" * 60)
print("ENGINE STATS")
print("=" * 60)
import json
print(json.dumps(engine.get_stats(), indent=2))