TokioAI-Coder IoT Package v1.0 -- Engine + IoT System Prompt + Tools

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#!/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))

	#!/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))