contextengine 0.1.2

Provider-agnostic AI orchestration, memory, and context-optimization layer for Flutter apps.

example/main.dart

import 'dart:io';
import 'package:contextengine/contextengine.dart';

/// Example: using ContextEngine with a mock provider, full pipeline.
///
/// Run: dart example/main.dart
void main() async {
  // 1. Set up the engine with all features enabled.
  final graphAdapter = LocalGraphAdapter();
  final memory = MemoryEngine(
    graphAdapter: graphAdapter,
    sessionId: 'pet_bruno',
  );
  final vectorStore = InMemoryVectorStore();
  final hybridRetrieval = HybridRetrieval(
    graphAdapter: graphAdapter,
    vectorStore: vectorStore,
  );
  final costEstimator = CostEstimator();

  final engine = ContextEngine(
    provider: MockProvider(name: 'mock', defaultModel: 'gpt-4o-mini'),
    storage: InMemoryStorage(),
    memory: memory,
    vectorStore: vectorStore,
    hybridRetrieval: hybridRetrieval,
    costEstimator: costEstimator,
    config: ContextEngineConfig(
      tokenBudget: TokenBudget(maxInputTokens: 6000),
      strategy: ContextStrategy.hybrid,
      systemPrompt: 'You are a helpful pet health assistant.',
      enableCostEstimation: true,
      privacyPolicy: PrivacyPolicy(redact: {Field.email, Field.phone}),
    ),
  );

  await engine.initialize(sessionId: 'pet_bruno');

  // 2. Direct fact ingestion (Lane 1 — no LLM cost).
  await engine.remember(Fact(
    id: 'fact_1',
    subject: 'Bruno',
    relation: 'HAS_ALLERGY',
    object: 'Chicken',
    confidence: 1.0,
  ));
  await engine.remember(Fact(
    id: 'fact_2',
    subject: 'Bruno',
    relation: 'IS_A',
    object: 'Dog',
  ));
  await engine.remember(Fact(
    id: 'fact_3',
    subject: 'Bruno',
    relation: 'LIKES',
    object: 'Walks',
  ));
  print('--- Facts stored ---');
  final facts = await engine.getAllFacts();
  for (final f in facts) {
    print('  $f');
  }

  // 3. Conversational message (Lane 2 — full pipeline).
  print('\n--- Sending message ---');
  final response = await engine.sendMessage('His ear is bothering him again');
  print('Response: ${response.text}');

  // 4. Observability — inspect what was sent.
  final debug = engine.lastPromptDebug()!;
  print('\n--- Pipeline log ---');
  for (final line in debug.pipelineLog) {
    print('  $line');
  }
  print('\n--- Token usage ---');
  print('  ${engine.lastTokenUsage()}');

  // 5. Cost estimation.
  print('\n--- Cost ---');
  print('  Estimated cost: \$${engine.lastCostEstimate()?.toStringAsFixed(6)}');

  // 6. Streaming.
  print('\n--- Streaming message ---');
  final stream = engine.sendMessageStream('Tell me about Bruno allergies');
  await for (final chunk in stream) {
    stdout.write(chunk.text);
  }
  print('');

  // 7. Show fact history after extraction.
  await Future.delayed(const Duration(milliseconds: 100));
  print('\n--- All facts after extraction ---');
  final allFacts = await engine.getAllFacts();
  for (final f in allFacts) {
    print('  $f');
  }

  // 8. Clean up.
  engine.dispose();
  print('\nDone.');
}