orchestrator.py

from cognitive_architecture import CognitiveArchitecture
from vector_db import VectorDB
from dynamic_tools import DynamicToolManager, ToolRegistry
from advanced_nlu import AdvancedNLU
from continual_learning import ContinualLearningManager
import numpy as np

class AgentZeroOrchestrator:
    def __init__(self):
        self.cognitive_architecture = CognitiveArchitecture()
        self.vector_db = VectorDB()
        self.tool_registry = ToolRegistry()
        self.dynamic_tool_manager = DynamicToolManager(self.tool_registry)
        self.nlu = AdvancedNLU()
        self.cl_manager = ContinualLearningManager()

    async def process_input(self, user_input: str) -> str:
        # 1. Natural Language Understanding
        nlu_result = self.nlu.understand(user_input)

        # 2. Cognitive Processing
        cognitive_input = np.concatenate([
            self._embed(nlu_result['intent']),
            self._embed(' '.join([e['value'] for e in nlu_result['entities']]))
        ])
        cognitive_output = self.cognitive_architecture.process(cognitive_input)

        # 3. Memory Retrieval
        memory_query = self._embed(user_input)
        relevant_memories = self.vector_db.search(memory_query, top_k=5)

        # 4. Dynamic Tool Selection
        selected_tool = self._select_tool(cognitive_output, nlu_result)

        # 5. Tool Execution
        tool_result = await self.dynamic_tool_manager.execute_tool(selected_tool, 
                                                                   input=user_input, 
                                                                   nlu_result=nlu_result, 
                                                                   memories=relevant_memories)

        # 6. Response Generation
        response = self.cl_manager.generate_response(f"Input: {user_input} Tool Result: {tool_result}")

        # 7. Continual Learning
        self.cl_manager.add_experience(user_input, response)
        if len(self.cl_manager.experiences) >= 100:  # Adapt model every 100 experiences
            await self._async_adapt_model()

        # 8. Memory Storage
        self.vector_db.insert([self._embed(user_input)], [response], [{"type": "interaction"}])

        return response

    def _embed(self, text: str) -> np.ndarray:
        # Implement text embedding logic here
        # For simplicity, we're using a random vector. In practice, use a proper embedding model.
        return np.random.randn(1536)

    def _select_tool(self, cognitive_output, nlu_result):
        # Implement logic to select the most appropriate tool based on cognitive output and NLU result
        # This is a placeholder implementation
        return self.tool_registry.list_tools()[0]

    async def _async_adapt_model(self):
        # Run model adaptation asynchronously to avoid blocking the main process
        import asyncio
        loop = asyncio.get_event_loop()
        await loop.run_in_executor(None, self.cl_manager.adapt_model)

# Usage
orchestrator = AgentZeroOrchestrator()

async def main():
    response = await orchestrator.process_input("What's the weather like in New York?")
    print(response)

import asyncio
asyncio.run(main())