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| """ | |
| MCP Core - Model Interface Adapters | |
| This module provides adapters for interfacing with various AI models, | |
| allowing the MCP system to work with different model providers and types | |
| while maintaining a consistent interface. | |
| """ | |
| from abc import ABC, abstractmethod | |
| from typing import Dict, Any, List, Optional, Union | |
| class ModelAdapter(ABC): | |
| """ | |
| Abstract base class for model adapters. | |
| Model adapters provide a consistent interface for interacting with different | |
| AI models, abstracting away the specifics of each model's API. | |
| """ | |
| async def process(self, input_data: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]: | |
| """ | |
| Process an input with the model and return the output. | |
| Args: | |
| input_data: The input data to process. | |
| context: Optional context information to guide the model. | |
| Returns: | |
| The model's output. | |
| """ | |
| pass | |
| def get_capabilities(self) -> Dict[str, Any]: | |
| """ | |
| Get the capabilities of this model. | |
| Returns: | |
| Dictionary describing the model's capabilities. | |
| """ | |
| pass | |
| class MockModelAdapter(ModelAdapter): | |
| """ | |
| A mock model adapter for testing and demonstration purposes. | |
| This adapter simulates responses without requiring an actual AI model. | |
| """ | |
| def __init__(self, model_id: str = "mock-model", capabilities: Dict[str, Any] = None): | |
| """ | |
| Initialize a new MockModelAdapter. | |
| Args: | |
| model_id: Identifier for this mock model. | |
| capabilities: Dictionary of capabilities this mock model supports. | |
| """ | |
| self.model_id = model_id | |
| self._capabilities = capabilities or { | |
| "text_generation": True, | |
| "question_answering": True, | |
| "content_evaluation": True, | |
| "max_input_length": 1000, | |
| "supports_streaming": False | |
| } | |
| async def process(self, input_data: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]: | |
| """ | |
| Process an input with the mock model. | |
| Args: | |
| input_data: The input data to process. | |
| context: Optional context information. | |
| Returns: | |
| A mock response based on the input. | |
| """ | |
| # Extract the input text or use a default | |
| input_text = input_data.get("text", "") | |
| # Generate a mock response based on the input | |
| if "question" in input_text.lower(): | |
| response = f"This is a mock answer to your question about {input_text.split('?')[0]}." | |
| elif "explain" in input_text.lower(): | |
| topic = input_text.lower().split("explain")[1].strip() | |
| response = f"Here's a mock explanation of {topic}. This would contain educational content in a real implementation." | |
| else: | |
| response = f"I've processed your input: '{input_text}'. This is a mock response that would be more relevant in a real implementation." | |
| # Include some educational metadata in the response | |
| return { | |
| "text": response, | |
| "model_id": self.model_id, | |
| "confidence": 0.85, | |
| "educational_metadata": { | |
| "complexity_level": "intermediate", | |
| "topics": ["sample", "mock", "demonstration"], | |
| "suggested_follow_ups": [ | |
| "Tell me more about this topic", | |
| "Can you provide an example?", | |
| "What are the key concepts I should understand?" | |
| ] | |
| } | |
| } | |
| def get_capabilities(self) -> Dict[str, Any]: | |
| """ | |
| Get the capabilities of this mock model. | |
| Returns: | |
| Dictionary describing the mock model's capabilities. | |
| """ | |
| return self._capabilities | |
| class OpenAIAdapter(ModelAdapter): | |
| """ | |
| Adapter for OpenAI models. | |
| This adapter provides an interface to OpenAI's models through their API. | |
| Note: This is a placeholder implementation. In a real application, you would | |
| need to implement the actual API calls to OpenAI. | |
| """ | |
| def __init__(self, model_name: str = "gpt-3.5-turbo", api_key: Optional[str] = None): | |
| """ | |
| Initialize a new OpenAIAdapter. | |
| Args: | |
| model_name: The name of the OpenAI model to use. | |
| api_key: OpenAI API key. If None, will attempt to use environment variable. | |
| """ | |
| self.model_name = model_name | |
| self.api_key = api_key | |
| # In a real implementation, you would initialize the OpenAI client here | |
| async def process(self, input_data: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]: | |
| """ | |
| Process an input with an OpenAI model. | |
| Args: | |
| input_data: The input data to process. | |
| context: Optional context information. | |
| Returns: | |
| The model's output. | |
| """ | |
| # This is a placeholder. In a real implementation, you would: | |
| # 1. Format the input and context for the OpenAI API | |
| # 2. Make the API call | |
| # 3. Process and return the response | |
| # Placeholder response | |
| return { | |
| "text": f"This is a placeholder response from {self.model_name}. In a real implementation, this would be the actual model output.", | |
| "model": self.model_name, | |
| "usage": { | |
| "prompt_tokens": 10, | |
| "completion_tokens": 20, | |
| "total_tokens": 30 | |
| } | |
| } | |
| def get_capabilities(self) -> Dict[str, Any]: | |
| """ | |
| Get the capabilities of this OpenAI model. | |
| Returns: | |
| Dictionary describing the model's capabilities. | |
| """ | |
| # This would be more accurate in a real implementation | |
| capabilities = { | |
| "text_generation": True, | |
| "question_answering": True, | |
| "content_evaluation": True, | |
| "supports_streaming": True | |
| } | |
| # Different capabilities based on model | |
| if "gpt-4" in self.model_name: | |
| capabilities.update({ | |
| "max_input_length": 8000, | |
| "reasoning_ability": "advanced" | |
| }) | |
| else: | |
| capabilities.update({ | |
| "max_input_length": 4000, | |
| "reasoning_ability": "intermediate" | |
| }) | |
| return capabilities | |
| class HuggingFaceAdapter(ModelAdapter): | |
| """ | |
| Adapter for Hugging Face models. | |
| This adapter provides an interface to models hosted on Hugging Face's model hub. | |
| Note: This is a placeholder implementation. In a real application, you would | |
| need to implement the actual API calls to Hugging Face. | |
| """ | |
| def __init__(self, model_id: str, api_key: Optional[str] = None): | |
| """ | |
| Initialize a new HuggingFaceAdapter. | |
| Args: | |
| model_id: The ID of the Hugging Face model to use. | |
| api_key: Hugging Face API key. If None, will attempt to use environment variable. | |
| """ | |
| self.model_id = model_id | |
| self.api_key = api_key | |
| # In a real implementation, you would initialize the Hugging Face client here | |
| async def process(self, input_data: Dict[str, Any], context: Dict[str, Any] = None) -> Dict[str, Any]: | |
| """ | |
| Process an input with a Hugging Face model. | |
| Args: | |
| input_data: The input data to process. | |
| context: Optional context information. | |
| Returns: | |
| The model's output. | |
| """ | |
| # This is a placeholder. In a real implementation, you would: | |
| # 1. Format the input and context for the Hugging Face API | |
| # 2. Make the API call | |
| # 3. Process and return the response | |
| # Placeholder response | |
| return { | |
| "text": f"This is a placeholder response from Hugging Face model {self.model_id}. In a real implementation, this would be the actual model output.", | |
| "model": self.model_id | |
| } | |
| def get_capabilities(self) -> Dict[str, Any]: | |
| """ | |
| Get the capabilities of this Hugging Face model. | |
| Returns: | |
| Dictionary describing the model's capabilities. | |
| """ | |
| # This would be determined dynamically in a real implementation | |
| return { | |
| "text_generation": True, | |
| "question_answering": True, | |
| "content_evaluation": True, | |
| "max_input_length": 2000, | |
| "supports_streaming": False | |
| } | |
| class ModelRegistry: | |
| """ | |
| Registry for model adapters. | |
| This class manages the available model adapters and provides methods | |
| for registering, retrieving, and listing them. | |
| """ | |
| def __init__(self): | |
| """Initialize a new ModelRegistry.""" | |
| self.adapters: Dict[str, ModelAdapter] = {} | |
| def register_adapter(self, name: str, adapter: ModelAdapter) -> None: | |
| """ | |
| Register a model adapter. | |
| Args: | |
| name: Name to register the adapter under. | |
| adapter: The ModelAdapter instance to register. | |
| """ | |
| self.adapters[name] = adapter | |
| def get_adapter(self, name: str) -> Optional[ModelAdapter]: | |
| """ | |
| Get a registered adapter by name. | |
| Args: | |
| name: Name of the adapter to retrieve. | |
| Returns: | |
| The ModelAdapter if found, None otherwise. | |
| """ | |
| return self.adapters.get(name) | |
| def list_adapters(self) -> List[str]: | |
| """ | |
| List all registered adapter names. | |
| Returns: | |
| List of adapter names. | |
| """ | |
| return list(self.adapters.keys()) | |
| def get_adapter_capabilities(self, name: str) -> Optional[Dict[str, Any]]: | |
| """ | |
| Get the capabilities of a registered adapter. | |
| Args: | |
| name: Name of the adapter. | |
| Returns: | |
| Dictionary of capabilities if the adapter exists, None otherwise. | |
| """ | |
| adapter = self.get_adapter(name) | |
| if adapter: | |
| return adapter.get_capabilities() | |
| return None | |