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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.
"""
@abstractmethod
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
@abstractmethod
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
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