モデル API インターフェース

def validate_provider_credentials(self, credentials: dict) -> None:
    """
    Validate provider credentials by making a test API call
    
    Parameters:
        credentials: Provider credentials as defined in `provider_credential_schema`
        
    Raises:
        CredentialsValidateFailedError: If validation fails
    """
    try:
        # Example implementation: validate using an LLM model instance
        model_instance = self.get_model_instance(ModelType.LLM)
        model_instance.validate_credentials(
            model="example-model", 
            credentials=credentials
        )
    except Exception as ex:
        logger.exception(f"Credential validation failed")
        raise CredentialsValidateFailedError(f"Invalid credentials: {str(ex)}")

def validate_credentials(self, model: str, credentials: dict) -> None:
    """
    Validate that the provided credentials work with the specified model
    
    Parameters:
        model: The specific model identifier (e.g., "gpt-4")
        credentials: Authentication details for the model
        
    Raises:
        CredentialsValidateFailedError: If validation fails
    """
    try:
        # Make a lightweight API call to verify credentials
        # Example: List available models or check account status
        response = self._api_client.validate_api_key(credentials["api_key"])
        
        # Verify the specific model is available if applicable
        if model not in response.get("available_models", []):
            raise CredentialsValidateFailedError(f"Model {model} is not available")
            
    except ApiException as e:
        raise CredentialsValidateFailedError(str(e))

@property
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
    """
    Map provider-specific exceptions to standardized Dify error types
    
    Returns:
        Dictionary mapping Dify error types to lists of provider exception types
    """
    return {
        InvokeConnectionError: [
            requests.exceptions.ConnectionError,
            requests.exceptions.Timeout,
            ConnectionRefusedError
        ],
        InvokeServerUnavailableError: [
            ServiceUnavailableError,
            HTTPStatusError
        ],
        InvokeRateLimitError: [
            RateLimitExceededError,
            QuotaExceededError
        ],
        InvokeAuthorizationError: [
            AuthenticationError,
            InvalidAPIKeyError,
            PermissionDeniedError
        ],
        InvokeBadRequestError: [
            InvalidRequestError,
            ValidationError
        ]
    }

def _invoke(
    self, 
    model: str, 
    credentials: dict,
    prompt_messages: list[PromptMessage], 
    model_parameters: dict,
    tools: Optional[list[PromptMessageTool]] = None, 
    stop: Optional[list[str]] = None,
    stream: bool = True, 
    user: Optional[str] = None
) -> Union[LLMResult, Generator[LLMResultChunk, None, None]]:
    """
    Invoke the language model
    """
    # Prepare API parameters
    api_params = self._prepare_api_parameters(
        model, 
        credentials, 
        prompt_messages, 
        model_parameters,
        tools, 
        stop
    )
    
    try:
        # Choose between streaming and non-streaming implementation
        if stream:
            return self._invoke_stream(model, api_params, user)
        else:
            return self._invoke_sync(model, api_params, user)
            
    except Exception as e:
        # Map errors using the error mapping property
        self._handle_api_error(e)

# Helper methods for streaming and non-streaming calls
def _invoke_stream(self, model, api_params, user):
    # Implement streaming call and yield chunks
    pass
    
def _invoke_sync(self, model, api_params, user):
    # Implement synchronous call and return complete result
    pass

def get_num_tokens(
    self, 
    model: str, 
    credentials: dict, 
    prompt_messages: list[PromptMessage],
    tools: Optional[list[PromptMessageTool]] = None
) -> int:
    """
    Calculate the number of tokens in the prompt
    """
    # Convert prompt_messages to the format expected by the tokenizer
    text = self._convert_messages_to_text(prompt_messages)
    
    try:
        # Use the appropriate tokenizer for this model
        tokenizer = self._get_tokenizer(model)
        return len(tokenizer.encode(text))
    except Exception:
        # Fall back to a generic tokenizer
        return self._get_num_tokens_by_gpt2(text)

def get_customizable_model_schema(
    self, 
    model: str, 
    credentials: dict
) -> Optional[AIModelEntity]:
    """
    Get parameter schema for custom models
    """
    # For fine-tuned models, you might return the base model's schema
    if model.startswith("ft:"):
        base_model = self._extract_base_model(model)
        return self._get_predefined_model_schema(base_model)
    
    # For standard models, return None to use the predefined schema
    return None

def _invoke(
    self, 
    model: str, 
    credentials: dict,
    texts: list[str], 
    user: Optional[str] = None
) -> TextEmbeddingResult:
    """
    Generate embedding vectors for multiple texts
    """
    # Set up API client with credentials
    client = self._get_client(credentials)
    
    # Handle batching if needed
    batch_size = self._get_batch_size(model)
    all_embeddings = []
    total_tokens = 0
    start_time = time.time()
    
    # Process in batches to avoid API limits
    for i in range(0, len(texts), batch_size):
        batch = texts[i:i+batch_size]
        
        # Make API call to the embeddings endpoint
        response = client.embeddings.create(
            model=model,
            input=batch,
            user=user
        )
        
        # Extract embeddings from response
        batch_embeddings = [item.embedding for item in response.data]
        all_embeddings.extend(batch_embeddings)
        
        # Track token usage
        total_tokens += response.usage.total_tokens
    
    # Calculate usage metrics
    elapsed_time = time.time() - start_time
    usage = self._create_embedding_usage(
        model=model,
        tokens=total_tokens,
        latency=elapsed_time
    )
    
    return TextEmbeddingResult(
        model=model,
        embeddings=all_embeddings,
        usage=usage
    )

def get_num_tokens(
    self, 
    model: str, 
    credentials: dict, 
    texts: list[str]
) -> int:
    """
    Calculate the number of tokens in the texts to be embedded
    """
    # Join all texts to estimate token count
    combined_text = " ".join(texts)
    
    try:
        # Use the appropriate tokenizer for this model
        tokenizer = self._get_tokenizer(model)
        return len(tokenizer.encode(combined_text))
    except Exception:
        # Fall back to a generic tokenizer
        return self._get_num_tokens_by_gpt2(combined_text)

def _invoke(
    self, 
    model: str, 
    credentials: dict,
    query: str, 
    docs: list[str], 
    score_threshold: Optional[float] = None, 
    top_n: Optional[int] = None,
    user: Optional[str] = None
) -> RerankResult:
    """
    Rerank documents based on relevance to the query
    """
    # Set up API client with credentials
    client = self._get_client(credentials)
    
    # Prepare request data
    request_data = {
        "query": query,
        "documents": docs,
    }
    
    # Call reranking API endpoint
    response = client.rerank(
        model=model,
        **request_data,
        user=user
    )
    
    # Process results
    ranked_results = []
    for i, result in enumerate(response.results):
        # Create RerankDocument for each result
        doc = RerankDocument(
            index=result.document_index,  # Original index in docs list
            text=docs[result.document_index],  # Original text
            score=result.relevance_score  # Relevance score
        )
        ranked_results.append(doc)
    
    # Sort by score in descending order
    ranked_results.sort(key=lambda x: x.score, reverse=True)
    
    # Apply score threshold filtering if specified
    if score_threshold is not None:
        ranked_results = [doc for doc in ranked_results if doc.score >= score_threshold]
    
    # Apply top_n limit if specified
    if top_n is not None and top_n > 0:
        ranked_results = ranked_results[:top_n]
    
    return RerankResult(
        model=model,
        docs=ranked_results
    )

def _invoke(
    self, 
    model: str, 
    credentials: dict,
    file: IO[bytes], 
    user: Optional[str] = None
) -> str:
    """
    Convert speech audio to text
    """
    # Set up API client with credentials
    client = self._get_client(credentials)
    
    try:
        # Determine the file format
        file_format = self._detect_audio_format(file)
        
        # Prepare the file for API submission
        # Most APIs require either a file path or binary data
        audio_data = file.read()
        
        # Call the speech-to-text API
        response = client.audio.transcriptions.create(
            model=model,
            file=("audio.mp3", audio_data),  # Adjust filename based on actual format
            user=user
        )
        
        # Extract and return the transcribed text
        return response.text
        
    except Exception as e:
        # Map to appropriate error type
        self._handle_api_error(e)
        
    finally:
        # Reset file pointer for potential reuse
        file.seek(0)

def _invoke(
    self, 
    model: str, 
    credentials: dict, 
    content_text: str, 
    streaming: bool,
    user: Optional[str] = None
) -> Union[bytes, Generator[bytes, None, None]]:
    """
    Convert text to speech audio
    """
    # Set up API client with credentials
    client = self._get_client(credentials)
    
    # Get voice settings based on model
    voice = self._get_voice_for_model(model)
    
    try:
        # Choose implementation based on streaming preference
        if streaming:
            return self._stream_audio(
                client=client,
                model=model,
                text=content_text,
                voice=voice,
                user=user
            )
        else:
            return self._generate_complete_audio(
                client=client,
                model=model,
                text=content_text,
                voice=voice,
                user=user
            )
    except Exception as e:
        self._handle_api_error(e)

def _invoke(
    self, 
    model: str, 
    credentials: dict,
    text: str, 
    user: Optional[str] = None
) -> bool:
    """
    Analyze text for harmful content
    
    Returns:
        bool: False if the text is safe, True if it contains harmful content
    """
    # Set up API client with credentials
    client = self._get_client(credentials)
    
    try:
        # Call moderation API
        response = client.moderations.create(
            model=model,
            input=text,
            user=user
        )
        
        # Check if any categories were flagged
        result = response.results[0]
        
        # Return True if flagged in any category, False if safe
        return result.flagged
        
    except Exception as e:
        # Log the error but default to safe if there's an API issue.
        # This is a conservative approach; production systems might want
        # different fallback behavior.
        logger.error(f"Moderation API error: {str(e)}")
        return False