Nextcloud MCP Server

"""Unified Anthropic provider for text generation.""" import logging from anthropic import AsyncAnthropic from .base import Provider logger = logging.getLogger(__name__) class AnthropicProvider(Provider): """ Anthropic provider for text generation. Supports Claude models via the Anthropic API. Note: Anthropic doesn't provide embedding models, only text generation. """ def __init__( self, api_key: str, generation_model: str = "claude-3-5-sonnet-20241022" ): """ Initialize Anthropic provider. Args: api_key: Anthropic API key generation_model: Model name (e.g., "claude-3-5-sonnet-20241022") """ self.client = AsyncAnthropic(api_key=api_key) self.model = generation_model logger.info(f"Initialized Anthropic provider (model={self.model})") @property def supports_embeddings(self) -> bool: """Whether this provider supports embedding generation.""" return False @property def supports_generation(self) -> bool: """Whether this provider supports text generation.""" return True async def embed(self, text: str) -> list[float]: """ Generate embedding vector for text. Raises: NotImplementedError: Anthropic doesn't provide embedding models """ raise NotImplementedError( "Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings" ) async def embed_batch(self, texts: list[str]) -> list[list[float]]: """ Generate embeddings for multiple texts. Raises: NotImplementedError: Anthropic doesn't provide embedding models """ raise NotImplementedError( "Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings" ) def get_dimension(self) -> int: """ Get embedding dimension. Raises: NotImplementedError: Anthropic doesn't provide embedding models """ raise NotImplementedError( "Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings" ) async def generate(self, prompt: str, max_tokens: int = 500) -> str: """ Generate text using Anthropic API. Args: prompt: The prompt to generate from max_tokens: Maximum tokens to generate Returns: Generated text """ message = await self.client.messages.create( model=self.model, max_tokens=max_tokens, temperature=0.7, messages=[{"role": "user", "content": prompt}], ) return message.content[0].text async def close(self) -> None: """Close the client (no-op for Anthropic SDK).""" pass