Chris Coutinho
5c73b85f65
- Increase sampling timeout from 30s to 300s in semantic.py to accommodate slower local LLMs like Ollama - Refactor RAG integration tests to support multiple providers (ollama, openai, anthropic, bedrock) - Remove unnecessary embedding_provider fixture since MCP server handles embeddings internally - Add --provider flag via tests/integration/conftest.py - Add provider_fixtures.py with factory functions for generation providers 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
100 lines
2.8 KiB
Python
100 lines
2.8 KiB
Python
"""Unified Anthropic provider for text generation."""
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import logging
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from anthropic import AsyncAnthropic
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from .base import Provider
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logger = logging.getLogger(__name__)
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class AnthropicProvider(Provider):
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"""
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Anthropic provider for text generation.
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Supports Claude models via the Anthropic API.
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Note: Anthropic doesn't provide embedding models, only text generation.
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"""
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def __init__(
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self, api_key: str, generation_model: str = "claude-3-5-sonnet-20241022"
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):
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"""
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Initialize Anthropic provider.
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Args:
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api_key: Anthropic API key
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generation_model: Model name (e.g., "claude-3-5-sonnet-20241022")
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"""
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self.client = AsyncAnthropic(api_key=api_key)
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self.model = generation_model
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logger.info(f"Initialized Anthropic provider (model={self.model})")
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@property
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def supports_embeddings(self) -> bool:
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"""Whether this provider supports embedding generation."""
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return False
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@property
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def supports_generation(self) -> bool:
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"""Whether this provider supports text generation."""
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return True
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async def embed(self, text: str) -> list[float]:
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"""
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Generate embedding vector for text.
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Raises:
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NotImplementedError: Anthropic doesn't provide embedding models
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"""
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raise NotImplementedError(
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"Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings"
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)
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async def embed_batch(self, texts: list[str]) -> list[list[float]]:
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"""
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Generate embeddings for multiple texts.
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Raises:
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NotImplementedError: Anthropic doesn't provide embedding models
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"""
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raise NotImplementedError(
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"Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings"
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)
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def get_dimension(self) -> int:
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"""
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Get embedding dimension.
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Raises:
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NotImplementedError: Anthropic doesn't provide embedding models
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"""
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raise NotImplementedError(
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"Embedding not supported by Anthropic - use Ollama or Bedrock for embeddings"
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)
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async def generate(self, prompt: str, max_tokens: int = 500) -> str:
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"""
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Generate text using Anthropic API.
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Args:
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prompt: The prompt to generate from
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max_tokens: Maximum tokens to generate
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Returns:
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Generated text
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"""
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message = await self.client.messages.create(
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model=self.model,
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max_tokens=max_tokens,
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temperature=0.7,
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messages=[{"role": "user", "content": prompt}],
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)
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return message.content[0].text
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async def close(self) -> None:
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"""Close the client (no-op for Anthropic SDK)."""
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pass
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