Chris CoutinhoandClaude Opus 4.7
665cb9b1eb
Sweep all 1676 G004 violations across 112 files, converting
`logger.<level>(f"…{x}…")` to `logger.<level>("…%s…", x)`.
Why: ruff rule G004 was added to pyproject.toml to enforce lazy
%-style logging — defers formatting until the log level is enabled
and lets structured log tooling match the unformatted template.
Conversion preserves rendered output byte-for-byte:
- `{x}` → `%s` + `x`
- `{x!r}` / `{x!s}` / `{x!a}` → `%r` / `%s` / `%a`
- Format specs (`{x:.2f}`, `{x:>10}`) → `%s` + `format(x, 'spec')`
(printf-style specs aren't 1:1 with Python format specs, so we
delegate to `format()` to keep identical output)
- Literal `%` → `%%`
- Concatenated f-strings (`f"a {x} " "b"`) flattened
- Trailing kwargs (`exc_info=True`) preserved
Verified:
- `uv run ruff check --select G004` → 0 violations
- `uv run ty check -- nextcloud_mcp_server` → passes
- `uv run pytest tests/unit/` → 1010 passed
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
100 lines
2.8 KiB
Python
100 lines
2.8 KiB
Python
"""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("Initialized Anthropic provider (model=%s)", 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
|