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https://github.com/catlog22/Claude-Code-Workflow.git
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bf66b095c7
- Create ccw-litellm Python package with AbstractEmbedder and AbstractLLMClient interfaces - Add BaseEmbedder abstraction and factory pattern to codex-lens for pluggable backends - Implement API Settings dashboard page for provider credentials and custom endpoints - Add REST API routes for CRUD operations on providers and endpoints - Extend CLI with --model parameter for custom endpoint routing - Integrate existing context-cache for @pattern file resolution - Add provider model registry with predefined models per provider type - Include i18n translations (en/zh) for all new UI elements 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
53 lines
1.2 KiB
Python
53 lines
1.2 KiB
Python
from __future__ import annotations
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import asyncio
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from abc import ABC, abstractmethod
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from typing import Any, Sequence
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import numpy as np
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from numpy.typing import NDArray
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class AbstractEmbedder(ABC):
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"""Embedding interface compatible with fastembed-style embedders.
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Implementers only need to provide the synchronous `embed` method; an
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asynchronous `aembed` wrapper is provided for convenience.
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"""
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@property
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@abstractmethod
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def dimensions(self) -> int:
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"""Embedding vector size."""
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@abstractmethod
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def embed(
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self,
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texts: str | Sequence[str],
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*,
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batch_size: int | None = None,
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**kwargs: Any,
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) -> NDArray[np.floating]:
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"""Embed one or more texts.
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Returns:
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A numpy array of shape (n_texts, dimensions).
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"""
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async def aembed(
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self,
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texts: str | Sequence[str],
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*,
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batch_size: int | None = None,
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**kwargs: Any,
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) -> NDArray[np.floating]:
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"""Async wrapper around `embed` using a worker thread by default."""
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return await asyncio.to_thread(
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self.embed,
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texts,
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batch_size=batch_size,
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**kwargs,
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)
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