mirror of
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>
65 lines
1.7 KiB
Python
65 lines
1.7 KiB
Python
from __future__ import annotations
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import asyncio
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from typing import Any, Sequence
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import numpy as np
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from ccw_litellm.interfaces import AbstractEmbedder, AbstractLLMClient, ChatMessage, LLMResponse
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class _DummyEmbedder(AbstractEmbedder):
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@property
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def dimensions(self) -> int:
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return 3
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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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) -> np.ndarray:
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if isinstance(texts, str):
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texts = [texts]
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_ = batch_size
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_ = kwargs
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return np.zeros((len(texts), self.dimensions), dtype=np.float32)
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class _DummyLLM(AbstractLLMClient):
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def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> LLMResponse:
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_ = kwargs
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return LLMResponse(content="".join(m.content for m in messages))
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def complete(self, prompt: str, **kwargs: Any) -> LLMResponse:
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_ = kwargs
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return LLMResponse(content=prompt)
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def test_embed_sync_shape_and_dtype() -> None:
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emb = _DummyEmbedder()
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out = emb.embed(["a", "b"])
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assert out.shape == (2, 3)
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assert out.dtype == np.float32
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def test_embed_async_wrapper() -> None:
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emb = _DummyEmbedder()
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out = asyncio.run(emb.aembed("x"))
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assert out.shape == (1, 3)
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def test_llm_sync() -> None:
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llm = _DummyLLM()
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out = llm.chat([ChatMessage(role="user", content="hi")])
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assert out == LLMResponse(content="hi")
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def test_llm_async_wrappers() -> None:
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llm = _DummyLLM()
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out1 = asyncio.run(llm.achat([ChatMessage(role="user", content="a")]))
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out2 = asyncio.run(llm.acomplete("b"))
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assert out1.content == "a"
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assert out2.content == "b"
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