feat: Add unified LiteLLM API management with dashboard UI and CLI integration

- 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>

ccw-litellm/tests/conftest.py

@@ -0,0 +1,11 @@
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+
+def pytest_configure() -> None:
+    project_root = Path(__file__).resolve().parents[1]
+    src_dir = project_root / "src"
+    sys.path.insert(0, str(src_dir))
+

ccw-litellm/tests/test_interfaces.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+
+import asyncio
+from typing import Any, Sequence
+
+import numpy as np
+
+from ccw_litellm.interfaces import AbstractEmbedder, AbstractLLMClient, ChatMessage, LLMResponse
+
+
+class _DummyEmbedder(AbstractEmbedder):
+    @property
+    def dimensions(self) -> int:
+        return 3
+
+    def embed(
+        self,
+        texts: str | Sequence[str],
+        *,
+        batch_size: int | None = None,
+        **kwargs: Any,
+    ) -> np.ndarray:
+        if isinstance(texts, str):
+            texts = [texts]
+        _ = batch_size
+        _ = kwargs
+        return np.zeros((len(texts), self.dimensions), dtype=np.float32)
+
+
+class _DummyLLM(AbstractLLMClient):
+    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> LLMResponse:
+        _ = kwargs
+        return LLMResponse(content="".join(m.content for m in messages))
+
+    def complete(self, prompt: str, **kwargs: Any) -> LLMResponse:
+        _ = kwargs
+        return LLMResponse(content=prompt)
+
+
+def test_embed_sync_shape_and_dtype() -> None:
+    emb = _DummyEmbedder()
+    out = emb.embed(["a", "b"])
+    assert out.shape == (2, 3)
+    assert out.dtype == np.float32
+
+
+def test_embed_async_wrapper() -> None:
+    emb = _DummyEmbedder()
+    out = asyncio.run(emb.aembed("x"))
+    assert out.shape == (1, 3)
+
+
+def test_llm_sync() -> None:
+    llm = _DummyLLM()
+    out = llm.chat([ChatMessage(role="user", content="hi")])
+    assert out == LLMResponse(content="hi")
+
+
+def test_llm_async_wrappers() -> None:
+    llm = _DummyLLM()
+    out1 = asyncio.run(llm.achat([ChatMessage(role="user", content="a")]))
+    out2 = asyncio.run(llm.acomplete("b"))
+    assert out1.content == "a"
+    assert out2.content == "b"