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/src/ccw_litellm/clients/litellm_embedder.py

@@ -0,0 +1,170 @@
+"""LiteLLM embedder implementation for text embeddings."""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Sequence
+
+import litellm
+import numpy as np
+from numpy.typing import NDArray
+
+from ..config import LiteLLMConfig, get_config
+from ..interfaces.embedder import AbstractEmbedder
+
+logger = logging.getLogger(__name__)
+
+
+class LiteLLMEmbedder(AbstractEmbedder):
+    """LiteLLM embedder implementation.
+
+    Supports multiple embedding providers (OpenAI, etc.) through LiteLLM's unified interface.
+
+    Example:
+        embedder = LiteLLMEmbedder(model="default")
+        vectors = embedder.embed(["Hello world", "Another text"])
+        print(vectors.shape)  # (2, 1536)
+    """
+
+    def __init__(
+        self,
+        model: str = "default",
+        config: LiteLLMConfig | None = None,
+        **litellm_kwargs: Any,
+    ) -> None:
+        """Initialize LiteLLM embedder.
+
+        Args:
+            model: Model name from configuration (default: "default")
+            config: Configuration instance (default: use global config)
+            **litellm_kwargs: Additional arguments to pass to litellm.embedding()
+        """
+        self._config = config or get_config()
+        self._model_name = model
+        self._litellm_kwargs = litellm_kwargs
+
+        # Get embedding model configuration
+        try:
+            self._model_config = self._config.get_embedding_model(model)
+        except ValueError as e:
+            logger.error(f"Failed to get embedding model configuration: {e}")
+            raise
+
+        # Get provider configuration
+        try:
+            self._provider_config = self._config.get_provider(self._model_config.provider)
+        except ValueError as e:
+            logger.error(f"Failed to get provider configuration: {e}")
+            raise
+
+        # Set up LiteLLM environment
+        self._setup_litellm()
+
+    def _setup_litellm(self) -> None:
+        """Configure LiteLLM with provider settings."""
+        provider = self._model_config.provider
+
+        # Set API key
+        if self._provider_config.api_key:
+            litellm.api_key = self._provider_config.api_key
+            # Also set environment-specific keys
+            if provider == "openai":
+                litellm.openai_key = self._provider_config.api_key
+            elif provider == "anthropic":
+                litellm.anthropic_key = self._provider_config.api_key
+
+        # Set API base
+        if self._provider_config.api_base:
+            litellm.api_base = self._provider_config.api_base
+
+    def _format_model_name(self) -> str:
+        """Format model name for LiteLLM.
+
+        Returns:
+            Formatted model name (e.g., "text-embedding-3-small")
+        """
+        provider = self._model_config.provider
+        model = self._model_config.model
+
+        # For some providers, LiteLLM expects explicit prefix
+        if provider in ["azure", "vertex_ai", "bedrock"]:
+            return f"{provider}/{model}"
+
+        return model
+
+    @property
+    def dimensions(self) -> int:
+        """Embedding vector size."""
+        return self._model_config.dimensions
+
+    def embed(
+        self,
+        texts: str | Sequence[str],
+        *,
+        batch_size: int | None = None,
+        **kwargs: Any,
+    ) -> NDArray[np.floating]:
+        """Embed one or more texts.
+
+        Args:
+            texts: Single text or sequence of texts
+            batch_size: Batch size for processing (currently unused, LiteLLM handles batching)
+            **kwargs: Additional arguments for litellm.embedding()
+
+        Returns:
+            A numpy array of shape (n_texts, dimensions).
+
+        Raises:
+            Exception: If LiteLLM embedding fails
+        """
+        # Normalize input to list
+        if isinstance(texts, str):
+            text_list = [texts]
+            single_input = True
+        else:
+            text_list = list(texts)
+            single_input = False
+
+        if not text_list:
+            # Return empty array with correct shape
+            return np.empty((0, self.dimensions), dtype=np.float32)
+
+        # Merge kwargs
+        embedding_kwargs = {**self._litellm_kwargs, **kwargs}
+
+        try:
+            # Call LiteLLM embedding
+            response = litellm.embedding(
+                model=self._format_model_name(),
+                input=text_list,
+                **embedding_kwargs,
+            )
+
+            # Extract embeddings
+            embeddings = [item["embedding"] for item in response.data]
+
+            # Convert to numpy array
+            result = np.array(embeddings, dtype=np.float32)
+
+            # Validate dimensions
+            if result.shape[1] != self.dimensions:
+                logger.warning(
+                    f"Expected {self.dimensions} dimensions, got {result.shape[1]}. "
+                    f"Configuration may be incorrect."
+                )
+
+            return result
+
+        except Exception as e:
+            logger.error(f"LiteLLM embedding failed: {e}")
+            raise
+
+    @property
+    def model_name(self) -> str:
+        """Get configured model name."""
+        return self._model_name
+
+    @property
+    def provider(self) -> str:
+        """Get configured provider name."""
+        return self._model_config.provider