feat: Enhance embedding management and model configuration

- Updated embedding_manager.py to include backend parameter in model configuration.
- Modified model_manager.py to utilize cache_name for ONNX models.
- Refactored hybrid_search.py to improve embedder initialization based on backend type.
- Added backend column to vector_store.py for better model configuration management.
- Implemented migration for existing database to include backend information.
- Enhanced API settings implementation with comprehensive provider and endpoint management.
- Introduced LiteLLM integration guide detailing configuration and usage.
- Added examples for LiteLLM usage in TypeScript.

codex-lens/src/codexlens/cli/embedding_manager.py

@@ -309,7 +309,7 @@ def generate_embeddings(
 
             # Set/update model configuration for this index
             vector_store.set_model_config(
-                model_profile, embedder.model_name, embedder.embedding_dim
+                model_profile, embedder.model_name, embedder.embedding_dim, backend=embedding_backend
             )
             # Use bulk insert mode for efficient batch ANN index building
             # This defers ANN updates until end_bulk_insert() is called

codex-lens/src/codexlens/cli/model_manager.py

@@ -107,8 +107,9 @@ def _get_model_cache_path(cache_dir: Path, info: Dict) -> Path:
         Path to the model cache directory
     """
     # HuggingFace Hub naming: models--{org}--{model}
-    model_name = info["model_name"]
-    sanitized_name = f"models--{model_name.replace('/', '--')}"
+    # Use cache_name if available (for mapped ONNX models), else model_name
+    target_name = info.get("cache_name", info["model_name"])
+    sanitized_name = f"models--{target_name.replace('/', '--')}"
     return cache_dir / sanitized_name