Refactor code structure and remove redundant changes

codex-lens/build/lib/codexlens/semantic/__init__.py

@@ -0,0 +1,118 @@
+"""Optional semantic search module for CodexLens.
+
+Install with: pip install codexlens[semantic]
+Uses fastembed (ONNX-based, lightweight ~200MB)
+
+GPU Acceleration:
+- Automatic GPU detection and usage when available
+- Supports CUDA (NVIDIA), TensorRT, DirectML (Windows), ROCm (AMD), CoreML (Apple)
+- Install GPU support: pip install onnxruntime-gpu (NVIDIA) or onnxruntime-directml (Windows)
+"""
+
+from __future__ import annotations
+
+SEMANTIC_AVAILABLE = False
+SEMANTIC_BACKEND: str | None = None
+GPU_AVAILABLE = False
+LITELLM_AVAILABLE = False
+_import_error: str | None = None
+
+
+def _detect_backend() -> tuple[bool, str | None, bool, str | None]:
+    """Detect if fastembed and GPU are available."""
+    try:
+        import numpy as np
+    except ImportError as e:
+        return False, None, False, f"numpy not available: {e}"
+
+    try:
+        from fastembed import TextEmbedding
+    except ImportError:
+        return False, None, False, "fastembed not available. Install with: pip install codexlens[semantic]"
+
+    # Check GPU availability
+    gpu_available = False
+    try:
+        from .gpu_support import is_gpu_available
+        gpu_available = is_gpu_available()
+    except ImportError:
+        pass
+
+    return True, "fastembed", gpu_available, None
+
+
+# Initialize on module load
+SEMANTIC_AVAILABLE, SEMANTIC_BACKEND, GPU_AVAILABLE, _import_error = _detect_backend()
+
+
+def check_semantic_available() -> tuple[bool, str | None]:
+    """Check if semantic search dependencies are available."""
+    return SEMANTIC_AVAILABLE, _import_error
+
+
+def check_gpu_available() -> tuple[bool, str]:
+    """Check if GPU acceleration is available.
+
+    Returns:
+        Tuple of (is_available, status_message)
+    """
+    if not SEMANTIC_AVAILABLE:
+        return False, "Semantic search not available"
+
+    try:
+        from .gpu_support import is_gpu_available, get_gpu_summary
+        if is_gpu_available():
+            return True, get_gpu_summary()
+        return False, "No GPU detected (using CPU)"
+    except ImportError:
+        return False, "GPU support module not available"
+
+
+# Export embedder components
+# BaseEmbedder is always available (abstract base class)
+from .base import BaseEmbedder
+
+# Factory function for creating embedders
+from .factory import get_embedder as get_embedder_factory
+
+# Optional: LiteLLMEmbedderWrapper (only if ccw-litellm is installed)
+try:
+    import ccw_litellm  # noqa: F401
+    from .litellm_embedder import LiteLLMEmbedderWrapper
+    LITELLM_AVAILABLE = True
+except ImportError:
+    LiteLLMEmbedderWrapper = None
+    LITELLM_AVAILABLE = False
+
+
+def is_embedding_backend_available(backend: str) -> tuple[bool, str | None]:
+    """Check whether a specific embedding backend can be used.
+
+    Notes:
+    - "fastembed" requires the optional semantic deps (pip install codexlens[semantic]).
+    - "litellm" requires ccw-litellm to be installed in the same environment.
+    """
+    backend = (backend or "").strip().lower()
+    if backend == "fastembed":
+        if SEMANTIC_AVAILABLE:
+            return True, None
+        return False, _import_error or "fastembed not available. Install with: pip install codexlens[semantic]"
+    if backend == "litellm":
+        if LITELLM_AVAILABLE:
+            return True, None
+        return False, "ccw-litellm not available. Install with: pip install ccw-litellm"
+    return False, f"Invalid embedding backend: {backend}. Must be 'fastembed' or 'litellm'."
+
+
+__all__ = [
+    "SEMANTIC_AVAILABLE",
+    "SEMANTIC_BACKEND",
+    "GPU_AVAILABLE",
+    "LITELLM_AVAILABLE",
+    "check_semantic_available",
+    "is_embedding_backend_available",
+    "check_gpu_available",
+    "BaseEmbedder",
+    "get_embedder_factory",
+    "LiteLLMEmbedderWrapper",
+]