feat(codexlens): add CodexLens code indexing platform with incremental updates

- Add CodexLens Python package with SQLite FTS5 search and tree-sitter parsing
- Implement workspace-local index storage (.codexlens/ directory)
- Add incremental update CLI command for efficient file-level index refresh
- Integrate CodexLens with CCW tools (codex_lens action: update)
- Add CodexLens Auto-Sync hook template for automatic index updates on file changes
- Add CodexLens status card in CCW Dashboard CLI Manager with install/init buttons
- Add server APIs: /api/codexlens/status, /api/codexlens/bootstrap, /api/codexlens/init

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

codex-lens/src/codexlens/entities.py

@@ -0,0 +1,73 @@
+"""Pydantic entity models for CodexLens."""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional, Tuple
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class Symbol(BaseModel):
+    """A code symbol discovered in a file."""
+
+    name: str = Field(..., min_length=1)
+    kind: str = Field(..., min_length=1)
+    range: Tuple[int, int] = Field(..., description="(start_line, end_line), 1-based inclusive")
+
+    @field_validator("range")
+    @classmethod
+    def validate_range(cls, value: Tuple[int, int]) -> Tuple[int, int]:
+        if len(value) != 2:
+            raise ValueError("range must be a (start_line, end_line) tuple")
+        start_line, end_line = value
+        if start_line < 1 or end_line < 1:
+            raise ValueError("range lines must be >= 1")
+        if end_line < start_line:
+            raise ValueError("end_line must be >= start_line")
+        return value
+
+
+class SemanticChunk(BaseModel):
+    """A semantically meaningful chunk of content, optionally embedded."""
+
+    content: str = Field(..., min_length=1)
+    embedding: Optional[List[float]] = Field(default=None, description="Vector embedding for semantic search")
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+    @field_validator("embedding")
+    @classmethod
+    def validate_embedding(cls, value: Optional[List[float]]) -> Optional[List[float]]:
+        if value is None:
+            return value
+        if not value:
+            raise ValueError("embedding cannot be empty when provided")
+        return value
+
+
+class IndexedFile(BaseModel):
+    """An indexed source file with symbols and optional semantic chunks."""
+
+    path: str = Field(..., min_length=1)
+    language: str = Field(..., min_length=1)
+    symbols: List[Symbol] = Field(default_factory=list)
+    chunks: List[SemanticChunk] = Field(default_factory=list)
+
+    @field_validator("path", "language")
+    @classmethod
+    def strip_and_validate_nonempty(cls, value: str) -> str:
+        cleaned = value.strip()
+        if not cleaned:
+            raise ValueError("value cannot be blank")
+        return cleaned
+
+
+class SearchResult(BaseModel):
+    """A unified search result for lexical or semantic search."""
+
+    path: str = Field(..., min_length=1)
+    score: float = Field(..., ge=0.0)
+    excerpt: Optional[str] = None
+    symbol: Optional[Symbol] = None
+    chunk: Optional[SemanticChunk] = None
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+