fix: Prepare package for PyPI publication

Python 3.10+ compatibility:
- Add tomli backport for Python < 3.11 (tomllib not available)
- Make numpy imports lazy in search.py (only for embeddings feature)

Code quality improvements:
- Remove unused tree-sitter imports in symbol_extractor.py
- Fix importlib usage in empathy_sync.py for module checking
- Clean up linting issues (down from 29 to 16 minor style issues)

Package successfully builds and installs on Python 3.10, 3.11, and 3.12.
All core commands work without optional dependencies.

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

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

Author: GeneAI

memdocs/exceptions.py

@@ -184,8 +184,10 @@ def validate_file_path(path: Path, must_exist: bool = True) -> None:
         raise FileNotFoundError(path)
 
 
-def validate_config_version(version: int, supported: list[int] = [1]) -> None:
+def validate_config_version(version: int, supported: list[int] = None) -> None:
     """Validate configuration version."""
+    if supported is None:
+        supported = [1]
     if version not in supported:
         raise ConfigurationError(
             f"Unsupported configuration version: {version}. "

memdocs/extract.py

@@ -9,16 +9,25 @@
 
 import json
 import re
-import tomllib
+import sys
 from dataclasses import dataclass
 from pathlib import Path
 
 import git
 from pygments import lexers
 from pygments.util import ClassNotFound
 
+# Python 3.11+ has tomllib built-in, earlier versions need tomli
+if sys.version_info >= (3, 11):
+    import tomllib
+else:
+    try:
+        import tomli as tomllib
+    except ImportError:
+        tomllib = None  # type: ignore
+
 from memdocs.schemas import Symbol, SymbolKind
-from memdocs.security import InputValidator, ConfigValidator
+from memdocs.security import InputValidator
 
 # Configuration constants
 MAX_FILE_SIZE_MB = 10  # Maximum file size to process (10MB)

memdocs/search.py

@@ -9,8 +9,6 @@
 from pathlib import Path
 from typing import Any, cast
 
-import numpy as np
-
 
 class LocalVectorSearch:
     """Local vector similarity search using FAISS.
@@ -80,6 +78,14 @@ def add_embeddings(
         if not embeddings:
             return []
 
+        # Import numpy (only needed for embeddings feature)
+        try:
+            import numpy as np
+        except ImportError:
+            raise ImportError(
+                "numpy not installed. " "Install with: pip install 'memdocs[embeddings]'"
+            ) from None
+
         # Convert to numpy array
         vectors = np.array(embeddings).astype("float32")
 
@@ -89,7 +95,7 @@ def add_embeddings(
 
         # Store metadata
         indices = list(range(start_idx, start_idx + len(embeddings)))
-        for idx, doc in zip(indices, documents):
+        for idx, doc in zip(indices, documents, strict=False):
             self.metadata[str(idx)] = doc
 
         # Save to disk (git-committable)
@@ -116,6 +122,14 @@ def search(
         if self.index.ntotal == 0:
             return []
 
+        # Import numpy (only needed for embeddings feature)
+        try:
+            import numpy as np
+        except ImportError:
+            raise ImportError(
+                "numpy not installed. " "Install with: pip install 'memdocs[embeddings]'"
+            ) from None
+
         # Convert to numpy array
         query = np.array([query_embedding]).astype("float32")
 
@@ -129,7 +143,7 @@ def search(
 
         # Build results
         results = []
-        for idx, similarity in zip(indices[0], similarities):
+        for idx, similarity in zip(indices[0], similarities, strict=False):
             if idx == -1:  # FAISS returns -1 for empty slots
                 continue
 

memdocs/summarize.py

@@ -18,7 +18,7 @@
     ReferenceSummary,
     ScopeInfo,
 )
-from memdocs.security import InputValidator, RateLimiter, ConfigValidator
+from memdocs.security import InputValidator, RateLimiter
 
 # Configuration constants
 DEFAULT_MAX_TOKENS = 4096  # Default max tokens for Claude API response (schema default)

memdocs/symbol_extractor.py

@@ -383,16 +383,9 @@ def _extract_typescript_symbols(self, code: str) -> list[ExtractedSymbol]:
         Returns:
             List of ExtractedSymbol objects
         """
-        try:
-            from tree_sitter import Language, Parser
-
-            # Try to load TypeScript language
-            # This requires tree-sitter-typescript to be built
-            # TODO: Add tree-sitter setup instructions
-            return []
-        except ImportError:
-            # tree-sitter not installed - return empty list
-            return []
+        # TODO: Implement TypeScript extraction with tree-sitter
+        # Requires: tree-sitter-typescript to be built
+        return []
 
     def _extract_javascript_symbols(self, code: str) -> list[ExtractedSymbol]:
         """
@@ -406,13 +399,9 @@ def _extract_javascript_symbols(self, code: str) -> list[ExtractedSymbol]:
         Returns:
             List of ExtractedSymbol objects
         """
-        try:
-            from tree_sitter import Language, Parser
-
-            # TODO: Implement JavaScript extraction
-            return []
-        except ImportError:
-            return []
+        # TODO: Implement JavaScript extraction with tree-sitter
+        # Requires: tree-sitter-javascript to be built
+        return []
 
 
 def extract_symbols_for_memdocs(

memdocs/wizards/empathy_sync.py

@@ -159,11 +159,11 @@ async def analyze_module(
     def _is_local_empathy(self) -> bool:
         """Check if Empathy Framework is available locally."""
         try:
-            # Try to import EmpathyService
-            from app.backend.services.empathy_service import EmpathyService
-
-            return True
-        except ImportError:
+            # Check if EmpathyService module is available
+            import importlib.util
+            spec = importlib.util.find_spec("app.backend.services.empathy_service")
+            return spec is not None
+        except (ImportError, ModuleNotFoundError):
             return False
 
     async def _analyze_local(
@@ -362,7 +362,7 @@ async def main():
     # Execute based on mode
     if args.file:
         print(f"Analyzing file: {args.file}")
-        result = await wizard.analyze_file(args.file, args.language, args.wizard, args.tier)
+        await wizard.analyze_file(args.file, args.language, args.wizard, args.tier)
         print("✓ Analysis complete. Stored in .memdocs/")
 
     elif args.module:

pyproject.toml

@@ -42,6 +42,7 @@ dependencies = [
     "tiktoken>=0.7.0",           # Token counting
     "python-dotenv>=1.0.0",      # Environment variables
     "rich>=13.0.0",              # Beautiful terminal formatting
+    "tomli>=2.0.0; python_version < '3.11'",  # TOML parsing (backport)
 ]
 
 [project.optional-dependencies]