Fix remaining ty type errors

- Fix slack_mcp_reader.py channel parameter can be None
- Fix embedding_compute.py ContextProp type issue
- Fix searcher_base.py method override signatures
- Fix chunking_utils.py chunk_text assignment
- Fix slack_rag.py and twitter_rag.py return types
- Fix email.py and image_rag.py method overrides

packages/leann-core/src/leann/chunking_utils.py

@@ -243,7 +243,7 @@ def create_ast_chunks(
                 astchunk_metadata: dict[str, Any] = {}
 
                 if hasattr(chunk, "text"):
-                    chunk_text = chunk.text
+                    chunk_text = str(chunk.text) if chunk.text else None
                 elif isinstance(chunk, str):
                     chunk_text = chunk
                 elif isinstance(chunk, dict):

packages/leann-core/src/leann/embedding_compute.py

@@ -451,7 +451,8 @@ def compute_embeddings_sentence_transformers(
             # TODO: Haven't tested this yet
             torch.set_num_threads(min(8, os.cpu_count() or 4))
             try:
-                torch.backends.mkldnn.enabled = True
+                # PyTorch's ContextProp type is complex; cast for type checker
+                torch.backends.mkldnn.enabled = True  # type: ignore[assignment]
             except AttributeError:
                 pass
 

packages/leann-core/src/leann/searcher_base.py

@@ -56,7 +56,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         with open(meta_path, encoding="utf-8") as f:
             return json.load(f)
 
-    def _ensure_server_running(self, passages_source_file: str, port: int, **kwargs) -> int:
+    def _ensure_server_running(self, passages_source_file: str, port: Optional[int], **kwargs) -> int:
         """
         Ensures the embedding server is running if recompute is needed.
         This is a helper for subclasses.
@@ -81,7 +81,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         }
 
         server_started, actual_port = self.embedding_server_manager.start_server(
-            port=port,
+            port=port if port is not None else 5557,
             model_name=self.embedding_model,
             embedding_mode=self.embedding_mode,
             passages_file=passages_source_file,
@@ -98,7 +98,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         self,
         query: str,
         use_server_if_available: bool = True,
-        zmq_port: int = 5557,
+        zmq_port: Optional[int] = None,
         query_template: Optional[str] = None,
     ) -> np.ndarray:
         """