Update package versions

python/packages/autogen-ext/src/autogen_ext/tools/langchain/__init__.py

@@ -1,3 +1,3 @@
-from ._langchain_adapter import LangChainToolAdapter
-
-__all__ = ["LangChainToolAdapter"]
+from ._langchain_adapter import LangChainToolAdapter
+
+__all__ = ["LangChainToolAdapter"]

python/packages/autogen-ext/src/autogen_ext/tools/langchain/_langchain_adapter.py

@@ -1,75 +1,75 @@
-import asyncio
-import inspect
-from typing import Any, Callable, Dict, Tuple, Type, cast
-
-from autogen_core.base import CancellationToken
-from autogen_core.components.tools import BaseTool
-from pydantic import BaseModel, Field, create_model
-from pydantic.fields import FieldInfo
-
-from langchain.tools import Tool as LangChainTool
-
-FieldDefinition = Tuple[Type[Any], FieldInfo]
-FieldsDict = Dict[str, FieldDefinition]
-
-
-class LangChainToolAdapter(BaseTool[BaseModel, Any]):
-    langchain_tool: LangChainTool
-    _callable: Callable[..., Any]
-
-    def __init__(self, langchain_tool: LangChainTool):
-        self.langchain_tool = langchain_tool
-
-        # Extract name and description
-        name = langchain_tool.name
-        description = langchain_tool.description or ""
-
-        # Determine the callable method
-        if hasattr(langchain_tool, "func") and callable(langchain_tool.func):
-            assert langchain_tool.func is not None
-            self._callable = langchain_tool.func
-        elif hasattr(langchain_tool, "_run") and callable(langchain_tool._run):  # pyright: ignore
-            self._callable = langchain_tool._run  # type: ignore
-        else:
-            raise AttributeError(
-                f"The provided LangChain tool '{name}' does not have a callable 'func' or '_run' method."
-            )
-
-        # Determine args_type
-        if langchain_tool.args_schema:  # pyright: ignore
-            args_type = langchain_tool.args_schema  # pyright: ignore
-        else:
-            # Infer args_type from the callable's signature
-            sig = inspect.signature(cast(Callable[..., Any], self._callable))
-            fields = {
-                k: (v.annotation, Field(...))
-                for k, v in sig.parameters.items()
-                if k != "self" and v.kind not in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD)
-            }
-            args_type = create_model(f"{name}Args", **fields)  # type: ignore
-            # Note: type ignore is used due to a LangChain typing limitation
-
-        # Ensure args_type is a subclass of BaseModel
-        if not issubclass(args_type, BaseModel):
-            raise ValueError(f"Failed to create a valid Pydantic v2 model for {name}")
-
-        # Assume return_type as Any if not specified
-        return_type: Type[Any] = object
-
-        super().__init__(args_type, return_type, name, description)
-
-    async def run(self, args: BaseModel, cancellation_token: CancellationToken) -> Any:
-        # Prepare arguments
-        kwargs = args.model_dump()
-
-        # Determine if the callable is asynchronous
-        if inspect.iscoroutinefunction(self._callable):
-            result = await self._callable(**kwargs)
-        else:
-            # Run in a thread to avoid blocking the event loop
-            result = await asyncio.to_thread(self._call_sync, kwargs)
-
-        return result
-
-    def _call_sync(self, kwargs: Dict[str, Any]) -> Any:
-        return self._callable(**kwargs)
+import asyncio
+import inspect
+from typing import Any, Callable, Dict, Tuple, Type, cast
+
+from autogen_core.base import CancellationToken
+from autogen_core.components.tools import BaseTool
+from pydantic import BaseModel, Field, create_model
+from pydantic.fields import FieldInfo
+
+from langchain.tools import Tool as LangChainTool
+
+FieldDefinition = Tuple[Type[Any], FieldInfo]
+FieldsDict = Dict[str, FieldDefinition]
+
+
+class LangChainToolAdapter(BaseTool[BaseModel, Any]):
+    langchain_tool: LangChainTool
+    _callable: Callable[..., Any]
+
+    def __init__(self, langchain_tool: LangChainTool):
+        self.langchain_tool = langchain_tool
+
+        # Extract name and description
+        name = langchain_tool.name
+        description = langchain_tool.description or ""
+
+        # Determine the callable method
+        if hasattr(langchain_tool, "func") and callable(langchain_tool.func):
+            assert langchain_tool.func is not None
+            self._callable = langchain_tool.func
+        elif hasattr(langchain_tool, "_run") and callable(langchain_tool._run):  # pyright: ignore
+            self._callable = langchain_tool._run  # type: ignore
+        else:
+            raise AttributeError(
+                f"The provided LangChain tool '{name}' does not have a callable 'func' or '_run' method."
+            )
+
+        # Determine args_type
+        if langchain_tool.args_schema:  # pyright: ignore
+            args_type = langchain_tool.args_schema  # pyright: ignore
+        else:
+            # Infer args_type from the callable's signature
+            sig = inspect.signature(cast(Callable[..., Any], self._callable))
+            fields = {
+                k: (v.annotation, Field(...))
+                for k, v in sig.parameters.items()
+                if k != "self" and v.kind not in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD)
+            }
+            args_type = create_model(f"{name}Args", **fields)  # type: ignore
+            # Note: type ignore is used due to a LangChain typing limitation
+
+        # Ensure args_type is a subclass of BaseModel
+        if not issubclass(args_type, BaseModel):
+            raise ValueError(f"Failed to create a valid Pydantic v2 model for {name}")
+
+        # Assume return_type as Any if not specified
+        return_type: Type[Any] = object
+
+        super().__init__(args_type, return_type, name, description)
+
+    async def run(self, args: BaseModel, cancellation_token: CancellationToken) -> Any:
+        # Prepare arguments
+        kwargs = args.model_dump()
+
+        # Determine if the callable is asynchronous
+        if inspect.iscoroutinefunction(self._callable):
+            result = await self._callable(**kwargs)
+        else:
+            # Run in a thread to avoid blocking the event loop
+            result = await asyncio.to_thread(self._call_sync, kwargs)
+
+        return result
+
+    def _call_sync(self, kwargs: Dict[str, Any]) -> Any:
+        return self._callable(**kwargs)