fix(dspy): accept LangChain tools with JSON schema args

dspy/utils/langchain_tool.py

@@ -9,7 +9,7 @@
 
 def convert_langchain_tool(tool: "BaseTool") -> Tool:
     """Build a DSPy tool from a LangChain tool.
-    
+
     This function converts a LangChain tool (either created with @tool decorator
     or by subclassing BaseTool) into a DSPy Tool.
 
@@ -29,13 +29,15 @@ async def func(**kwargs):
     # Get args_schema from the tool
     # https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#langchain_core.tools.base.BaseTool.args_schema
     args_schema = tool.args_schema
-    args, _, arg_desc = convert_input_schema_to_tool_args(args_schema.model_json_schema())
-
-    # The args_schema of Langchain tool is a pydantic model, so we can get the type hints from the model fields
-    arg_types = {
-        key: field.annotation if field.annotation is not None else Any
-        for key, field in args_schema.model_fields.items()
-    }
+    if isinstance(args_schema, dict):
+        args, arg_types, arg_desc = convert_input_schema_to_tool_args(args_schema)
+    else:
+        args, _, arg_desc = convert_input_schema_to_tool_args(args_schema.model_json_schema())
+        # Preserve model annotations for nested Pydantic arguments.
+        arg_types = {
+            key: field.annotation if field.annotation is not None else Any
+            for key, field in args_schema.model_fields.items()
+        }
 
     return Tool(
         func=func,

tests/utils/test_langchain_tool.py

@@ -50,3 +50,39 @@ def get_age(profile: Profile) -> int:
     assert tool.arg_types == {"profile": Profile}
     assert tool.arg_desc == {"profile": "No description provided. (Required)"}
     assert await tool.acall(profile=Profile(name="John", age=20)) == 20
+
+
+@pytest.mark.asyncio
+@pytest.mark.extra
+async def test_convert_tool_with_json_schema_dict():
+    from langchain_core.tools import StructuredTool
+
+    schema = {
+        "type": "object",
+        "properties": {
+            "query": {"type": "string"},
+            "limit": {"type": "integer", "default": 5},
+        },
+        "required": ["query"],
+    }
+
+    calls = []
+
+    async def search(query: str, limit: int = 5) -> str:
+        calls.append((query, limit))
+        return f"{query}:{limit}"
+
+    langchain_tool = StructuredTool.from_function(
+        coroutine=search,
+        name="search",
+        description="Search records.",
+        args_schema=schema,
+    )
+    dspy_tool = convert_langchain_tool(langchain_tool)
+
+    assert await dspy_tool.acall(query="cats") == "cats:5"
+    assert dspy_tool.args == schema["properties"]
+    assert dspy_tool.format_as_litellm_function_call()["function"]["parameters"]["required"] == ["query"]
+    with pytest.raises(ValueError, match="Arg query"):
+        await dspy_tool.acall(query=3)
+    assert calls == [("cats", 5)]