feat: (and fix) Add enable_streaming_passthrough to ToolInvoker and add missing params to to_dict

haystack/components/tools/tool_invoker.py

@@ -7,7 +7,7 @@
 import json
 from concurrent.futures import ThreadPoolExecutor
 from functools import partial
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Set, Union
 
 from haystack import component, default_from_dict, default_to_dict, logging
 from haystack.components.agents import State
@@ -24,6 +24,7 @@
 )
 from haystack.tools.errors import ToolInvocationError
 from haystack.tracing.utils import _serializable_value
+from haystack.utils.callable_serialization import deserialize_callable, serialize_callable
 
 logger = logging.getLogger(__name__)
 
@@ -167,6 +168,7 @@ def __init__(
         convert_result_to_json_string: bool = False,
         streaming_callback: Optional[StreamingCallbackT] = None,
         *,
+        enable_streaming_callback_passthrough: bool = False,
         async_executor: Optional[ThreadPoolExecutor] = None,
     ):
         """
@@ -186,6 +188,11 @@ def __init__(
             A callback function that will be called to emit tool results.
             Note that the result is only emitted once it becomes available — it is not
             streamed incrementally in real time.
+        :param enable_streaming_callback_passthrough:
+            If True, the `streaming_callback` will be passed to the tool invocation if the tool supports it.
+            This allows tools to stream their results back to the client.
+            Note that this requires the tool to have a `streaming_callback` parameter in its `invoke` method signature.
+            If False, the `streaming_callback` will not be passed to the tool invocation.
         :param async_executor:
             Optional ThreadPoolExecutor to use for async calls. If not provided, a single-threaded executor will be
             initialized and used.
@@ -198,6 +205,7 @@ def __init__(
         # could be a Toolset instance or a list of Tools
         self.tools = tools
         self.streaming_callback = streaming_callback
+        self.enable_streaming_callback_passthrough = enable_streaming_callback_passthrough
 
         # Convert Toolset to list for internal use
         if isinstance(tools, Toolset):
@@ -329,18 +337,12 @@ def _prepare_tool_result_message(self, result: Any, tool_call: ToolCall, tool_to
                 raise conversion_error from e
         return ChatMessage.from_tool(tool_result=tool_result_str, error=error, origin=tool_call)
 
-    @staticmethod
-    def _inject_state_args(tool: Tool, llm_args: Dict[str, Any], state: State) -> Dict[str, Any]:
+    def _get_func_params(self, tool: Tool) -> Set:
         """
-        Combine LLM-provided arguments (llm_args) with state-based arguments.
+        Returns the function parameters of the tool's invoke method.
 
-        Tool arguments take precedence in the following order:
-          - LLM overrides state if the same param is present in both
-          - local tool.inputs mappings (if any)
-          - function signature name matching
+        This method inspects the tool's function signature to determine which parameters the tool accepts.
         """
-        final_args = dict(llm_args)  # start with LLM-provided
-
         # ComponentTool wraps the function with a function that accepts kwargs, so we need to look at input sockets
         # to find out which parameters the tool accepts.
         if isinstance(tool, ComponentTool):
@@ -352,6 +354,20 @@ def _inject_state_args(tool: Tool, llm_args: Dict[str, Any], state: State) -> Di
         else:
             func_params = set(inspect.signature(tool.function).parameters.keys())
 
+        return func_params
+
+    def _inject_state_args(self, tool: Tool, llm_args: Dict[str, Any], state: State) -> Dict[str, Any]:
+        """
+        Combine LLM-provided arguments (llm_args) with state-based arguments.
+
+        Tool arguments take precedence in the following order:
+          - LLM overrides state if the same param is present in both
+          - local tool.inputs mappings (if any)
+          - function signature name matching
+        """
+        final_args = dict(llm_args)  # start with LLM-provided
+        func_params = self._get_func_params(tool)
+
         # Determine the source of parameter mappings (explicit tool inputs or direct function parameters)
         # Typically, a "Tool" might have .inputs_from_state = {"state_key": "tool_param_name"}
         if hasattr(tool, "inputs_from_state") and isinstance(tool.inputs_from_state, dict):
@@ -417,6 +433,8 @@ def run(
         messages: List[ChatMessage],
         state: Optional[State] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
+        *,
+        enable_streaming_callback_passthrough: Optional[bool] = None,
     ) -> Dict[str, Any]:
         """
         Processes ChatMessage objects containing tool calls and invokes the corresponding tools, if available.
@@ -427,6 +445,12 @@ def run(
         :param streaming_callback: A callback function that will be called to emit tool results.
             Note that the result is only emitted once it becomes available — it is not
             streamed incrementally in real time.
+        :param enable_streaming_callback_passthrough:
+            If True, the `streaming_callback` will be passed to the tool invocation if the tool supports it.
+            This allows tools to stream their results back to the client.
+            Note that this requires the tool to have a `streaming_callback` parameter in its `invoke` method signature.
+            If False, the `streaming_callback` will not be passed to the tool invocation.
+            If None, the value from the constructor will be used.
         :returns:
             A dictionary with the key `tool_messages` containing a list of ChatMessage objects with tool role.
             Each ChatMessage objects wraps the result of a tool invocation.
@@ -443,6 +467,12 @@ def run(
         if state is None:
             state = State(schema={})
 
+        resolved_enable_streaming_passthrough = (
+            enable_streaming_callback_passthrough
+            if enable_streaming_callback_passthrough is not None
+            else self.enable_streaming_callback_passthrough
+        )
+
         # Only keep messages with tool calls
         messages_with_tool_calls = [message for message in messages if message.tool_calls]
         streaming_callback = select_streaming_callback(
@@ -468,6 +498,16 @@ def run(
                 llm_args = tool_call.arguments.copy()
                 final_args = self._inject_state_args(tool_to_invoke, llm_args, state)
 
+                # Check whether to inject streaming_callback
+                if (
+                    resolved_enable_streaming_passthrough
+                    and streaming_callback is not None
+                    and "streaming_callback" not in final_args
+                ):
+                    invoke_params = self._get_func_params(tool_to_invoke)
+                    if "streaming_callback" in invoke_params:
+                        final_args["streaming_callback"] = streaming_callback
+
                 # 2) Invoke the tool
                 try:
                     tool_result = tool_to_invoke.invoke(**final_args)
@@ -520,6 +560,8 @@ async def run_async(
         messages: List[ChatMessage],
         state: Optional[State] = None,
         streaming_callback: Optional[StreamingCallbackT] = None,
+        *,
+        enable_streaming_callback_passthrough: Optional[bool] = None,
     ) -> Dict[str, Any]:
         """
         Asynchronously processes ChatMessage objects containing tool calls and invokes the corresponding tools.
@@ -530,6 +572,12 @@ async def run_async(
         :param streaming_callback: An asynchronous callback function that will be called to emit tool results.
             Note that the result is only emitted once it becomes available — it is not
             streamed incrementally in real time.
+        :param enable_streaming_callback_passthrough:
+            If True, the `streaming_callback` will be passed to the tool invocation if the tool supports it.
+            This allows tools to stream their results back to the client.
+            Note that this requires the tool to have a `streaming_callback` parameter in its `invoke` method signature.
+            If False, the `streaming_callback` will not be passed to the tool invocation.
+            If None, the value from the constructor will be used.
         :returns:
             A dictionary with the key `tool_messages` containing a list of ChatMessage objects with tool role.
             Each ChatMessage objects wraps the result of a tool invocation.
@@ -546,6 +594,12 @@ async def run_async(
         if state is None:
             state = State(schema={})
 
+        resolved_enable_streaming_passthrough = (
+            enable_streaming_callback_passthrough
+            if enable_streaming_callback_passthrough is not None
+            else self.enable_streaming_callback_passthrough
+        )
+
         # Only keep messages with tool calls
         messages_with_tool_calls = [message for message in messages if message.tool_calls]
         streaming_callback = select_streaming_callback(
@@ -571,6 +625,16 @@ async def run_async(
                 llm_args = tool_call.arguments.copy()
                 final_args = self._inject_state_args(tool_to_invoke, llm_args, state)
 
+                # Check whether to inject streaming_callback
+                if (
+                    resolved_enable_streaming_passthrough
+                    and streaming_callback is not None
+                    and "streaming_callback" not in final_args
+                ):
+                    invoke_params = self._get_func_params(tool_to_invoke)
+                    if "streaming_callback" in invoke_params:
+                        final_args["streaming_callback"] = streaming_callback
+
                 # 2) Invoke the tool asynchronously
                 try:
                     tool_result = await asyncio.get_running_loop().run_in_executor(
@@ -626,11 +690,18 @@ def to_dict(self) -> Dict[str, Any]:
         :returns:
             Dictionary with serialized data.
         """
+        if self.streaming_callback is not None:
+            streaming_callback = serialize_callable(self.streaming_callback)
+        else:
+            streaming_callback = None
+
         return default_to_dict(
             self,
             tools=serialize_tools_or_toolset(self.tools),
             raise_on_failure=self.raise_on_failure,
             convert_result_to_json_string=self.convert_result_to_json_string,
+            streaming_callback=streaming_callback,
+            enable_streaming_callback_passthrough=self.enable_streaming_callback_passthrough,
         )
 
     @classmethod
@@ -644,4 +715,8 @@ def from_dict(cls, data: Dict[str, Any]) -> "ToolInvoker":
             The deserialized component.
         """
         deserialize_tools_or_toolset_inplace(data["init_parameters"], key="tools")
+        if data["init_parameters"].get("streaming_callback") is not None:
+            data["init_parameters"]["streaming_callback"] = deserialize_callable(
+                data["init_parameters"]["streaming_callback"]
+            )
         return default_from_dict(cls, data)

releasenotes/notes/add-enable-streaming-passthrough-and-fix-sede-toolinvoker-bc2e906ddcff0e0d.yaml

@@ -0,0 +1,7 @@
+---
+features:
+  - |
+    Added the `enable_streaming_callback_passthrough` to the `ToolInovker` init, run and run_async methods. If set to True the ToolInvoker will try and pass the `streaming_callback` function to a tool's invoke method only if the tool's invoke method has `streaming_callback` in its signature.
+fixes:
+  - |
+    Fixed the `to_dict` and `from_dict` of `ToolInvoker` to properly serialize the `streaming_callback` init parameter.