dosubot[bot]
commented
3 weeks ago To resolve the TypeError: sequence item 0: expected str instance, dict found error, you need to ensure that all items in the sequence being joined are strings. The error typically occurs when you try to join a list of dictionaries or other non-string items.
In the provided code, the issue likely arises when converting the result to a string. Specifically, the result["output"] might be a dictionary or a list of dictionaries instead of a string.
To fix this, you should convert the result["output"] to a string before returning it. Here is the modified part of the run_agent method:
async def run_agent(self) -> Message:
if "input" not in self.user_prompt:
raise ValueError("Prompt must contain 'input' key.")
messages = [
("system", self.system_prompt),
("placeholder", "{chat_history}"),
("human", self.user_prompt),
("placeholder", "{agent_scratchpad}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
agent = create_tool_calling_agent(self.llm, self.tools, prompt)
runnable = AgentExecutor.from_agent_and_tools(
agent=cast(BaseSingleActionAgent, agent),
tools=self.tools,
verbose=True,
handle_parsing_errors=self.handle_parsing_errors,
)
input_dict: dict[str, str | list[Dict[str, str]]] = {"input": self.input_value}
if hasattr(self, "memory") and self.memory:
input_dict["chat_history"] = self.convert_chat_history(self.memory)
result = await runnable.ainvoke(input_dict)
self.status = result
if "output" not in result:
raise ValueError("Output key not found in result. Tried 'output'.")
# Ensure result_string is a string
result_string = str(result["output"])
return Message(text=result_string)By converting result["output"] to a string using str(result["output"]), you ensure that the Message object receives a string, thus avoiding the TypeError [1].
Additionally, ensure that the user_prompt contains the 'input' key as required by the ToolCallingAgentComponent class:
async def build(
self,
input_value: str,
llm: BaseLanguageModel,
tools: List[Tool],
user_prompt: str = "{input}",
message_history: Optional[List[Record]] = None,
system_message: str = "You are a helpful assistant",
handle_parsing_errors: bool = True,
) -> Text:
if "input" not in user_prompt:
raise ValueError("Prompt must contain 'input' key.")
messages = [
("system", system_message),
("placeholder", "{chat_history}"),
("human", user_prompt),
("placeholder", "{agent_scratchpad}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
agent = create_tool_calling_agent(llm, tools, prompt)
result = await self.run_agent(agent, input_value, tools, message_history, handle_parsing_errors)
self.status = result
return resultThis ensures that the prompt is correctly formatted and the agent can process the input without errors [1].
In the context of using the Flow as Tool through the Tool Call Agent, ensure that the FlowToolComponent is correctly configured and that the result["output"] is properly handled. The FlowToolComponent class is designed to construct a Tool from a function that runs the loaded Flow, and it includes methods to retrieve and manage flows:
class FlowToolComponent(CustomComponent):
display_name = "Flow as Tool"
description = "Construct a Tool from a function that runs the loaded Flow."
field_order = ["flow_name", "name", "description", "return_direct"]
trace_type = "tool"
beta = True
def get_flow_names(self) -> List[str]:
flow_datas = self.list_flows()
return [flow_data.data["name"] for flow_data in flow_datas]
def get_flow(self, flow_name: str) -> Optional[Data]:
flow_datas = self.list_flows()
for flow_data in flow_datas:
if flow_data.data["name"] == flow_name:
return flow_data
return None
def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
logger.debug(f"Updating build config with field value {field_value} and field name {field_name}")
if field_name == "flow_name":
build_config["flow_name"]["options"] = self.get_flow_names()
return build_config
def build_config(self):
return {
"flow_name": {
"display_name": "Flow Name",
"info": "The name of the flow to run.",
"options": [],
"real_time_refresh": True,
"refresh_button": True,
},
"name": {
"display_name": "Name",
"description": "The name of the tool.",
},
"description": {
"display_name": "Description",
"description": "The description of the tool.",
},
"return_direct": {
"display_name": "Return Direct",
"description": "Return the result directly from the Tool.",
"advanced": True,
},
}
async def build(self, flow_name: str, name: str, description: str, return_direct: bool = False) -> Tool:
FlowTool.update_forward_refs()
flow_data = self.get_flow(flow_name)
if not flow_data:
raise ValueError("Flow not found.")
graph = Graph.from_payload(flow_data.data["data"])
inputs = get_flow_inputs(graph)
tool = FlowTool(
name=name,
description=description,
graph=graph,
return_direct=return_direct,
inputs=inputs,
flow_id=str(flow_data.id),
user_id=str(self._user_id),
)
description_repr = repr(tool.description).strip("'")
args_str = "\n".join([f"- {arg_name}: {arg_data['description']}" for arg_name, arg_data in tool.args.items()])
self.status = f"{description_repr}\nArguments:\n{args_str}"
return tool # type: ignoreEnsure that the FlowToolComponent is properly instantiated and that the result["output"] is correctly converted to a string before being used [1].
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carlosrcoelho
Describe the bug When invoking the Flow as Tool through the Tool Call Agent, the system displayed an error message:
TypeError: sequence item 0: expected str instance, dict foundBrowser and Version
To Reproduce Steps to reproduce the behavior:
Additional context The issue was resolved after I made the following modifications to the code. Details are as follows:
langflow/base/flow_processing/utils.py, line 91:
Before the modification:
After the modification: