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The
The Ensure that the tool name you are using matches exactly with the name defined in your tools list. For example, if your tool is named "荔枝知识增强检索", make sure this name is used consistently throughout your code. Additionally, the required fields for the
This is defined as: class RetrieverInput(BaseModel):
"""Input to the retriever."""
query: str = Field(description="query to look up in retriever")[7]. Make sure that the input to your tool includes the 'query' field correctly. If the tool name and input fields are correct, the validation error should be resolved. |
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Yes, the provided code snippet demonstrates the correct way to use the Here is the relevant code snippet for creating and using the agent: # Import relevant functionality
from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage
from langgraph.checkpoint.memory import MemorySaver
from langgraph.prebuilt import create_react_agent
# Create the agent
memory = MemorySaver()
model = ChatAnthropic(model_name="claude-3-sonnet-20240229")
search = TavilySearchResults(max_results=2)
tools = [search]
agent_executor = create_react_agent(model, tools, checkpointer=memory)
# Use the agent
config = {"configurable": {"thread_id": "abc123"}}
for chunk in agent_executor.stream(
{"messages": [HumanMessage(content="hi im bob! and i live in sf")]}, config
):
print(chunk)
print("----")
for chunk in agent_executor.stream(
{"messages": [HumanMessage(content="whats the weather where I live?")]}, config
):
print(chunk)
print("----")To verify the tool's invocation, you can check the LangSmith trace links provided in the documentation: These traces will show you the detailed execution flow, including the inputs and outputs at each step, ensuring that the tool is being called with the correct input format. Additionally, the LangChain documentation provides detailed guidance on using LangSmith for tracing and inspecting the execution flow of your applications. You can enable LangSmith tracing by setting the appropriate environment variables: import getpass
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()For more information on building and inspecting complex applications with LangChain, you can refer to the LangChain tutorial on Q&A with chat history [1][2][3][4][5]. |
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@dosu tool 荔枝知识增强检索 input is |
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The Here's how you can adjust the input format to match the expected structure:
By ensuring that the input format matches the expected structure for the {
"query": "深圳的荔枝品种有哪些？"
}If the input is nested within |
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@dosu |
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The tool requires specific input parameters, which can be understood from the
The format of the input parameters is defined as follows: class Schema(BaseModel):
arg1: int = Field(..., description="foo")
arg2: Literal["bar", "baz"] = Field(..., description="one of 'bar', 'baz'")Additionally, the Here is an example of how the tool parameters are defined and converted: def _convert_to_cohere_tool(
tool: Union[Dict[str, Any], BaseTool, Type[BaseModel]],
) -> Dict[str, Any]:
if isinstance(tool, BaseTool):
return Tool(
name=tool.name,
description=_remove_signature_from_tool_description(
tool.name, tool.description
),
parameter_definitions={
param_name: ToolParameterDefinitionsValue(
description=param_definition.get("description")
if "description" in param_definition
else "",
type=JSON_TO_PYTHON_TYPES.get(
param_definition.get("type"), param_definition.get("type")
),
required="default" not in param_definition,
)
for param_name, param_definition in tool.args.items()
},
).dict()
elif isinstance(tool, dict):
if not all(k in tool for k in ("title", "description", "properties")):
raise ValueError(
"Unsupported dict type. Tool must be passed in as a BaseTool instance, JSON schema dict, or BaseModel type."
)
return Tool(
name=tool.get("title"),
description=tool.get("description"),
parameter_definitions={
param_name: ToolParameterDefinitionsValue(
description=param_definition.get("description"),
type=JSON_TO_PYTHON_TYPES.get(
param_definition.get("type"), param_definition.get("type")
),
required="default" not in param_definition,
)
for param_name, param_definition in tool.get("properties", {}).items()
},
).dict()
elif issubclass(tool, BaseModel):
as_json_schema_function = convert_to_openai_function(tool)
parameters = as_json_schema_function.get("parameters", {})
properties = parameters.get("properties", {})
return Tool(
name=as_json_schema_function.get("name"),
description=as_json_schema_function.get(
"description",
as_json_schema_function.get("name"),
),
parameter_definitions={
param_name: ToolParameterDefinitionsValue(
description=param_definition.get("description"),
type=JSON_TO_PYTHON_TYPES.get(
param_definition.get("type"), param_definition.get("type")
),
required=param_name in parameters.get("required", []),
)
for param_name, param_definition in properties.items()
},
).dict()
else:
raise ValueError(
f"Unsupported tool type {type(tool)}. Tool must be passed in as a BaseTool instance, JSON schema dict, or BaseModel type."
)This ensures that the input parameters are correctly formatted and validated according to the tool's requirements [1][2][3][4]. |
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Example Code
Description
I run the code and use langsmith to see what happen in each step,
In the (tools --荔枝知识增强检索)，error as follow:
What cause the error, because I did have the 'query'
System Info
python 3.11.9
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