Tool Input Schema

默认情况下，工具会通过检查函数签名来推断参数模式。如果需要更严格的要求，可以指定自定义的输入模式，并附带自定义的验证逻辑。

from typing import Any, Dict
from langchain.agents import AgentType, initialize_agent
from langchain.llms import OpenAI
from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper
from pydantic import BaseModel, Field, root_validator

llm = OpenAI(temperature=0)

!pip install tldextract > /dev/null

[notice] A new release of pip is available: 23.0.1 -> 23.1
[notice] To update, run: pip install --upgrade pip

import tldextract

_APPROVED_DOMAINS = {
    "langchain",
    "wikipedia",
}

class ToolInputSchema(BaseModel):
    url: str = Field(...)

    @root_validator
    def validate_query(cls, values: Dict[str, Any]) -> Dict:
        url = values["url"]
        domain = tldextract.extract(url).domain
        if domain not in _APPROVED_DOMAINS:
            raise ValueError(
                f"Domain {domain} is not on the approved list:"
                f" {sorted(_APPROVED_DOMAINS)}"
            )
        return values

tool = RequestsGetTool(
    args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper()
)

agent = initialize_agent(
    [tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False
)

# This will succeed, since there aren't any arguments that will be triggered during validation
answer = agent.run("What's the main title on langchain.com?")
print(answer)

The main title of langchain.com is "LANG CHAIN 🦜️🔗 Official Home Page"

agent.run("What's the main title on google.com?")

---------------------------------------------------------------------------

ValidationError                           Traceback (most recent call last)

Cell In[7], line 1
----> 1 agent.run("What's the main title on google.com?")


File ~/code/lc/lckg/langchain/chains/base.py:213, in Chain.run(self, *args, **kwargs)
    211     if len(args) != 1:
    212         raise ValueError("`run` supports only one positional argument.")
--> 213     return self(args[0])[self.output_keys[0]]
    215 if kwargs and not args:
    216     return self(kwargs)[self.output_keys[0]]


File ~/code/lc/lckg/langchain/chains/base.py:116, in Chain.__call__(self, inputs, return_only_outputs)
    114 except (KeyboardInterrupt, Exception) as e:
    115     self.callback_manager.on_chain_error(e, verbose=self.verbose)
--> 116     raise e
    117 self.callback_manager.on_chain_end(outputs, verbose=self.verbose)
    118 return self.prep_outputs(inputs, outputs, return_only_outputs)


File ~/code/lc/lckg/langchain/chains/base.py:113, in Chain.__call__(self, inputs, return_only_outputs)
    107 self.callback_manager.on_chain_start(
    108     {"name": self.__class__.__name__},
    109     inputs,
    110     verbose=self.verbose,
    111 )
    112 try:
--> 113     outputs = self._call(inputs)
    114 except (KeyboardInterrupt, Exception) as e:
    115     self.callback_manager.on_chain_error(e, verbose=self.verbose)


File ~/code/lc/lckg/langchain/agents/agent.py:792, in AgentExecutor._call(self, inputs)
    790 # We now enter the agent loop (until it returns something).
    791 while self._should_continue(iterations, time_elapsed):
--> 792     next_step_output = self._take_next_step(
    793         name_to_tool_map, color_mapping, inputs, intermediate_steps
    794     )
    795     if isinstance(next_step_output, AgentFinish):
    796         return self._return(next_step_output, intermediate_steps)


File ~/code/lc/lckg/langchain/agents/agent.py:695, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)
    693         tool_run_kwargs["llm_prefix"] = ""
    694     # We then call the tool on the tool input to get an observation

--> 695 observation = tool.run( 696 agent_action.tool_input, 697 verbose=self.verbose, 698 color=color, 699 **tool_run_kwargs, 700 ) 701 else: 702 tool_run_kwargs = self.agent.tool_run_logging_kwargs()

File ~/code/lc/lckg/langchain/tools/base.py:110, in BaseTool.run(self, tool_input, verbose, start_color, color, **kwargs)
    101 def run(
    102     self,
    103     tool_input: Union[str, Dict],
   (...)
    107     **kwargs: Any,
    108 ) -> str:
    109     """Run the tool."""
--> 110     run_input = self._parse_input(tool_input)
    111     if not self.verbose and verbose is not None:
    112         verbose_ = verbose