llm type_error ai_generated true

TypeError: 'NoneType' object is not iterable in tool call arguments parsing

ID: llm/langchain-tool-call-argument-type-error

Also available as: JSON · Markdown · 中文
87%Fix Rate
84%Confidence
1Evidence
2024-03-22First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
langchain==0.2.5 active — — —
langchain-core==0.2.5 active — — —
pydantic==2.7.0 active — — —

Root Cause

LangChain's tool call parser receives a 'None' value for a required list or dict parameter from the LLM, often when the model fails to generate arguments for a tool invocation.

generic

中文

LangChain 的工具调用解析器从 LLM 接收到必需列表或字典参数的 'None' 值，通常发生在模型未能为工具调用生成参数时。

Official Documentation

https://python.langchain.com/docs/modules/agents/tools/custom_tools#handling-errors

Workarounds

  1. 90% success Add validation in the tool's `_run` method to handle None defaults: `def _run(self, items: List[str] = None): items = items or []` 
    Add validation in the tool's `_run` method to handle None defaults: `def _run(self, items: List[str] = None): items = items or []`
  2. 95% success Use LangChain's `PydanticToolsParser` with a BaseModel that has default values for optional fields: `class MyArgs(BaseModel): items: List[str] = Field(default_factory=list)` 
    Use LangChain's `PydanticToolsParser` with a BaseModel that has default values for optional fields: `class MyArgs(BaseModel): items: List[str] = Field(default_factory=list)`
  3. 85% success Implement a retry mechanism that re-prompts the LLM with a clear instruction to provide all required arguments: `f'Please provide all required arguments for the tool. Missing: {missing_fields}'` 
    Implement a retry mechanism that re-prompts the LLM with a clear instruction to provide all required arguments: `f'Please provide all required arguments for the tool. Missing: {missing_fields}'`

中文步骤

  1. Add validation in the tool's `_run` method to handle None defaults: `def _run(self, items: List[str] = None): items = items or []`
  2. Use LangChain's `PydanticToolsParser` with a BaseModel that has default values for optional fields: `class MyArgs(BaseModel): items: List[str] = Field(default_factory=list)`
  3. Implement a retry mechanism that re-prompts the LLM with a clear instruction to provide all required arguments: `f'Please provide all required arguments for the tool. Missing: {missing_fields}'`

Dead Ends

Common approaches that don't work:

  1. 60% fail

    Even at temperature=0, the model can still output incomplete or missing arguments due to model behavior, not randomness.

  2. 80% fail

    The issue is structural (missing argument), not truncation; more tokens won't fix a None value.

  3. 85% fail

    Silently ignoring means the tool call is lost, breaking the agent's chain of reasoning and potentially producing incorrect results.