SparkLLM throws PydanticUserError: class-not-fully-defined

[Xmind0]

from langchain_community.llms import SparkLLM

llm = SparkLLM(
    app_id="xxx",
    api_key="xxx",
    api_secret="xxx",
)
PydanticUserError: SparkLLM is not fully defined; you should define Union, then call SparkLLM.model_rebuild().
Name: langchain
Version: 0.3.0
pydantic
Version: 2.11.3

[dosubot[bot]]

Hello @Xmind0! I'm Dosu, your friendly bot here to help you solve bugs, answer questions, and assist with contributions while you wait for a human maintainer. Let's tackle this issue together!

The error you're encountering suggests that the SparkLLM class is not fully defined, and it requires the definition of Union and a call to model_rebuild(). This type of error can occur when using certain versions of LangChain and Pydantic.

To resolve this, you might need to ensure that the class is properly set up by defining any necessary components or using an updated approach. In some cases, replacing the implementation with a more current method, such as using create_sql_agent for SQL-related tasks, might be recommended ^[1].

If the issue persists, consider checking for any updates or patches in the LangChain repository that might address compatibility with Pydantic 2.11.3.

To continue talking to Dosu, mention @dosu.

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[Xmind0]

i want to know whether is it the Pydantic/LangChain version compatibility

[dosubot[bot]]

LangChain version 0.3.0 requires Pydantic version 2, and it is compatible with Pydantic 2.11.3 as long as you avoid using the pydantic.v1 namespace with LangChain APIs. The langchain.pydantic_v1 module was a compatibility shim for Pydantic v1 and should no longer be used. You should update your code to import directly from Pydantic, for example, replace imports like from langchain.pydantic_v1 import BaseModel with from pydantic import BaseModel ^[1].

To continue talking to Dosu, mention @dosu.

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