Repository: langchain
Issue: I am getting pydantic Validation error when expecting tools in a response
Labels: 🤖:bug,investigate

### Checked other resources

- [X] I added a very descriptive title to this issue.
- [X] I searched the LangChain documentation with the integrated search.
- [X] I used the GitHub search to find a similar question and didn't find it.
- [X] I am sure that this is a bug in LangChain rather than my code.
- [X] The bug is not resolved by updating to the latest stable version of LangChain (or the specific integration package).

### Example Code

The Following code:
```python
from datetime import datetime
from typing import List

from langchain_core.messages import (
    SystemMessage,
    HumanMessage,
    ToolMessage,
    trim_messages,
)
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

# from openai import BaseModel
from pydantic.v1 import BaseModel, Field

## My messages in this format are accepted
 def build_message_list(self) -> List:
        # Convert internal history to a list of SystemMessage, HumanMessage, AIMessage, ToolMessage
        messages = [
            SystemMessage(
                content=""" \n Do Not make Vague assumptions use 
        appropriate tools provided whenever required to extract new or next Question."""
            )
        ]
        for entry in self.history:
            if entry["role"] == "human":
                messages.append(HumanMessage(content=entry["content"]))
            elif entry["role"] == "assistant":
                messages.append(AIMessage(content=entry["content"]))
            elif entry["role"] == "tool":
                messages.append(ToolMessage(content=entry["content"]))
        return messages
# this is the function where i should expect a AIMessage.
  def invoke_tool_or_model(self, messages) -> dict:
      """Handles whether to invoke a tool or continue with the LLM."""
      last_message = messages[-1]
      if hasattr(last_message, "tool_calls") and last_message.tool_calls:
          return self.call_tools(last_message.tool_calls)
      else:
          prompt = self.format_trimmed_history(messages)

          response = self.llm.invoke(prompt)
          return response
          
```

### Error Message and Stack Trace (if applicable)

the json response is giving the error inside the "tool_calls" where the accepted format is not decoded.

```
D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\pydantic\main.py:390: UserWarning: Pydantic serializer warnings:
  Expected `str` but got `dict` with value `{'category': 'math'}` - serialized value may not be as expected
  return self.__pydantic_serializer__.to_python(
2024-09-23 18:39:55.381 Uncaught app exception
Traceback (most recent call last):
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\streamlit\runtime\scriptrunner\exec_code.py", line 88, in exec_func_with_error_handling
    result = func()
             ^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 590, in code_to_exec
    exec(code, module.__dict__)
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\ai_interviewer_streamlit\new_interviewer.py", line 256, in <module>
    main()
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\ai_interviewer_streamlit\new_interviewer.py", line 249, in main
    handle_chat(interviewer)
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\ai_interviewer_streamlit\new_interviewer.py", line 200, in handle_chat
    response = interviewer.text_to_text(user_input)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\ai_interviewer_streamlit\new_interviewer.py", line 82, in text_to_text
    response = self.invoke_tool_or_model(trimmed_messages)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\ai_interviewer_streamlit\new_interviewer.py", line 114, in invoke_tool_or_model
    response = self.llm.invoke(prompt)
               ^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\runnables\base.py", line 5343, in invoke
    return self.bound.invoke(
           ^^^^^^^^^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\language_models\chat_models.py", line 284, in invoke
    self.generate_prompt(
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\language_models\chat_models.py", line 784, in generate_prompt
    return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\language_models\chat_models.py", line 641, in generate
    raise e
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\language_models\chat_models.py", line 631, in generate
    self._generate_with_cache(
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\language_models\chat_models.py", line 853, in _generate_with_cache
    result = self._generate(
             ^^^^^^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_openai\chat_models\base.py", line 671, in _generate
    return self._create_chat_result(response, generation_info)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_openai\chat_models\base.py", line 708, in _create_chat_result
    message = _convert_dict_to_message(res["message"])
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_openai\chat_models\base.py", line 127, in _convert_dict_to_message
    return AIMessage(
           ^^^^^^^^^^
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\messages\ai.py", line 94, in __init__
    super().__init__(content=content, **kwargs)
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\messages\base.py", line 76, in __init__
    super().__init__(content=content, **kwargs)
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\langchain_core\load\serializable.py", line 110, in __init__
    super().__init__(*args, **kwargs)
  File "D:\Work\Company_Product_Brainstorming\Sample_projects\GPTInterviewer\Final\envinter\Lib\site-packages\pydantic\main.py", line 212, in __init__
    validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
pydantic_core._pydantic_core.ValidationError: 1 validation error for AIMessage
invalid_tool_calls.0.args
  Input should be a valid string [type=string_type, input_value={'category': 'math'}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.9/v/string_type

```

### Description

* Currently i'm trying to fetch tools arguments from langchain response which can then use to call the function and return the answer from the tool to the llm.
* Also i used akjindal53244/Llama-3.1-Storm-8B as my llm model with koboldcpp as backend server.

### System Info

System Information
------------------
> OS:  Windows
> OS Version:  10.0.19045
> Python Version:  3.12.5 | packaged by conda-forge | (main, Aug  8 2024, 18:24:51) [MSC v.1940 64 bit (AMD64)]

Package Information
-------------------
> langchain_core: 0.3.5
> langchain: 0.2.16
> langchain_community: 0.2.16
> langsmith: 0.1.125
> langchain_openai: 0.2.0
> langchain_text_splitters: 0.2.2

Optional packages not installed
-------------------------------
> langgraph
> langserve

Other Dependencies
------------------
> aiohttp: 3.10.5
> async-timeout: Installed. No version info available.
> dataclasses-json: 0.6.7
> httpx: 0.27.2
> jsonpatch: 1.33
> numpy: 1.26.4
> openai: 1.46.0
> orjson: 3.10.7
> packaging: 23.2
> pydantic: 2.8.2
> PyYAML: 6.0.2
> requests: 2.32.3
> SQLAlchemy: 2.0.32
> tenacity: 8.5.0
> tiktoken: 0.7.0
> typing-extensions: 4.12.2