I've encountered an error while working with the langchain_core langchain-openailibrary and I'm hoping someone can assist me in resolving this issue.
AttributeError: 'str' object has no attribute 'model_dump'
import pandas as pd
from data_api import *
from langchain_openai import ChatOpenAI
# from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv, find_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from perplexity.perplexity import search_chat_completion
from langchain.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambda
from operator import itemgetter
import json
import os
from typing import List, Dict, Any
from tqdm import tqdm
self.llm = ChatOpenAI(model='gpt-4o', temperature=0)
self.mini = ChatOpenAI(model='gpt-4o-mini', temperature=0)
self.pplx = ChatOpenAI(base_url="https://api.perplexity.ai",
model='llama-3.1-sonar-huge-128k-online')
self.o1 = ChatOpenAI(model='o1-preview', temperature=1)
chain = (
{
"chain1": chain1,
"chain2": chain2,
"chain3": chain3,
"chain4": chain4,
"chain5": chain5,
"chain6": chain6,
"chain7": chain7,
"company": itemgetter("company")
}
| PromptTemplate.from_template(
"""
<text>
{chain1}
{chain2}
{chain3}
{chain4}
{chain5}
{chain6}
{chain7}
</text>
"""
) | self.o1 | StrOutputParser()
)
return chain.invoke({"company": symbol})
AttributeError("'str' object has no attribute 'model_dump'")Traceback (most recent call last):
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3022, in invoke
input = context.run(step.invoke, input, config, **kwargs)
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3727, in invoke
output = {key: future.result() for key, future in zip(steps, futures)}
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3727, in <dictcomp>
output = {key: future.result() for key, future in zip(steps, futures)}
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/concurrent/futures/_base.py", line 439, in result
return self.__get_result()
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3711, in _invoke_step
return context.run(
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3022, in invoke
input = context.run(step.invoke, input, config, **kwargs)
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3727, in invoke
output = {key: future.result() for key, future in zip(steps, futures)}
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3727, in <dictcomp>
output = {key: future.result() for key, future in zip(steps, futures)}
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/concurrent/futures/_base.py", line 446, in result
return self.__get_result()
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/concurrent/futures/_base.py", line 391, in __get_result
raise self._exception
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3711, in _invoke_step
return context.run(
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/runnables/base.py", line 3024, in invoke
input = context.run(step.invoke, input, config)
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py", line 286, in invoke
self.generate_prompt(
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py", line 786, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py", line 643, in generate
raise e
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py", line 633, in generate
self._generate_with_cache(
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_core/language_models/chat_models.py", line 851, in _generate_with_cache
result = self._generate(
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_openai/chat_models/base.py", line 718, in _generate
return self._create_chat_result(response, generation_info)
File "/home/azureuser/miniconda3/envs/llm39/lib/python3.9/site-packages/langchain_openai/chat_models/base.py", line 745, in _create_chat_result
response if isinstance(response, dict) else response.model_dump()
AttributeError: 'str' object has no attribute 'model_dump'
Environment Information:
| Package | Version |
|---|---|
| aiohappyeyeballs | 2.4.4 |
| aiohttp | 3.11.10 |
| aiosignal | 1.3.1 |
| annotated-types | 0.7.0 |
| anyio | 4.7.0 |
| asttokens | 3.0.0 |
| async-timeout | 4.0.3 |
| attrs | 24.2.0 |
| beautifulsoup4 | 4.12.3 |
| certifi | 2024.8.30 |
| charset-normalizer | 3.4.0 |
| comm | 0.2.2 |
| dataclasses-json | 0.6.7 |
| debugpy | 1.8.10 |
| decorator | 5.1.1 |
| distro | 1.9.0 |
| et_xmlfile | 2.0.0 |
| exceptiongroup | 1.2.2 |
| executing | 2.1.0 |
| fastjsonschema | 2.21.1 |
| frozenlist | 1.5.0 |
| greenlet | 3.1.1 |
| h11 | 0.14.0 |
| httpcore | 1.0.7 |
| httpx | 0.28.1 |
| httpx-sse | 0.4.0 |
| idna | 3.10 |
| import-ipynb | 0.2 |
| importlib_metadata | 8.5.0 |
| ipykernel | 6.29.5 |
| ipython | 8.18.1 |
| jedi | 0.19.2 |
| jiter | 0.8.2 |
| jsonpatch | 1.33 |
| jsonpointer | 3.0.0 |
| jsonschema | 4.23.0 |
| jsonschema-specifications | 2024.10.1 |
| jupyter_client | 8.6.3 |
| jupyter_core | 5.7.2 |
| langchain | 0.3.13 |
| langchain-community | 0.3.13 |
| langchain-core | 0.3.28 |
| langchain-openai | 0.2.14 |
| langchain-text-splitters | 0.3.4 |
| langgraph | 0.2.59 |
| langgraph-checkpoint | 2.0.9 |
| langgraph-sdk | 0.1.44 |
| langsmith | 0.2.3 |
| marshmallow | 3.23.2 |
| matplotlib-inline | 0.1.7 |
| msgpack | 1.1.0 |
| multidict | 6.1.0 |
| mypy-extensions | 1.0.0 |
| nbformat | 5.10.4 |
| nest_asyncio | 1.6.0 |
| numpy | 1.26.4 |
| openai | 1.58.1 |
| openpyxl | 3.1.5 |
| orjson | 3.10.12 |
| packaging | 24.2 |
| pandas | 2.2.3 |
| parso | 0.8.4 |
| pexpect | 4.9.0 |
| pickleshare | 0.7.5 |
| pip | 24.2 |
| platformdirs | 4.3.6 |
| prompt_toolkit | 3.0.48 |
| propcache | 0.2.1 |
| psutil | 6.1.0 |
| ptyprocess | 0.7.0 |
| pure_eval | 0.2.3 |
| pydantic | 2.10.4 |
| pydantic_core | 2.27.2 |
| pydantic-settings | 2.7.0 |
| Pygments | 2.18.0 |
| python-dateutil | 2.9.0.post0 |
| python-dotenv | 1.0.1 |
| pytz | 2024.2 |
| PyYAML | 6.0.2 |
| pyzmq | 26.2.0 |
| referencing | 0.35.1 |
| regex | 2024.11.6 |
| requests | 2.32.3 |
| requests-toolbelt | 1.0.0 |
| rpds-py | 0.22.3 |
| setuptools | 75.1.0 |
| six | 1.17.0 |
| sniffio | 1.3.1 |
| soupsieve | 2.6 |
| SQLAlchemy | 2.0.36 |
| stack_data | 0.6.3 |
| tenacity | 9.0.0 |
| tiktoken | 0.8.0 |
| tornado | 6.4.2 |
| tqdm | 4.67.1 |
| traitlets | 5.14.3 |
| typing_extensions | 4.12.2 |
| typing-inspect | 0.9.0 |
| tzdata | 2024.2 |
| urllib3 | 2.2.3 |
| wcwidth | 0.2.13 |
| wheel | 0.44.0 |
| yarl | 1.18.3 |
| zipp | 3.21.0 |
This error occurs because one of your chains returns a string instead of a proper LangChain message type. Here's how to fix it:
from langchain_core.messages import HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain.prompts import PromptTemplate
from operator import itemgetter
from langchain_core.runnables import RunnablePassthrough
def wrap_chain_output(chain_output):
if isinstance(chain_output, str):
return HumanMessage(content=chain_output)
return chain_output
chain = (
{
"chain1": chain1 | (lambda x: wrap_chain_output(x)),
"chain2": chain2 | (lambda x: wrap_chain_output(x)),
"chain3": chain3 | (lambda x: wrap_chain_output(x)),
"chain4": chain4 | (lambda x: wrap_chain_output(x)),
"chain5": chain5 | (lambda x: wrap_chain_output(x)),
"chain6": chain6 | (lambda x: wrap_chain_output(x)),
"chain7": chain7 | (lambda x: wrap_chain_output(x)),
"company": itemgetter("company")
}
| PromptTemplate.from_template(
"""
<text>
{chain1}
{chain2}
{chain3}
{chain4}
{chain5}
{chain6}
{chain7}
</text>
"""
)
| self.o1
| StrOutputParser()
)
result = chain.invoke({"company": symbol})
The error occurs because the OpenAI chat model expects either a dictionary or an object with a model_dump() method, but it's receiving a string. These modifications should resolve the issue by ensuring proper type handling throughout the chain. If that doesn't work then you can try this way to handle string outputs directly like this-
chain = (
{
"chain1": chain1,
"chain2": chain2,
"chain3": chain3,
"chain4": chain4,
"chain5": chain5,
"chain6": chain6,
"chain7": chain7,
"company": itemgetter("company")
}
| PromptTemplate.from_template(
"""
<text>
{chain1.content if hasattr(chain1, 'content') else chain1}
{chain2.content if hasattr(chain2, 'content') else chain2}
{chain3.content if hasattr(chain3, 'content') else chain3}
{chain4.content if hasattr(chain4, 'content') else chain4}
{chain5.content if hasattr(chain5, 'content') else chain5}
{chain6.content if hasattr(chain6, 'content') else chain6}
{chain7.content if hasattr(chain7, 'content') else chain7}
</text>
"""
)
| self.o1
| StrOutputParser()
)