Closed viduz closed 2 months ago
Answer generated by a 🤖
I understand that you're encountering a TypeError: "multiple bases have instance lay-out conflict" when trying to import modules from LangChain in Azure Synapse Studio. This error seems to be originating from the HumanMessageChunk class in the langchain/schema/messages.py file and is likely due to a conflict arising from multiple inheritance in your classes.
In Python, multiple inheritance can sometimes lead to conflicts when a class inherits from two or more classes that have instance variables with the same name. In your case, it appears that the classes HumanMessageChunk
, AIMessageChunk
, SystemMessageChunk
, FunctionMessageChunk
, and ChatMessageChunk
are all inheriting from two classes: a specific message class (HumanMessage
, AIMessage
, etc.) and BaseMessageChunk
. Both of these parent classes, in turn, inherit from BaseMessage
, which could be causing the conflict.
One potential solution to this issue is to refactor your code to avoid multiple inheritance. For instance, you could have BaseMessageChunk
inherit from BaseMessage
, and then have the specific message chunk classes (HumanMessageChunk
, AIMessageChunk
, etc.) only inherit from BaseMessageChunk
. This would create a clear inheritance hierarchy and eliminate the potential for conflict.
Here's an example of how you could refactor BaseMessageChunk
and HumanMessageChunk
:
class BaseMessageChunk(BaseMessage):
# ... existing code ...
class HumanMessageChunk(BaseMessageChunk):
"""A Message from a human."""
example: bool = False
"""Whether this Message is being passed in to the model as part of an example
conversation.
"""
@property
def type(self) -> str:
"""Type of the message, used for serialization."""
return "human"
You would need to make similar changes for AIMessageChunk
, SystemMessageChunk
, FunctionMessageChunk
, and ChatMessageChunk
.
Please note that this is just one potential solution. If you believe this is a bug that could impact other users, I encourage you to make a pull request with your proposed changes. If this solution doesn't align with your experience or doesn't resolve your issue, I recommend upgrading to the latest version of LangChain, as the issue may have been resolved in a more recent update.
This response is meant to be useful, save you time, and share context. It is not meant to be a precise solution, but rather a starting point for your own research.
Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.
hi,
I believe this is a bug.
Answer generated by a 🤖
Hello,
Thank you for reaching out and providing detailed information about the issue you're facing. The error you're encountering, "TypeError: multiple bases have instance lay-out conflict", typically occurs when a class is derived from multiple parent classes that have conflicting instance layouts. This is likely happening because the HumanMessageChunk
class in LangChain is inheriting from both HumanMessage
and BaseMessageChunk
, which both inherit from BaseMessage
.
A potential solution to this issue is to refactor the HumanMessageChunk
class to use composition instead of multiple inheritance. This would involve creating an instance of the BaseMessageChunk
class within the HumanMessageChunk
class and delegating method calls to this instance. Here's an example of how you can do it:
class HumanMessageChunk(HumanMessage):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.chunk = BaseMessageChunk(*args, **kwargs)
def __add__(self, other: Any) -> 'HumanMessageChunk':
result = super().__add__(other)
if isinstance(other, BaseMessageChunk):
result.chunk = self.chunk + other
return result
def _merge_kwargs_dict(self, left: Dict[str, Any], right: Dict[str, Any]) -> Dict[str, Any]:
return self.chunk._merge_kwargs_dict(left, right)
In this refactored version, an instance of BaseMessageChunk
is created within HumanMessageChunk
and the __add__
and _merge_kwargs_dict
methods are delegated to this instance. This allows HumanMessageChunk
to use the functionality of BaseMessageChunk
without directly inheriting from it, which should resolve the instance layout conflict.
Please try this solution and let me know if it resolves your issue or if you have any further questions.
This response is meant to be useful, save you time, and share context. It is not meant to be a precise solution, but rather a starting point for your own research.
Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.
Hi Team, Latest update on this, I have cleared out noises around this and finally. Isolated issues to these two imports.
install Langchian package.
!pip install langchain
from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
Then I'm getting bellow issue
ImportError Traceback (most recent call last)
/tmp/ipykernel_6996/3251986536.py in
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/init.py in
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/agents/init.py in
~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/agents/agent.py in
~/cluster-env/clonedenv/lib/python3.8/site-packages/pydantic/init.cpython-38-x86_64-linux-gnu.so in init pydantic.init()
~/cluster-env/clonedenv/lib/python3.8/site-packages/pydantic/dataclasses.cpython-38-x86_64-linux-gnu.so in init pydantic.dataclasses()
ImportError: cannot import name dataclass_transform
Answer generated by a 🤖
Hello,
The ImportError you're encountering seems to be related to the dataclass_transform
function. However, based on the information provided, it's not clear whether this function is part of the LangChain v0.0.249 release or if it was introduced in a later version.
One possible cause of the issue could be a version mismatch between LangChain and its dependencies. LangChain v0.0.249 requires Pydantic version 1.0.0 or above. Could you please check the version of Pydantic installed in your environment? You can do this by running the following command in your Python environment:
import pydantic
print(pydantic.__version__)
If the version of Pydantic is below 1.0.0, you can upgrade it using pip:
pip install --upgrade pydantic
If the Pydantic version is not the issue, it would be helpful to know if the dataclass_transform
function is part of the LangChain v0.0.249 release. Unfortunately, I couldn't find this information in the repository.
Lastly, it's worth noting that the latest version of LangChain is v0.0.250. If it's possible for you, you might want to consider upgrading to the latest version. This can be done using pip:
pip install --upgrade langchain
Please let me know if any of these suggestions help resolve the issue, or if you need further assistance.
Best, Dosu
This response is meant to be useful, save you time, and share context. It is not meant to be a precise solution, but rather a starting point for your own research.
Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.
I am seeing this in 0.251 as well
Was also seeing this - Was able to work around it by upgrading Pydantic 1.8.2 -> 1.10.12.
Was also seeing this - Was able to work around it by upgrading Pydantic 1.8.2 -> 1.10.12.
good job
I am also getting same error
TypeError: multiple bases have instance lay-out conflict
How to solve it ?
I have the same error today
I meet this error when use jupyter notebook in pycharm. but it does not occur when i use jupyter lab in terminal
Having the same issue in VS terminal, the same was working a week back. Any solution here?
Same issue here, upgrading Pydantic 1.8.2 -> 1.10.12 does not help.
I'm facing the same error, could isolate it with instruction from langchain.schema.messages import BaseMessage
. As far as I can inspect it then goes into file langchain.schema.__init__.py
, in which tries to execute first row from langchain.schema.agent import AgentAction, AgentFinish
, goes into the file langchain.schema.agent
and there tries to import BaseMessage
again which in my understanding should cause an infinite loop... I'm inspecting version 0.0.322 which corresponds to commit 4d6243fa879fddef0e85904a3538af715da627e8. And the error "multiple bases have instance lay-out conflict" arises only in jupyter notebook, when I run the same code in terminal everything is fine.
I had a similar issue popup as well, but found it wasnt specific to importing a specific module from langchain - it was just importing anything from langchain. I uninstalled my env and reinstalled everything and that solved the issue.
Looks like it has something to do with this: https://github.com/pydantic/pydantic/issues/3523
still facing the issue
In my case, it is solved by pip install pydantic==2.5.2
. (Sorry if it does not help though)
My error resolved by upgrading pydantic,
pip install -U pydantic pip install --upgrade pydantic pip install pydantic
having the same issue
langchain 0.1.0
I get the same in slightly different spot:
File "/Users/benm/Library/Python/3.11/lib/python/site-packages/langchain_core/messages/ai.py", line 29, in <module>
class AIMessageChunk(AIMessage, BaseMessageChunk):
File "/usr/local/lib/python3.11/site-packages/pydantic/main.py", line 367, in __new__
cls = super().__new__(mcs, name, bases, new_namespace, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen abc>", line 106, in __new__
TypeError: multiple bases have instance lay-out conflict
Using latest:
Name: langchain
Version: 0.1.7
Name: pydantic
Version: 2.6.1
🤖
Based on the traceback you provided, it seems like the issue is related to the pydantic
package. The error message "cannot import name dataclass_transform" suggests that there might be a compatibility issue with the version of pydantic
you're using.
From the context provided, it appears that LangChain v0.0.249 requires a version of pydantic
that is ">=1,<3". This means that the pydantic
package version should be greater than or equal to 1 and less than 3. You can check your current version of pydantic
by running pip show pydantic
in your Python environment.
If your pydantic
version is not within the required range, you can install a compatible version using pip. For example, you can install version 1.8.2 (which is within the required range) by running pip install pydantic==1.8.2
.
However, please note that downgrading pydantic
might lead to incompatibility issues with other packages that require a newer version of pydantic
, as mentioned in this issue.
As for Python 3.8, there are no known compatibility issues with LangChain v0.0.249. The pyproject.toml
file specifies that LangChain is compatible with Python versions greater than or equal to 3.8.1 and less than 4.0. Therefore, Python 3.8 is within the compatible range.
If the issue persists after ensuring the correct pydantic
version, it would be helpful to know more about your environment. Are there any other packages installed that might conflict with pydantic
or LangChain? Also, could you provide the full error message and traceback? This additional information could help identify the root cause of the issue.
I get the same in slightly different spot:
File "/Users/benm/Library/Python/3.11/lib/python/site-packages/langchain_core/messages/ai.py", line 29, in <module> class AIMessageChunk(AIMessage, BaseMessageChunk): File "/usr/local/lib/python3.11/site-packages/pydantic/main.py", line 367, in __new__ cls = super().__new__(mcs, name, bases, new_namespace, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "<frozen abc>", line 106, in __new__ TypeError: multiple bases have instance lay-out conflict
Using latest:
Name: langchain Version: 0.1.7 Name: pydantic Version: 2.6.1
Ah...in my case, I was running it via pyhttpdbg or streamlit, and those were launched from different python env. So paying attention to paths that show up in error can help you verify it's running on expected venv.
In my case, it is solved by
pip install pydantic==2.5.2
. (Sorry if it does not help though)
This solved my problem. Thank you
System Info
langchain Version 0.0.249
Python Version 3.8
Other notes
Using this on notebook in Azure Synapse Studio
Error
Other installed packages in the system
Package Version
absl-py 0.13.0 adal 1.2.7 adlfs 0.7.7 aiohttp 3.8.5 aiosignal 1.3.1 annotated-types 0.5.0 appdirs 1.4.4 applicationinsights 0.11.10 argon2-cffi 21.3.0 argon2-cffi-bindings 21.2.0 astor 0.8.1 astunparse 1.6.3 async-timeout 4.0.2 attrs 21.2.0 azure-common 1.1.27 azure-core 1.16.0 azure-datalake-store 0.0.51 azure-graphrbac 0.61.1 azure-identity 1.4.1 azure-mgmt-authorization 0.61.0 azure-mgmt-containerregistry 8.0.0 azure-mgmt-core 1.3.0 azure-mgmt-keyvault 2.2.0 azure-mgmt-resource 13.0.0 azure-mgmt-storage 11.2.0 azure-storage-blob 12.8.1 azure-synapse-ml-predict 1.0.0 azureml-core 1.34.0 azureml-dataprep 2.22.2 azureml-dataprep-native 38.0.0 azureml-dataprep-rslex 1.20.2 azureml-dataset-runtime 1.34.0 azureml-mlflow 1.34.0 azureml-opendatasets 1.34.0 azureml-synapse 0.0.1 azureml-telemetry 1.34.0 backcall 0.2.0 backports.functools-lru-cache 1.6.4 backports.tempfile 1.0 backports.weakref 1.0.post1 beautifulsoup4 4.9.3 bleach 5.0.1 blinker 1.4 bokeh 2.3.2 Brotli 1.0.9 brotlipy 0.7.0 cachetools 4.2.2 certifi 2021.5.30 cffi 1.14.5 chardet 4.0.0 charset-normalizer 3.2.0 click 8.0.1 cloudpickle 1.6.0 conda-package-handling 1.7.3 configparser 5.0.2 contextlib2 0.6.0.post1 cryptography 3.4.7 cycler 0.10.0 Cython 0.29.23 cytoolz 0.11.0 dash 1.20.0 dash-core-components 1.16.0 dash-cytoscape 0.2.0 dash-html-components 1.1.3 dash-renderer 1.9.1 dash-table 4.11.3 dask 2021.6.2 databricks-cli 0.12.1 dataclasses-json 0.5.14 debugpy 1.3.0 decorator 4.4.2 defusedxml 0.7.1 dill 0.3.4 distlib 0.3.6 distro 1.7.0 dnspython 2.4.1 docker 4.4.4 dotnetcore2 2.1.23 entrypoints 0.3 et-xmlfile 1.1.0 fastjsonschema 2.16.1 filelock 3.8.0 fire 0.4.0 Flask 2.0.1 Flask-Compress 0.0.0 flatbuffers 1.12 frozenlist 1.4.0 fsspec 2021.10.0 fsspec-wrapper 0.1.6 fusepy 3.0.1 future 0.18.2 gast 0.3.3 gensim 3.8.3 geographiclib 1.52 geopy 2.1.0 gevent 21.1.2 gitdb 4.0.7 GitPython 3.1.18 google-auth 1.32.1 google-auth-oauthlib 0.4.1 google-pasta 0.2.0 greenlet 1.1.0 grpcio 1.37.1 h5py 2.10.0 html5lib 1.1 hummingbird-ml 0.4.0 idna 2.10 imagecodecs 2021.3.31 imageio 2.9.0 importlib-metadata 4.6.1 importlib-resources 5.9.0 ipykernel 6.0.1 ipython 7.23.1 ipython-genutils 0.2.0 ipywidgets 7.6.3 isodate 0.6.0 itsdangerous 2.0.1 jdcal 1.4.1 jedi 0.18.0 jeepney 0.6.0 Jinja2 3.0.1 jmespath 0.10.0 joblib 1.0.1 jsonpickle 2.0.0 jsonschema 4.15.0 jupyter-client 6.1.12 jupyter-core 4.7.1 jupyterlab-pygments 0.2.2 jupyterlab-widgets 3.0.3 Keras-Applications 1.0.8 Keras-Preprocessing 1.1.2 keras2onnx 1.6.5 kiwisolver 1.3.1 koalas 1.8.0 KqlmagicCustom 0.1.114.post8 langchain 0.0.249 langsmith 0.0.16 liac-arff 2.5.0 library-metadata-cooker 0.0.7 lightgbm 3.2.1 lime 0.2.0.1 llvmlite 0.36.0 locket 0.2.1 loguru 0.7.0 lxml 4.6.5 Markdown 3.3.4 MarkupSafe 2.0.1 marshmallow 3.20.1 matplotlib 3.4.2 matplotlib-inline 0.1.2 mistune 2.0.4 mleap 0.17.0 mlflow-skinny 1.18.0 msal 1.12.0 msal-extensions 0.2.2 msrest 0.6.21 msrestazure 0.6.4 multidict 5.1.0 mypy 0.780 mypy-extensions 0.4.3 nbclient 0.6.7 nbconvert 7.0.0 nbformat 5.4.0 ndg-httpsclient 0.5.1 nest-asyncio 1.5.5 networkx 2.5.1 nltk 3.6.2 notebook 6.4.12 notebookutils 3.1.2-20230518.1 numba 0.53.1 numexpr 2.8.4 numpy 1.24.4 oauthlib 3.1.1 olefile 0.46 onnx 1.9.0 onnxconverter-common 1.7.0 onnxmltools 1.7.0 onnxruntime 1.7.2 openai 0.27.8 openapi-schema-pydantic 1.2.4 openpyxl 3.0.7 opt-einsum 3.3.0 packaging 21.0 pandas 1.2.3 pandasql 0.7.3 pandocfilters 1.5.0 parso 0.8.2 partd 1.2.0 pathspec 0.8.1 patsy 0.5.1 pexpect 4.8.0 pickleshare 0.7.5 Pillow 8.2.0 pinecone-client 2.2.2 pip 23.2.1 pkgutil_resolve_name 1.3.10 platformdirs 2.5.2 plotly 4.14.3 pmdarima 1.8.2 pooch 1.4.0 portalocker 1.7.1 prettytable 2.4.0 prometheus-client 0.14.1 prompt-toolkit 3.0.19 protobuf 3.15.8 psutil 5.8.0 ptyprocess 0.7.0 py4j 0.10.9 pyarrow 3.0.0 pyasn1 0.4.8 pyasn1-modules 0.2.8 pycairo 1.20.1 pycosat 0.6.3 pycparser 2.20 pydantic 1.8.2 pydantic_core 2.4.0 Pygments 2.9.0 PyGObject 3.40.1 PyJWT 2.1.0 pyodbc 4.0.30 pyOpenSSL 20.0.1 pyparsing 2.4.7 pyperclip 1.8.2 PyQt5 5.12.3 PyQt5_sip 4.19.18 PyQtChart 5.12 PyQtWebEngine 5.12.1 pyrsistent 0.18.1 PySocks 1.7.1 pyspark 3.1.2 python-dateutil 2.8.1 pytz 2021.1 pyu2f 0.1.5 PyWavelets 1.1.1 PyYAML 5.4.1 pyzmq 22.1.0 regex 2023.6.3 requests 2.31.0 requests-oauthlib 1.3.0 retrying 1.3.3 rsa 4.7.2 ruamel.yaml 0.17.4 ruamel.yaml.clib 0.2.6 ruamel-yaml-conda 0.15.100 SALib 1.3.11 scikit-image 0.18.1 scikit-learn 0.23.2 scipy 1.5.3 seaborn 0.11.1 SecretStorage 3.3.1 Send2Trash 1.8.0 setuptools 49.6.0.post20210108 shap 0.39.0 six 1.16.0 skl2onnx 1.8.0 sklearn-pandas 2.2.0 slicer 0.0.7 smart-open 5.1.0 smmap 3.0.5 soupsieve 2.2.1 SQLAlchemy 1.4.20 sqlanalyticsconnectorpy 1.0.1 statsmodels 0.12.2 synapseml-cognitive 0.10.2.dev1 synapseml-core 0.10.2.dev1 synapseml-deep-learning 0.10.2.dev1 synapseml-internal 0.0.0.dev1 synapseml-lightgbm 0.10.2.dev1 synapseml-opencv 0.10.2.dev1 synapseml-vw 0.10.2.dev1 tabulate 0.8.9 tenacity 8.2.2 tensorboard 2.4.1 tensorboard-plugin-wit 1.8.0 tensorflow 2.4.1 tensorflow-estimator 2.4.0 termcolor 1.1.0 terminado 0.15.0 textblob 0.15.3 threadpoolctl 2.1.0 tifffile 2021.4.8 tiktoken 0.4.0 tinycss2 1.1.1 toolz 0.11.1 torch 1.8.1 torchvision 0.9.1 tornado 6.1 tqdm 4.65.0 traitlets 5.0.5 typed-ast 1.4.3 typing_extensions 4.5.0 typing-inspect 0.8.0 urllib3 1.26.4 virtualenv 20.14.0 wcwidth 0.2.5 webencodings 0.5.1 websocket-client 1.1.0 Werkzeug 2.0.1 wheel 0.36.2 widgetsnbextension 3.5.2 wrapt 1.12.1 xgboost 1.4.0 XlsxWriter 3.0.3 yarl 1.6.3 zipp 3.5.0 zope.event 4.5.0 zope.interface 5.4.0
``
Who can help?
No response
Information
Related Components
Reproduction
Go to Azure Synapse Studio Open a Notebook Select Pyspark(Python) as Language Please note your node should run python 3.8
Then put bellow
Installed bellow to the session.
Here are my imports
Run the notebook.
Expected behavior
Import should be happend without any issues.