langchain: multiple bases have instance lay-out conflict on HumanMessageChunk class on langchain 0.0.249

System Info

langchain Version 0.0.249

Python Version 3.8

Other notes

Using this on notebook in Azure Synapse Studio

Error

When import happening getting bellow error message 
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_17180/722847199.py in <module>
----> 1 from langchain.vectorstores import Pinecone
      2 from langchain.embeddings.openai import OpenAIEmbeddings
      3 
      4 import datetime
      5 from datetime import date, timedelta

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/__init__.py in <module>
      4 from typing import Optional
      5 
----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
      7 from langchain.cache import BaseCache
      8 from langchain.chains import (

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/agents/__init__.py in <module>
      1 """Interface for agents."""
----> 2 from langchain.agents.agent import (
      3     Agent,
      4     AgentExecutor,
      5     AgentOutputParser,

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/agents/agent.py in <module>
     13 from pydantic import BaseModel, root_validator
     14 
---> 15 from langchain.agents.agent_iterator import AgentExecutorIterator
     16 from langchain.agents.agent_types import AgentType
     17 from langchain.agents.tools import InvalidTool

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/agents/agent_iterator.py in <module>
     19 )
     20 
---> 21 from langchain.callbacks.manager import (
     22     AsyncCallbackManager,
     23     AsyncCallbackManagerForChainRun,

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/callbacks/__init__.py in <module>
      1 """Callback handlers that allow listening to events in LangChain."""
      2 
----> 3 from langchain.callbacks.aim_callback import AimCallbackHandler
      4 from langchain.callbacks.argilla_callback import ArgillaCallbackHandler
      5 from langchain.callbacks.arize_callback import ArizeCallbackHandler

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/callbacks/aim_callback.py in <module>
      3 
      4 from langchain.callbacks.base import BaseCallbackHandler
----> 5 from langchain.schema import AgentAction, AgentFinish, LLMResult
      6 
      7 

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/schema/__init__.py in <module>
      1 from langchain.schema.agent import AgentAction, AgentFinish
      2 from langchain.schema.document import BaseDocumentTransformer, Document
----> 3 from langchain.schema.memory import BaseChatMessageHistory, BaseMemory
      4 from langchain.schema.messages import (
      5     AIMessage,

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/schema/memory.py in <module>
      5 
      6 from langchain.load.serializable import Serializable
----> 7 from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage
      8 
      9 

~/cluster-env/clonedenv/lib/python3.8/site-packages/langchain/schema/messages.py in <module>
    136 
    137 
--> 138 class HumanMessageChunk(HumanMessage, BaseMessageChunk):
    139     pass
    140 

~/cluster-env/clonedenv/lib/python3.8/site-packages/pydantic/main.cpython-38-x86_64-linux-gnu.so in pydantic.main.ModelMetaclass.__new__()

~/cluster-env/clonedenv/lib/python3.8/abc.py in __new__(mcls, name, bases, namespace, **kwargs)
     83         """
     84         def __new__(mcls, name, bases, namespace, **kwargs):
---> 85             cls = super().__new__(mcls, name, bases, namespace, **kwargs)
     86             _abc_init(cls)
     87             return cls

TypeError: multiple bases have instance lay-out conflict

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

The official example notebooks/scripts
My own modified scripts

Related Components

LLMs/Chat Models
Embedding Models
Prompts / Prompt Templates / Prompt Selectors
Output Parsers
Document Loaders
Vector Stores / Retrievers
Memory
Agents / Agent Executors
Tools / Toolkits
Chains
Callbacks/Tracing
Async

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.

!pip install --upgrade pip
!pip install tqdm
!pip install pinecone-client
!pip install typing-extensions==4.5.0
!pip install langchain
!pip install openai
!pip install tiktoken

Here are my imports

from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings

import datetime
from datetime import date, timedelta
import time
import csv

import openai
import requests
import pandas as pd
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, lit, current_timestamp
from pyspark.sql import functions as F
from pyspark.sql import Row
from pyspark.sql.utils import AnalysisException
from pyspark.sql.types import StructType, StructField, StringType, TimestampType, LongType
from pyspark.sql.window import Window
from pyspark.sql import types as T
import concurrent.futures
import pinecone
import tiktoken

Run the notebook.

Expected behavior

Import should be happend without any issues.

About this issue

Original URL
State: open
Created a year ago
Reactions: 9
Comments: 23

Most upvoted comments

Was also seeing this - Was able to work around it by upgrading Pydantic 1.8.2 -> 1.10.12.

+37

dunnkevin on Aug 8, 2023

Answer generated by a 🤖

Answer

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.

Sources

libs/langchain/langchain/schema/messages.py

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.

+23

dosubot[bot] on Aug 1, 2023

Answer generated by a 🤖

Answer

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.

Sources

libs/langchain/langchain/schema/messages.py

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.

+18

dosubot[bot] on Aug 2, 2023

Answer generated by a 🤖

Answer

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

Sources

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.

+13

dosubot[bot] on Aug 3, 2023

hi,

I believe this is a bug.

viduz on Aug 2, 2023

I am also getting same error

TypeError: multiple bases have instance lay-out conflict

How to solve it ?

aadhil96 on Aug 24, 2023

🤖

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,❤️”. 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.

Sources

About Dosu

This response is meant to be useful and save you time. 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.

If you want to continue the conversation, start your reply with @dosu-bot.

dosubot[bot] on Feb 19, 2024

My error resolved by upgrading pydantic,

pip install -U pydantic pip install --upgrade pydantic pip install pydantic

Ezhil6999 on Dec 1, 2023

In my case, it is solved by pip install pydantic==2.5.2. (Sorry if it does not help though)

uchiiii on Nov 28, 2023

Same issue here, upgrading Pydantic 1.8.2 -> 1.10.12 does not help.

Fiztech-ai on Oct 6, 2023