Notebook Langchain ModuleNotFoundError: No module named 'langchain.retrievers.merger_retriever'

[bento]

Hi,
As mentioned in the title, receiving this error despite

%pip install --upgrade langchain

Specific line of code:

from langchain.retrievers.merger_retriever import MergerRetriever

All other langchain import works when this is commented out. Same line works on my local VS Code.

Appreciate any help with this issue. Thanks!

[Kaniz]

Hi @bento , • The error message "ModuleNotFoundError: No module named 'langchain.retrievers.merger_retriever'" indicates that the Python module 'langchain.retrievers.merger_retriever' is not found in the current environment.

• The code suggests that the "langchain" library is installed using the pip command.
• However, the error message suggests that the 'langchain.retrievers.merger_retriever' module is not available in the installed "langchain" package.
• The code provided does not import or use a module named 'langchain.retrievers.merger_retriever', so it's unclear why the error is occurring.

• Potential solutions to the issue are:
  - Ensure that the "langchain" package is installed correctly and restart the Python environment if needed.
  - Check if the 'langchain.retrievers.merger_retriever' module is part of the "langchain" package or a separate package that needs to be installed separately.
  - If 'langchain.retrievers.merger_retriever' is a custom module, ensure it is located in a directory included in the Python path.
• Remember that the notebook environment in Databricks is isolated, so installed packages are only available in the notebook where they were installed. Consider installing packages at the cluster level for use across multiple notebooks.

[sean_owen · Accepted solution]

More specifically: langchain releases a new update every few days, and it is likely that you are using code or a library that needs a later version of langchain than you have (or, perhaps, a later version that removed whatever part of langchain you rely on). You need to figure out what version of langchain works for your use case and pin it. Updating to the latest version every time makes you vulnerable to breaking changes like this.