Chroma Image Search Bug: A LangChain Fix
Hey everyone! π I'm here to discuss a frustrating issue I encountered while working with Chroma's image similarity search feature in LangChain. Specifically, the similarity_search_by_image function seems to be acting up, and I think I've pinpointed the root cause. Let's dive in and see if we can get this sorted out, shall we?
The Problem: Chroma Image Search Not Delivering
So, the main issue is that when you try to use Chroma's similarity_search_by_image method with an OpenCLIPEmbeddings embedding function, it throws a ValueError. This error essentially boils down to the format of the image embeddings. Chroma is expecting a list of floats or a numpy array, but it's getting something it doesn't quite understand β a list containing a list of floats. Confused? I was too, at first. The error message looks something like this:
ValueError: Expected embeddings to be a list of floats or ints, a list of lists, a numpy array, or a list of numpy arrays, got [[[0.0287, ... ]]]
This means that the image embedding, which is generated by the OpenCLIPEmbeddings class, isn't being passed to Chroma in the correct format. Let's break this down further and look at the specifics of what's going on.
Reproducible Example
To make things super clear, here's a minimal, reproducible example. This is important because it allows anyone to easily replicate the issue and confirm that it's not a problem with their specific code, but rather a more general issue:
from langchain_chroma import Chroma
from langchain_experimental.open_clip import OpenCLIPEmbeddings
# Initialize Chroma vectorstore with OpenCLIP embeddings
vs = Chroma(embedding_function=OpenCLIPEmbeddings())
# Attempt to search by image
# Replace 'my_local_file.jpg' with the actual path to your image
# This line will trigger the ValueError
vs.similarity_search_by_image(uri="my_local_file.jpg")
Just run this snippet, and you should see the same ValueError pop up. This makes it really easy for you or anyone else to confirm that the issue is not related to your specific environment and data, but is a general issue with the current implementation. Pretty neat, right?
The Root Cause: Embedding Format
After digging into the LangChain code, I believe the problem lies in how the image_embedding is being handled within the similarity_search_by_image function. Specifically, it seems that the embed_image method from OpenCLIPEmbeddings returns a list containing a list of floats. But Chroma's similarity_search_by_vector method, which is ultimately called to perform the search, expects a list of floats or a numpy array. The current implementation in langchain_chroma/vectorstores.py passes the image embedding in the wrong shape. The similarity_search_by_image function is not properly extracting the actual embedding vector from the nested list structure that embed_image returns.
This leads me to think that we need to modify how the image embeddings are processed before being passed to similarity_search_by_vector. The key is to correctly extract the list of floats representing the image embedding.
Potential Solution: Modifying the Embedding Extraction
I think a simple change could fix this! Let's examine the code snippet that's causing the problem. Based on the file location provided in the original issue ( langchain_chroma/vectorstores.py), we can see the probable reason for the error. The problematic lines of code in the similarity_search_by_image function seem to be along the lines of:
image_embedding = self._embedding_function.embed_image(uris=[uri]) # Returns [[float,...]]
return self.similarity_search_by_vector(embedding=image_embedding, ...)
The embed_image method, as implemented by OpenCLIPEmbeddings, produces a list of lists. To fix this, we likely need to extract the inner list (the actual embedding) before passing it to similarity_search_by_vector. Here's a suggested fix:
image_embedding = self._embedding_function.embed_image(uris=[uri])[0] # Extracts [float,...]
return self.similarity_search_by_vector(embedding=image_embedding, ...)
This simple change should ensure that we're passing the correct format to the similarity_search_by_vector method. We're essentially extracting the inner list, which contains the actual embedding vector. In essence, we're flattening the nested list structure to match what Chroma expects.
Implementation Details
By accessing the first element of the list returned by embed_image, we get the embedding vector in the correct format that similarity_search_by_vector requires. It transforms a list of lists (e.g., [[0.1, 0.2, ...]]) into a simple list (e.g., [0.1, 0.2, ...]).
System Information and Package Versions
To give you a better idea of my setup, here's the system information. Knowing the environment helps others reproduce the issue and test any potential fixes. This includes the Python version, and the package versions of the key dependencies involved.
Python Version: 3.11.11 (main, Dec 4 2024, 08:55:07) [GCC 11.4.0]
Package Information
-------------------
langchain_core: 0.3.79
langchain: 0.3.27
langchain_community: 0.3.31
langsmith: 0.4.41
langchain_chroma: 0.2.6
langchain_experimental: 0.3.4
langchain_google_community: 2.0.10
langchain_google_vertexai: 2.1.2
langchain_text_splitters: 0.3.11
The versions of LangChain, Chroma, and associated libraries are critical here. This information enables anyone to replicate the environment and test the fix. Knowing the versions helps ensure the fix works and prevents potential conflicts. All the relevant information is included, so there's no guesswork needed for replication.
Conclusion: Seeking Confirmation and Next Steps
So, to wrap things up, the Chroma image search is broken because of how the embedding is being formatted. I believe this change will solve the problem. However, I'd love to get some confirmation from the LangChain developers or other users. If you've encountered this issue, please let me know. If you are a developer, can you confirm that my suggested fix is correct and if you can integrate it into the codebase? Confirming this helps validate my analysis. It's a way to double-check that the proposed change aligns with best practices and doesn't introduce any unforeseen issues.
By fixing the embedding extraction, we can ensure that image search works as intended. We need to modify the code to unpack the list correctly. Ultimately, this will improve the functionality and usability of LangChain's image search capabilities.
Thanks for reading! Hopefully, we can get this bug squashed and get back to building awesome things with LangChain. Let me know what you think in the comments. Let's make this work, folks! π