image query with retriever

[WithFoxSquirrel]

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Example Code

vectorstore = Chroma(
    collection_name="mm_rag_clip_museum", embedding_function=OpenCLIPEmbeddings(), persist_directory="./data/database/"
)

# Make retriever
# retriever = vectorstore.as_retriever()        

retriever = vectorstore.as_retriever(
                    search_type="mmr",
                search_kwargs={'k': 1, 'fetch_k': 5}
)
...

vectorstore.add_images(uris=image_uris, metadatas=texts_as_dicts, idx=uuids)
...
question="Where is the car?"
docs = retriever.invoke(question, k=10)

Description

How can i retriever with image query? for example, pass the car image to retriever.invoke() and expectate return the matched image and text?

System Info

langchain 0.1.9
langchain-chroma 0.1.1
langchain-community 0.0.24
langchain-core 0.1.27
langchain-experimental 0.0.52
langchain-google-genai 0.0.9
langchain-openai 0.0.7
langchain-text-splitters 0.2.1
langchainhub 0.1.2

[dosubot[bot]]

To retrieve information using an image query with LangChain, you need to ensure that your retriever can handle image inputs and that the retriever.invoke() method is called with the appropriate input format. Here is an example of how you can achieve this:

1. Ensure your retriever is capable of processing image inputs.
2. Pass the image data to the retriever.invoke() method.

Here is a sample code snippet:

from langchain_core.retrievers import BaseRetriever, RetrieverOutput
from langchain_core.runnables import Runnable, RunnablePassthrough
from PIL import Image
import base64
import io

# Assuming you have a retriever that can handle image inputs
class ImageRetriever(BaseRetriever):
    def retrieve(self, image: Image.Image) -> RetrieverOutput:
        # Implement your image retrieval logic here
        pass

# Create an instance of your image retriever
image_retriever = ImageRetriever()

# Create the combine_docs_chain as per your requirements
combine_docs_chain = ...  # Your implementation here

# Create the retrieval chain
retrieval_chain = create_retrieval_chain(image_retriever, combine_docs_chain)

# Load your image
image = Image.open('path_to_your_image.jpg')

# Encode the image to base64
def encode_image(image: Image.Image) -> str:
    buffered = io.BytesIO()
    image.save(buffered, format="JPEG")
    return base64.b64encode(buffered.getvalue()).decode("utf-8")

encoded_image = encode_image(image)

# Invoke the retrieval chain with the image input
result = retrieval_chain.invoke({"input": encoded_image})

# The result will contain the matched image and text
print(result)

In this example, ImageRetriever is a custom retriever that you need to implement to handle image inputs. The invoke method is called with a dictionary containing the base64 encoded image under the key "input". The retrieval_chain will process this input and return the matched image and text. This approach aligns with the multi-modal retrieval and generation (RAG) techniques described in the LangChain cookbook, which utilize multimodal LLMs like GPT-4V for image summarization and retrieval ^[1][2][3][4].

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