LlamaIndex is a powerful framework for building applications that leverage large language models (LLMs) over your own data. However, modern data often comes in a variety of formats, including text, images, audio, and video. Effectively handling these mixed data types is crucial for building more robust and insightful applications. This article will focus on how to handle text and images within LlamaIndex, providing a comprehensive guide with practical examples to help you navigate the complexities of multimodal data processing. We will explore various techniques, from basic image description to more advanced visual question answering, ensuring you can build compelling applications that truly leverage the strengths of both text and visual information. By the end of this read, you'll be equipped with the knowledge to seamlessly integrate images alongside your textual dataset for rich and insightful data analysis using LlamaIndex. Furthermore, we will cover efficient indexing strategies, query routing, and response augmentation techniques tailored for mixed modalities.
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Working with mixed data types, especially text and images, presents unique challenges. LLMs are primarily trained on text data, meaning they don't inherently 'understand' images. Therefore, we need to find ways to bridge the gap between the textual representation expected by the LLM and the visual information contained in the image. One common approach is to use image captioning models to generate textual descriptions of images. These descriptions can then be treated as regular text documents within LlamaIndex. Another popular strategy involves using visual embeddings, which represent images as numerical vectors that capture their semantic content. These embeddings can be integrated alongside text embeddings to provide a unified representation of both modalities. The choice of approach depends on the specific application and the available resources. It's essential to consider the trade-offs between accuracy, computational cost, and model complexity when deciding how to handle these mixed data types. Choosing the right strategy is crucial for ensuring the LLM can effectively leverage the information present in both textual and visual contexts.
Before you can start indexing and querying your data, you need to prepare it properly. This means loading your text and image data, and then transforming the images into a format that LlamaIndex can understand. The simplest approach is to create textual descriptions for each image. This involves using an image captioning model to generate a sentence or paragraph that summarizes the content of the image. These descriptions can then be stored alongside the image file path or other relevant metadata. Alternatively, you can use a visual feature extractor like CLIP or ResNet to generate a vector embedding for each image. These embeddings can be stored in a vector database alongside the text embeddings of your text documents. The document structure would need to be setup so that both image and corresponding text can be ingested. This structured representation allows LlamaIndex to understand the links between the two different forms of this multi-modal data. The choice between captions, embeddings, or a combination of both depends on the specific task. Captions provide human-readable descriptions that are easy for LLMs to understand, while embeddings capture more nuanced semantic information.
One practical way to prepare image data is with the pipeline functionality that Hugging Face offer. You will need access to the transformer library and the required model to run these image to text pipelines.
from transformers import pipeline
# 1. Load the image captioning pipeline
image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
def caption_image(image_path):
"""Generates a caption for an image using the image-to-text pipeline."""
try:
caption = image_to_text(image_path)[0]['generated_text']
return caption
except Exception as e:
print(f"Error processing image {image_path}: {e}")
return None
# Example usage
image_path = "path/to/your/image.jpg" # Replace with the actual path
caption = caption_image(image_path)
if caption:
print(f"Image Caption: {caption}")
else:
print("Failed to generate caption.")
This code snippet demonstrates the basic process of using an image captioning model to generate textual descriptions of images. The 'caption_image' function encapsulates this process, taking an image path as input and returning the generated caption. Remember to replace "path/to/your/image.jpg" with the actual path to your image file. Also, note that the first time you download the model, the process might take a longer time.
Several visual feature extractors are available. CLIP is a popular choice for its ability to generate embeddings that are aligned with text embeddings. This makes it easier to compare text and images based on their semantic content. ResNet is another well-established architecture that can be used for extracting features from images. The choice of feature extractor depends on the specific application and the desired trade-off between accuracy and computational cost. CLIP is designed to align with both text and image, but might be more computational. ResNet is a basic image recognition model, but it is very computationally cheap. You can also fine-tune these models on your specific dataset to improve their performance. This involves training the model on a set of labeled images to make it better at extracting features that are relevant to your specific task. Don't overlook fine-tuning as an option to improve the performance of your visual extraction feature.
Once you have prepared your data, you can start indexing it in LlamaIndex. This involves creating a Document object for each text document and image. For images, you can store the image caption or embedding in the document's text field. You can also store the image file path in the document's metadata. This allows you to retrieve the image when you retrieve the document. Next, you need to create an index from the documents. LlamaIndex supports several types of indexes, including vector store indexes, tree indexes, and keyword table indexes. Vector store indexes are particularly well-suited for handling image embeddings, as they allow you to perform similarity searches based on the vector representations of the images and text. LlamaIndex supports both flat indexes and hierarchical indexes. Which type of index to choose is dependent on the specifics of your dataset. You should spend some time evaluating each index type with your data to find the best indexing strategy.
ImageDocument for Visual InformationLlamaIndex has introduced the ImageDocument class, which facilitates the handling of image data during the indexing process. The flexibility in LlamaIndex is in how it handles images. You can point directly to a file path, or directly to the raw bytes of the image.
from llama_index.core import ImageDocument
# Create a 'Document' object
image_path = "path/to/your/image.jpg"
image_document = ImageDocument(image=image_path)
# The same can be done with raw image bytes
with open(image_path, "rb") as f:
image_bytes = f.read()
image_document = ImageDocument(image=image_bytes)
You can incorporate both text and images into a single document for more comprehensive representation. This is particularly useful when the text is directly related to the image. The benefit is that everything lives in one place, making it very manageable. The drawback is that if you have a lot of images, the data store can be very large very quickly. You have to consider what your model will be looking for. If you are looking for images that are similar to your text query, then this may be a good option.
from llama_index.core import Document, ImageDocument
# Create a 'Document' with surrounding text
text = "This image shows a beautiful sunset over the ocean."
image_path = "path/to/your/image.jpg"
document = Document(
text=text,
metadata={"image_path": image_path}
)
# Combine text and images using a 'MultiModalVectorStoreIndex'
index = VectorStoreIndex.from_documents([document])
Once you have indexed your data, you can start querying it with mixed modalities. This means you can use both text and images as input to your queries. For example, you could ask the question "Find images of sunsets that are similar to this text description: 'A vibrant sunset over the ocean with orange and purple hues'." To do this, you would need to convert the text description into a vector embedding. Then, you would perform a similarity search on the vector store index to find images with similar embeddings. You can also use the image caption as part of your text query. Another way to phrase the query could be "Find images that shows this caption: 'A vibrant sunset over the ocean with orange and purple hues'." Remember to adapt your strategy based on how you set up your data.
QueryBundle for Multi-Modal QueriesLlamaIndex provides the QueryBundle class to construct efficient multi-modal queries. This allows you to combine image representations and text inputs into a single query object.
from llama_index.core import QueryBundle
from llama_index.core.schema import ImageDocument
# 1. Represent query as a QueryBundle, with image document specified
query_bundle = QueryBundle(
query_str="What is in this image?",
image_documents=[
ImageDocument(image="path/to/image.jpg"),
],
)
# 2. Pass query bundle to query engine
response = query_engine.query(query_bundle)
Because LlamaIndex is designed to be flexible, you can handle the images in various ways. As demonstrated in the code snippet, you can store the path of the image in the QueryBundle, but you can also provide raw bytes. The key is to make the image query that suits your index schema. If you have used captions, make sure your query is aligned. If you use image embeddings, then the QueryBundle should be set up with the specific query of those embeddings.
In some cases, you might want to route queries to different indexes based on the modality of the query. For example, you might have one index for text data and another index for image data. When a query contains only text, you would route it to the text index. When a query contains only an image, you would route it to the image index. When a query contains both text and an image, you would need to decide how to combine the information from both indexes. One approach is to query both indexes separately and then combine the results. Another approach is to create a new index that contains both text and image data. LlamaIndex provides the tools to implement these routing strategies effectively, allowing you to optimize your indexing and querying pipeline for mixed modalities. However, it is recommended that you try to store all the images and caption pairs into one index unless there is a specific objective to split them.
After querying the index, you can augment the responses with visual information. This means you can display the images alongside the text responses. This can make the responses more informative and engaging. For example, if you ask the question "Find images of sunsets", you could display the images that are retrieved alongside their captions. Integrating images into the response can significantly improve the user experience, making the results more intuitive and visually appealing. Moreover, displaying images can help clarify the context of the response, providing additional information that might not be explicitly mentioned in the text. This is particularly useful when dealing with complex topics or when the image provides a visual representation of the information being conveyed. The ultimate goal is to create a seamless and integrated experience that leverages the strengths of both text and visual modalities.
LlamaIndex supports modules such as ImageRAGRetriever for integrating image data. This is very cutting edge and it expands the multi-modal tooling available in LlamaIndex. This class is a retriever that uses an MM Reranker to rerank nodes.
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import MarkdownNodeParser
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.multi_modal_llms import OpenAIMultiModal
from llama_index.core import QueryBundle
from llama_index.schema import ImageDocument
from llama_index.retrievers.mmr import MMRetrievalReranker
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.callbacks import CallbackManager, LlamaDebugHandler
from llama_index.retrievers import ImageRAGRetriever
# example text
markdown_text = """
# This is a document about cats
Here is an image of a cat:
Here is another image of a cat:
"""
# save to markdown file
with open("cats.md", "w") as f:
f.write(markdown_text)
# load documents
documents = SimpleDirectoryReader(input_files=["cats.md"]).load_data()
# define node parser
node_parser = MarkdownNodeParser(include_image=True, image_dir="./")
# get nodes
nodes = node_parser.get_nodes_from_documents(documents)
index = VectorStoreIndex(nodes)
# define base retriever
retriever = VectorIndexRetriever(
index=index, similarity_top_k=10
)
# define reranker
reranker = MMRetrievalReranker(
callback_manager=callback_manager,
top_n=5,
)
# define image retriever
image_rag_retriever = ImageRAGRetriever(
retriever=retriever,
reranker=reranker,
image_key="image",
callback_manager=callback_manager,
)
# create query engine
query_engine = RetrieverQueryEngine.from_args(
retriever=image_rag_retriever,
callback_manager=callback_manager,
)
To display images in the response, you can use HTML or other formatting techniques. For example, in a web application, you could use HTML's <img> tag to display the images. In a Jupyter notebook, you can use the IPython.display module to display images. In a command-line application, you can use a library like Pillow to display images in a separate window. You can also integrate the images directly into the text of the response. For example, you could include a URL to the image in the text of the response, or you could embed the image data directly into the text using base64 encoding. This is especially useful in messaging or email applications, where you might not be able to display images directly.
Handling mixed data types, especially images and text, in LlamaIndex requires careful planning and execution. By combining image captioning models, visual embeddings, and LlamaIndex's indexing and querying capabilities, you can build powerful applications that leverage the strengths of both modalities. Remember to choose the right approach based on your specific application and data characteristics. Experiment with different techniques and evaluate their performance to find the best combination. With the right approach, you can create truly impactful applications that bridge the gap between text and visual information.
