LlamaIndex Multi-Modal
FreeEnd-to-end multimodal RAG
FreeFree tier
Inputs: text, imageOutputs: text
About LlamaIndex Multi-Modal
LlamaIndex Multi-Modal is an extension of the LlamaIndex framework that enables building Retrieval-Augmented Generation (RAG) systems over both text and image data. It provides a MultiModalLLM abstraction that supports text+image models like GPT-4V, allowing users to jointly input images and text and receive text outputs. The module includes tools for loading image documents from URLs or local directories, building MultiModalVectorStoreIndex with separate text and image vector stores, and using multi-modal retrievers and query engines for end-to-end multi-modal workflows.
Key Features
MultiModalLLM abstraction for text+image models
Support for GPT-4V and other large multi-modal models
Image document loading from URLs and local directories
MultiModalVectorStoreIndex with separate text and image stores
Multi-modal retriever and query engine
End-to-end multi-modal RAG workflow orchestration
Non-streaming and streaming completion support
Integration with LlamaIndex data connectors and indices
Pros & Cons
Pros
- Open-source and free to use
- Part of the comprehensive LlamaIndex ecosystem
- Flexible abstraction supports multiple LMM providers
- Enables seamless combination of text and image reasoning
- Active development and community support
Cons
- Some features are marked as experimental or unreliable
- Primary example uses proprietary model (GPT-4V)
- Limited to text output from multi-modal models
- Documentation notes naming may change
Best For
Building RAG systems that answer questions about imagesMulti-modal search and retrieval over mixed text-image datasetsAnalyzing and summarizing visual content with text contextCreating chatbots that understand and reference imagesAutomated extraction of information from image-heavy documents
FAQ
What models does LlamaIndex Multi-Modal support?
The MultiModalLLM abstraction currently supports GPT-4V (via the OpenAIMultiModal class) and is designed to be extensible to other large multi-modal models.
Can I use LlamaIndex Multi-Modal without OpenAI?
The documentation primarily demonstrates OpenAI's GPT-4V, but the MultiModalLLM abstraction can be adapted to other providers. Custom implementations may be required.
What types of data can I use with LlamaIndex Multi-Modal?
It supports text and image data. Images can be loaded from URLs or local directories using SimpleDirectoryReader.
Is there a limit on how many images I can process?
The documentation does not specify a limit; processing capacity depends on the underlying model and infrastructure.