Multi-modal#
LlamaIndex offers capabilities to not only build language-based applications but also multi-modal applications - combining language and images.
Types of Multi-modal Use Cases#
This space is actively being explored right now, but some fascinating use cases are popping up.
RAG (Retrieval Augmented Generation)#
All the core RAG concepts: indexing, retrieval, and synthesis, can be extended into the image setting.
The input could be text or image.
The stored knowledge base can consist of text or images.
The inputs to response generation can be text or image.
The final response can be text or image.
Check out our guides below:
- Advanced Multi-Modal Retrieval using GPT4V and Multi-Modal Index/Retriever
- Multi-modal retrieval with CLIP
- Image to Image Retrieval
- Semi-structured Image Retrieval
- Chroma Multi-Modal Demo with LlamaIndex
- Multi-Modal on PDF’s with tables.
- Multi-Modal LLM using Google’s Gemini model for image understanding and build Retrieval Augmented Generation with LlamaIndex
- Multimodal Ollama Cookbook
Structured Outputs#
You can generate a structured output with the new OpenAI GPT4V via LlamaIndex. The user just needs to specify a Pydantic object to define the structure of the output.
Check out the guide below:
Retrieval-Augmented Image Captioning#
Oftentimes understanding an image requires looking up information from a knowledge base. A flow here is retrieval-augmented image captioning - first caption the image with a multi-modal model, then refine the caption by retrieving it from a text corpus.
Check out our guides below:
Agents#
Here are some initial works demonstrating agentic capabilities with GPT-4V.
Evaluations and Comparisons#
These sections show comparisons between different multi-modal models for different use cases.
LLaVa-13, Fuyu-8B, and MiniGPT-4 Multi-Modal LLM Models Comparison for Image Reasoning#
These notebooks show how to use different Multi-Modal LLM models for image understanding/reasoning. The various model inferences are supported by Replicate or OpenAI GPT4-V API. We compared several popular Multi-Modal LLMs:
GPT4-V (OpenAI API)
LLava-13B (Replicate)
Fuyu-8B (Replicate)
MiniGPT-4 (Replicate)
CogVLM (Replicate)
Check out our guides below:
Simple Evaluation of Multi-Modal RAG#
In this notebook guide, we’ll demonstrate how to evaluate a Multi-Modal RAG system. As in the text-only case, we will consider the evaluation of Retrievers and Generators separately. As we alluded to in our blog on the topic of Evaluating Multi-Modal RAGs, our approach here involves the application of adapted versions of the usual techniques for evaluating both Retriever and Generator (used for the text-only case). These adapted versions are part of the llama-index library (i.e., evaluation module), and this notebook will walk you through how you can apply them to your evaluation use cases.
Model Guides#
Here are notebook guides showing you how to interact with different multimodal model providers.