ADaSci Banner 2024

Hands-on Guide to LLava for Enhanced Multimodal Integration in AI

Discover how LLava integrates text and visual data to enhance AI capabilities in multimodal applications.

In Artificial Intelligence, integrating multimodal data, combining text, images, and sometimes audio, represents a significant advancement. LLava is an innovative framework (large language models with Visual Augmentation) that aims to bridge the gap between visual and textual understanding, enhancing the capabilities of language models to process and generate content contextually enriched with visual information. In this article, we will understand LLava, its architecture, and its use cases. We will then move further and apply LLava with LlamaIndex to retrieve image contexts.

Table of Contents

  1. What is LLava?
  2. Key components of LLava’s architecture 
  3. Use Cases of LLava
  4. Application of LLava with LlamaIndex To Retrieve Image Contexts

Let us first understand what is LLava and then we will integrate it with LLamaIndex to retrieve the context of an image. 

What is LLava?

LLava enhances the strengths of Large Language Models (LLMs) such as GPT-4, which are already proficient in understanding and generating human-like text. LLava enhances LLMs by integrating visual information. This allows them to interpret and describe visual content, such as images, alongside text. This is achieved through a multimodal training approach. Here, the model is exposed to paired visual and textual data, learning to associate images with descriptive text.

LLava builds on the strengths of large language models (LLMs) like GPT-4, which are already adept at understanding and generating text. By integrating visual information, LLava takes these capabilities a step further, enabling models to interpret and describe visual content, such as images, in conjunction with text. This multimodal approach involves training the model with paired visual and textual data, teaching it to associate images with descriptive text effectively.

Source: Llava

Key components of LLava’s architecture 

Visual Encoder

Visual Encoder processes images and converts them into a format that can be understood by the language model. Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) are used to achieve this.

Multimodal Fusion Layer

This layer integrates the encoded visual data with textual data. Techniques like attention mechanisms ensure that the model can focus on relevant parts of the image when generating or interpreting text.

Language Decoder

The final component is the Language Decoder. This generates text based on the combined visual and textual inputs. It ensures that the output is coherent and contextually accurate, whether the task is image captioning, visual question answering, or another application.

Use Cases of LLava

LLava can understand and generate content based on both text and images. This opens up numerous applications:

Image Captioning

LLava can generate descriptive captions for images, which can be used in digital libraries, social media platforms, and assistive technologies for the visually impaired.

Visual Question Answering

It can answer questions about the content of an image, useful in educational tools, customer support systems, and interactive AI applications.

Content Creation

It assists in the creation of multimodal content. This includes generating visually enriched articles, reports, and presentations.

Enhanced Search Engines

It improves the search results by understanding and indexing images along with textual data. This provides more relevant and contextually accurate results.

Application of LLava with LlamaIndex To Retrieve Image Contexts

LLava modal can be used to understand, read, and caption both the visual and textual context of an image. LLava uses its architectural components to easily give us the response. Here, we will be using LLava-13b modal along with LlamIndex to study an image and then ask a query regarding the image. 

Let us first begin with installing all the required packages and then import the libraries.

%pip install unstructured replicate
%pip install llama_index ftfy regex tqdm
%pip install git+
%pip install torch torchvision
%pip install matplotlib scikit-image
%pip install -U qdrant_client
%pip install llama-index-vector-stores-qdrant
%pip install llama-index-readers-file
%pip install llama-index-multi-modal-llms-replicate

from pathlib import Path
from llama_index.core import SimpleDirectoryReader
from PIL import Image
import matplotlib.pyplot as plt
import os
import openai
from llama_index.core import VectorStoreIndex
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.schema import ImageDocument
from llama_index.multi_modal_llms.replicate.base import (

We will need OpenIAI API Key and Replicate Token. So, import these two into the environment.

os.environ["REPLICATE_API_TOKEN"] = "******"
os.environ["OPENAI_API_KEY"] = "sk-******"

LLava can combine textural context and an image. So, we will be loading a textual document first.

image_documents = SimpleDirectoryReader("Document/").load_data()

Next, load the image into the notebook.

imageUrl = "./Image/HP.jpeg"
image ="RGB")
plt.figure(figsize=(16, 5))

This is the image we will be using. 

We will now use the ReplicateMultiModal to activate and initiate the llava-13b modal.

llava_multi_modal_llm = ReplicateMultiModal(

Let us now give a prompt to the llava multi-modal and pass our image URL as an attribute.

prompt = "What is the image about?"

llava_response = llava_multi_modal_llm.complete(

The image features a collage of various Harry Potter movie posters, showcasing the characters and scenes from the popular film series. The posters are arranged in a visually appealing manner, highlighting the different elements of the Harry Potter universe. In the collage, there are three main characters: Harry Potter, Hermione Granger, and Ron Weasley. They are positioned in various poses and locations, representing their roles in the movies. Additionally, there are other characters and elements from the Harry Potter films, such as magical creatures, Hogwarts School of Witchcraft and Wizardry, and various magical objects.

The collage captures the essence of the Harry Potter movies and serves as a tribute to the iconic film series.

This is the output we got for the prompt. To check if the information we are getting from the image are relevant and related to the Harry Potter series, we will pass the response into a query engine. 

We will begin with creating a vector storage and then creating a query engine.

# construct top-level vector index + query engine
vector_index = VectorStoreIndex.from_documents(image_documents)
query_engine = vector_index.as_query_engine(similarity_top_k=5, verbose=True)
rag_response = query_engine.query(llava_response.text)

The output will be something like this:

The collage of various Harry Potter movie posters showcases the main characters, Harry Potter, Hermione Granger, and Ron Weasley, in different poses and settings that reflect their roles in the films. Alongside them are representations of magical creatures, Hogwarts School of Witchcraft and Wizardry, and various magical objects from the series. This visually appealing arrangement pays homage to the beloved Harry Potter film franchise, encapsulating its essence and iconic elements.

Thus, by using LLava-13b with LlamaIndex we were able to extract the information from image and textual context. 


LLava represents a significant advancement in AI by integrating visual augmentation into large language models. This capability to process and generate content that combines text and images opens up new possibilities across various industries. Whether enhancing accessibility, improving content creation, or developing intelligent search engines, LLava provides a powerful tool for leveraging the synergy between visual and textual data. By integrating LLava with frameworks like LlamaIndex and Hugging Face, users can explore and develop sophisticated multimodal AI applications, pushing the boundaries of what AI can achieve.


  1. Link to the above code
  2. LLava Documentation
  3. LLava – LlamaIndex Documentation

Enroll in the course below to learn more about RAG with Pinecone:

Picture of Shreepradha Hegde

Shreepradha Hegde

Shreepradha is an accomplished Associate Lead Consultant at AIM, showcasing expertise in AI and data science, specifically Generative AI. With a wealth of experience, she has consistently demonstrated exceptional skills in leveraging advanced technologies to drive innovation and insightful solutions. Shreepradha's dedication and strategic mindset have made her a valuable asset in the ever-evolving landscape of artificial intelligence and data science.

The Chartered Data Scientist Designation

Achieve the highest distinction in the data science profession.

Elevate Your Team's AI Skills with our Proven Training Programs

Strengthen Critical AI Skills with Trusted Generative AI Training by Association of Data Scientists.

Our Accreditations

Get global recognition for AI skills

Chartered Data Scientist (CDS™)

The highest distinction in the data science profession. Not just earn a charter, but use it as a designation.

Certified Data Scientist - Associate Level

Global recognition of data science skills at the beginner level.

Certified Generative AI Engineer

An upskilling-linked certification initiative designed to recognize talent in generative AI and large language models

Join thousands of members and receive all benefits.

Become Our Member

We offer both Individual & Institutional Membership.