Multimodal RAG: Guide to Gemini’s Free AI Development Tools

Introduction

In the evolving landscape of artificial intelligence, Retrieval-Augmented Generation (RAG) has become a powerful tool. It enhances model responses by combining retrieval and generation capabilities. This innovative approach enables AI to pull in relevant external information. As a result, it generates meaningful and contextually aware responses. This extends the AI’s knowledge base beyond pre-trained data. However, the rise of multimodal data presents new challenges. Traditional text-based RAG systems struggle to comprehend and process visual content alongside text. Multimodal RAG systems address this gap. They allow AI models to integrate various input formats. This provides comprehensive responses that are crucial for applications in e-commerce, education, and content generation.

With the introduction of Google Generative AI’s Gemini models, developers can now build advanced multimodal systems. These systems come without typical financial constraints. Gemini is available for free and offers both text and vision models. This empowers developers to create cutting-edge AI solutions that seamlessly integrate retrieval and generation. This blog will present a real-world case study. It will demonstrate how to build a multimodal RAG system using Gemini’s free models. Developers will be guided through querying images and text inputs. They will learn how to retrieve the necessary information and generate insightful responses.

Learning Objectives

• Understand the concept of Retrieval-Augmented Generation (RAG) and its importance in creating more intelligent AI systems.
• Explore the advantages of multimodal systems that integrate both text and image processing.
• Learn how to build a multimodal RAG system using Google’s free Gemini models, including practical coding examples.
• Gain insights into the key concepts of text embedding and image processing, along with their implementation.
• Discover potential applications and future directions for multimodal RAG systems in various industries.

This article was published as a part of the Data Science Blogathon.

Power of Multimodal RAGs

At its core, retrieval-augmented generation (RAG) is a hybrid approach that combines two AI techniques: retrieval and generation. Traditional language models generate responses based only on their pre-trained knowledge, but RAG enhances this by retrieving relevant external data before generating a response. This means that RAG systems can provide more accurate, contextually relevant, and up-to-date responses, especially when they are connected to large databases or expansive knowledge sources.

For example, a standard language model might struggle with complex or niche queries requiring specific information not covered during training. A RAG system can query external knowledge sources, retrieve relevant information, and combine it with the model’s generative capabilities to deliver a superior response.

By integrating retrieval with generation, RAG systems become dynamic and adaptable. This makes them ideal for applications that require fact-based, knowledge-heavy, or timely responses. Industries such as customer support, research, and data analytics are increasingly adopting RAG. They recognize its effectiveness in improving AI interactions.

Multimodality: Bridging the Gap Between Text and Images

The growing need for AI to handle multiple input types—such as images, text, and audio—has led to the development of multimodal systems. Multimodal AI processes and combines inputs from various data formats, allowing for richer, more comprehensive outputs. A system that can both read and interpret a text query while analyzing an image can deliver more insightful and accurate answers.

Some real-world applications include:

• Visual Search: Systems that understand both text and images can offer superior search results, such as recommending products based on both a description and an image.
• Education: Multimodal systems can enhance learning by analyzing diagrams, images, or videos and combining them with textual explanations, making complex topics more digestible.
• Content Generation: Multimodal AI can generate content from both written prompts and visual inputs, blending information creatively.

Multimodal RAG systems expand these possibilities by enabling AI to retrieve external information from various modalities and generate responses that synthesize this data.

Gemini Models: Unlocking Free Multimodal Power

At the core of this blog’s case study are the Gemini models from Google Generative AI. Gemini provides both text and vision models, making it a strong foundation for building multimodal RAG systems. What makes Gemini particularly attractive is its free availability, which allows developers, researchers, and hobbyists to build advanced AI systems without incurring significant costs.

• Text Models: Gemini’s text models are designed for conversational and contextual tasks, making them ideal for generating intelligent responses to textual queries.
• Vision Models: Gemini’s vision models allow the system to process and understand images, making it a key player in multimodal systems that combine text and visual input.

In the next section, we will walk through a case study demonstrating how to build a multimodal RAG system using Gemini’s free models.

Case Study: Querying Images with Text using a Multimodal RAG System

In this case study, we will build a practical system that allows users to query both text and images. The goal is to retrieve detailed responses by utilizing a multimodal RAG system. For instance, a user can upload an image of a bird and ask the system for specific information, such as the bird’s habitat, behavior, or characteristics. The system will use the Gemini models to process the image and text and return relevant information.

Problem Statement

Imagine a scenario where users can interact with an AI system by uploading an image of a bird (to make it difficult, we will use a cartoon image) and asking for additional details about it, such as its habitat, migration patterns, or native regions. The challenge is to combine image analysis capabilities with text-based querying to provide an insightful response that blends visual and textual data.

Step by Step Guide

We will now go through the steps of building this system using Gemini’s text and vision models. The code will be explained in detail, and the expected outcomes of each code block will be highlighted.

Step1: Importing Required Libraries and Setting Up the Environment

%pip install --upgrade langchain langchain-google-genai "langchain[docarray]" faiss-cpu pypdf langchain-community
!pip install -q -U google-generativeai

We start by installing and upgrading the necessary packages. These include langchain for building the RAG system, faiss-cpu for vector search capabilities, and google-generativeai for interacting with the Gemini models.

Expected Outcome: All required libraries should be installed successfully, preparing the environment for further development.

Step2: Configuring the Gemini API Key

import google.generativeai as genai
from google.colab import userdata
GOOGLE_API_KEY=userdata.get('Gemini_API_Key')
genai.configure(api_key=GOOGLE_API_KEY)

Here, we configure the Gemini API key, which is required to interact with Google Generative AI services. We retrieve it from Colab’s user data and set it up for further API calls.

Expected Outcome: Gemini API should be configured correctly, allowing us to use text and vision models in subsequent steps.

Step3: Loading the Gemini Model

def load_model(model_name):
  if model_name=="gemini-pro":
    llm = ChatGoogleGenerativeAI(model="gemini-1.0-pro-latest")
  else:
    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
  return llm

model_text = load_model("gemini-1.0-pro-latest")

This function allows us to load the Gemini model based on the version needed. In this case, we are using gemini-1.0-pro-latest for text-based generation. The same method can be extended for vision models.

Expected Outcome: The text-based Gemini model should be loaded, enabling it to generate responses to text queries.

Step4: Loading Text Documents and Splitting into Chunks

loader = TextLoader("/content/your txt file")
text = loader.load()[0].page_content

def get_text_chunks_langchain(text):
  text_splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=10)
  docs = [Document(page_content=x) for x in text_splitter.split_text(text)]
  return docs

docs = get_text_chunks_langchain(text)

We load a text document (in this example, about birds) and split it into smaller chunks using CharacterTextSplitter from LangChain. This ensures the text is manageable for retrieval and matching.

Expected Outcome: The text should be split into smaller chunks, which will be used later for vector-based retrieval.

Step5: Vectorizing the Text Chunks

embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
vectorstore = FAISS.from_documents(docs, embedding=embeddings)
retriever = vectorstore.as_retriever()

Next, we generate embeddings for the text chunks using Google Generative AI’s embedding model. We then store these embeddings in a FAISS vector store, enabling us to retrieve relevant text snippets based on queries.

Expected Outcome: The embeddings of the text should be stored in FAISS, allowing for efficient retrieval when querying.

Step6: Building the RAG Chain for Text and Image Queries

template = """
```
{context}
```

{query}


Provide brief information and store location.
"""
prompt = ChatPromptTemplate.from_template(template)
rag_chain = (
    {"context": retriever, "query": RunnablePassthrough()}
    | prompt
    | llm_text
    | StrOutputParser()
)
result = rag_chain.invoke("can you give me a detail of a eagle?")

We set up the retrieval-augmented generation (RAG) chain by combining text retrieval (context) with a language model prompt. The user queries the system (in this case, about an eagle), and the system retrieves relevant context from the document before passing it to the Gemini model for generation.

Expected Outcome: The system retrieves relevant chunks of text about an eagle and generates a response containing detailed information.

Note: The above prompt will retrieve all instances of an eagle. Details must be specified for specific information retrieval.

Step7: Full Multimodal Chain with Image and Text Queries

full_chain = (
    RunnablePassthrough() | vision_model | StrOutputParser() | rag_chain
)

image3 = "/content/path_to_your_image_file"

message = HumanMessage(
    content=[
        {
            "type": "text",
            "text": "Provide information on given bird and native location.",
        },
        {"type": "image_url", "image_url": image3},
    ]
)

result = full_chain.invoke([message])

Finally, we create a complete multimodal RAG system by chaining the vision model with the text-based RAG chain. The user provides an image and a text query, and the system processes both inputs to return an enriched response.

Expected Outcome: The system processes the image and text query together and generates a detailed response combining visual and textual information. So now, after this step, given the image of any bird, if the information exists in the external database, the RAG pipeline should be able to retrieve the respective information. The visual abstract of the problem state shown before will be achieved in this step.

For a better understanding and to give the readers a hands-on experience, the entire notebook can be found here. Feel free to use and develop those codes for more awesome ideas!

Key Concepts from Case Study with Demo Code Snippets

Text embedding is a technique for transforming text into numerical representations (vectors) that capture its semantic meaning. By embedding text, we can represent words, phrases, or entire documents in a multidimensional space, allowing us to measure similarities and relationships between them. This is particularly useful for retrieving relevant information quickly from large datasets.

The process typically involves:

• Text Splitting: Dividing large pieces of text into smaller, manageable chunks.
• Embedding: Converting these text chunks into numerical vectors using embedding models.
• Vector Stores: Storing these vectors in a structure (like FAISS) that allows for efficient similarity search and retrieval.

# Import necessary libraries
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import GoogleGenerativeAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.schema import Document

# Load the text document
loader = TextLoader("/content/birds.txt")
text = loader.load()[0].page_content

# Split the text into chunks for better manageability
text_splitter = CharacterTextSplitter(chunk_size=20, chunk_overlap=10)
docs = [Document(page_content=x) for x in text_splitter.split_text(text)]

# Create embeddings for the text chunks
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")

# Store the embeddings in a FAISS vector store
vectorstore = FAISS.from_documents(docs, embedding=embeddings)
retriever = vectorstore.as_retriever()

Expected Outcome: After running this code, you will have:

• A set of text chunks representing the original document.
• Each chunk embedded into a numerical vector.
• A FAISS vector store containing these embeddings, ready for efficient retrieval based on user queries.

Efficient retrieval of information is crucial in many applications, such as chatbots, recommendation systems, and search engines. As datasets grow larger, traditional keyword-based search methods become inadequate, leading to irrelevant or incomplete results. By embedding text and storing it in a vector space, we can:

• Enhance search accuracy by finding semantically similar documents, even if the exact wording differs.
• Reduce response time, as vector search methods like those provided by FAISS are optimized for quick similarity searches.
• Improve the user experience by delivering more relevant and context-aware responses, ultimately leading to better interaction with AI systems.

Vision Model for Image Processing

The Gemini vision model is designed to analyze images and extract meaningful information from them. This capability can be utilized to summarize content, identify objects, and understand context within images. By combining image processing with text querying, we can create powerful multimodal systems that provide rich, informative responses based on both visual and textual inputs.

# Load the vision model
from google.generativeai import ChatGoogleGenerativeAI

vision_model = load_model("gemini-pro-vision")

# Prepare a prompt for the vision model
prompt = "Summarize this image in 5 words"
image_path = "/content/sample_image.jpg"

# Create a message containing the prompt and image
message = HumanMessage(
    content=[
        {
            "type": "text",
            "text": prompt,
        },
        {
            "type": "image_url",
            "image_url": image_path
        }
    ]
)

# Invoke the vision model to get a summary
image_summary = vision_model.invoke([message]).content
print(image_summary)

Expected Outcome: This code snippet allows the vision model to process an image and respond to the prompt. The output will be a concise five-word summary of the image, showcasing the model’s ability to extract and convey information based on visual content.A

The importance of the vision model lies in its ability to enhance our understanding of images across various applications:

• Improved User Interaction: Users can upload images for intuitive queries.
• Rich Contextual Understanding: Extracts key insights for education and e-commerce.
• Multimodal Integration: Combines vision and text for comprehensive responses.
• Efficiency in Information Retrieval: Speeds up detail extraction from large datasets.
• Enhanced Content Generation: Generates richer content for various platforms.

By understanding these key concepts—text embedding and the functionality of vision models—we can leverage the power of multimodal RAG systems effectively. This approach enhances our ability to interact with AI by allowing for rich, context-aware responses that blend information from both text and images. The code samples provided above illustrate how to implement these concepts, laying the foundation for creating sophisticated AI systems capable of advanced querying and information retrieval.

Benefits of Free Access to Gemini Models and Use Cases for Multimodal RAG Systems

The free availability of Gemini models significantly lowers the entry barriers for developers, researchers, and hobbyists, enabling them to build advanced AI systems without incurring costs. This democratization of access fosters innovation and allows a diverse range of users to explore the capabilities of multimodal AI.

Cost Savings: With free access, developers can experiment with and refine their projects without the financial strain typically associated with AI development. This accessibility encourages more individuals to contribute ideas and applications, enriching the AI ecosystem.

Scalability: These systems are designed to grow with user needs. Developers can efficiently scale their solutions to handle increasingly complex queries and larger datasets, leveraging free resources to enhance system capabilities.

Availability of Complementary Tools: The integration of tools like FAISS and LangChain complements the capabilities of Gemini models, allowing for the construction of end-to-end AI pipelines. These tools facilitate efficient data retrieval and management, which are crucial for developing robust multimodal applications.

Possible Use Cases for Multimodal RAG Systems

The potential applications of multimodal RAG systems are diverse and impactful:

• E-Commerce: These systems can enable visual product searches, allowing users to upload images and retrieve relevant product information instantly. This enhances the shopping experience by making it more intuitive and engaging.
• Education: Multimodal RAG systems can facilitate interactive learning in educational settings. Students can ask questions about images, leading to richer discussions and deeper understanding of the material.
• Healthcare: Multimodal systems can assist in medical diagnostics by allowing practitioners to upload medical images alongside text queries, retrieving relevant information about conditions and treatments.
• Social Media: In platforms focused on user-generated content, these systems can enhance user engagement by allowing users to interact with images and text seamlessly, improving content discovery and interaction.
• Research and Development: Researchers can utilize multimodal RAG systems to analyze data across different modalities, extracting insights from text and images in a unified manner, which can lead to innovative discoveries.

By harnessing the capabilities of Gemini models and exploring these use cases, developers can create impactful applications that leverage the power of multimodal RAG systems to meet real-world needs.

Future Directions for Multimodal RAG Systems

As the field of artificial intelligence continues to evolve, the future of multimodal RAG systems holds exciting possibilities. Here are some key directions that developers and researchers can explore:

Advanced Applications: The versatility of multimodal RAG systems allows for a wide range of applications across various domains. Potential advancements include:

• Enhanced E-Commerce Experiences: Future systems could integrate augmented reality (AR) features, allowing users to visualize products in their own environments while accessing detailed information through text queries.
• Interactive Education Tools: By incorporating real-time feedback mechanisms, educational platforms can adapt to individual learning styles, using multimodal inputs to enhance understanding and retention.
• Healthcare Innovations: Integrating multimodal RAG systems with wearable health technology can facilitate personalized medical insights by analyzing both user-provided data and real-time health metrics.
• Art and Creativity: These systems could empower artists and creators by generating inspiration from both text and image inputs, leading to collaborative creative processes between human and AI.

Next Steps for Developers

To further develop multimodal RAG systems, developers can consider the following approaches:

• Utilizing Larger Datasets: Expanding the datasets used for training models can enhance their performance, allowing for more accurate retrieval and generation of information.
• Exploring Additional Retrieval Strategies: Implementing diverse retrieval techniques, such as content-based image retrieval or semantic search, can improve the effectiveness of the system in responding to complex queries.
• Integrating Video Inputs: The future of multimodal RAG systems may involve video alongside text and image inputs, allowing users to query and retrieve information from dynamic content, further enriching the user experience.
• Cross-Domain Applications: Exploring how multimodal RAG systems can be applied across different domains—such as combining historical data with contemporary information—can yield innovative insights and solutions.
• User-Centric Design: Focusing on user experience will be crucial. Future systems should prioritize intuitive interfaces and responsive designs that make it easy for users to interact with the technology, regardless of their technical expertise.

Conclusion

In this blog, we explored the powerful capabilities of multimodal RAG systems, specifically leveraging the free availability of Google’s Gemini models. By integrating text and image processing, these systems enable more interactive and engaging user experiences, making information retrieval more intuitive and efficient. The practical case study demonstrated how developers can implement these advanced tools to create robust applications that cater to diverse needs.

As the field continues to grow, the opportunities for innovation within multimodal systems are vast. Developers are encouraged to experiment with these technologies, extend their capabilities, and explore new applications across various domains. With tools like Gemini at their disposal, the potential for creating impactful AI-driven solutions is more accessible than ever.

Explore the code behind this article on GitHub!

Key Takeaways

• Multimodal RAG systems combine text and image processing to enhance information retrieval and user interaction.
• Google’s Gemini models, available for free, empower developers to build advanced AI applications without financial constraints.
• Real-world applications include e-commerce enhancements, interactive educational tools, and innovative healthcare solutions.
• Future developments can focus on integrating larger datasets, exploring diverse retrieval strategies, and incorporating video inputs.
• User experience should be a priority, with an emphasis on intuitive design and responsive interaction.

By embracing these advancements, developers can harness the full potential of multimodal RAG systems to drive innovation and improve how we access and engage with information.

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Neil D

Neil is a research professional currently working on the development of AI agents. He has successfully contributed to various AI projects across different domains, with his works published in several high-impact, peer-reviewed journals. His research focuses on advancing the boundaries of artificial intelligence, and he is deeply committed to sharing knowledge through writing. Through his blogs, Neil strives to make complex AI concepts more accessible to professionals and enthusiasts alike.