Retrieving relevant information from a diverse set of sources, including text and images, can significantly enhance the user experience and provide a more comprehensive understanding of a given topic. Traditional Retrieval Augmented Generation (RAG) systems have primarily focused on text-based information, but the inclusion of multimodal data can unlock new possibilities.

By incorporating both textual and visual information, multimodal RAG systems can offer several key benefits:

1. Improved Context Understanding: The combination of text and images can provide a richer context, allowing the system to better comprehend the nuances and relationships within the data.

2. Enhanced Information Retrieval: Multimodal retrieval can surface relevant information that may not be easily accessible through text-only searches, such as visual cues, diagrams, or data visualizations.

3. Increased Engagement and Comprehension: The integration of text and images can make the information more engaging and easier to understand, particularly for complex or technical topics.

4. Broader Applicability: Multimodal RAG systems can be applied to a wider range of domains, from scientific research to product documentation, where visual information plays a crucial role in conveying information.

5. Adaptability to User Preferences: By catering to different learning styles and preferences, multimodal RAG systems can provide a more personalized and effective information retrieval experience.

To implement a benefit-driven multimodal RAG system, the key steps involve:

1. Extracting and Embedding Multimodal Data: Separate the text and images from the source documents, and create embeddings for both modalities using appropriate models (e.g., CLIP for text-image embeddings).

2. Constructing a Multimodal Vector Store: Combine the text and image embeddings into a unified vector store, enabling efficient retrieval across both modalities.

3. Implementing Multimodal Retrieval and Ranking: Develop a retrieval mechanism that can query the multimodal vector store and rank the most relevant text and image chunks based on the user's query.

4. Integrating Multimodal Generation: Leverage a multimodal language model to generate responses that seamlessly incorporate both textual and visual information, providing a comprehensive and engaging output.

By following this approach, you can create a multimodal RAG system that delivers enhanced information retrieval capabilities, ultimately improving the user experience and unlocking new possibilities for knowledge discovery and dissemination.

The first approach we'll explore for building multimodal RAC (Retrieval-Augmented Generation) systems is to embed all different modalities, such as text and images, into a single vector space. This allows us to leverage the power of a unified embedding model, like CLIP (Contrastive Language-Image Pre-training), to create embeddings that can work across both text and visual data.

The key steps in this approach are:

1. Extract Text and Images: We start by extracting the text and images from our input data, such as Wikipedia articles.
2. Create Unified Embeddings: We use a model like CLIP to create embeddings that can represent both the text and images in a shared vector space.
3. Store Embeddings in a Vector Store: We store these unified embeddings in a multimodal vector store, such as Quadrant, that can handle both text and image data.
4. Retrieve Relevant Chunks: When a user query comes in, we create embeddings for the query and perform retrieval on the unified vector store to get the most relevant text chunks and images.
5. Pass to Multimodal LLM: If the retrieved context includes images, we can pass the text chunks and images through a multimodal language model to generate the final response.

This approach is relatively straightforward, but it requires a powerful multimodal embedding model like CLIP to create the unified vector space. The advantage is that it allows for seamless retrieval and integration of both text and visual information to support the user's query.

In the code example provided, we demonstrate how to implement this approach using the Llama Index library and the Quadrant vector store. We extract text and images from Wikipedia articles, create CLIP embeddings for the images and GPT embeddings for the text, and then store them in a multimodal vector store. We then show how to perform retrieval on this vector store and display the relevant text chunks and images.

While this is a good starting point, in later videos, we'll explore more advanced approaches, such as grounding all modalities to a primary modality (text) and using separate vector stores for different modalities with a multimodal re-ranker. Stay tuned for those exciting developments!

The second approach to building multimodal RAC systems involves grounding all the different modalities into a primary modality, which in this case is text. This approach aims to unify the various data sources, including text and images, into a single text-based vector space for retrieval.

Here's how the process works:

1. Extract Text and Images: The input data, such as Wikipedia articles, is processed to extract both the text and the images.

2. Create Text Embeddings: For the text data, standard text embeddings are created, such as using OpenAI's text embeddings.

3. Generate Text Descriptions for Images: The images are passed through a multimodal model, like GPT-4 or Gemini Pro, to generate text descriptions of the images. These text descriptions are then used to create text embeddings.

4. Unify in a Text Vector Store: The text embeddings, whether from the original text or the image descriptions, are combined into a unified text-based vector store.

When a user query comes in, the retrieval process happens on this unified text vector space. The retrieved context may contain both text and image-based descriptions. If the retrieved content is pure text, it can be directly passed through a language model to generate responses. However, if the retrieved content includes image-based descriptions, those are passed through a multimodal model to generate the final responses.

This approach has the advantage of simplicity, as it unifies everything into a single modality. However, it may potentially lose some nuances from the original images, as the focus is primarily on the text-based representation.

In the next videos, we will explore more advanced solutions, including the use of separate vector stores for different modalities and the implementation of a multimodal re-ranker to effectively combine the text and image-based retrieval results.

The third approach to building multimodal RAC systems involves using separate vector stores for different modalities. This approach allows for more granular control and optimization of the retrieval process for each modality.

Here's how it works:

1. Text Vector Store: For the text data, we create text embeddings and store them in a dedicated text vector store.

2. Image Vector Store: For the images, we use a specialized model (e.g., CLIP) to create embeddings, and store them in a separate image vector store.

3. Dual Retrieval: When a user query comes in, we perform retrieval separately on both the text vector store and the image vector store. This gives us relevant chunks from the text as well as relevant images.

4. Multimodal Re-Ranking: Since we have retrieved relevant chunks from both text and images, we need to use a multimodal re-ranking model to determine the most relevant combination of text and image chunks for the given query. This re-ranking model should be capable of understanding the importance and relevance of both modalities.

5. Final Response: After re-ranking the retrieved chunks, we can pass the most relevant combination of text and image chunks through a multimodal language model to generate the final response.

This approach provides several benefits:

1. Modality-Specific Optimization: By maintaining separate vector stores for text and images, we can optimize the embedding and retrieval process for each modality independently, allowing for better performance.

2. Flexible Retrieval: The dual retrieval process gives us the flexibility to adjust the number of text and image chunks retrieved based on the specific query and requirements.

3. Multimodal Understanding: The multimodal re-ranking step ensures that the final response takes into account the relevance and importance of both text and image information.

However, this approach also requires a more complex system design and the development of a capable multimodal re-ranking model, which can add to the overall complexity and computational cost of the system.

In the next video, we will dive deeper into the implementation details of this advanced multimodal retrieval approach with re-ranking.

In this video, we explored three different approaches for building multimodal Retrieval Augmented Generation (RAG) systems. The focus was on the first approach, where we embedded all different modalities (text and images) into a single vector space using a CLIP model.

We walked through the code implementation, where we:

1. Extracted text and images from Wikipedia articles.
2. Created text embeddings using GPT embeddings and image embeddings using the CLIP model.
3. Stored the embeddings in a multimodal vector store using the Quadrant library.
4. Performed retrieval on the multimodal vector store to get the top relevant text chunks and images for a given query.

While this approach is relatively simple, it requires a capable multimodal embedding model like CLIP to effectively capture the relationship between text and images.

In the future videos, we will explore the other two approaches, where we ground all modalities into a primary modality (text) or use separate vector stores for different modalities. These approaches offer different trade-offs in terms of performance, nuance preservation, and complexity.

Additionally, we will dive into the generation part of the multimodal RAG system, where we will use the retrieved text and image chunks to generate the final response using a multimodal language model.

Stay tuned for more advanced multimodal RAG system implementations in the upcoming videos. Don't forget to subscribe to the channel to stay updated.