The key advantages of multimodel retrieval augmented generation systems are:

1. Improved Contextual Understanding: By directly processing images, tables, and other multimodal data within the documents, these systems can better capture the full context and nuance of the information, rather than relying solely on textual descriptions which may miss important visual details.

2. Enhanced Reasoning Capabilities: The combination of a vision-based retrieval system and a powerful vision-language model enables the system to not only extract relevant information, but also perform reasoning and calculations on the visual data, allowing it to answer more complex questions.

3. Reduced Preprocessing Overhead: Traditional text-based systems often require extensive preprocessing steps to extract and embed textual information from multimodal documents. Multimodel systems can bypass these steps and directly process the raw data, improving efficiency and reducing the risk of information loss.

4. Broader Applicability: By handling a wide range of data types, including images, tables, and text, multimodel systems can be applied to a wider range of real-world scenarios and use cases, making them more versatile and valuable in enterprise settings.

5. Improved Accuracy and Relevance: The ability to directly process visual information can lead to more accurate and relevant retrieval results, as the system can better understand the context and content of the documents.

Overall, the adoption of multimodel retrieval augmented generation systems represents a significant advancement in the field of enterprise search and knowledge management, enabling organizations to unlock the full value of their diverse data assets.

Traditional approaches to handling multimodal data, such as images and tables within documents, have several limitations:

1. Loss of Contextual Information: When converting images and tables into text-based captions or descriptions, a significant amount of contextual information is lost. The quality of the descriptions is heavily dependent on the prompting and the capabilities of the language model used.

2. Increased Memory Requirements: Approaches like PolyEncoder that directly encode images and create multi-level embeddings can lead to high memory requirements, as most existing vector stores do not support this type of multi-level representation.

3. Suboptimal Performance: Text-based descriptions of images and tables may not capture the nuances and details present in the original multimodal data, leading to suboptimal performance in tasks like retrieval and question answering.

4. Complexity of Pre-processing: The traditional approach of parsing images and tables, generating text-based descriptions, and then embedding them as part of the document chunks can be a complex and time-consuming process.

To address these challenges, the video introduces a more efficient and effective approach using the Embed-V4 multimodal embedding model from Cohere, along with a vision-language model like Gemini for generation. This approach allows for direct processing of images and tables, preserving the contextual information and enabling more accurate and efficient multimodal search and question answering.

Embed V4 is a state-of-the-art multimodel embedding solution introduced by Cohere, a Frontier Foundation model company. This new embedding approach has shown promising results in vision-based retrieval, even outperforming models like CLIP.

The key advantages of Embed V4 are:

1. Fixed-Size Embeddings: Embed V4 generates embeddings of a fixed size, which makes them compatible with a wide range of vector stores, unlike previous approaches like POLI that produced multi-level embeddings with high memory requirements.

2. Quantization Support: The Embed V4 embeddings can be quantized, similar to the quantization of language model weights. This allows for significant reductions in compute and storage costs without a substantial impact on performance.

3. Multimodel Capabilities: Embed V4 can process both image and text data, enabling a unified approach for indexing and retrieving multimodel content, such as documents with images and tables.

The workflow for using Embed V4 in a multimodel retrieval-augmented generation system is as follows:

1. Embed the images (or other visual content) from the documents using the Embed V4 model.
2. Store the resulting embeddings in a vector store of your choice.
3. When a user query comes in, use the same Embed V4 model to generate an embedding for the query.
4. Perform a similarity search in the vector store to retrieve the most relevant images.
5. Pass the retrieved images and the original query to a multimodel generation model, such as Gemini, to produce the final answer.

This approach allows for direct processing of multimodel data, avoiding the need for intermediate steps like generating text descriptions of images. The vision-language model can then leverage the rich visual information to provide more accurate and contextual responses.

In this section, we will explore how to build a multimodel retrieval augmented generation system that can process images, text, and tables. We will look at both proprietary API-based solutions as well as a local solution that can be run privately.

First, we will discuss the limitations of traditional text-based retrieval systems when dealing with multimodal data. We will then introduce the Embed-V4 model from Cohere, which enables direct processing of images as part of the indexing process. We will also discuss the cost-saving benefits of embedding quantization.

Next, we will dive into the code implementation. We will start by using the Cohere proprietary API to embed images and perform retrieval. Then, we will pass the retrieved images to a multimodel generation model like Gemini to generate the final answers.

To provide a fully local solution, we will also demonstrate how to use the CallPoly model from the BLD package for image-based retrieval, and then integrate it with the Gemini model for generation.

Throughout the implementation, we will highlight the key advantages of this multimodel approach, such as the ability to directly process complex visual information and perform reasoning on the retrieved data, without the need for extensive preprocessing steps required in traditional text-based systems.

The use of multi-vector representations or multimodal vector representations of images can be costly in terms of both compute and storage. However, there are techniques to optimize these costs without significantly impacting performance.

One such technique is embedding quantization. Just like the quantization of language model weights, the resultant vectors generated by an embedding model can also be quantized. This can provide significant savings in both compute and storage costs.

The idea is to use lower bit-width quantization, such as 4-bit or 8-bit, instead of the default 32-bit floating-point representation. As shown in the plot, this quantization can preserve a large portion of the performance while drastically reducing the cost.

For example, using 4-bit or 8-bit quantization can provide very similar levels of performance compared to the 32-bit representation, but at a much lower cost. This makes the use of multimodal vector representations more feasible and practical, especially in scenarios with limited compute resources or storage constraints.

The impact of embedding quantization on retrieval performance has been covered in one of the author's previous videos. Interested viewers can refer to the link provided in the video description for more details on this topic.

In this section, we will explore how to build a multimodel retrieval system using local models, without relying on proprietary APIs. This approach allows you to keep your data and processing entirely within your own infrastructure, providing more control and flexibility.

We will be using the BLD package to leverage the CallPoly or CallQuinn models for image-based retrieval. These models can be run locally, eliminating the need to send data to external API endpoints. Additionally, we will use the Gemini model for the vision-language generation component, as it is relatively easy to set up.

First, we need to install the required packages, including the BLD package for the local image retrieval models and the popular package for converting PDF files into images. We also need to provide our Hugging Face API token.

Next, we load the CallPoly model using the BLD package and point it to the folder containing the images we want to index. This will create a local index for us.

To perform the search, we simply call the search function, passing in the user's query. The function will compute the embeddings for the query using the CallPoly model and return the top-ranked image that is most relevant.

We then convert the retrieved image into bytes and can display the result. This process is repeated for different queries, and the retrieved images are passed along with the original user query to the Gemini model for the final answer generation.

By using this local approach, we maintain full control over the data and processing, while still benefiting from the capabilities of advanced multimodel retrieval and generation systems.

The integration of multimodal data, including images and tables, into retrieval-augmented generation systems is a crucial step in addressing the limitations of text-based approaches. The presented solutions, both the proprietary API-based and the local implementation using CallPoly, demonstrate the ability to effectively process and retrieve relevant visual information to enhance the overall performance of these systems.

The key advantages of the multimodal approach include:

1. Preserving Contextual Information: By directly processing the images and tables, the system avoids the potential loss of contextual information that can occur when converting them to text-based representations.

2. Improved Retrieval Accuracy: The use of specialized vision-based embedding models, such as Embed-V4, enables more accurate retrieval of relevant visual content, which can then be effectively leveraged by the vision-language model for generating accurate responses.

3. Reasoning on Visual Data: The integration of vision-language models, like Gemini, allows the system to perform complex reasoning on the visual data, enabling it to answer questions that would be challenging for text-based systems alone.

The presented examples showcase the potential of multimodal retrieval-augmented generation systems in various applications, such as financial analysis, product information, and complex visual data processing. As the field of multimodal AI continues to evolve, the integration of these techniques into enterprise-level search and retrieval systems holds great promise for addressing the diverse needs of real-world data.