Exploring Multimodal RAG Models for Enhanced Language Understanding
Retrieval Augmented Generation (RAG) models have emerged as a promising approach to enhance the capabilities of language models by incorporating external knowledge from large text corpora. However, despite their impressive performance in various natural language processing tasks, RAG models still face several limitations that need to be addressed.
Naive RAG models face limitations such as missing content, reasoning mismatch, and challenges in handling multimodal data. Although they can retrieve relevant information, they may struggle to generate complete and coherent responses when required information is absent, leading to incomplete or inaccurate outputs. Additionally, even with relevant information retrieved, the models may have difficulty correctly interpreting and reasoning over the content, resulting in inconsistencies or logical errors. Furthermore, effectively understanding and reasoning over multimodal data remains a significant challenge for these primarily text-based models.
In this post, we present a new approach named multimodal RAG (mmRAG) to tackle those existing limitations in greater detail. The solution intends to address these limitations for practical generative artificial intelligence (AI) assistant use cases. Additionally, we examine potential solutions to enhance the capabilities of large language models (LLMs) and visual language models (VLMs) with advanced LangChain capabilities, enabling them to generate more comprehensive, coherent, and accurate outputs while effectively handling multimodal data. The solution uses Amazon Bedrock, a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies, providing a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Solution architecture
The mmRAG solution is based on a straightforward concept: to extract different data types separately, you generate text summarization using a VLM from different data types, embed text summaries along with raw data accordingly to a vector database, and store raw unstructured data in a document store. The query will prompt the LLM to retrieve relevant vectors from both the vector database and document store and generate meaningful and accurate answers.
The following architecture diagram illustrates the mmRAG architecture that integrates advanced reasoning and retrieval mechanisms. It combines text, table, and image (including chart) data into a unified vector representation, enabling cross-modal understanding and retrieval. The process begins with diverse data extractions from various sources such as URLs and PDF files by parsing and preprocessing text, table, and image data types separately, while table data is converted into raw text and image data into captions.
These parsed data streams are then fed into a multimodal embedding model, which encodes the various data types into uniform, high-dimensional vectors. The resulting vectors, representing the semantic content regardless of the original format, are indexed in a vector database for efficient approximate similarity searches. When a query is received, the reasoning and retrieval component performs similarity searches across this vector space to retrieve the most relevant information from the vast integrated knowledge base.
The retrieved multimodal representations are then used by the generation component to produce outputs such as text, images, or other modalities. The VLM component generates vector representations specifically for textual data, further enhancing the system’s language understanding capabilities. Overall, this architecture facilitates advanced cross-modal reasoning, retrieval, and generation by unifying different data modalities into a common semantic space.
Developers can access the mmRAG source codes on the GitHub repo.
Configure Amazon Bedrock with LangChain
You start by configuring Amazon Bedrock to integrate with various components from the LangChain Community library. This allows you to work with the core FMs. You use the BedrockEmbeddings class to create two different embedding models: one for text (embedding_bedrock_text) and one for images (embeddings_bedrock_image). These embeddings represent textual and visual data in a numerical format, which is essential for various natural language processing (NLP) tasks.
Additionally, you use the LangChain Bedrock and BedrockChat classes to create VLM model instances from Anthropic Claude 3 Haiku and Sonnet models. These instances are used for advanced query reasoning, argumentation, and retrieval tasks.
Parse content from data sources and embed both text and image data
In this section, we explore how to harness the power of Python to parse text, tables, and images from URLs and PDFs efficiently using two powerful packages: Beautiful Soup and PyMuPDF. Beautiful Soup, a library designed for web scraping, makes it easy to sift through HTML and XML content, allowing you to extract the desired data from web pages. PyMuPDF offers an extensive set of functionalities for interacting with PDF files, enabling you to extract not just text but also tables and images with ease.
The following code snippets demonstrate how to generate image captions and embed image pixels along with image captions using the Amazon Titan image embedding model.
Embedding and vectorizing multimodality data
The mmRAG architecture enables the system to understand and process multimodal queries, retrieve relevant information from various sources, and generate multimodal answers by combining textual, tabular, and visual information in a unified manner. The system seamlessly operates across vector databases and object stores, marking a significant advancement in the quest for more efficient, accurate, and contextually aware search mechanisms.
The provided code examples showcase the accuracy and comprehensive understanding achieved with the multimodal capability. The mmRAG approach can grasp the intent behind queries, extract relevant information from provided charts, estimate costs, and perform mathematical calculations to determine differences. It offers a high level of accuracy in analyzing complex visual and textual data.
Use cases and limitations
Amazon Bedrock offers a comprehensive set of generative AI models for enhancing content comprehension across various modalities. By using the latest advancements in VLMs and image embedding models, Amazon Bedrock enables businesses to expand document understanding beyond text to include tables, charts, and images. While these solutions excel at understanding visual and textual data, the multi-step query decomposition, reciprocal ranking, and fusion processes involved can lead to increased inference latency, making them less suitable for real-time applications.
Conclusion
In conclusion, multimodal RAG presents a powerful solution to address the limitations in multimodal generative AI assistants. Through the integration of advanced models and technologies, such as Amazon Bedrock, LangChain, and cutting-edge VLMs, businesses can gain deeper insights, make informed decisions, and drive innovation driven by more accurate data.
By overcoming the existing limitations, mmRAG offers a glimpse into the future of AI-driven content comprehension, opening up new possibilities for research and development in the field of artificial intelligence.
Acknowledgement
We would like to express our sincere gratitude to the reviewers who contributed to the comprehensive review of this post: Nausheen Sayed, Karen Twelves, Li Zhang, Sophia Shramko, Mani Khanuja, Santhosh Kuriakose, and Theresa Perkins.
About the Authors
Alfred Shen, Changsha Ma, and Julianna Delua are experienced professionals in the field of artificial intelligence and machine learning with a passion for creating innovative solutions to drive business growth and transformation.