Traditional RAG Systems and Visual Data Challenges In recent years, the advancement of deep learning and natural language processing (NLP) has led to the widespread adoption of Retrieval-Augmented Generation (RAG) systems. These systems excel in handling textual data, making them valuable tools in various applications like customer service, medical consultations, and legal inquiries. However, traditional RAG systems have a significant limitation: they can only rely on text embeddings to retrieve information from documents. This poses a challenge when dealing with visual data such as charts, tables, and images, which often contain critical information, especially in financial reports, investment research literature, and market presentations. To address this, developers are exploring multi-modal RAG systems that combine text and image understanding capabilities, thereby enhancing the accuracy and comprehensiveness of information retrieval and generation. Construction of Multi-Modal RAG Systems The project leverages Cohere’s multi-modal embedding technology and Google's Gemini 2.5 Flash engine to build a RAG system capable of understanding and retrieving both text and images from PDF files. The core technical process involves several steps: PDF Conversion: The first step is to convert each page of the uploaded PDF into high-resolution images using the pdf2image library. Embedding Vectors: Both text and image content are then processed through Cohere’s services to generate embedding vectors. For images, the system converts them into Base64 encoding before processing. Vector Storage and Search: A unified vector index is created using the FAISS library, which supports a fast search engine in mixed modes. Answer Generation with Gemini: When a user poses a question, the system retrieves relevant context—whether it be text or images—and uses Gemini 2.5 Flash to generate accurate answers. Gemini can intelligently parse chart titles, layouts, and related values. Video Demonstration and Test Results The project team created a 9-minute video demonstrating the multi-modal RAG system's workflow. The video showcases the system's ability to interpret complex data in charts and tables and generate immediate, accurate responses. To validate the system’s superiority, researchers conducted comparative tests using the same ETF PDF document with both traditional text-only RAG systems and the multi-modal RAG system. The results were striking: When asked about Invesco Investment Management's total assets under management (AUM), the multi-modal RAG system successfully found the answer in a bar chart, while the traditional text-based system missed this crucial information. For the question “How much did BlackRock earn through technical services?” based on image content, the multi-modal RAG system extracted the data from a profit table image, whereas the traditional system failed to find an answer. When queried about Apple's percentage in the S&P 500 index, the multi-modal system provided a precise value derived from a pie chart, while the traditional system offered an approximate figure. In response to “What were the top ten S&P 500 index stocks during the COVID-19 pandemic?”, the multi-modal RAG system parsed a timeline chart to provide specific details, which the traditional RAG system overlooked. For the question “How does one track bitcoin in an ETF?”, the multi-modal RAG system located the relevant information in a table image, again outperforming the traditional text-based system. These examples clearly demonstrate the effectiveness of multi-modal RAG systems in enhancing the quality of information retrieval, particularly for visually intensive documents. Project Evaluation and Company Background The successful implementation of this multi-modal RAG project not only provides a new tool for data analysis but also highlights the powerful potential of Cohere and Gemini 2.5 Flash technologies. Industry insiders view this as a significant step forward in AI application across various complex scenarios, especially in finance, healthcare, and scientific documentation. Cohere is a prominent AI service provider known for developing high-quality language models and multi-modal embedding tools. Gemini 2.5 Flash, a high-performance content generation service by Google, is designed to handle complex queries and multi-modal data. Their combination suggests a rich and practical direction for future AI technology development. Improving LLM Response Quality with Custom RAG Systems The emergence of Large Language Models (LLMs) like GPT-4 and Claude has revolutionized how AI assistants interact with users, generating human-like text and providing vast information. However, these models are not infallible. They often suffer from "hallucinations," where they produce non-existent or inaccurate information, especially when dealing with questions outside their training data. Enter Retrieval-Augmented Generation (RAG) systems, which bridge this knowledge gap by integrating external data sources into the response generation process. What is RAG? RAG enhances the ability of LLMs to answer questions by retrieving relevant information from external sources. While traditional models such as GPT and BERT can only use the data they were trained on, RAG allows for real-time access to new or dynamic information without the need for retraining. This makes RAG particularly useful in scenarios like customer support, medical consultations, and legal inquiries. Technical Stack for RAG Implementation The author of the article used a suite of open-source tools to implement RAG, including Hugging Face Transformers, FAISS, and SentenceTransformers. Hugging Face Transformers: This widely-used library supports loading and fine-tuning various pre-trained language models. FAISS: Developed by Facebook AI Research, FAISS is an efficient similarity search tool that quickly finds the most relevant document fragments from large datasets. SentenceTransformers: It encodes text into vectors, enabling effective retrieval by FAISS. Steps to Build a RAG Pipeline Document Preparation and Preprocessing: Convert custom documents, wiki pages, or articles into text form and preprocess them (e.g., tokenization, removal of stop words). Index Construction: Use SentenceTransformers to encode preprocessed documents into vectors and create an index with FAISS for quick retrieval. Model Configuration: Choose a pre-trained Transformer model (e.g., BERT, T5) and configure it to accept retrieved information as input. Retrieval and Generation: For each user query, use FAISS to retrieve the most relevant information fragments and input them along with the original query into the Transformer model to generate accurate responses. Case Study The author demonstrated how to implement these steps using a medical FAQ document set. After preprocessing the text, encoding it with SentenceTransformers, and indexing with FAISS, they configured a T5 model to serve as the generator. The system could automatically retrieve and integrate relevant information from the documents to answer medical questions accurately. This process significantly improved the quality of responses and reduced the risk of incorrect answers being generated. Results and Future Prospects Testing showed that the RAG model outperformed traditional pre-trained models in answering specific domain questions. By incorporating external knowledge sources, RAG ensures that the model can stay up-to-date with the latest information, adapting to rapidly changing knowledge environments. Looking ahead, RAG technology is expected to expand its applications to areas such as legal consulting, technical support, and personalized education. Industry Feedback Industry experts see RAG as a critical enhancement to traditional generative models. By effectively integrating external information, RAG can significantly boost the practical value of AI systems. Hugging Face, a leading NLP platform, provides robust tools for RAG implementation, and FAISS and SentenceTransformers offer a solid foundation for efficient retrieval and information fusion. The author, a technology blogger active in the AI field, has shared the project code openly, encouraging other developers to explore and learn from their work. Cohere and Hugging Face are both driving forces in AI research and development. Cohere focuses on high-quality language models and multi-modal embedding tools, while Hugging Face is renowned for its Transformer library, a standard tool in NLP. Their contributions, along with advanced retrieval and generation engines like FAISS and Gemini, point towards a more integrated and dynamic future for AI applications.