Feb 12, 2024 · Learn how to build multimodal RAG systems using vector databases and LLMs. Compare methods for embedding text and images together to enhance AI applications

Oct 20, 2023 · Seamless question-answering across diverse data types (images, text, tables) is one of the holy grails of RAG. We’re releasing three new cookbooks that showcase the multi-vector retriever for RAG on documents that contain a mixture of content types.

Nov 28, 2023 · In the realm of information retrieval, Retrieval Augmented Generation (RAG) has emerged as a powerful tool for extracting knowledge from vast amounts of text data. This versatile technique ...

Mar 6, 2025 · Yes, an Image RAG (Multimodal RAG) approach exists. You can implement it using Azure OpenAI + Vector Search. Start by generating embeddings for images, store them in a vector database, retrieve relevant matches for queries, and …

Feb 12, 2025 · Retrieval-augmented generation, or RAG, is a framework that enhances LLM output using external knowledge. In multi-modal RAG systems, we utilize data other than simple text, such as image and audio data. In this article, we have implemented multi-modal RAG using text, audio, and image data.

Jul 7, 2024 · In this article, I talk about how I used the LangChain Expression Language (LCEL) to create a feature-rich RAG chatbot. I loaded the RAG pipeline with my own resume and project reports, so it...

Feb 13, 2025 · RAG Architecture (image by author) Let’s dive deeper into each component in detail! Pre-Processing Pipeline. Data Extraction — Converting documents into a structured format by extracting text, images, and other assets. Basic Extraction: Outputs flat text without structure.

Feb 13, 2025 · Retrieval in RAG Model (image by author) There are a few different techniques it can use to know what’s relevant: Indexing process — organizes the data into your vector database in a way that makes it easily searchable. This allows the RAG to access relevant information when responding to a query.

Apr 10, 2024 · Adopting the approach from the clothing matchmaker cookbook, we directly embed images for similarity search, bypassing the lossy process of text captioning, to boost retrieval accuracy. Using CLIP-based embeddings further allows fine-tuning with specific data or updating with unseen images.

We are leveraging the positional information of images in a vast array of multi-modal (text/image) documents for ingesting image information alongside text, followed by advanced retrieval and prompt engineering techniques to develop an RAG system that maintains the integrity of textual and visual data correlation in responses to queries ...