?? Dive into the Multi-Vector Retriever - RAG for Tables, Text, and Images! ???

Excited to tell you about a cool new thing we've discovered in our journey to make getting information super easy – the Multi-Vector Retriever for RAG on tables, text, and images! ????

This fantastic tool combines powerful language models like GPT4-V, LLaVA, and Fuyu-8b with the Multi-Vector Retriever, with the main goal of revolutionizing how we extract information, especially when dealing with tables, text, and images. ????????

?? Why It Matters:

The Multi-Vector Retriever is made to make finding information super easy! Whether checking out tables, reading through text details, or figuring out what's happening in pictures, it's your all-in-one tool for getting knowledge in a simple way. ??????

??? Key Features:

? Tables: Pinpoint accuracy with top-notch retrieval on structured data.

? Text: Contextual brilliance with summaries and broader contextual insights.

? Images: Visual storytelling with multimodal approaches and image-focused retrieval.

???? How It Works:

The Multi-Vector Retriever leverages the brilliance of language models and Retrieval Augmented Generation (RAG) techniques to enhance information assimilation. Think of it as your go-to solution for navigating through the diverse landscape of data formats effortlessly. ??????

?? Discover the Future of Info Retrieval! Ready to explore? Jump in now and see the magic unfold! ???

Combining advanced language models like GPT4-V, LLaVA, and Fuyu-8b with the Multi-Vector Retriever introduces a sophisticated approach, especially when dealing with image-related queries. These Large Language Models (LLMs) have two key ways of learning new information: through weight updates, such as fine-tuning, and Retrieval Augmented Generation (RAG). The latter involves passing relevant context to the LLM via a prompt, and it holds significant promise for factual recall.

RAG's strength lies in its ability to merge the reasoning capability of LLMs with external data sources. This combination proves particularly powerful for enterprise data, enhancing the model's capacity to recall and comprehend information effectively. In essence, it enriches the understanding of data by marrying the inherent knowledge within the LLMs with the broader context provided by external sources.

This integration of multimodal Large Language Models with the Multi-Vector Retriever signifies a strategic alignment of cutting-edge technologies. It not only refines the learning process of language models but also augments their capacity to handle complex image-related inquiries. This sophisticated synergy holds tremendous potential, especially in scenarios where a nuanced understanding of data, particularly in the context of images, is paramount. ??????

??? Techniques to Enhance RAG (Retrieval Augmented Generation)

• ???? Base case RAG: Choose the Top K from the document to get accurate answers. ??
• ???? Summary embedding: Fetch summaries of documents to understand the context, and grab the full document to bring all the information together. ???
• ???? Windowing: Choose the top K bits from embedded chunks or sentences to see a bigger picture. ????
• ???? Metadata filtering: Select the top K pieces with chunks filtered by metadata. ????
• ???? Fine-tune RAG embeddings: Customize the embedding model to match your data perfectly. ???
• ???? 2-stage RAG: Start with a keyword search, then do a smart Top K retrieval for even better accuracy. ????

?? Multimodal Approaches: Redefining Image-Related RAG Queries with 3 Techniques

• ???? Using Mix-and-Match Embeddings: Take advantage of blended embeddings like CLIP to mix image and text data. This opens up the possibility for similarity-based retrieval and easy connections to images stored in a document. After that, send the raw images and text pieces to a clever mixed LLM for putting it all together. ??????
• ???? Harnessing Multimodal LLM Power: Use a smart multimodal LLM to create text summaries from images. Then, embed and retrieve to put together answers. Refer to raw text bits or tables from a document store, leaving out the images. ??????

???? Similar to Option 2, but a bit different: Concentrate on getting summary of pictures while still keeping track of the original images. This method works well in situations where using different types of data isn't possible. ??????

?? Explore Further ??

• LangChain blog: ?? LangChain blog
• Multi-vector retriever: ??Multi-vector retriever

?? Cookbooks

• ?? Semi-structured (tables + text) RAG: Semi-structured (tables + text) RAG
• ?? Multi-modal (text + tables + images) RAG: Multi-modal (text + tables + images) RAG
• ?? Private multi-modal (text + tables + images) RAG: Private multi-modal (text + tables + images) RAG