#VectorDB

The rise of #LLMs has made vector embeddings and #VectorDB immensely popular and useful, particularly in #AI, #GenerativeAI, and #LLMOps applications. 🌟💯🌟 @bindureddy from @abacusai explains in this “RAG - Vector Retrieval - Comprehensive Study”… ⬇️⬇️

RAG - Vector Retrieval - A Comprehensive Study The rise of LLMs has made vector embeddings immensely popular and useful. You must create embeddings for all your data - text images, speech, or a mix of these. When you have a large number of embeddings, you want to be able to retrieve it on the fly and send it to an LLM in real-time. Vector retrieval, in general, has been a pretty large research topic and has exploded lately. It's non-trivial to do a good job of retrieval. Over at Abacus AI, we spend a lot of time and effort experimenting, iterating, and inventing different retrieval techniques (research paper coming soon). This excellent and comprehensive study, "Foundations of Vector Retrieval" by Sebastian Bruch, gives you a sense of the depth and complexity of the topic (link in alt). It covers fundamental concepts along with advanced data structures and algorithms for vector retrieval.

From @Aurimas_Gr >> #Infographic explains RAGs (Retrieval Augmented Generation) systems, including #VectorDB (Vector #Databases), #LLMs, #AI, #GenerativeAI Source: Details (from source article): 𝟭: Split text corpus of the entire #KnowledgeBase into chunks - a chunk will represent a single piece of context available to be queried. Data of interest can be from multiple sources, e.g. Documentation in Confluence supplemented by PDF reports. 𝟮: Use the Embedding Model to transform each of the chunks into a vector embedding. 𝟯: Store all vector embeddings in a Vector Database. 𝟰: Save text that represents each of the embeddings separately together with the pointer to the embedding (we will need this later). Next constructing the answer to user question/query: 𝟱: Embed a question/query you want to ask using the same Embedding Model that was used to embed the knowledge base itself. 𝟲: Use the resulting Vector Embedding to run a query against the index in the Vector Database. Choose how many vectors you want to retrieve from the Vector Database - it will equal the amount of context you will be retrieving and eventually using for answering the query question. 𝟳: Vector DB performs an Approximate Nearest Neighbour (ANN) search for the provided vector embedding against the index and returns previously chosen amount of context vectors. The procedure returns vectors that are most similar in a given Embedding/Latent space. 𝟴: Map the returned Vector Embeddings to the text chunks that represent them. 𝟵: Pass a question together with the retrieved context text chunks to the LLM via prompt. Instruct the LLM to only use the provided context to answer the given question. This does not mean that no Prompt Engineering will be needed - you will want to ensure that the answers returned by LLM fall into expected boundaries, e.g. if there is no data in the retrieved context that could be used make sure that no made up answer is provided.

How RAGs (Retrieval Augmented Generation) work... #infographic source: #GenerativeAI #VectorDB #AI #MachineLearning #DeepLearning #DataScience #Knowledgebase #LLMs

Here's a walkthrough for building your own state-of-the-art multilingual search w/ some performance + cost comparison between embedding providers Credit to @Nils_Reimers for the vast majority of the code, data, etc here #NLProc #vectordb

The rise of #LLMs has made vector embeddings and #VectorDB immensely popular and useful, particularly in #AI, #GenerativeAI, and #LLMOps applications. 🌟💯🌟 @bindureddy from @abacusai explains in this “RAG - Vector Retrieval - Comprehensive Study”… ⬇️⬇️

Example of a simple #RAG-based Chatbot to query your Private #KnowledgeBase: ———— #VectorDB #LLMs #GenAI #AI #GenerativeAI #Infographic #DeepLearning #MachineLearning

#leann #vectordb #database #bytemuni