Augmenting LLM Apps with Vector Databases

Andrew Ng on X: "Vector databases are a key part of many LLM ...

Vector databases are a key part of many LLM applications that need search or data retrieval, for example with Retrieval Augmented Generation ...

All You Need to Know about Vector Databases and How to Use ...

All You Need to Know about Vector Databases and How to Use Them to Augment Your LLM Apps _ by Dominik Polzer _ Towards Data Science - Free download as PDF ...

Using Vector Databases for Multimodal Search and Retrieval ...

Ask your questions using Slido: https://app.sli.do/event/eqcH8oqPiznBRhfg2VaA8R Join the Data Phoenix Discord community: ...

Vector databases in LLMs and search - InfoWorld

One of my first projects as a software developer was developing genetic analysis algorithms. We built software to scan electrophoresis samples ...

Use Your Data in LLMs With the Vector Database You Already Have

Vector databases allow you to enhance your LLM models with data from your internal data stores. Prompting the LLM with local, factual knowledge ...

The Utility of Vector Databases in LLMs - Daily Dose of Data Science

AssemblyAI LeMUR is a framework that allows you to build LLM apps on speech data in <2 minutes and <10 lines of code.

What is Retrieval Augmented Generation (RAG)? - Confluent

These embeddings are then stored in a vector database. A user or machine submits a query, which becomes a prompt for the LLM and goes to the generative AI ...

Integrating Vector Databases with LLM: Techniques & Challenges

This article explains vector database LLM integration to enable you to enhance the search and data retrieval capabilities of any LLM. It ...

Vector Databases in AI and LLM Use Cases - KDnuggets

Vector databases have risen in popularity thanks to the introduction of Generative AI to the public, especially the LLM. Many LLM products, such ...

Mitigating Security Risks in RAG LLM Applications | CSA

The key components are the knowledge source, indexer, vector database, retriever, and generator. The workflow involves indexing the knowledge ...

Vector Database Use Cases: Retrieval Augmented Generation - Zilliz

Learn LLMs' limitations, vector database benefits, and ... LLM Limitations. Lacking domain-specific ... apps today with Zilliz Cloud Serverless. Get ...

Vector Search for LLM and Generative AI Applications - DataStax

Similarity Search: ... Semantic Caching: ... High-Scale Database Applications: ... Enhanced Search Accuracy: By considering the semantic meaning of data points, ...

Leverage Vector Search to Use Embeddings and Generative AI

Introduction · The role of vector store and vector similarity search · Retrieval Augmented Generation (RAG) · Vector store in CrateDB · LLM ...

Retrieval-Augmented Generation (RAG): How to Work with Vector ...

Once similar vectors are identified, we extract the associated data from the database. This data is then combined with the original query to ...

RAG: LLM performance boost with retrieval-augmented generation

In the retrieval step (discussed above), the vector database fetches likely relevant chunks based on the prompt's embedding. “Relevance,” in ...

Announcing Select AI with Retrieval Augmented Generation (RAG ...

Announcing Select AI with Retrieval Augmented Generation (RAG) on Autonomous Database · Create and populate vector stores using your private data ...

RAG with Atlas Vector Search, LangChain, and OpenAI - MongoDB

However, there is also another emerging trend that many are unaware of: the rise of vector stores. Vector stores or vector databases play a ...

What Is A Vector Database? - IBM

Beyond APIs, many vector databases use programming-language-specific software development kits (SDKs) that can wrap around the APIs. Using the ...

What Is A Vector Database? Top 12 Use Cases - lakeFS

The retrieval augmented generation (RAG) method is used to give an LLM (Large Language Model) more information about the context it is given.

Intro to Retrieval Augmented Generation (RAG) - MLQ.ai

Vector Database: Vector databases, or vector ... applications that involve large ... LLM, we first need to transform the input text into vector embeddings.