Databricks built a RAG agent it says can handle every kind of enterprise search

Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.
ZDNet

Want generative AI LLMs integrated with your business data? You need RAG

RAG is an approach that combines Gen AI LLMs with information retrieval techniques. Essentially, RAG allows LLMs to access external knowledge stored in databases, documents, and other information ...
Finextra

How Financial Services Companies Secure Data in a RAG GenAI Environment

Few industries have the competitive pressure to innovate — while under as much public and regulatory scrutiny for data privacy and security — as the financial services sector. So, as companies ...
dbta

Memgraph’s Atomic GraphRAG Speeds Up the Use of Graph-Based RAG Across Multiple Data Sources

Memgraph, a leader in open-source, in-memory graph databases, is introducing a new capability designed to accelerate business adoption of graph-based retrieval-augmented generation (GraphRAG), Atomic ...
VentureBeat

Snowflake builds new intelligence that goes beyond RAG to query and aggregate thousands of documents at once

Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document ...
Geeky Gadgets

How to use Embeddings in RAG with Llama-Index

In the realm of natural language processing (NLP), the concept of embeddings plays a pivotal role. It is a technique that converts words, sentences, or even entire documents into numerical vectors.