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Local LLM setup: how to use RAG and an embedding model to stop wasting context

Local LLMs degrade fast when context fills up. An embedding model and RAG pipeline fixes that — and runs entirely on your machine.

Architectural patterns for graph-enhanced RAG: Moving beyond vector search in production

The standard architecture — chunking documents, embedding them into a vector database, and retrieving top-k results via ...
InfoWorld

Retrieval-augmented generation, step by step

RAG is a pragmatic and effective approach to using large language models in the enterprise. Learn how it works, why we need it, and how to implement it with OpenAI and LangChain. Typically, the use of ...
VentureBeat

LLM integration takes Cloudera data lakehouse from Big Data to Big AI

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Cloudera got its start in the Big Data era and is now moving quickly into ...
InfoWorld

AWS looks to cut storage costs for LLM embeddings with Amazon S3 Vectors

AWS is previewing a specialized storage offering, Amazon S3 Vectors, that it claims can cut the cost of uploading, storing, and querying vectors by up to 90% compared to using a vector database, a ...
SiliconANGLE

AI-focused big data startup Unstructured raises $40M to make raw data LLM-ready

Generative artificial intelligence data processing startup Unstructured Technologies Inc. has closed on its second major funding round in less than a year, announcing a $40 million fundraising.
TechCrunch

Qdrant, an open source vector database startup, wants to help AI developers leverage unstructured data

For many, ChatGPT and the generative AI hype train signals the arrival of artificial intelligence into the mainstream. But while there’s little question of a seismic sea change these past six months ...