MUO on MSN

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 ...
techtimes

How RAG and Vector Databases Improve LLMs

Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) are two distinct yet complementary AI technologies. Understanding the differences between them is crucial for leveraging their ...

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 ...
InfoQ

Enhancing AI Capabilities: Google Cloud Integrates Vector Search in Managed Databases

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...
Business Wire

New Vector Database in Salesforce Data Cloud Will Power AI, Analytics, and Automation Using LLMs with Business Data for Use Across the Einstein 1 Platform

Data Cloud Vector Database will unify all business data, including unstructured data like PDFs, emails, and transcripts, with CRM data to enable grounding of AI prompts and Einstein Copilot, ...
InfoWorld

Using PostgreSQL as a vector database in RAG

As many developers have come to realize, “Just use Postgres” is generally a good strategy. If and when your needs grow, you might want to swap in a larger and more performant vector database. Until ...
IPWatchdog

The Licensing Vector: A Fair Approach to Content Use in LLMs

“Despite efforts by LLM providers to avoid reproducing lengthy excerpts from single works, strings of words from ingested works persist in LLMs. This has significant legal implications….” A spate of ...