Generic Search is Dead.

Weaviate vector database · Intermediate ·🔍 RAG & Vector Search ·1y ago
Skills: RAG Basics85%

Key Takeaways

Introducing Personalization Agent for adaptive search using user profiles, real-time interactions, and LLM reasoning

Original Description

After introducing the Query Agent and the Transformation Agent, it's time to meet the Personalization Agent - search that adapts to each user. This isn't just another ranking tool. It's the first step toward truly personalized search that understands: • User profiles (preferences, interests, characteristics) • Real-time interactions (likes, dislikes, engagement patterns) • LLM reasoning included: Custom instructions for specific use cases Whether you're building a clothing brand or recipe platform (recipes below 🧑‍🍳), imagine search results that actually match what each user wants to see. The coolest part? You can choose classic ML or go full LLM with agent ranking that even explains its reasoning. Blog + Recipes: https://weaviate.io/blog/personalization-agent?utm_source=channels&utm_medium=w_social&utm_campaign=agents&utm_content=video_post_268065893 Tuana Socials - Twitter/X: https://x.com/tuanacelik - LinkedIn: https://www.linkedin.com/in/tuanacelik/ Femke Socials - Twitter/X: https://x.com/femke_plantinga - LinkedIn: https://www.linkedin.com/in/femke-plantinga/ - TikTok: https://www.tiktok.com/@femkeplant.ai - Instagram: https://www.instagram.com/femkeplant.ai/ ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT WITH US ▬▬▬▬▬▬▬▬▬▬▬▬ - Visit [weaviate.io](http://weaviate.io/) - Star us on GitHub https://github.com/weaviate/weaviate - Stay updated and subscribe to our newsletter: https://newsletter.weaviate.io/ - Try out Weaviate Cloud Services for free here: https://console.weaviate.cloud/ Got a question? - Forum: https://forum.weaviate.io/ - Slack: https://weaviate.io/slack Connect with us on - Twitter: https://twitter.com/weaviate_io - LinkedIn: https://www.linkedin.com/company/weaviate-io/
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
The AI Series : The Hardest Part of RAG Is Not the Vector DB — It’s Parsing PDFs
Learn to overcome the challenges of parsing PDFs in RAG systems, a crucial step in building production-grade document AI systems
Medium · AI
📰
The AI Series : The Hardest Part of RAG Is Not the Vector DB — It’s Parsing PDFs
Building production-grade document AI systems requires handling broken OCR, messy tables, and unstructured chaos in PDFs, which is harder than working with vector databases
Medium · Python
📰
Building a Grounded RAG Assistant: Why Citation Enforcement Matters More Than Retrieval
Learn why citation enforcement is crucial for building trustworthy RAG assistants and how to implement it
Dev.to AI
📰
Why RAG gives wrong answers (and how to fix retrieval failures)
Learn how to identify and fix retrieval failures in RAG systems, ensuring accurate answers from your AI model
Dev.to AI
Up next
LLM Wiki vs RAG Explained | Complete LLM Wiki Implementation Guide
Pavithra’s Podcast
Watch →