Vectorize: NEW RAG Engine - Semantic Search, Embeddings, Vector Search, & More!

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Welcome to our channel! In this video, we explore Vectorize, a powerful new RAG engine that enhances semantic search, embeddings, vector search, and more. Discover how Vectorize can streamline your AI application development by automating data extraction and optimizing real-time RAG pipelines.

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Features:
- RAG Evaluation Tools: Automatically assesses and recommends the best chunking and embedding strategies for your data.
- RAG Pipeline Builder: Easily construct vector search indexes from unstructured data sources like documents and knowledge bases.
- Real-Time Updates: Keep your vector indexes synchronized with your unstructured data for always up-to-date results.

Whether you're an AI developer or a data engineer, Vectorize has something for everyone! Check out the links in the description for more resources and to get started.

Tags: Vectorize, RAG engine, semantic search, embeddings, vector search, AI development, data engineering, machine learning, cloud service, automation, data extraction

Hashtags: #Vectorize #RAGEngine #SemanticSearch #Embeddings #VectorSearch #ai #dataengineering w

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💗 Thank you so much for watching guys! I would highly appreciate it if you subscribe (turn on notifcation bell), like, and comment what else you want to see!
Love y'all and have an amazing day fellas. Thank you so much guys! Love yall!

intheworldofai

You would post this when I just spent $25 for replit 😂 DAMN DAMN DAMN I'm going broke trying to build an application.

AITester-ju

I have one pdf of total 125pages and each page covering different topics. Example page from 10to20 covers about Health, than the page 21 to 30 covers the topic Education.

First I need to store this pdf 125pages data into the vector database.

Then the actual requirement is if the user gives a topic as Health I need to retrieve the whole content from the page 10to20 and make it as simple blog.

How to make this?

vasanthravi

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