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Search PDFs at Scale with MongoDB and Nomic

Blog post from MongoDB

Post Details
Company
Date Published
Author
Luca Napoli, Richard Guo (Nomic)
Word Count
1,662
Company Posts That Month
24
Language
English
Hacker News Points
-
Post removed?
No
Summary

The latest advancements in AI have disrupted the way unstructured data is accessed, making it easier for companies to extract information from PDFs. Nomic Embed, a machine learning company specializing in explainable and accessible AI, has partnered with MongoDB Atlas Vector Search to provide an affordable and powerful AI-powered search solution for large PDF collections. This partnership enables organizations to efficiently process high volumes of PDFs and improve database retrieval speed. The combination of Nomic Embeddings and MongoDB Atlas offers a cost-effective and integrated toolset for advanced retrieval-augmented generation (RAG) applications, allowing users to ask natural language questions about the content of PDFs and receive structured answers. This technology has various industry use cases, including financial services, retail, and manufacturing, where it can significantly enhance information discovery and operational efficiency.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
Vector Search 30 2,613 257 91 +44%
RAG 5 1,795 223 72 +55%
LLM 4 3,398 379 136 +44%
Real-time 2 2,334 631 194 -8%
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