finally published our latest research on text embeddings!
TLDR: Vector databases are NOT safe. 😳 Text embeddings can be inverted. We can do this exactly for sentence-length inputs and get very close with paragraphs...
finally published our latest research on text embeddings! TLDR: Vector databases are NOT safe. 😳 Text embeddings can be inverted. We can do this exactly for sentence-length inputs and get very close with paragraphs...
Today, I'm announcing Alexandria, an open-source initiative to embed the internet.
To start, we're releasing the embeddings for every research paper on the Arxiv. That's over 4m items, 600m tokens, and 3.07 billion vector dimensions.
We're not stopping here. Show more
Open source, high-throughput, fault-tolerant vector embedding pipeline
Simple API endpoint that ingests large volumes of raw data, processes, and stores or returns the vectors quickly and reliably
Simple API endpoint that ingests large volumes of raw data, processes, and stores or returns the vectors quickly and reliably
dgarnitz • GitHub - dgarnitz/vectorflow: VectorFlow is a high volume vector embedding pipeline that ingests raw data, transforms it into vectors and writes it to a vector DB of your choice.
A serverless vector database
built from first principles on object storage: 10-100x cheaper, usage-based pricing, massive scalability
built from first principles on object storage: 10-100x cheaper, usage-based pricing, massive scalability
turbopuffer
Harnessing the Universal Geometry of Embeddings
arxiv.org
- Cohere introduced Embed v3, an advanced model for generating document embeddings, boasting top performance on a few benchmarks. It excels in matching document topics to queries and content quality, improving search applications and retrieval-augmentation generation (RAG) systems. The new version offers models with 1024 or 384 dimensions, supports o