A knowledge can be created from a pdf Rle, a podcast, or a CSV Rle. Vector databases are multi-region and serverless. Dria is fully decentralized, and every index is available as a smart contract. Allowing permissionless access to knowledge without needing Drias services. Dria provides: An API for knowledge retrieval implementing search with natural language and query. A docker image for running local APIs without permission. Knowledge uploaded to Dria is public and permanent. 4.01.2024 16:56 Page 6 of 15 Knowledge Interface between Humans and AI FirstBatch DRIA operates as: A public RAG model. A Serverless and Multi-Region vector database, accessible with natural language and its API, characterized by its massive scalability and low fees. A Decentralized knowledge hub, where each vectorDB can be run locally in a permissionless manner. A platform enabling searchability of any knowledge. An open-source embedding lake.

First Principles Every index should be rebuildable with a different indexing algorithm or embedding model using the same knowledge. This is crucial for serving in state-of-the-art retrieval at all times. Every piece of knowledge should be accessible without depending on a central authority. Uploading quality knowledge to collective memory should be rewarded. Specs Dria supports the following Rle formats for knowledge creation: PDF, CSV, and audio Rles for podcasts. 4.01.2024 16:56 Page 7 of 15 Knowledge Interface between Humans and AI FirstBatch The indexing algorithms or embedding models can be changed without affecting the existing knowledge structure. It implements a reward system for uploading quality knowledge to the collective memory. Dria supports index types: HNSW, ANNOY Annoy is static. HNSW is dynamic. Vector databases are multi-region; requests are geo-steered. Regions currently include: ** us-east-1 us-west-1

The benchmark for deep-image-angular-96 with top_n=10, requests sent within the region. QPS wont throttle but performance may reduce after a certain threshold. 4.01.2024 16:56 Page 8 of 15 Knowledge Interface between Humans and AI FirstBatch Paving the way for open AGI: Core Solutions 1-) Cost of Knowledge at Its Lowest: Dria modernizes AI interfacing by indexing and delivering the world's knowledge via LLMs. Drias Public RAG Models Democratize knowledge access with cost-effective, shared RAG models. Today, Dria eSciently handles Wikipedia's entire 23GB database and its annual 56 billion traSc at just $258.391, a scale unattainable by other vector databases. Dria operates as a Decentralized Knowledge Hub serving multiple regions, offering natural language access and API integration. 4.01.2024 16:56 Page 9 of 15

Knowledge Interface between Humans and AI FirstBatch Dria supports multiple advanced indexing algorithms and embed models. This offers the \exibility to seamlessly switch between algorithms or embed models using the same data, ensuring consistently state-of-the-art retrieval quality. 2-) Contributing is easy and incentivized for everyone: Drias zero technical mumbo jumbo approach allows everyone to contribute knowledge to LLMs: Drias Drag & Drop Public RAG Model effortlessly transforms knowledge into a retrievable format with an intuitive drag-and-drop upload feature. As a permissionless and decentralized protocol, Dria creates an environment where knowledge uploaders can earn rewards for the value of their veriRable work: Users worldwide can contribute valuable knowledge with permissionless access to shared