We witnessed one of the largest on-chain transaction ever - 10 million vector storage and search queries in just 24 hours.

Entire Wikipedia as Smart Contract Public RAG model, Embeddings Lives On-Chain.

Grinding dria.co | Building the Infinite Data Generation Engine @driaforall: Everything Synthetic Data, LLMs, and Multi-Agent Systems

- Enabled combining vectors and keywords for precise results.

- Free, permanent availability on decentralized storage.

- All packed in a dead-simple CLI `dria serve`

The Wikipedia.en Index is not just a tool; it's a gateway to enhancing local LLMs, including fine-tunes of corporate giants like Llama-70B, Mistral7B or Stable LLM. Stay tuned for benchmark evaluations!

How to Use?

Dria can be used with the Dria CLI. 

You only need to have Node and Docker installed, then you simply:

dria fetch uaBIB4kh7gYh6vSNL7V2eygfbyRu9vGZ_nJ6jKVn_x8


dria serve wikipedia.20220301.en

https://github.com/firstbatchxyz/dria-cli

You can unlock the full potential of local LLMs, bringing a large external knowledge corpus right to your local environment, working on HDDs instead of RAM.

- Embedded entire articles, not only titles with bge-large-en-v1.5

- Summarized Long articles for better retrieval