substrate VS MeiliSearch

Substrate — a framework for building application-specific blockchains (written in Rust);

frame-benchmarking = { version = "4.0.0-dev", default-features = false, optional = true, git = "https://github.com/paritytech/substrate.git", branch = "polkadot-v1.0.0" }

To offer some perspective outside of the typical "all crypto is a scam", Parity is doing some cool stuff with a rust modular blockchain library called Substrate https://github.com/paritytech/substrate.

Rust takes the cake in the blockchain space: Substrate, Cosmos (CosmWasm), and Solana. All of the zero knowledge cryptography libraries used for layer 2 solutions are written in Rust, compiling to Wasm (see arkworks, Risc0). Ethereum's next version of smart contracts will even use a restricted subset of Wasm ("Ewasm") instead of EVM.

Verifying NPoS election solution graph and its score is a memory-intensive operation that needs to be performed within a single block time. Under the current runtime constraints, a solution graph with 22,500 nominators can be verified well within the block production time on Polkadot. There are plans to improve on this and implement multi-block election solution verification, after which, the NPoS system can scale to incorporate more nominators.

Fast-unstake is now available on Kusama. This allows instant unstaking if you have not participated in staking in the last 28 eras. https://github.com/paritytech/substrate/pull/12129

First, any outstanding multisig calls (ones where one signatory has signed, but not the threshold) will not be able to execute. Please either finish your multisig calls before this upgrade, or wait until after it is completed. https://github.com/paritytech/substrate/pull/12072

Github: https://github.com/paritytech/substrate

We needed to introduce a new service for search. As we settled on using meilisearch, we needed a way to sync updates on our models with the records in meilisearch. We could've continued to use callbacks but we needed something better.

Meilisearch

In this case, a good strategy is to use vector search only when the keyword/prefix search returns none or just a small number of results. A good candidate for this is MeiliSearch. It uses custom ranking rules to provide results as fast as the user can type.

With the VertexAI endpoint set up and tested, our next step is to work with LibreChat. LibreChat is an open-source ChatGPT clone that can integrate with various AI models, including the PaLM 2 models via the VertexAI API. It's built using React, MongoDB, and Meilisearch technologies.

Meilisearch seems like it is the best open source option.

https://www.meilisearch.com/

Starting with v1.3, you can use Meilisearch as a vector store. Meilisearch allows you to store vector embeddings alongside your documents conveniently. You will need to create the vector embeddings using your third-party tool of choice (Hugging Face, OpenAI). As we published the first v1.3 release candidate, you can try out vector search today.

I work at Meilisearch, an open-source search engine built in Rust. 🦀

https://www.meilisearch.com/ https://github.com/meilisearch