langchainjs VS rayon

*** all code examples are using LangChain TS on the main branch on September 22nd, 2023 (roughly version 0.0.153).

At the time of the prototype's development, the Langchain GitHub loader sent one request per file to fetch the repository sequentially, leading to prolonged download times. In our case about 2 minutes for the insights.opensauced.pizza repository. This issue was later resolved in hwchase17/langchainjs#2224, enabling parallel requests for faster retrieval.

Another llm orchestration library for js/ts

You will probably need to create a custom agent with custom tools to do what you want to do. Look at Langchain (seems like there is an open PR for Google calendar tools here: https://github.com/hwchase17/langchainjs/pull/1777). There are a lot of great integration examples on their website (including for vectorDB memory https://python.langchain.com/docs/modules/memory/how_to/vectorstore_retriever_memory)

You can checkout there docs (here)[https://js.langchain.com/docs/].

The strategy I tried, was to point scalablytyped, at langchainJS.

TL;DR the BANGER TWEET BRAINSToRMER 💥 🐦 🧠 is an open-source, fullstack React/Express/Postgres/Pinecone app that brainstorms new ideas and tweet drafts based on your own notes/ideas and the tweets of your favorite twitter users. This isn't a bot, but you can think of it rather as your personal twitter intern that monitors current twitter **trends**, keeps note of your **ideas**, helps you **brainstorm** new ones, and write **draft** tweets. It's your job to find and edit the best ideas before saving them to your personal notes database or tweeting them out from the app itself. it uses pg-boss cron jobs via Wasp, OpenAI, langChain, and Pinecone for the vector store https://github.com/vincanger/twitter-brainstorming-agent

It is not great. It has a lot of limitations, but can be used under certain conditions. https://github.com/hwchase17/langchainjs/pull/655

Do some operations with .iter() then later use rayon to parallelize. So you can show how easy is to add a dependency and how easy is to parallelize.

rayon: Async CPU runtime for parallelism.

In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.

With a lot of rayon, blood, sweat and tears I finally managed to simulate a million buildings at 60fps :) Feel free to AMA, game is Combine And Conquer

(see https://github.com/rayon-rs/rayon/tree/master/src/iter/plumbing)

I did some very basic testing with this crate : https://crates.io/crates/rayon and it seems to work :

Sequentially, generating tree-sitter AST for each file and querying for the links of each file takes around 2.3 seconds. However, I randomly remembered this crate rayon, and I decided to test it. It ended up improving the performance (just by changing 2 lines of code) to 200-300ms by parallelizing the iterators and tree-sitter queries. MAJOR.

Now if you really want to use Rust, you can rewrite only the part that are slowing down your consumer. It's easy by using Py03 and maturin. Maybe also rayon to parallelize.