4 items tagged “jina”
2024
Bridging Language Gaps in Multilingual Embeddings via Contrastive Learning (via) Most text embeddings models suffer from a "language gap", where phrases in different languages with the same semantic meaning end up with embedding vectors that aren't clustered together.
Jina claim their new jina-embeddings-v3 (CC BY-NC 4.0, which means you need to license it for commercial use if you're not using their API) is much better on this front, thanks to a training technique called "contrastive learning".
There are 30 languages represented in our contrastive learning dataset, but 97% of pairs and triplets are in just one language, with only 3% involving cross-language pairs or triplets. But this 3% is enough to produce a dramatic result: Embeddings show very little language clustering and semantically similar texts produce close embeddings regardless of their language
Jina AI Reader. Jina AI provide a number of different AI-related platform products, including an excellent family of embedding models, but one of their most instantly useful is Jina Reader, an API for turning any URL into Markdown content suitable for piping into an LLM.
Add r.jina.ai to the front of a URL to get back Markdown of that page, for example https://r.jina.ai/https://simonwillison.net/2024/Jun/16/jina-ai-reader/ - in addition to converting the content to Markdown it also does a decent job of extracting just the content and ignoring the surrounding navigation.
The API is free but rate-limited (presumably by IP) to 20 requests per minute without an API key or 200 request per minute with a free API key, and you can pay to increase your allowance beyond that.
The Apache 2 licensed source code for the hosted service is on GitHub - it's written in TypeScript and uses Puppeteer to run Readabiliy.js and Turndown against the scraped page.
It can also handle PDFs, which have their contents extracted using PDF.js.
There's also a search feature, s.jina.ai/search+term+goes+here, which uses the Brave Search API.
Exploring Hacker News by mapping and analyzing 40 million posts and comments for fun
(via)
A real tour de force of data engineering. Wilson Lin fetched 40 million posts and comments from the Hacker News API (using Node.js with a custom multi-process worker pool) and then ran them all through the BGE-M3 embedding model using RunPod, which let him fire up ~150 GPU instances to get the whole run done in a few hours, using a custom RocksDB and Rust queue he built to save on Amazon SQS costs.
Then he crawled 4 million linked pages, embedded that content using the faster and cheaper jina-embeddings-v2-small-en model, ran UMAP dimensionality reduction to render a 2D map and did a whole lot of follow-on work to identify topic areas and make the map look good.
That's not even half the project - Wilson built several interactive features on top of the resulting data, and experimented with custom rendering techniques on top of canvas to get everything to render quickly.
There's so much in here, and both the code and data (multiple GBs of arrow files) are available if you want to dig in and try some of this out for yourself.
In the Hacker News comments Wilson shares that the total cost of the project was a couple of hundred dollars.
One tiny detail I particularly enjoyed - unrelated to the embeddings - was this trick for testing which edge location is closest to a user using JavaScript:
const edge = await Promise.race(
EDGES.map(async (edge) => {
// Run a few times to avoid potential cold start biases.
for (let i = 0; i < 3; i++) {
await fetch(`https://${edge}.edge-hndr.wilsonl.in/healthz`);
}
return edge;
}),
);
2023
Execute Jina embeddings with a CLI using llm-embed-jina
Berlin-based Jina AI just released a new family of embedding models, boasting that they are the “world’s first open-source 8K text embedding model” and that they rival OpenAI’s text-embedding-ada-002 in quality.