Jina AI itself is also a great framework to expose APIs from deep neural net models and deploy them to Kubernetes clusters, which I think is very promising, but they didn't get as much hype as I predicted that they deserved.
Jina AI launches open-source 8k text embedding
51–60 of 217 posts
Re: Jina AI launches open-source 8k text embedding
#52Earlier quoted context omitted.
LLMs and embedding models are certainly different, but it's a useful benchmark to calibrate expectations. OpenAI released text-embedding-ada-002 a year ago, and they describe the ada model as[1] "the original GPT-3 base model [...] capable of very simple tasks, usually the fastest model in the GPT-3 series". It's fair to expect GPT3-level results - not GPT 3.5 and certainly not open-source tiny GPT4 as some might thi…
No, you're confusing two things here. "text-ada-001" is LLM in the GPT3 family, described as "Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost" "text-embedding-ada-002" is entirely different - that page describes it as "Our second generation embedding model, text-embedding-ada-002 is a designed to replace the previous 16 first-generation embedding models at a fraction of th…
tl;dr OpenAI is bad at product naming.
Re: Jina AI launches open-source 8k text embedding
#53Earlier quoted context omitted.
LLMs and embedding models are certainly different, but it's a useful benchmark to calibrate expectations. OpenAI released text-embedding-ada-002 a year ago, and they describe the ada model as[1] "the original GPT-3 base model [...] capable of very simple tasks, usually the fastest model in the GPT-3 series". It's fair to expect GPT3-level results - not GPT 3.5 and certainly not open-source tiny GPT4 as some might thi…
No, you're confusing two things here. "text-ada-001" is LLM in the GPT3 family, described as "Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost" "text-embedding-ada-002" is entirely different - that page describes it as "Our second generation embedding model, text-embedding-ada-002 is a designed to replace the previous 16 first-generation embedding models at a fraction of th…
OpenAI doesn't say directly what text-embedding-ada-002 is, but in the release blog post they show that performance is comparable to davinci/curie, which places it firmly in the universe of GPT3. I understand it's not a straight line comparison, but to me it's still a useful mental heuristic about what to expect.
[1] https://openai.com/blog/new-and-improved-embedding-model (see "Model improvements")
Re: Jina AI launches open-source 8k text embedding
#54Pardon my ignorance in advance but could it be used to "chat" with PDFs and websites? I am looking for OpenAI alternatives as I am in learning phase
Re: Jina AI launches open-source 8k text embedding
#55Earlier quoted context omitted.
I think I'm missing something: like, yeah, it's vector search for bigger text chunks. But arguably vector search with bigger text chunks is _definitively_ worse -- this isn't doing summarization, just turning about 25 pages of text to 1024 floats, which you then can use cosine similarity to measure the semantic similarity to other text I'd much rather know what paragraph to look in than what 25 pages to look in
I imagine it's more useful for finding related articles and clustering things than for semantic search, which will work much better against smaller chunks - especially if you're implementing Retrieval Augmented Generation.
I think the point is: if you compress 25 pages of text into 1024 floats, you will lose a ton of information, regardless of what the use case is, so you're probably still better of with chunking.
Re: Jina AI launches open-source 8k text embedding
#56I just shipped a new llm-embed-jina plugin for my LLM tool which provides access to these new Jina models: https://github.com/simonw/llm-embed-jina Here's how to try it out. First, install LLM. Use pip or pipx or brew: brew install llm Next install the new plugin: llm install llm-embed-jina You can confirm the new models are now available to LLM by running: llm embed-models You should see a list that includes "jina-e…
The only feedback I had from your embedding post was
wish we could create the array of floating points without openai
Re: Jina AI launches open-source 8k text embedding
#57Earlier quoted context omitted.
I imagine it's more useful for finding related articles and clustering things than for semantic search, which will work much better against smaller chunks - especially if you're implementing Retrieval Augmented Generation.
I think the point is: if you compress 25 pages of text into 1024 floats, you will lose a ton of information, regardless of what the use case is, so you're probably still better of with chunking.
I've been getting great results for related documents by embedding entire blog posts, e.g. here: https://til.simonwillison.net/gis/pmtiles#related
I'm not sure how I would do that after chunking.
Re: Jina AI launches open-source 8k text embedding
#58How well do LLMS like this work with a non-English language? Or are these open source models limited to English?
Re: Jina AI launches open-source 8k text embedding
#59Pardon my ignorance in advance but could it be used to "chat" with PDFs and websites? I am looking for OpenAI alternatives as I am in learning phase
Check out my little side project for chatting with PDFs. You should be able to load most models including this one. https://github.com/clarkmcc/chitchat
Re: Jina AI launches open-source 8k text embedding
#60Earlier quoted context omitted.
Is this what you mean by RAG? https://www.promptingguide.ai/techniques/rag ?
I have an explanation of RAG in the context of embeddings here: https://simonwillison.net/2023/Oct/23/embeddings/#answering-...
You could just sum it up for us all rather than do a divert to your blog?
It's Retrieval Augmented Generation btw.
To quote:
> The key idea is this: a user asks a question. You search your private documents for content that appears relevant to the question, then paste excerpts of that content into the LLM (respecting its size limit, usually between 3,000 and 6,000 words) along with the original question.
> The LLM can then answer the question based on the additional content you provided.