jxodwyer1

8 ms·

1.
How to Get to Production with LLMs(getmetal.io)
3 points by jxodwyer1 3y ago|0 comments
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Slack Similarity Search, Metal X Beam(docs.beam.cloud)
4 points by jxodwyer1 3y ago|0 comments
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by jxodwyer1 4y ago
We will be adding hdbscan in the coming days! Right now we only offer kmeans but for dimensionality reduction we offer pca, tnse, & pca .
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by jxodwyer1 4y ago
Thank you so much for your questions! - As a managed service there are some overheads. We need to auth, validate and parse the inputs, fetch the index that is getting queried as we then need to use the index’s model to generate the embeddin
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by jxodwyer1 4y ago
right on! Sounds like you have a lot of the foundation for your infra setup, which is great
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by jxodwyer1 4y ago
We have some open-source tooling in the works! :) We understand that some users are sensitive to managed services, we’re starting with this, but we’re planning to open source tools to improve developer experience around information retrieva
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by jxodwyer1 4y ago
You’re right! If you want to do that in a notebook, it’s pretty straightforward. But if you want to have it running in production, it’s a bit more complicated. Also, providing users with a gui to run these operations without a notebook has
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by jxodwyer1 4y ago
Hey! I appreciate the comment, and we would love to hear about other pains you've encountered. I can't find a way to DM on HN, but please email us at founders@getmetal.io, and we can connect there!
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by jxodwyer1 4y ago
Redis provides indexes for vector similarity. And we have a lot of experience with Redis. We have plans to expand into offering other data stores, like Qdrant
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by jxodwyer1 4y ago
We love langchain! That’s a great idea – we want to provide examples using langchain and look into ways to better integrate into libraries like this.
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by jxodwyer1 4y ago
Chroma is awesome <3 - We have some overlap with them as we store the embeddings. But, we provide additional operations on top of the data, such as clustering/fine-tuning. We're also looking into open-sourcing some tools in the
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by jxodwyer1 4y ago
It does compare with them, but we want to lower the barrier of entry for any developer to build features that use embeddings. So we want to give regular software engineers superpowers in providing this technology within their stack and out
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by jxodwyer1 4y ago
We store the vectors, but we also provide additional operations that would require additional code/infra if you just use a vectorDB. We also have the infrastructure in place to ingest all the data, generate the embeddings (we also take
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by jxodwyer1 4y ago
Thank you! We’ve looked into instructor-xl, and it’s really awesome! We also accept custom embeddings, allowing developers to use whatever model they want. But we want to keep adding models to allow for better experimentation.
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by jxodwyer1 4y ago
We’ve looked into FAISS and Milvus. Milvus is possibly an excellent option for us in the future. What’s your experience with these so far?
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by jxodwyer1 4y ago
There’s some overlap with information retrieval for chat GPT applications. As a managed service, we handle all of the infrastructure and maintenance. Also, we support additional use cases for web applications/backends, such as clusteri
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by jxodwyer1 4y ago
Whoa! Thanks for sharing -- we haven't seen this!
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by jxodwyer1 4y ago
This is great, and that makes a ton of sense! Would you want to define + experiment with these various configurations yourself explicitly, or would you expect a system to determine this automatically? I like the concept of rolling-up chunk
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by jxodwyer1 4y ago
We don’t support this use case yet, but we could by exposing an API to update the non-filterable metadata of the records. This is a cool use case; we would love to learn more about it. Would you want to create embeddings from the product na
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by jxodwyer1 4y ago
Hey! I'd love to understand what you're referring to with this
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by jxodwyer1 4y ago
We don’t offer this through the API, yet! You can however run clustering in the UI. We are working on exposing classification so that you can generate clusters on specific topics. We plan to offer both in the API within the next week or two
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by jxodwyer1 4y ago
Qdrant is awesome :). Redis also supports metadata filtering we’re currently building. We are considering adding a different data store option and Qdrant might be our next choice.
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by jxodwyer1 4y ago
Redis provides indexes for vector similarity. And we have a lot of experience with Redis. We see a future where we can offer more than one datastore, and we’ve been considering Qdrant as the next datastore to support.
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by jxodwyer1 4y ago
We agree with the sentiment; we’re currently figuring out the pieces we want to open source, as much of it is just infra (like the ingest pipeline). But the search server and some of our future work around memory will get open-sourced first
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by jxodwyer1 4y ago
We agree; this is precisely the problem area we’re focusing on!! We’re currently working on the ability for users to specify chunking strategies while providing a ton of guidance on this selection based on their particular data.
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by jxodwyer1 4y ago
Great question; while it’s still super early, we believe that some of the most critical problems to solve will involve making current APIs compatible with AI use cases. Products like ChatGPT Plugins are game changers, but they will still be
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by jxodwyer1 4y ago
We see ourselves a layer above vectorDB; we use Redis to index the data. We focused on building the ingest pipeline and operations on top of the embeddings, such as clustering and fine-tuning (embedding customization). Ultimately we want to
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by jxodwyer1 4y ago
Yes, we do! We allow users to run `metal.tune` to determine whether two vectors should be close to each other. Then we use that to recalculate the embeddings similar to the customized embeddings cookbook from OpenAI. Then the queries get em
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by jxodwyer1 4y ago
Search is one use case we support, but you can perform a few other operations on your data, like clustering or fine-tuning. We're also working on a classification feature. Are there other async jobs you'd like to see?
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by jxodwyer1 4y ago
Hey! We do support multiple versions of an Index under an App. When you fine-tune an embedding, we autogenerate the new embeddings for the entire dataset into a unique index. We store the raw data uploaded to our system via text or file imp

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