21 ms·

This sounds less like Embeddings as a Service and more like Semantic Search (which happens to be using embeddings) as a Service.

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?
- crosen99 4y ago
  The problem I'd like solved is that when I want to retrieve chunks of data for retrieval augmented generation, it's challenging to optimize the choice of embeddings model, chunking strategy, and overall retrieval algorithm. I'm not sure if that's the sort of problem you're focused on.
  - 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.
    - crosen99 4y ago
      In addition to the choices for how to chunk (i.e. defining chunk size, chunk boundaries, chunk overlap, etc.), there's also the question of what actually gets returned once finding the chunks that match. For example, perhaps I have a document with 100 1-page sections where each section is broken into roughly 5 chunks. I may get optimal performance in my RAG application not by retrieving the top K chunks from the index, but rather by returning the top K sections fom the document, where sections might be scored based on the number and scores of child chunks. It also might be useful to incorporate section summaries, etc., in the retrieval process.
      - 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 scores!
  - jn2clark 4y ago
    if you want some more options (chunking, models, +more) check here https://github.com/marqo-ai/marqo https://github.com/marqo-ai/marqo and an example for RAG using context aware trimming of text for fitting into context windows https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT-examples/article/article.md https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT...

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?

crosen99 4y ago
The problem I'd like solved is that when I want to retrieve chunks of data for retrieval augmented generation, it's challenging to optimize the choice of embeddings model, chunking strategy, and overall retrieval algorithm. I'm not sure if that's the sort of problem you're focused on.
- 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.
  - crosen99 4y ago
    In addition to the choices for how to chunk (i.e. defining chunk size, chunk boundaries, chunk overlap, etc.), there's also the question of what actually gets returned once finding the chunks that match. For example, perhaps I have a document with 100 1-page sections where each section is broken into roughly 5 chunks. I may get optimal performance in my RAG application not by retrieving the top K chunks from the index, but rather by returning the top K sections fom the document, where sections might be scored based on the number and scores of child chunks. It also might be useful to incorporate section summaries, etc., in the retrieval process.
    - 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 scores!
- jn2clark 4y ago
  if you want some more options (chunking, models, +more) check here https://github.com/marqo-ai/marqo https://github.com/marqo-ai/marqo and an example for RAG using context aware trimming of text for fitting into context windows https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT-examples/article/article.md https://github.com/marqo-ai/marqo/blob/mainline/examples/GPT...