4 ms·

URGENT - Does anyone have an alternative to OpenAI's embeddings API? I do have alternative to GPT's API (e.g. Anthropic Claude) but I'm not able to use them wit

URGENT - Does anyone have an alternative to OpenAI's embeddings API? I do have alternative to GPT's API (e.g. Anthropic Claude) but I'm not able to use them without embeddings API (used to generate semantic representation of my knowledge base and also to create embeddings from user's queries). We need to have an alternative to OpenAI's embeddings as a fallback in case of outages.

politelemon 3y ago
What about Azure? You can set up an ADA 002 Embeddings deployment there.
deleted 3y ago
[deleted]
comment_ran 3y ago
[dead]
thecal 3y ago
Choose whichever one outperforms ada-002 for your task here: https://huggingface.co/spaces/mteb/leaderboard https://huggingface.co/spaces/mteb/leaderboard
fitzoh 3y ago
Amazon Bedrock has an embeddings option
btown 3y ago
https://www.anthropic.com/product https://www.anthropic.com/product recommends the open-source SBERT: https://www.sbert.net/examples/applications/computing-embeddings/README.html https://www.sbert.net/examples/applications/computing-embedd... Highly recommend preemptively saving multiple types of embeddings for each of your objects; that way, you can shift to an alternate query embedding at any time, or combine the results from multiple vector searches. As one of my favorite quotes from Contact says: "first rule in government spending: why build one when you can have two at twice the price?" https://www.youtube.com/watch?v=EZ2nhHNtpmk https://www.youtube.com/watch?v=EZ2nhHNtpmk
nonfamous 3y ago
Azure OpenAI Service is up, and provides the same models as OpenAI https://azure.status.microsoft/status https://azure.status.microsoft/status
- capableweb 3y ago
  Is it still "private" as in you have to request access?
  - nonfamous 3y ago
    It’s publicly available, but you still do have to request access I believe.
kordlessagain 3y ago
I've implemented alternate embeddings in SlothAI using Instructor, which is running an early preview at https://ai.featurebase.com/ https://ai.featurebase.com/. Currently working on the landing page, which I'm doing manually because ChatGPT is down. The plan is to add Llama 2 completions to the processors, which would include dictionary completion (keyterm/sentiment/etc), chat completion, code completion, for reasons exactly like what we're discussing. Here's the code for the Instructor embeddings: https://github.com/FeatureBaseDB/Laminoid/blob/main/sloth/sloth.py https://github.com/FeatureBaseDB/Laminoid/blob/main/sloth/sl... To do Instructor embeddings, do the imports then reference the embed() function. It goes without saying that these vectors can't be mixed with other types of vectors, so you would have to reindex your data to make them compatible.
enoch2090 3y ago
This reminds us that, what if our databases are maintained using OpenAI's embeddings, and the API suddenly goes down? How do we find alternatives to match the already generated database?
- rolisz 3y ago
  I don't think you can do that easily. If you already have a list of embeddings from a different model, you might be able to generate an alignment somehow, but in general, I wouldn't recommend it.
  - enoch2090 3y ago
    That's my point, maybe VectorDBs in production should have a fallback mechanism, for the documents inserted, 1. Generate embeddings using services such as OpenAI, which is usually more powerful; 2. Generate backup embeddings using local, more stable models, such as Llama2 embeddings or simply some BERT-family-model (which is more affordable). When outages comes up you simply switch from one vector space to another. Though possible, model alignments are much harder and more expensive to achieve.
Silasdev 3y ago
To my knowledge, you cannot mix embeddings from different models. Each dimension has a different meaning for each model.
- TeMPOraL 3y ago
  There's been some success in creating translation layers that can convert between different LLM embeddings, and even between LLM and an image generation model.
tropicalbeach 3y ago
Oh no the 3 line ai wrapper apps are panicking because they actually don't know to write any code.
m3kw9 3y ago
Be careful because one embedding may not be compatible with your current embeddings
deleted 3y ago
[deleted]

politelemon 3y ago

What about Azure? You can set up an ADA 002 Embeddings deployment there.

deleted 3y ago

[deleted]

comment_ran 3y ago

[dead]

thecal 3y ago

Choose whichever one outperforms ada-002 for your task here: https://huggingface.co/spaces/mteb/leaderboard https://huggingface.co/spaces/mteb/leaderboard

fitzoh 3y ago

Amazon Bedrock has an embeddings option

btown 3y ago

https://www.anthropic.com/product https://www.anthropic.com/product recommends the open-source SBERT: https://www.sbert.net/examples/applications/computing-embeddings/README.html https://www.sbert.net/examples/applications/computing-embedd... Highly recommend preemptively saving multiple types of embeddings for each of your objects; that way, you can shift to an alternate query embedding at any time, or combine the results from multiple vector searches. As one of my favorite quotes from Contact says: "first rule in government spending: why build one when you can have two at twice the price?" https://www.youtube.com/watch?v=EZ2nhHNtpmk https://www.youtube.com/watch?v=EZ2nhHNtpmk

nonfamous 3y ago

Azure OpenAI Service is up, and provides the same models as OpenAI https://azure.status.microsoft/status https://azure.status.microsoft/status

capableweb 3y ago
Is it still "private" as in you have to request access?
- nonfamous 3y ago
  It’s publicly available, but you still do have to request access I believe.

kordlessagain 3y ago

I've implemented alternate embeddings in SlothAI using Instructor, which is running an early preview at https://ai.featurebase.com/ https://ai.featurebase.com/. Currently working on the landing page, which I'm doing manually because ChatGPT is down. The plan is to add Llama 2 completions to the processors, which would include dictionary completion (keyterm/sentiment/etc), chat completion, code completion, for reasons exactly like what we're discussing. Here's the code for the Instructor embeddings: https://github.com/FeatureBaseDB/Laminoid/blob/main/sloth/sloth.py https://github.com/FeatureBaseDB/Laminoid/blob/main/sloth/sl... To do Instructor embeddings, do the imports then reference the embed() function. It goes without saying that these vectors can't be mixed with other types of vectors, so you would have to reindex your data to make them compatible.

enoch2090 3y ago

This reminds us that, what if our databases are maintained using OpenAI's embeddings, and the API suddenly goes down? How do we find alternatives to match the already generated database?

rolisz 3y ago
I don't think you can do that easily. If you already have a list of embeddings from a different model, you might be able to generate an alignment somehow, but in general, I wouldn't recommend it.
- enoch2090 3y ago
  That's my point, maybe VectorDBs in production should have a fallback mechanism, for the documents inserted, 1. Generate embeddings using services such as OpenAI, which is usually more powerful; 2. Generate backup embeddings using local, more stable models, such as Llama2 embeddings or simply some BERT-family-model (which is more affordable). When outages comes up you simply switch from one vector space to another. Though possible, model alignments are much harder and more expensive to achieve.

Silasdev 3y ago

To my knowledge, you cannot mix embeddings from different models. Each dimension has a different meaning for each model.

TeMPOraL 3y ago
There's been some success in creating translation layers that can convert between different LLM embeddings, and even between LLM and an image generation model.

tropicalbeach 3y ago

Oh no the 3 line ai wrapper apps are panicking because they actually don't know to write any code.

m3kw9 3y ago

Be careful because one embedding may not be compatible with your current embeddings

deleted 3y ago

[deleted]