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Updated 11 months ago

i am looking to use xinfrence through

i am looking to use xinfrence through

At a glance

The community members are discussing how to use the Xinference API for language model embeddings in the llama-index library. They consider extending the OpenAI embedding metadata or waiting for a dedicated llama-index-embeddings-openai-like feature. Some suggestions include creating a custom embedding class, using the ollama library, or modifying the OpenAIEmbedding class to work with the Xinference API. The community members also discuss the complexity of the llama-index codebase and how to best approach the integration.

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i am looking to use xinfrence through their openapi compatible rest api. for llm usage there is llama-index-llms-openai-like, but not for embedding.

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20 comments

do we need to somehow extend the openai embedding metadata with the hosted xinference models?

or should we wait for llama-index-embeddings-openai-like (i.e. open a feature request)

You can try creating a custom embedding class: https://docs.llamaindex.ai/en/stable/examples/embeddings/custom_embeddings.html#custom-embeddings-implementation

define your endpoint and update the method from where it can call to your model.

Second option would be to use https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/embeddings/llama-index-embeddings-ollama/llama_index/embeddings/ollama/base.py

Not sure if this will work out of the box by just providing the base_url to your model but can be a good place to start

Also: If you want to contribute you are most welcome for this feature!!💪

@WhiteFang_Jr i am not familiar with ollama, but from https://github.com/ollama/ollama/issues/305 it's unclaer if they also have an openai compatble api

teh custom embedding class is not what i want i think

i will try to monkeypath the openai predefined models

and see how far i get with that

the new code structure is a bit ... euhm ... complex

in volume it's 90% poetry.lock files

anyway

It's pretty organized tbh

For openai-like embeddings, you can just change the api_base and model_name kwargs for OpenAIEmbedding

@Logan M thx, i'll have a look

wrt the code, i guess it's in some hybrid state and the intend is to make them all separate repos? anyway, atm it looks odd but i understand why this is better

the get_engine code won't like unknown models

but it looks easy to patch it

if you pass model_name="model" it will skip get_engine

@Logan M ah, thanks. now i see it.

@Logan M wrt llm openai vs openai-like, it is onlyt the context length and if a model is chat an/or generate?

pretty much 🙂

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