[R-313] Adding more LLM/Embedding Providers

[jjmachan]

A running issue to get feedback on which LLM or Embedding Providers we need to add for you

_R-313

[kent-william007]

If the output of my model is obtained through a method call, for example, def query_model(query): return response. Then how should I write the code to load the model? In addition, the Embedding model is also generated through a similar method call, providing methods for converting words into vectors and comparing vector similarities.

[jjmachan]

hey @kent-william007, in that case you will have to implement

ragas/src/ragas/llms/base.py

Lines 64 to 83 in 1e7121b

	@abstractmethod
	def generate_text(
	self,
	prompt: PromptValue,
	n: int = 1,
	temperature: float = 1e-8,
	stop: t.Optional[t.List[str]] = None,
	callbacks: Callbacks = None,
	) -> LLMResult: ...
	@abstractmethod
	async def agenerate_text(
	self,
	prompt: PromptValue,
	n: int = 1,
	temperature: t.Optional[float] = None,
	stop: t.Optional[t.List[str]] = None,
	callbacks: Callbacks = None,
	) -> LLMResult: ...

on your own and make sure make sure that the result is of the type https://python.langchain.com/api_reference/core/outputs/langchain_core.outputs.llm_result.LLMResult.html

similarly for https://github.com/explodinggradients/ragas/blob/main/src/ragas/embeddings/base.py

let me know if you have further doubts, we'll help you out 🙂

btw are you on discord?

[kent-william007]

Is it possible to support model invocation through an API? This way, we can leverage the model capabilities provided by the server without needing to worry about which underlying model is being used.