Repository: explodinggradients/ragas
Issue: docs: add proper docs about the change in how v0.1 fixes the class of issues with passing your own LLMs and Embeddings
Root cause: Documentation Deficiency

**Describe the bug**
I was trying to follow the steps mentioned here: https://docs.ragas.io/en/v0.0.22/howtos/customisations/llms.html  to bring my own llm(AzureOpenAI) into ragas evaluation. I was interested in 3 of the following metrics:
 result = evaluate(
            dataset=dataset,
            metrics=[
                #context_precision,
                #faithfulness,
                answer_relevancy,
            ],
        )

Where I follow the steps mentioned to use LangchainLLM method to bring my azure endpoint into.
ragas_azure_model = LangchainLLM(llm)
        faithfulness.llm = ragas_azure_model
        answer_relevancy.llm = ragas_azure_model
        context_precision.llm = ragas_azure_model

But during evaluate, faithfulness and context_precision looks ok where i can get the result, But for answer_relevancy it keep complained on OpenAI API key not found.  I was using the same endpoint as stated above but how come only this specific metric bumps into error.. Is there any extra steps needed for "answer_relevancy" metric?
Ragas version: 0.0.22
Python version: 3.10.13

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**Error trace**

**Expected behavior**
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**Additional context**
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Seems to be related to the pip version, if you install from source it works.

> Seems to be related to the pip version, if you install from source it works.

Thanks @AugustDS  for your response. Means that i cannot direct pip install ragas? I have to build from scratch and install it myself?

For me this worked:
`pip uninstall ragas`

In Python site-packages dir:
`git clone https://github.com/explodinggradients/ragas && cd ragas`
`pip install -e .`

RUN cd /usr/local/lib/python3.10/site-packages/ && git clone https://github.com/explodinggradients/ragas && cd ragas && pip install -e .
but when i start it complain
from ragas.llms import LangchainLLM
ragas    | ImportError: cannot import name 'LangchainLLM' from 'ragas.llms' (/usr/local/lib/python3.10/site-packages/ragas/src/ragas/llms/__init__.py)

> RUN cd /usr/local/lib/python3.10/site-packages/ && git clone https://github.com/explodinggradients/ragas && cd ragas && pip install -e . but when i start it complain from ragas.llms import LangchainLLM ragas | ImportError: cannot import name 'LangchainLLM' from 'ragas.llms' (/usr/local/lib/python3.10/site-packages/ragas/src/ragas/llms/**init**.py)

@hteeyeoh It is now called LangchainLLMWrapper

https://github.com/explodinggradients/ragas/blob/ff449fccfb7d594c582071694b00f4c2192c5465/src/ragas/llms/base.py#L66-L74

And the usage is also different from pip version since langchain have dramatically changed in version 0.1.0. They deprecated `langchain.chat_models`, which ragas pip version is using, and replaced it with `from langchain_core.language_models import BaseLanguageModel`.

So I think the documents might need an update to catch on these changes?

Ok my mistakes. I tried to recompile with v0.0.22 tag yet the issue still occur:
ERROR:    OpenAI API key not found! Seems like your trying to use Ragas metrics with OpenAI endpoints. Please set 'OPENAI_API_KEY' environment variable
Will try to compile and use the latest main branch code and try

From https://docs.ragas.io/en/v0.0.22/howtos/customisations/azure-openai.html,
I observed following:
# init and change the embeddings
# only for answer_relevancy
azure_embeddings = AzureOpenAIEmbeddings(
    deployment="your-embeddings-deployment-name",
    model="your-embeddings-model-name",
    openai_api_base="https://your-endpoint.openai.azure.com/",
    openai_api_type="azure",
)
# embeddings can be used as it is
answer_relevancy.embeddings = azure_embeddings

Is it a must to have embeddings in case I want to measure answer_relevancy metrics? Cause so far without this i observed other metrics like context_precision, and faithfulness im able to get. Just answer_relevancy will hit into the error mentioned in title.


keeping this open but will use this to track the documentation change required so that you folks can actually use the fix as easily as possible.