Repository: explodinggradients/ragas
Issue: Tried model using lanchain_together
Root cause: Interface Contract Mismatch

[ ] I checked the [documentation](https://docs.ragas.io/) and related resources and couldn't find an answer to my question.

**Your Question**
Getting Error: **AttributeError: 'Generation' object has no attribute 'message'**

**Code Examples**
```py
from ragas.dataset_schema import SingleTurnSample
from ragas.metrics import NoiseSensitivity
from langchain_together import Together
import asyncio
from ragas.llms import LangchainLLMWrapper
from custom_embeddings import LocalEmbeddings

generator_llm = Together(
        model="mistralai/Mistral-7B-Instruct-v0.2",
        temperature=0.7,
        top_k=50,
        top_p=0.7,
        repetition_penalty=1,
    )
evaluator_llm = LangchainLLMWrapper(Together(
        model="mistralai/Mistral-7B-Instruct-v0.2"
    ))
evaluator_embedding = LocalEmbeddings(model_path="")


sample = SingleTurnSample(
    user_input="What is the Life Insurance Corporation of India (LIC) known for?",
    response="The Life Insurance Corporation of India (LIC) is the largest insurance company in India, known for its vast portfolio of investments. LIC contributes to the financial stability of the country.",
    reference="The Life Insurance Corporation of India (LIC) is the largest insurance company in India, established in 1956 through the nationalization of the insurance industry. It is known for managing a large portfolio of investments.",
    retrieved_contexts=[
        "The Life Insurance Corporation of India (LIC) was established in 1956 following the nationalization of the insurance industry in India.",
        "LIC is the largest insurance company in India, with a vast network of policyholders and huge investments.",
        "As the largest institutional investor in India, LIC manages substantial funds, contributing to the financial stability of the country.",
        "The Indian economy is one of the fastest-growing major economies in the world, thanks to sectors like finance, technology, manufacturing etc."
    ]
)

scorer = NoiseSensitivity(llm=evaluator_llm)

async def main():
    response = await scorer.single_turn_ascore(sample)
    return response
asyncio.run(main())
```
**Additional context**
Anything else you want to share with us? 


hey @Eknathabhiram use `ChatTogether` - it only works with that - could you try it out and let me know if it works.

also can you run `evaluate()` with `raise_exceptions=True`? and share you're entire trace? I'll add a better error message so others don't get confused here 🙂 

    raise LLMDidNotFinishException()
ragas.exceptions.LLMDidNotFinishException: The LLM generation was not completed. Please increase try increasing the max_tokens and try again.

I have used chatTogther but still I am facing this issue.., Tried max tokens 2096 too, 



can you increase it to the max possible - 8k preferably 

I have the same problem when generating the dataset during summary extraction.

_Applying [SummaryExtractor, HeadlinesExtractor]:   0%|          | 0/14 [00:00<?, ?it/s]
ERROR:ragas.testset.transforms.engine:unable to apply transformation: The LLM generation was not completed. Please increase try increasing the max_tokens and try again._

I checked the SummaryExtractor(LLMBasedExtractor) class and the given LLM (Google Gemini) has an output token of 8190, so I don't know how to fix it.

@Villanua Google Gemini has long context models too right? could you use that?

also do you have tracing setup? you can easily view the traces and see how big the node was for the one that failed

Also getting `LLMDidNotFinishException(The LLM generation was not completed. Please increase try increasing the max_tokens and try again.)` when running the code below.

ragas `0.2.7`, Python 3.10.15

I tried using different models e.g. `gemini-1.5-pro-001`,`gemini-1.5-pro-002`,  `gemini-1.5-flash` and all of them returned the same result.

```python
from langchain_google_vertexai import VertexAI
from langchain_google_vertexai import VertexAIEmbeddings
from ragas.embeddings import LangchainEmbeddingsWrapper
from ragas.llms import LangchainLLMWrapper
from ragas import evaluate
from ragas.metrics import LLMContextRecall, Faithfulness, FactualCorrectness, SemanticSimilarity
from ragas import EvaluationDataset
from datasets import load_dataset

dataset = load_dataset(
    "explodinggradients/amnesty_qa",
    "english_v3",
    trust_remote_code=True
)


eval_dataset = EvaluationDataset.from_hf_dataset(dataset["eval"])

project_id = 'MY_PROJECT'
location = 'MY_LOCATION'
llm_model = 'gemini-1.5-pro-002'
embedding_model = 'text-embedding-004'

llm = VertexAI(model_name=llm_model)

embedding_function = VertexAIEmbeddings(
    model_name=embedding_model,
    project=project_id,
    location=location,
    request_parallelism=1,
)


evaluator_llm = LangchainLLMWrapper(llm)
evaluator_embeddings = LangchainEmbeddingsWrapper(embedding_function)

metrics = [
    LLMContextRecall(llm=evaluator_llm),
    FactualCorrectness(llm=evaluator_llm),
    Faithfulness(llm=evaluator_llm),
    SemanticSimilarity(embeddings=evaluator_embeddings)
]
results = evaluate(dataset=eval_dataset, metrics=metrics)

df = results.to_pandas()
df.head()
```

I was having the same issue using google's api. Seens like the #1728 works here with the googles api. 
with the:
```python is_finished_parser=gemini_is_finished_parser```