Repository: explodinggradients/ragas
Issue: LLMContextPrecisionWithoutReference is only producing 0.0 as the score
Root cause: External Dependency Breakage

I have checked the [documentation](https://docs.ragas.io/) and related resources and couldn't resolve my bug.

**Describe the bug**
LLMContextPrecisionWithoutReference is only producing 0.0 as the score

Ragas version: 0.2.6
Python version: 3.10.12

**Code to Reproduce**
```py
import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass()

from ragas.llms import LangchainLLMWrapper
from ragas.embeddings import LangchainEmbeddingsWrapper
from ragas.run_config import RunConfig
from langchain_openai import ChatOpenAI
from langchain_openai import OpenAIEmbeddings

# Create a RunConfig with a specified timeout
run_config = RunConfig(timeout=60)  # Set timeout to 60 seconds
evaluator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"), run_config=run_config)
evaluator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings(), run_config=run_config)

df_all_gpt_decomp_rags = pd.DataFrame({
    'user_input': [RAG_query, RAG_query2, RAG_query3, RAG_query4, RAG_query5, RAG_query6],
    'response': [combined_themes, combined_themes2, combined_themes3, combined_themes4, combined_themes5, combined_themes6],
    'retrieved_contexts': [context, context2, context3, context4, context5, context6]
    })

from datasets import Dataset
from ragas import SingleTurnSample, EvaluationDataset
from ragas.metrics import LLMContextPrecisionWithoutReference
from ragas import evaluate


# Bring dataframe into right format
dataset = Dataset.from_pandas(df_all_gpt_decomp_rags)

# Set up context relevancy metric
metrics = [LLMContextPrecisionWithoutReference(llm=evaluator_llm)]

results = evaluate(dataset=dataset, metrics=metrics)
df_results = results.to_pandas()
df_results
```

**Error trace**
None

**Expected behavior**
I expected the score to vary but all scores for llm_context_precision_without_reference came out to be 0.0. 

**Additional context**
I ran this code last week and it worked without issue. Now I only get a score of 0.0



Hi @nbarnett19,

Are you still facing the issue? I tried to reproduce it, but it's working fine on my end. Here’s the code I ran, and I’m getting varied output:
```py
from ragas import SingleTurnSample
from ragas.metrics import NonLLMContextPrecisionWithReference

context_precision = NonLLMContextPrecisionWithReference()

sample = SingleTurnSample(
    retrieved_contexts=["The Eiffel Tower is located in Paris."], 
    reference_contexts=["Paris is the capital of France.", 
    "The Eiffel Tower is one of the most famous landmarks in Paris."]
)

await context_precision.single_turn_ascore(sample)
```
Output
```
0.9999999999
```

```py
sample = SingleTurnSample(
    retrieved_contexts=["The Eiffel Tower is located in Paris."], 
    reference_contexts=["Paris is the capital of France.", 
    "The Eiffel Tower is one of the most famous landmarks in SF."]
)

await context_precision.single_turn_ascore(sample)
```
Output
```
0.0
```



hi @sahusiddharth 
I'm no longer facing the issue. It randomly started working again a few days after submitting this request. Maybe something to do with the openai instead of ragas itself?

If you encounter similar issues in the future, you may find it helpful to observe and analyse using the Ragas app. You can refer to the documentation [here](https://docs.ragas.io/en/latest/getstarted/rag_eval/#analyze-results) for more details.