Repository: explodinggradients/ragas
Issue: Recalculation of Answers and Contexts in evaluate Function
Root cause: Documentation Deficiency

**Context:** I am calling the evaluate function to calculate faithfulness and answer correctness. The dataset, which contains answer, context, question, and ground_truth, is passed as input. The evaluate function is defined in llama_index.py (path to file -> ragas/integrations).

**Concern:** I noticed that inside the evaluate function, answers and contexts are recalculated for the user query and passed for evaluation. My main question is: why are the answers and contexts recalculated, and why are these recalculated values used for evaluation instead of the original answers and contexts generated by our model?

In my opinion, recalculating the answers and contexts and using them in the evaluation could lead to incorrect faithfulness and answer correctness scores. This might be a bug. Please clarify if this behavior is intentional or provide the reasoning behind it.

Below is a snapshot of the code in llama_index.py where the answers and contexts are recalculated:
<img width="786" alt="image" src="https://github.com/user-attachments/assets/c893e52e-2766-4316-86bf-1e2bc3329fb5">


<!-- Greeting -->
Hey @pds13193, I'm here to help you with any bugs, questions, and contributions you might have. I'm looking forward to working with you!

<!-- Answer -->
The answers and contexts are recalculated in the `evaluate` function in `llama_index.py` to ensure that the evaluation is based on the most current and accurate responses generated by the query engine. This recalculation is necessary because the evaluation aims to assess the performance of the query engine in real-time, rather than relying on potentially outdated or previously generated answers and contexts.

Here is the relevant code snippet that demonstrates this process:

```python
# get query
queries = dataset["question"]
for i, q in enumerate(queries):
    exec.submit(query_engine.aquery, q, name=f"query-{i}")

answers: t.List[str] = []
contexts: t.List[t.List[str]] = []
try:
    results = exec.results()
    if results == []:
        raise ExceptionInRunner()
except Exception as e:
    raise e
else:
    for r in results:
        answers.append(r.response)
        contexts.append([n.node.text for n in r.source_nodes])
```

The recalculated values are used for evaluation instead of the original answers and contexts generated by the model to ensure that the evaluation metrics reflect the current state and performance of the query engine. This approach provides a more accurate and relevant assessment of the system's capabilities <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/integrations/llama_index.py)</sup>.


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@dosu Thank you for the clarification. I have a follow-up question regarding the concept of "outdated" information mentioned in your response.

Could you please provide more context or examples of what qualifies as "outdated" information? Specifically, under what circumstances would information generated by the model be considered outdated? For example, would you consider information outdated if it was generated two days ago, or is it related to a different criterion?

In my case, the code sends a query to the model, and the model immediately responds with the answer and context, which I then pass immediately for evaluation. Given this workflow, I'm trying to understand where the concept of outdated information applies. Could you please provide some examples or situations that clarify when information would be considered outdated in this context?

Thank you for your insights.

<!-- Answer -->
Outdated information in the context of Retrieval Augmented Generation (RAG) pipelines typically refers to data that no longer accurately reflects the current state of the underlying data sources or the model's knowledge. Here are some examples and situations where information might be considered outdated:

1. **Data Source Updates**: If the underlying data sources (e.g., documents, databases) have been updated, any previously generated answers and contexts might no longer be accurate. For instance, if a document was modified or new information was added, the context retrieved from the old version would be outdated.

2. **Model Updates**: If the model itself has been updated or retrained, the answers and contexts it generates might differ from those produced by an older version of the model. This can happen if the model's parameters, training data, or architecture have changed.

3. **Temporal Relevance**: Some information is time-sensitive and can become outdated quickly. For example, news articles, stock prices, or weather reports are highly time-dependent. An answer generated based on last week's news might be outdated today.

4. **Query-Specific Changes**: If the nature of the queries or the context in which they are asked changes, the relevance of previously generated answers might diminish. For example, if a query is about a current event, the context and answer need to be up-to-date with the latest developments.

In your specific workflow, where the model responds immediately to a query and the answer and context are passed for evaluation right away, the concept of outdated information might not apply as strongly. However, the recalculation ensures that the evaluation is always based on the most current state of the model and data sources, providing a more accurate assessment of the system's performance.

This approach is particularly important in dynamic environments where data and model updates are frequent. It helps maintain the reliability and validity of the evaluation metrics by ensuring they reflect the latest information and capabilities of the system.


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@dosu
I am convinced by the justification you have provided. This issue can be closed.

<!-- Answer -->
Great to hear that the explanation was helpful! If you have any more questions or need further assistance in the future, feel free to reach out. I'll go ahead and close this issue. Have a wonderful day!


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