Repository: explodinggradients/ragas
Issue: expectation error
GitHub labels: bug

question

answered

I am working with ragas and I Have my own embeddings and textual data. i see this error what should I do?
```
Exception has occurred: ValueError
Dataset feature "contexts" should be of type Sequence[string], got <class 'datasets.features.features.Sequence'>
  File "C:\Users\Amin\git\ragas-prototype\src\rag_eva.py", line 320, in <module>
    evaluation_report = evaluate(new_ragas_dataset, metrics=metrics, embeddings=ragas_embeddings, llm=custom_llm, column_map=column_map)  # Pass your custom LLM here
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Dataset feature "contexts" should be of type Sequence[string], got <class 'datasets.features.features.Sequence'>
```

Based on the error codetype, it sounds like your embedding model is not the problem, but rather the context row in the new_ragas_dataset that makes up the textual data is not a List[str], so you should check the data type first.

When you check the data once, if you load new_ragas_dataset['context'],
`Expected output : List[str]`

thanks for helping out @hundredeuk2 🙂 ❤️ 

@amin-kh96 like Heondeuk suggest this is likely do to an inconsistency with how you have formatted the data, do check it out. 

@dosubot

in my dataset, the context should be a list of lists like this.
I am using ragas version 0.1.7
{
    "contexts": [["context1_chunk1", "context1_chunk2"], ["context2_chunk1"]],
    "questions": ["question1", "question2"],
    "answers": ["answer1", "answer2"]
}

<!-- Answer -->
I found similar threads that might help you resolve the issue. In one closed issue, users encountered a similar `ValueError` and resolved it by ensuring that the "contexts" column in their dataset is a list of strings rather than a list of lists. You can achieve this by flattening the list of lists into a single list of strings. Here's a code snippet that might help:

```python
# Assuming new_ragas_dataset is your dataset
new_ragas_dataset['contexts'] = [item for sublist in new_ragas_dataset['contexts'] for item in sublist]
```

This transformation ensures that each entry in the "contexts" field is a single list of strings, which should resolve the error you're encountering <sup>[[1]](https://github.com/explodinggradients/ragas/issues/1807)</sup>.


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@dosu
i did it but i face this error
Exception has occurred: TypeError
'Dataset' object does not support item assignment
  File "C:\Users\Amin\git\ragas-prototype\src\rag_eva.py", line 178, in <module>
    new_ragas_dataset['contexts'] = [item for sublist in new_ragas_dataset['contexts'] for item in sublist]
    ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
TypeError: 'Dataset' object does not support item assignment

<!-- Answer -->
The error you're encountering is due to the fact that the `Dataset` object in the ragas framework does not support direct item assignment. To modify a column, you can use the `map` function to transform the data. Here's how you can flatten the "contexts" column:

```python
# Assuming new_ragas_dataset is your dataset
def flatten_contexts(example):
    example['contexts'] = [item for sublist in example['contexts'] for item in sublist]
    return example

new_ragas_dataset = new_ragas_dataset.map(flatten_contexts)
```

This approach uses the `map` method to apply the transformation function to each example in the dataset, effectively flattening the "contexts" column.


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@dosu
I see this what should I do
Failed to parse output. Returning None.
Failed to parse output. Returning None.
Failed to parse output. Returning None.
Failed to parse output. Returning None.
Failed to parse output. Returning None.

<!-- Answer -->
The "Failed to parse output" message indicates that the Ragas framework is encountering issues when trying to parse the output generated by the language model. This could be due to the output not conforming to the expected format or schema.

Here are some steps you can take to troubleshoot and resolve this issue:

1. **Check Output Format**: Ensure that the output from your language model matches the expected format. The Ragas framework expects outputs to be in a specific JSON format that can be parsed by the `RagasOutputParser`. Verify that the output adheres to this format.

2. **Enable Debugging**: Set the `RAGAS_DEBUG` environment variable to `True` to enable debugging mode. This will provide more detailed error messages and stack traces, which can help identify the root cause of the parsing failure <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/_analytics.py)</sup>.

3. **Review Retry Logic**: The framework includes a retry mechanism for parsing outputs. If the initial parsing fails, it attempts to fix the output format and retry. Ensure that this retry logic is functioning correctly and that the output can be corrected within the allowed retries <sup>[[2]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/prompt/pydantic_prompt.py)</sup>.

4. **Inspect Logs**: Check the logs for any additional error messages or warnings that might provide more context about the parsing failure. The logs can give insights into what part of the output is causing the issue <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/_analytics.py)</sup>.

By following these steps, you should be able to identify and address the cause of the output parsing errors.


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@dosu

i see this
Failed to parse output. Returning None.
Failed to parse output. Returning None.
Failed to parse output. Returning None.
Failed to parse output. Returning None.
Failed to parse output. Returning None.

<!-- Answer -->
To resolve the "Failed to parse output. Returning None." error in the Ragas framework, you should handle the `RagasOutputParserException` that is raised when there is an error parsing the output. Here are some steps to help you troubleshoot and fix the issue:

1. **Check Output Format**: Ensure that the output from your language model is in the expected JSON format. The expected format is typically:

   ```json
   {
       "text": "str"
   }
   ```

   Make sure the output adheres to this structure <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/tests/unit/test_prompt.py)</sup>.

2. **Enable Debugging**: Set the `RAGAS_DEBUG` environment variable to `True` to enable detailed logging. This can help you identify the specific part of the output that is causing the parsing failure:

   ```python
   import os
   os.environ["RAGAS_DEBUG"] = "True"
   ```

3. **Review Retry Logic**: The framework includes a retry mechanism for parsing outputs. If the initial parsing fails, it attempts to fix the output format and retry. Ensure that this retry logic is functioning correctly and that the output can be corrected within the allowed retries <sup>[[2]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/prompt/pydantic_prompt.py)</sup>.

4. **Inspect Logs**: Check the logs for any additional error messages or warnings that might provide more context about the parsing failure. The logs can give insights into what part of the output is causing the issue.

By following these steps, you should be able to identify and address the cause of the output parsing errors.


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@dosu
I have a question which I am not sure.
I chose the metric context_utilization and also i created a custombaseragasllm subclass.
my question is that when the ragasoutput parser inside the metric wants to work, it requires 
reason: str = Field(..., description="Reason for verification")
    verdict: int = Field(..., description="Binary (0/1) verdict of verification")
from the output of the custom or its done inside the metric itself?

<!-- Answer -->
The `RagasOutputParser` requires the `reason` and `verdict` fields to be provided by the custom output. These fields are explicitly defined in the `AspectCriticOutput` class, which is used for the output:

```python
class AspectCriticOutput(BaseModel):
    reason: str = Field(description="Reason for the verdict")
    verdict: int = Field(description="The verdict (0 or 1) for the submission")
```

This means your custom output must include these fields, as they are part of the expected output model <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/metrics/_aspect_critic.py)</sup>.


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@dosu
I don't understand it which part or segment should do this reason and verdict?
the baseragasllm or the metric?

<!-- Answer -->
The `reason` and `verdict` fields should be provided by the `BaseRagasLLM` subclass. This is because the `AspectCritic` class, which uses these fields, expects the language model to generate responses that include them. The `BaseRagasLLM` is responsible for generating the output that contains the `reason` and `verdict` as part of its response <sup>[[1]](https://github.com/explodinggradients/ragas/blob/main/src/ragas/metrics/_aspect_critic.py)</sup>.


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@dosu
if I am using a subclass of baseragasllm (not using ChatGPT or other models), how it should be done?


<!-- Answer -->
When using a subclass of `BaseRagasLLM`, you need to ensure that your implementation of the `generate_text` or `agenerate_text` methods produces outputs that include the `reason` and `verdict` fields. These fields are part of the expected output structure for certain metrics like `context_utilization`.

Here's a general approach to achieve this:

1. **Implement `generate_text` Method**: In your subclass, implement the `generate_text` method to return an `LLMResult` where each `Generation` object includes the `reason` and `verdict` fields. You can structure your output to include these fields as part of the generated text.

2. **Format Output**: Ensure that the output from your language model is formatted to include the `reason` and `verdict`. This might involve appending these fields to the generated text or structuring the output as a JSON object that includes these fields.

3. **Example Implementation**:
   ```python
   def generate_text(self, prompt: PromptValue, n=1, temperature=1e-8, stop=None, callbacks=[]):
       # Example of generating text with reason and verdict
       generations = [
           [Generation(text=json.dumps({"reason": "Example reason", "verdict": 1}))]
           for _ in range(n)
       ]
       return LLMResult(generations=generations)
   ```

By ensuring your `BaseRagasLLM` subclass outputs the required fields, you can integrate seamlessly with the Ragas framework's metrics that depend on these fields.


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@dosu
but one thing:, if we set this verdict equal to 1, it means that the output is always one, and there is no dynamic processing. right? i think we should find a way that this be done inside the baseragsllm.

<!-- Answer -->
Yes, setting the `verdict` to a constant value like 1 would mean that the output is always the same, which eliminates any dynamic processing. To achieve dynamic processing, your `BaseRagasLLM` subclass should implement logic to evaluate the input context and generate the `reason` and `verdict` fields based on specific criteria.

Here's how you can approach this:

1. **Dynamic Evaluation**: Implement logic within your `generate_text` method to analyze the input context and determine the `verdict` dynamically. This could involve using natural language processing techniques or predefined rules to assess the context and generate a corresponding `reason`.

2. **Example Implementation**:
   ```python
   def generate_text(self, prompt: PromptValue, n=1, temperature=1e-8, stop=None, callbacks=[]):
       # Example of dynamic processing
       def evaluate_context(context):
           # Implement your logic to evaluate the context
           # Return a reason and a dynamic verdict (0 or 1)
           if "certain condition" in context:
               return "Condition met", 1
           else:
               return "Condition not met", 0

       generations = [
           [Generation(text=json.dumps({"reason": reason, "verdict": verdict}))]
           for context in prompt.contexts
           for reason, verdict in [evaluate_context(context)]
       ]
       return LLMResult(generations=generations)
   ```

By implementing such logic, your `BaseRagasLLM` subclass can dynamically generate the `reason` and `verdict` fields based on the input context, allowing for more meaningful and context-aware evaluations.


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