Unable to Save Generated Data to JSONL File - Always Resulting in "Wrote 0 examples to finetuning_events.jsonl" Message

[loyal812]

Bug Description

When attempting to generate JSONL data using Llama Index, the process works well until the final step where the results are saved to a JSONL file. However, every time I try to save the data, it seems to be unsuccessful as I always receive the message "Wrote 0 examples to finetuning_events.jsonl". I am unsure of the reason behind this issue.

This is my code

def jsonl_generation(self):
        """
        Generate JSONL file for fine-tuning events and perform model refinement.
        """
        # Initialize OpenAI FineTuningHandler and CallbackManager
        finetuning_handler = OpenAIFineTuningHandler()
        callback_manager = CallbackManager([finetuning_handler])

        llm = OpenAI(model=self.model, temperature=self.temperature)
        llm.callback_manager = callback_manager

        # Load questions for fine-tuning from a file
        questions = []
        with open(f'{self.dataset_path}/train_questions.txt', "r", encoding='utf-8') as f:
            for line in f:
                questions.append(line.strip())

        try:
            # Generate responses to the questions using GPT-4 and save the fine-tuning events to a JSONL file
            index = VectorStoreIndex.from_documents(
                self.documents
            )
            query_engine = index.as_query_engine(similarity_top_k=2, llm=llm)
            for question in questions:
                response = query_engine.query(question)
                print("each response:", response)
        except Exception as e:
            # Handle the exception here, you might want to log the error or take appropriate action
            print(f"An error occurred: {e}")
        finally:
            # Save the fine-tuning events to a JSONL file
            finetuning_handler.save_finetuning_events(f'{self.dataset_path}/finetuning_events.jsonl')

Thank you again.

Version

0.10.20

Steps to Reproduce

1. Successfully generated JSONL data using Llama Index.
2. Attempted to save the results to a JSONL file.
3. Received the message "Wrote 0 examples to finetuning_events.jsonl".

Relevant Logs/Tracbacks

Wrote 0 examples to ./dataset_data/finetuning_events.jsonl

[dosubot[bot]]

Hey there @loyal812! 🎉 Great to see you back diving into the depths of Llama Index. What's cooking in your coding kitchen today? Let's get that JSONL file beefed up with more than just air!

Based on the code snippet you've provided, it seems like the finetuning_handler is not being used to collect the responses generated by the query_engine. The finetuning_handler is responsible for collecting the responses and saving them to a JSONL file. However, in your code, the responses are being printed out but not added to the finetuning_handler.

To resolve this issue, you need to add the responses to the finetuning_handler. You can do this by calling the add_finetuning_event method of the finetuning_handler for each response. Here's how you can modify your code:

for question in questions:
    response = query_engine.query(question)
    print("each response:", response)
    finetuning_handler.add_finetuning_event(question, response)

This will ensure that the responses are collected by the finetuning_handler and subsequently saved to the JSONL file when save_finetuning_events is called.

Regarding your question about changes in the OpenAIFineTuningHandler class between LlamaIndex version 0.10.20 and the latest version 0.10.25, I wasn't able to find any specific changes in the repository. However, it's always a good idea to keep your software up-to-date, so you might want to consider updating to the latest version of LlamaIndex.

I hope this helps! If you have any further questions or if the issue persists, please provide more details so I can assist you better.

Sources

• [Bug]: Unable to Save Generated Data to JSONL File - Always Resulting in "Wrote 0 examples to finetuning_events.jsonl" Message
• llama-index-legacy/llama_index/legacy/finetuning/rerankers/dataset_gen.py
• llama-index-finetuning/llama_index/finetuning/callbacks/finetuning_handler.py

About Dosu

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Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[dosubot]

It appears there's been a misunderstanding regarding the functionality of the OpenAIFineTuningHandler in LlamaIndex v0.10.20. The class is designed to automatically handle and add responses to the finetuning events within the on_event_end method, rather than requiring manual addition of responses through an add_finetuning_event function.

When an event concludes, such as receiving a response from the language model, the on_event_end method is invoked. This method is responsible for checking if the event is of type CBEventType.LLM and if the event ID is recognized within _finetuning_events. If these conditions are satisfied, the response is added to the list of messages associated with that event ID in _finetuning_events.

Here's a snippet that outlines this process:

if (
    event_type == CBEventType.LLM
    and event_id in self._finetuning_events
    and payload is not None
):
    if isinstance(payload[EventPayload.RESPONSE], str):
        response = ChatMessage(
            role=MessageRole.ASSISTANT, text=str(payload[EventPayload.RESPONSE])
        )
    else:
        response = payload[EventPayload.RESPONSE].message

    self._finetuning_events[event_id].append(response)

The save_finetuning_events method in OpenAIFineTuningHandler is then used to save these events to a JSONL file. This method iterates over all events, converts them to the format expected by OpenAI's API, and writes them to the specified file.

To ensure responses are added for saving to a JSONL file, it's crucial that the on_event_end method is correctly invoked with the appropriate parameters (event type, payload, and event ID) after receiving a response from the LLM. The implementation provided in your context supports this functionality without needing an explicit add_finetuning_event function.

For more detailed information, you can refer to the llama-index-legacy/llama_index/legacy/callbacks/finetuning_handler.py file.

If you're still facing issues, sharing more about the specific error messages and the context in which they occur could provide further insights into whether there's a problem with the event handling or the saving process.

Sources

• [Bug]: Unable to Save Generated Data to JSONL File - Always Resulting in "Wrote 0 examples to finetuning_events.jsonl" Message
• llama-index-legacy/llama_index/legacy/callbacks/finetuning_handler.py
• llama-index-finetuning/llama_index/finetuning/callbacks/finetuning_handler.py
• llama-index-legacy/llama_index/legacy/callbacks/simple_llm_handler.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[loyal812]

@dosu-bot
I think on_event_end method not working well in my side. Please let me know how can I update my code.

[dosubot]

It appears the issue you're encountering with the on_event_end method not functioning as expected in Llama Index v0.10.20 is due to it not being triggered properly, which in turn prevents your data from being saved to the JSONL file. The on_event_end method is crucial for handling the end of an event by processing the event's details, specifically for events of type CBEventType.LLM with a non-None payload.

To address this, you'll need to ensure that the on_event_end method is correctly implemented to include functionality for writing the generated data to a JSONL file. This involves serializing the data to a JSON string and appending it to a file with a .jsonl extension. Here's a revised snippet that demonstrates how to update your on_event_end method:

import json

def on_event_end(
    self,
    event_type: CBEventType,
    payload: Optional[Dict[str, Any]] = None,
    event_id: str = "",
    **kwargs: Any,
) -> None:
    """Handle the end of an event and save data to JSONL."""
    if event_type == CBEventType.LLM and payload is not None:
        self._print_llm_event(payload)
        # Append the payload to a JSONL file
        with open(self.output_file, 'a') as f:
            json_record = json.dumps(payload)
            f.write(json_record + '\n')

This modification ensures that whenever an event of type CBEventType.LLM concludes and its payload is not None, the payload is serialized and written to the specified JSONL file. Each record is placed on a new line, adhering to the JSONL format.

For further details on the on_event_end method, you can refer to the SimpleLLMHandler class in the LlamaIndex repository.

If you encounter any more issues or require additional assistance, feel free to reach out.

Sources

• [Bug]: Unable to Save Generated Data to JSONL File - Always Resulting in "Wrote 0 examples to finetuning_events.jsonl" Message
• llama-index-core/llama_index/core/callbacks/simple_llm_handler.py
• llama-index-finetuning/llama_index/finetuning/callbacks/finetuning_handler.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[loyal812]

@dosu-bot
So do I need to adjust llama-index's finetuning_handler.py file?
I don't want to change the module. I'd like to fix this in my code if possible.

[dosubot]

To address the issue with the on_event_end method not functioning as expected and preventing data from being saved to the JSONL file, it's essential to ensure that this method is correctly implemented and invoked. Here are some steps and adjustments you can make in your code without modifying the Llama Index's finetuning_handler.py file:

1. Verify on_event_end Invocation: Confirm that the on_event_end method is being called at the end of each event. This can be done by adding logging or print statements within the method.

2. Check Event Type and Payload: Ensure that the events processed by on_event_end are of type CBEventType.LLM and that the payload contains a valid response.

3. Validate Response Format: The method expects the response in the payload to be either a string or an object with a message attribute. Adjust your code if the response format differs.

4. Data Integrity Before Saving: Before invoking save_finetuning_events, verify that the data structure (self._finetuning_events) contains the expected data.

5. File Writing Permissions: Check that your script has the necessary permissions to write to the specified path.

6. JSON Serialization: Ensure that the data structure is serializable and does not contain non-serializable objects.

Here's how you might add logging to the on_event_end method to confirm its execution and data integrity:

def on_event_end(
    self,
    event_type: CBEventType,
    payload: Optional[Dict[str, Any]] = None,
    event_id: str = "",
    **kwargs: Any,
) -> None:
    print(f"Event ending: {event_id}, Type: {event_type}")
    from llama_index.legacy.core.llms.types import ChatMessage, MessageRole

    if (
        event_type == CBEventType.LLM
        and event_id in self._finetuning_events
        and payload is not None
    ):
        print(f"Processing payload for event: {event_id}")
        if isinstance(payload[EventPayload.RESPONSE], str):
            response = ChatMessage(
                role=MessageRole.ASSISTANT, text=str(payload[EventPayload.RESPONSE])
            )
        else:
            response = payload[EventPayload.RESPONSE].message

        self._finetuning_events[event_id].append(response)
        print(f"Response added for event: {event_id}")

And for handling exceptions in the save_finetuning_events method:

def save_finetuning_events(self, path: str) -> None:
    try:
        # Existing logic to save events
        print(f"Wrote {len(json_strs)} examples to {path}")
    except Exception as e:
        print(f"Failed to write to file: {e}")

These adjustments can help diagnose the issue. If problems persist, providing more details about the error message and additional relevant code snippets will help in offering a more precise solution.

For further assistance, you can refer to the source code of the OpenAIFineTuningHandler class in the LlamaIndex repository.

Sources

• [Bug]: Unable to Save Generated Data to JSONL File - Always Resulting in "Wrote 0 examples to finetuning_events.jsonl" Message
• llama-index-legacy/llama_index/legacy/callbacks/finetuning_handler.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

[loyal812]

I just solved the problem.
It's my solution. Currently, It's storing the dataset to jsonl data.

def jsonl_generation(self):
        """
        Generate JSONL file for fine-tuning events and perform model refinement.
        """
        # Initialize OpenAI FineTuningHandler and CallbackManager
        finetuning_handler = OpenAIFineTuningHandler()
        callback_manager = CallbackManager([finetuning_handler])

        llm = OpenAI(model="gpt-4", temperature=0.3)
        Settings.callback_manager, = (callback_manager,)

        # Load questions for fine-tuning from a file
        questions = []
        with open(f'{self.dataset_path}/train_questions.txt', "r", encoding='utf-8') as f:
            for line in f:
                questions.append(line.strip())

        try:
            from llama_index.core import VectorStoreIndex
            # Generate responses to the questions using GPT-4 and save the fine-tuning events to a JSONL file
            index = VectorStoreIndex.from_documents(
                self.documents
            )
            query_engine = index.as_query_engine(similarity_top_k=2, llm=llm)
            for question in questions:
                response = query_engine.query(question)
        except Exception as e:
            # Handle the exception here, you might want to log the error or take appropriate action
            print(f"An error occurred: {e}")
        finally:
            # Save the fine-tuning events to a JSONL file
            finetuning_handler.save_finetuning_events(f'{self.dataset_path}/finetuning_events.jsonl')