For fine tuning my embedding model I need to send custo...

bbalanp

For fine tuning my embedding model I need to send custom prompts regarding the data contained in nodes and what I kind of questions I want to be generated. How can I do this using generate_qa_embedding_pairs?

17 comments

kkapa.ai

Please stand by @balanp as I search the knowledge base. I normally take about 20 seconds or so. :wave:

kkapa.ai

----------------------
The generate_qa_embedding_pairs function from LlamaIndex's finetuning module is used to generate a structured dataset containing question and context pairs. This function takes in any set of text nodes (chunks) as input.

Here is an example of how you can use this function:

Plain Text

from llama_index.finetuning import (
    generate_qa_embedding_pairs,
    EmbeddingQAFinetuneDataset,
)

train_dataset = generate_qa_embedding_pairs(train_nodes)
val_dataset = generate_qa_embedding_pairs(val_nodes)

train_dataset.save_json("train_dataset.json")
val_dataset.save_json("val_dataset.json")

In this example, train_nodes and val_nodes are the sets of text nodes that you want to generate questions from. The function generate_qa_embedding_pairs generates questions using each text chunk in the corpus as context. Each pair of (generated question, text chunk used as context) becomes a datapoint in the finetuning dataset.

If you want to load the generated dataset, you can do so with the following code:

Plain Text

train_dataset = EmbeddingQAFinetuneDataset.from_json("train_dataset.json")
val_dataset = EmbeddingQAFinetuneDataset.from_json("val_dataset.json")

For more details, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

bbalanp

@kapa.ai I want to make the generated questions as close to my question corpus as possible.Is there a way to do this automatically?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
Yes, LlamaIndex provides a way to generate questions that are similar to your question corpus. This is done using the DatasetGenerator class. Here is an example of how you can use it:

Plain Text

from llama_index import SimpleDirectoryReader, ServiceContext
from llama_index.llms import OpenAI
from llama_index.evaluation import DatasetGenerator

# build service context
llm = OpenAI(model="gpt-4", temperature=0.0)
service_context = ServiceContext.from_defaults(llm=llm)

# build documents
documents = SimpleDirectoryReader("./data").load_data()

# define generator, generate questions
data_generator = DatasetGenerator.from_documents(documents)

eval_questions = data_generator.generate_questions_from_nodes()

In this example, the DatasetGenerator is initialized with a set of documents. The generate_questions_from_nodes method is then used to generate questions from the nodes (chunks of text) in these documents. The generated questions will be similar to the ones in your question corpus.

For more details, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

bbalanp

@kapa.ai is there a sample size limit in the corpus of questions or will a single representative question of all types suffice?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
The size of the question corpus can vary depending on your specific needs and the complexity of the task. However, it's generally beneficial to have a diverse and representative set of questions that cover all the types of queries you expect the model to handle. This helps in training the model to understand and respond to a wide range of queries.

A single representative question for each type might not be sufficient, especially for complex tasks. The model might not generalize well from a single example. It's usually better to provide multiple examples for each type of question to help the model learn the nuances and variations.

In terms of a limit, there's no hard limit on the number of questions you can use. However, keep in mind that more data might require more computational resources and time to process.

For more details on how to generate a question corpus and use it for fine-tuning, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

bbalanp

@kapa.ai in the code example of data generation you showed above, the eval_questions will be sed for finetuning embedding.Am I correct?

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
No, the eval_questions generated in the code example are typically used for evaluation, not for fine-tuning the embeddings.

The fine-tuning process usually involves two distinct datasets: a training dataset and a validation (or evaluation) dataset. The training dataset is used to adjust the model parameters, while the validation dataset is used to evaluate the model's performance during and/or after training.

In the context of LlamaIndex, the generate_qa_embedding_pairs function is used to generate both the training and validation datasets. Here's an example:

Plain Text

from llama_index.finetuning import generate_qa_embedding_pairs, EmbeddingQAFinetuneDataset

# Generate training and validation datasets
train_dataset = generate_qa_embedding_pairs(train_nodes)
val_dataset = generate_qa_embedding_pairs(val_nodes)

# Save datasets
train_dataset.save_json("train_dataset.json")
val_dataset.save_json("val_dataset.json")

In this example, train_nodes and val_nodes are your training and validation sets of text nodes, respectively.

For more details, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

bbalanp

@kapa.ai If I am right the following line
"eval_questions = data_generator.generate_questions_from_nodes()"
will generate questions similar to my questions based on the data in the nodes. That dataset is required for finetuning embedding

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

----------------------
Yes, you are correct. The line eval_questions = data_generator.generate_questions_from_nodes() generates a set of questions based on the data in the nodes. These questions are intended to be similar to the ones in your question corpus.

This set of generated questions can indeed be used as part of the process for fine-tuning the embeddings. However, it's important to note that these questions are typically used for evaluation purposes, to assess the performance of the model after fine-tuning.

If you want to use these generated questions for fine-tuning, you would need to pair them with appropriate answers to create a training dataset. This could be done using a method similar to the generate_qa_embedding_pairs function, which generates pairs of questions and their corresponding answers.

Here's an example of how you might use the generated questions for fine-tuning:

Plain Text

from llama_index.finetuning import generate_qa_embedding_pairs, EmbeddingQAFinetuneDataset

# Assume eval_questions are your generated questions and eval_answers are the corresponding answers
eval_dataset = list(zip(eval_questions, eval_answers))

# Generate the fine-tuning dataset
finetune_dataset = generate_qa_embedding_pairs(eval_dataset)

# Save the dataset
finetune_dataset.save_json("finetune_dataset.json")

In this example, eval_answers would be the set of answers corresponding to your eval_questions. The generate_qa_embedding_pairs function is then used to generate the fine-tuning dataset.

For more details, you can refer to the LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------

bbalanp

@kapa.ai If I am right fine tuning embedding dose not requires answers (questios,generated question, text chunk used as context) are used as data point

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

@balanp That's a great question, but unfortunately I am only capable of responding to a maximum of five questions per thread. Don't hesitate to create a new thread for any further questions you have though. I'm here to help!

Add a reply

Find answers from the community

For fine tuning my embedding model I need to send custom prompts regarding the data contai