How to Generate Synthetic Dataset for RAG?

• Blockchain Council
• September 15, 2024

Retrieval-Augmented Generation (RAG) systems blend language models with external data to produce context-aware responses. These systems need high-quality datasets for training and evaluation. One major challenge in building RAG systems is the lack of sufficient high-quality data. Generating synthetic datasets offers a practical way to address this issue.

Steps to Create Synthetic Data for RAG

RAG models are essential for tasks that demand contextually relevant answers, such as customer support and knowledge retrieval. They enable language models to fetch pertinent information from external sources before forming a response. Synthetic datasets, which mimic real-world conditions, are generated using large language models (LLMs) like GPT-4. These datasets fill gaps where labeled data is limited, saving time and cost compared to manual data creation.

Here’s a step-wise guide to generating synthetic datasets for RAG:

1. Setting Up the Models

To create synthetic data for RAG, three main components are required:

• Generator Model: This model produces question-answer pairs based on specific contexts. Models like GPT-3 or GPT-4 can be used to generate realistic queries and responses.
• Embedding Model: This converts text into vector formats (embeddings) that capture semantic meanings, assisting in retrieving relevant information.
• Critic Model: This checks the generated data to ensure quality and relevance, serving as a filter for accuracy and appropriate context.

2. Loading the Knowledge Base

Start by loading raw data, which forms the basis for generating synthetic questions and answers. This can include various texts, such as articles, research papers, or domain-specific documents. For example, a legal assistant might use a database of legal texts.

3. Generating Synthetic Question-Answer Pairs

You can use models like GPT-3 to generate synthetic pairs. Below is a simple Python script demonstrating how to create a query-response pair using an LLM:

import openai

 

def generate_synthetic_data(prompt, examples, model=”text-davinci-003″, max_tokens=50):

    response = openai.Completion.create(

        engine=model,

        prompt=prompt + “\n\n” + “\n\n”.join(examples),

        max_tokens=max_tokens

    )

    return response.choices[0].text.strip()

 

# Example usage

prompt = “Generate a query about renewable energy in China and a relevant document snippet.”

examples = [

    “Query: What are the benefits of solar energy in China?\nDocument Snippet: Solar energy reduces electricity bills in China and is eco-friendly.”,

    “Query: How does wind power contribute to energy efficiency in China?\nDocument Snippet: Wind power is a renewable energy source that helps in reducing carbon emissions in China.”

]

synthetic_query_response = generate_synthetic_data(prompt, examples)

print(synthetic_query_response)

 

This code generates a new query-response pair using the provided examples’ structure. By varying prompts and examples, you can create a diverse dataset that mirrors real-world interactions.

4. Fine-Tuning with Synthetic Data

After creating the synthetic dataset, the next step is fine-tuning the retrieval model’s embeddings. This involves refining how the model interprets and retrieves relevant content. Fine-tuning enhances the model’s performance by making it more responsive to specific queries and contexts. Techniques like contrastive loss are used, where the model learns from both relevant and irrelevant examples to improve retrieval skills.

Here’s a basic approach to fine-tune embeddings with Python:

from sentence_transformers import SentenceTransformer, InputExample, losses

from torch.utils.data import DataLoader

 

model = SentenceTransformer(‘all-MiniLM-L6-v2’)

 

# Creating training examples

train_examples = [InputExample(texts=[‘Query: ‘ + q, ‘Document Snippet: ‘ + d], label=1) for q, d in synthetic_data]

 

# Preparing the data loader

train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=16)

train_loss = losses.ContrastiveLoss(model)

 

# Fine-tuning the model

model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=1, warmup_steps=100)

 

# Saving the fine-tuned model

model.save(‘fine-tuned-rag-model’)

Fine-tuning aligns the model’s understanding of domain-specific questions, improving the relevance of answers generated by the RAG system.

Why Generate Synthetic Data for RAG?

Creating synthetic data for RAG offers numerous advantages. First, it allows for the rapid creation of large, varied datasets tailored to specific fields, improving model performance in areas lacking labeled data. Second, synthetic data supports testing and refining retrieval models, enhancing their ability to provide relevant context. This data also helps minimize biases and ensures consistent performance across different settings.

Importance of Synthetic Data in RAG

The generation of synthetic data provides several key benefits:

• Contextual Relevance: Synthetic datasets help train RAG systems to respond more accurately in specific areas, improving the model’s contextual understanding.
• Scalability and Cost Efficiency: Synthetic data generation is faster and cheaper than manual annotation, enabling quick experimentation and development.
• Reduction in Hallucinations: Using synthetic data allows RAG systems to update their knowledge dynamically, reducing errors and outdated responses.
• Flexibility in Data Creation: Synthetic data enables the creation of tailored question-answer pairs, which can be adjusted for specific needs, diversifying the training dataset.

Conclusion

Generating synthetic datasets for RAG is a vital approach to overcoming challenges related to data availability, domain specificity, and retrieval accuracy. By configuring the appropriate models and refining them with synthetic data, the performance of RAG systems can be significantly enhanced. This process not only boosts the quality of responses but also strengthens the adaptability and effectiveness of RAG models across various applications.