GenAI Financial Synthetic Data Generator [Mimic Your Data] | LLM + RAG [ Zypher 7B LLM ] Mistral LLM

Education

Introduction

Welcome to Part 8 of our series on RAG plus LLM use cases in the finance domain. If you haven't watched my 75 Heart Gen Challenge playlist, please check it out—I’ll put the link in the description.

In this session, we will focus on synthetic data, which is artificially generated data used to validate mathematical or statistical models. It can train large models requiring high amounts of data with specific patterns. Synthetic data finds applications in forecasting market crashes, detecting system failures, policy-making, data labeling, and fraud detection frameworks. The future will see an increasing need for this type of data to train large language models or new machine learning and deep learning models.

In the finance domain, generative AI can create synthetic data that mimics existing patterns in your domain-specific data. Using RAG or a vector DB, we can store domain-specific data and use large language models to generate synthetic data.

In this project, we will use Mistral’s fine-tuned large language model, Zypher 7B LLM, to generate financial synthetic data that can be employed to train or validate models where there is insufficient data.

Project Overview

Step-by-Step Process:

  1. Generating Initial Synthetic Data:

    • Start by generating synthetic data by straightforward means. This initial data can be used in various tasks and scenarios.

  2. Data Preprocessing:

    • Using Python, preprocess the initially generated data to create a dataset that represents realistic financial behaviors and patterns.

  3. Storing in Vector DB:

    • Load the preprocessed data into a Vector DB (Chroma DB) using embedding models. This provides a way to retrieve specific data patterns later.

  4. Implementing RAG QA Chain:

    • Use the RAG QA Chain to connect the data in Vector DB with a large language model. This chain handles queries and data generation tasks by retrieving relevant data patterns.

  5. Using Zypher 7B LLM:

    • Load the Zypher 7B LLM model in a quantized format to reduce space, time, and memory consumption.

  6. Building Query Pipeline:

    • Construct a query pipeline to process prompts and generate responses.

  7. Generating Synthetic Data:

    • Pass your financial data into the model and request new synthetic data rows. The model examines existing patterns and generates new data that mimics these patterns.

Practical Example

After generating the initial dataset, I added columns without manually specifying operations, allowing the model to infer patterns. This dataset was then stored in Chroma DB. By creating a retrieval system, I could efficiently use Zypher 7B to generate new data rows that maintain the behaviors seen in the original dataset.

Using prompts, the RAG system retrieves and processes the relevant data to generate realistic synthetic financial data, which can be used for training and validating models.

Conclusion

This method offers significant advantages over using libraries like Faker, as the generated data closely mimics real-world customer behavior patterns.

Stay tuned for our next video focusing on portfolio optimization using RAG and large language models.

If you're curious about prompt engineering, machine learning, and generative AI, check out more of my videos on YouTube and my articles on Medium.

Thank you, and see you in the next video!

Keywords

  • Synthetic Data
  • Generative AI
  • Finance Domain
  • RAG (Retrieval-Augmented Generation)
  • Vector DB (Chroma DB)
  • Large Language Model (LLM)
  • Zypher 7B LLM
  • Mistral LLM
  • Machine Learning
  • Data Preprocessing

FAQ

Q1: What is synthetic data? A: Synthetic data is artificially generated data used to validate and train models requiring high amounts of data with specific patterns.

Q2: Why is synthetic data significant in finance? A: It allows for the forecasting of market crashes, detection of system failures, policy-making, data labeling, and fraud detection, providing realistic data patterns when original data is insufficient.

Q3: What is RAG (Retrieval-Augmented Generation)? A: RAG involves using retrieved data patterns from a database to assist in generating new data via a large language model.

Q4: Why use Zypher 7B LLM in this project? A: Zypher 7B LLM, particularly in its quantized format, efficiently processes large datasets, reducing space, time, and memory requirements.

Q5: How is synthetic data different from data generated by the Faker library? A: Synthetic data generated using LLMs like Zypher 7B mimics real-world customer behaviors and specific patterns, unlike Faker which produces random data without such patterns.