Home

Project Name: SynthDataGen

Problem Statement: Data science and machine learning teams face a significant bottleneck in developing and fine-tuning custom Large Language Models (LLMs). The primary hurdle is the creation of high-quality, domain-specific datasets, which is a laborious, time-consuming, and often demotivating manual process. The absence of such datasets stalls projects aimed at fine-tuning, few-shot prompting, or system prompt optimization, preventing companies from leveraging their proprietary knowledge effectively.

Proposed Solution: The SynthDataGen project aims to automate the creation of synthetic question-answer datasets for LLM training. The system will ingest a company's internal, unstructured documents (such as PDFs and DOCX files), process their content, and use a powerful generator LLM to create relevant, context-aware question-answer pairs. These pairs will be stored in a structured database, ready for review and export, dramatically reducing the manual effort and time required for dataset creation.

Key Features:

  • Automated Document Ingestion: Seamlessly pulls documents from a MinIO object store.
  • Content Extraction & Chunking: Extracts text from PDF and DOCX files and breaks it down into manageable, logical chunks.
  • Context-Aware Q\&A Generation: Intelligently groups chunks to form rich context and uses an LLM to generate high-quality question-answer pairs.
  • Structured Storage: Stores all generated data, metadata, and document relationships in a PostgreSQL database.
  • Easy Data Export: Provides a one-click export of the dataset into a JSON format suitable for fine-tuning frameworks.
  • Developer SDK: A Python SDK will be provided for programmatic integration into existing MLOps pipelines.

Business Impact:

  • Accelerate AI Development: Drastically reduces dataset creation time from weeks to hours.
  • Improve Model Performance: Enables the creation of highly specialized models trained on proprietary company data.
  • Empower Teams: Removes a major blocker for data science teams, increasing motivation and productivity.
  • Unlock Knowledge Assets: Transforms static, unstructured documents into valuable, queryable training data.