Challenge: AI-Powered Data Processing

Justification

AI-driven data processing, encompassing Generative AI, Machine Learning, semantic search, and Retrieval Augmented Generation (RAG), can be applied in various areas. This includes improving search engine accuracy, enabling more effective data retrieval, enhancing chatbot capabilities, and powering intelligent applications. It plays a crucial role in industries like e-commerce, customer support, and knowledge management by enabling users to access and interact with information in more intuitive and meaningful ways.

Objective

Using Apache Beam’s capabilities (turnkey transforms such as RAG, RunInference), build a data pipeline that leverages AI techniques (such as GenAI, ML, semantic search, RAG, etc.) to process and derive insights from data.

Things to consider

• Embedding generation (for semantic search/RAG)
• Integration with AI/ML models or services
• Vector database integration (if applicable)
• Techniques for similarity search or pattern recognition
• Data preprocessing and transformation for AI models

Expected result

In the simplest scenario, a data pipeline implemented in Google Colab that ingests data, applies an AI-driven process (e.g., semantic search, classification, generation), and returns the results. It can be enhanced by making it power an application (a search app, a chatbot, a data summarization tool, etc.).