Pathway Live Data Framework + AG2 Sample Document

This is a sample document for testing the AG2 multi-agent RAG example with the Pathway Live Data Framework.

About the Pathway Live Data Framework

The Pathway Live Data Framework is a Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG. It uses a high-performance Rust engine under the hood, providing multithreading and multiprocessing capabilities.

Key features of the Pathway Live Data Framework include:

• Real-time document indexing and re-indexing
• Support for various data sources (Kafka, S3, GDrive, PostgreSQL, and more)
• Built-in LLM integration with the xpacks.llm module
• VectorStoreServer for serving document embeddings via REST API
• Automatic handling of document updates without manual re-indexing

About AG2

AG2 (formerly AutoGen) is a multi-agent conversation framework that enables multiple AI agents to collaborate on complex tasks. It has 500K+ monthly PyPI downloads, 4,300+ GitHub stars, and 400+ contributors.

Key features of AG2 include:

• Multi-agent conversations with GroupChat
• Tool registration for agents via decorator pattern
• Support for various LLM providers
• Flexible agent orchestration and conversation management