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Pathway is a Python ETL framework designed for building real-time data streaming applications, analytics, and integrated LLM/RAG pipelines with ease.
Pathway is an open-source Python framework enabling developers to build high-throughput, low-latency data pipelines that can process and react to streaming data in real-time. It provides built-in capabilities for integrating Large Language Models (LLMs) and implementing Retrieval Augmented Generation (RAG) systems.
Building real-time data pipelines that handle continuous data streams, perform complex analytics on the fly, and integrate advanced AI models like LLMs is challenging. Pathway simplifies this by providing a unified, reactive framework that automatically handles data updates and computations.
Processes data streams as they arrive, providing up-to-date results instantly.
Simplifies the integration of LLMs and the development of sophisticated RAG pipelines for intelligent applications.
Efficiently updates results only when input data changes, minimizing computation and latency.
Connects to various data sources, including Kafka, Redpanda, files, S3, and databases.
Pathway is ideal for applications requiring low-latency processing and real-time insights, especially when integrating AI:
Build dashboards that update instantly as new data streams in, providing always-current business metrics or operational insights.
Enables proactive decision-making based on fresh data.
Develop chatbots that use RAG to provide accurate, context-aware answers based on internal documents or external data sources, updated in real-time.
Improves chatbot accuracy and relevance by incorporating up-to-date information.
Process transaction streams in real-time to detect suspicious patterns and flag potential fraud as it happens.
Minimizes financial losses by enabling immediate response to fraudulent activities.
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