llm-app: what it is, what problem it solves & why it's gaining traction

What it solves

Pathway AI Pipelines provide a way to quickly deploy high-accuracy RAG (Retrieval-Augmented Generation) and enterprise search applications at scale. It solves the problem of keeping AI applications up-to-date by automatically syncing with live data sources, eliminating the need for separate infrastructure for vector databases, caches, and API frameworks.

How it works

The framework uses the Pathway Live Data Framework (a Python library with a Rust engine) to synchronize data from sources like Google Drive, S3, Kafka, PostgreSQL, and local file systems. It provides ready-to-deploy templates that include built-in in-memory indexing (using usearch for vector search and Tantivy for full-text search). These pipelines can be run as Docker containers and expose an HTTP API for frontend integration.

Who it’s for

Developers and enterprises looking to build and scale RAG applications that require real-time data synchronization and minimal infrastructure overhead.

Highlights

• Live Data Sync: Automatically handles additions, deletions, and updates from various cloud and on-premises data sources.
• Integrated Stack: Combines data indexing, retrieval, and LLM logic into a single framework, removing the need for external vector DBs or caches.
• Diverse Templates: Includes templates for basic QA RAG, multimodal RAG (using GPT-4o), private local RAG (via Ollama), and unstructured-to-SQL pipelines.
• Scalability: Capable of scaling up to millions of pages of documents.