# Pathway

> Python ETL framework for stream processing and real-time analytics with built-in vector search capabilities. Features real-time document synchronization, in-memory vector index, and adaptive RAG technology for always-current AI applications.

- **URL:** https://pathway.com
- **Category:** LLM Frameworks
- **Tags:** Real Time, streaming, Rag
- **Updated:** 2026-03-11 13:24
- **Canonical page:** https://vectordb.ever.works/items/pathway

## Details

## Overview

Pathway is a Python framework for building real-time AI applications with native vector search capabilities. Unlike traditional RAG systems that work with static data, Pathway provides streaming data processing with automatic index updates.

## Features

- **Real-Time Vector Index**: Built-in in-memory vector index, no separate database needed
- **Streaming Data**: Automatic document sync from SharePoint, Google Drive, etc.
- **Auto-Indexing**: Detects document changes and re-indexes within seconds
- **Adaptive RAG**: Dynamically adjusts document quantity, reducing token costs by 4x
- **Rust Engine**: Powered by scalable Rust engine based on Differential Dataflow
- **Incremental Computation**: Efficient updates without full recomputation
- **Python API**: Simple Python interface with multithreading and multiprocessing
- **Distributed**: Supports distributed computations across multiple nodes

## Use Cases

- Real-time RAG applications
- Live document search systems
- Always-current AI assistants
- Streaming analytics with vector search
- Enterprise knowledge bases with frequent updates
- Anomaly detection with embeddings

## Performance

Handles high-throughput streaming data with low-latency vector search. Incremental updates are significantly faster than rebuilding entire indexes.

## Integration

Integrates with LangChain and other LLM frameworks. Supports various data sources including file systems, databases, and APIs.

## Pricing

Open-source framework with commercial support and managed offerings available.

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