sageFlow

sageFlow is a cutting-edge, vector-native stream processing engine designed specifically to maintain and materialize semantic state snapshots for real-time, LLM-based generation tasks. The engine offers a declarative API to compose stateful vector operations within temporal windows, enabling fast and efficient updates to semantic context for dynamically changing datasets.

Features

• Vector-Native Stream Processing: At its core, sageFlow is built to handle high-dimensional vector streams efficiently.
• Declarative API: Easily compose complex, stateful vector operations such as TopK, Filter, and Join within defined temporal windows.
• Incremental Low-Latency Updates: Optimized for incremental computations, ensuring semantic states are updated with minimal delay.
• Optimized Three-Phase Pipeline: Abstracts stream processing into three distinct phases—ingestion, state materialization, and snapshot exposure—unlocking significant optimization opportunities.
• Stateful and Windowed Operations: Natively supports windowing to create time-bound semantic snapshots from continuous data streams.

Key Use Cases

• Real-time LLM Generation: Provide large language models with fresh, stateful context snapshots for more accurate and timely responses.
• Dynamic Context Maintenance: Ideal for conversational AI or interactive applications where the context evolves rapidly over time.
• Streaming Data Analytics: Serve high-velocity data analysis use cases that require complex, stateful semantic queries on vector data.
• Adaptive Recommendation Systems: Build systems that can update recommendations in real-time based on the most recent user interactions and streaming events.

Setup

To setup sageFlow and it's dependencies, begin by making sure that you have docker installed, or any Linux release version that contains apt, such as Ubuntu or Debian

We suggest first begin with docker before you are familiar with sageFlow.

Docker

make sure you have installed Docker and Docker is running

Windows

cd <PATH_TO_REPO>/setup
./start_win.bat

Linux

cd <PATH_TO_REPO>/setup
./start.sh

Linux with apt

check the dependencies in <PATH_TO_REPO>/setup/Dockerfile, and build your env

sageFlow: examples

run the following commands to generate examples

cmake -B build
cmake --build build -j $(nproc)

This will generate the examples in the build/bin directory. You can run the examples with:

./build/bin/itopk