Flowise is an open source low-code tool for developers to build customized LLM orchestration flows & AI agents. With Upstash Vector and Upstash Redis, you can extend your Flowise flows to include semantic search, caching, and conversation memory.

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Install

To get started, you can install Flowise locally using npm. Run: 
npm install -g flowise
Start Flowise: 
npx flowise start
Open: http://localhost:3000 You also need to set up Upstash services:
  1. Create a Vector Index in the Upstash Console. To learn more about index creation, you can check out this page.
  2. Create a Redis Database in the Upstash Console. To learn more about Redis database creation, you can check out this page.

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Nodes Overview

Flowise supports multiple Upstash integrations. Below are the nodes and their functionalities:

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1. Upstash Vector Node

Use the Upstash Vector node to perform semantic search and store document embeddings. Connect the node to document loaders and embedding components for indexing and querying.

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2. Upstash Redis Cache Node

The Upstash Redis Cache node caches LLM responses in a serverless Redis database.

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3. Upstash Redis-Backed Chat Memory Node

The Upstash Redis-Backed Chat Memory node summarizes conversations and stores the memory in Redis. This enables persistent, context-aware interactions across multiple sessions.

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Example Flow

Below is an example flow using Upstash Vector:

You can use a document loader to upload documents and connect it to the Upstash Vector node for indexing.

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Learn More

For more details, visit the Flowise documentation.