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FlowiseAI Service

FlowiseAI is a powerful visual workflow builder that enables developers to create LLM-based applications through an intuitive drag-and-drop interface. It supports integration with multiple AI services and provides a low-code solution for complex AI workflows.

Architecture Overview

graph TB
    A[User Interface] -->|Drag & Drop| B[Workflow Designer]
    B -->|Configure| C[Node Components]
    C -->|Connect| D[LLM Providers]
    C -->|Connect| E[Data Sources]
    C -->|Connect| F[Output Handlers]
    D --> G[OpenAI]
    D --> H[Anthropic]
    D --> I[Local Models]
    E --> J[Vector Stores]
    E --> K[Databases]
    F --> L[APIs]
    F --> M[Webhooks]

    %% Styling
    classDef uiClass fill:#e3f2fd,stroke:#0d47a1,stroke-width:2px,color:#000
    classDef designerClass fill:#f1f8e9,stroke:#33691e,stroke-width:2px,color:#000
    classDef componentClass fill:#fff8e1,stroke:#f57f17,stroke-width:2px,color:#000
    classDef providerClass fill:#fce4ec,stroke:#ad1457,stroke-width:2px,color:#000
    classDef dataClass fill:#e0f2f1,stroke:#00695c,stroke-width:2px,color:#000
    classDef outputClass fill:#f3e5f5,stroke:#6a1b9a,stroke-width:2px,color:#000
    classDef serviceClass fill:#ede7f6,stroke:#4527a0,stroke-width:2px,color:#000

    class A uiClass
    class B designerClass
    class C componentClass
    class D,G,H,I providerClass
    class E,J,K dataClass
    class F,L,M outputClass

Key Features

Visual Workflow Builder

  • Drag-and-drop interface for creating complex AI workflows
  • Pre-built components for common AI tasks
  • Real-time flow execution and debugging
  • Visual data flow representation

LLM Integrations

  • Support for multiple LLM providers (OpenAI, Anthropic, Cohere, etc.)
  • Local model integration via Ollama
  • Custom model endpoint configuration
  • Chain multiple models in sequence

Data Connectors

  • Vector database integration (Pinecone, Weaviate, etc.)
  • Document loaders (PDF, Word, web scraping)
  • Database connectors (PostgreSQL, MongoDB)
  • API integration capabilities

Advanced Capabilities

  • Memory management for conversational flows
  • Custom function nodes with JavaScript/Python
  • Conditional logic and branching
  • Error handling and retry mechanisms

Configuration Schema

Environment Variables

# Basic Configuration
PORT=3000
DATABASE_TYPE=postgres
DATABASE_HOST=postgres
DATABASE_USER=postgres
DATABASE_PASSWORD=postgres
DATABASE_NAME=flowise

# Authentication
JWT_AUTH_TOKEN_SECRET=${JWT_AUTH_TOKEN_SECRET}
FLOWISE_USERNAME=${FLOWISE_USERNAME}
FLOWISE_PASSWORD=${FLOWISE_PASSWORD}

# Storage Paths
SECRETKEY_PATH=/root/.flowise
LOG_PATH=/root/.flowise/logs
BLOB_STORAGE_PATH=/root/.flowise/storage

# Telemetry Settings
DISABLE_FLOWISE_TELEMETRY=true

Node Configuration Example

{
  "id": "openai-node",
  "type": "LLMChain",
  "data": {
    "model": "gpt-3.5-turbo",
    "temperature": 0.7,
    "maxTokens": 1000,
    "prompt": "Answer the following question: {question}"
  },
  "inputs": ["question"],
  "outputs": ["response"]
}

Access

FlowiseAI is accessible at:

http://localhost:3001/

Supported Integrations

LLM Providers

  • OpenAI (GPT-3.5, GPT-4)
  • Anthropic (Claude)
  • Cohere
  • Hugging Face
  • Azure OpenAI
  • Local models via Ollama

Vector Databases

  • Pinecone
  • Weaviate
  • Qdrant
  • Chroma
  • Supabase

Document Loaders

  • PDF files
  • CSV/Excel files
  • Web scraping
  • Notion pages
  • GitHub repositories

Online Resources

Use Cases

  • Chatbots: Build intelligent conversational agents
  • Document Q&A: Create systems for querying large document collections
  • Content Generation: Automate content creation workflows
  • Data Analysis: Build AI-powered analytics pipelines
  • API Integration: Connect multiple services with AI processing

FlowiseAI is perfect for developers, product managers, and AI enthusiasts who want to rapidly prototype and deploy AI applications without extensive coding.