Flowise Builder

Overview

Flowise is an open-source visual tool for building LLM workflows. It provides a drag-drop interface to connect LLMs, document loaders, vector stores, tools, and chains — then deploy as API endpoints.

Capabilities

• Build chatflows visually with drag-drop nodes
• Connect to OpenAI, Anthropic, Ollama, and local models
• Add document loaders (PDF, web, CSV, Notion)
• Integrate vector stores (Pinecone, FAISS, Chroma, Qdrant)
• Add tools (web search, calculator, API calls)
• Deploy as REST API with streaming support
• Embed chatbot widget in websites

When to Use

Trigger phrases:

• "flowise builder"
• "Flowise visual LLM workflow builder — drag-drop chatflows, API endpoints, docume"

• Building LLM apps without writing code
• Prototyping RAG chatbots quickly
• Needing visual workflow design for AI pipelines
• Deploying AI chatbots as APIs or website widgets
• Self-hosting AI infrastructure

When NOT to Use

• Task requires custom AI model training (use ML tools)
• You need complex AI agent logic (use LangChain directly)
• Task is about data processing, not AI app building
• You don't have Flowise instance running
• Task requires real-time AI inference (use dedicated AI services)
• You need to build a custom AI application (use development tools)

Pseudo Code

Implementation patterns for common use cases with this skill.

Installation

# npm
npm install -g flowise
npx flowise start

# Docker
docker run -d -p 3000:3000 flowiseai/flowise

# Access at http://localhost:3000

Chatflow Architecture

Document Loader → Text Splitter → Embedding → Vector Store
                                                    ↓
User Question → Embedding → Vector Store Retriever → LLM Chain → Response

API Usage

# Prediction
curl -X POST http://localhost:3000/api/v1/prediction/{chatflow-id} \
  -H "Content-Type: application/json" \
  -d '{"question": "What is the return policy?", "overrideConfig": {}}'

# Streaming
curl -X POST http://localhost:3000/api/v1/prediction/{chatflow-id} \
  -H "Content-Type: application/json" \
  -d '{"question": "Hello", "streaming": true}'

Embed Widget

  import Chatbot from "https://cdn.jsdelivr.net/npm/flowise-embed/dist/web.js"
  Chatbot.init({
    chatflowid: "your-chatflow-id",
    apiHost: "http://localhost:3000",
  })

Node Configuration

| Node | Config | |------|--------| | ChatOpenAI | model, temperature, maxTokens, apiKey | | OpenAIEmbeddings | modelName, apiKey | | VectorStoreRetriever | topK, filter | | TextSplitter | chunkSize, chunkOverlap | | Calculator | — | | RequestsGet | url, headers | | CustomJS | code |

Common Patterns

| Pattern | When to Use | |---------|------------| | Document Loader → Vector Store | Index knowledge base | | Retriever → LLM Chain | RAG chatbot | | Agent + Tools | Autonomous assistant | | Conditional Branches | Different paths based on input | | Memory | Multi-turn conversations |

Error Handling

| Error | Cause | Fix | |-------|-------|-----| | API key not set | Missing env var | Set OPENAIAPIKEY in .env | | Vector store empty | Documents not indexed | Re-upload and process documents | | Node connection error | Invalid node config | Check node settings in UI | | Streaming not working | Missing streaming flag | Add streaming: true in API call |

Red Flags

• Not testing flows before deployment
• Ignoring error handling in flows
• Missing logging and monitoring
• Not documenting flow logic
• Ignoring rate limits and quotas

Verification

• [ ] Flows are tested end-to-end
• [ ] Error handling is in place
• [ ] Logging and monitoring are configured
• [ ] Flow logic is documented
• [ ] Rate limits are respected

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization

| Rationalization | Reality | |---|---| | "Manual is faster for one-off tasks" | One-off tasks become recurring. Automate early, save time later. | | "I will add error handling later" | You never do. Handle errors from day one. | | "Automation is overkill" | If you do it twice, automate it. If you do it daily, it is critical infrastructure. |

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: oyi77
• Source: oyi77/1ai-skills
• License: MIT
• Homepage: https://oyi77.github.io/1ai-skills

Install and usage instructions live in the source repository linked above.