Quick Start Checklist: Flowise + OrgHunter RAG Integration

Introduction

📋 Pre-Integration Checklist

Account Setup

• [ ] Sign up for OrgHunter API at https://orghunter.3scale.net/
• [ ] Get your OrgHunter API key from dashboard
• [ ] Sign up for OpenAI (or alternative LLM provider)
• [ ] Get your OpenAI API key from https://platform.openai.com/api-keys
• [ ] Test both API keys with simple curl requests

Software Installation

Choose one method:

Option A: Local Installation

• [ ] Install Node.js (v18+) from https://nodejs.org/
• [ ] Run: npm install -g flowise
• [ ] Run: npx flowise start
• [ ] Access at http://localhost:3000
• [ ] Verify Flowise interface loads correctly

Option B: Docker

• [ ] Install Docker Desktop
• [ ] Run: docker run -d --name flowise -p 3000:3000 flowiseai/flowise
• [ ] Access at http://localhost:3000

Option C: Cloud

• [ ] Sign up at https://flowiseai.com/
• [ ] Access hosted version (no installation needed)

🔧 Initial Configuration (15 minutes)

Environment Setup

• [ ] Open Flowise at http://localhost:3000
• [ ] Go to Settings → Environment Variables
• [ ] Add: ORGHUNTER_API_KEY = your_orghunter_key
• [ ] Add: OPENAI_API_KEY = your_openai_key
• [ ] Save environment variables
• [ ] Restart Flowise if needed

Create New Chatflow

• [ ] Click "Add New" button
• [ ] Name: "OrgHunter Charity Intelligence"
• [ ] Description: "RAG system for charity discovery and analysis"
• [ ] Save the new chatflow

🛠️ Build Phase 1: Basic Search (30 minutes)

Custom Tool #1: Charity Search

• [ ] Drag "Custom Tool" from Tools category onto canvas
• [ ] Set Tool Name: search_charities
• [ ] Copy tool description from tutorial
• [ ] Copy input schema JSON from tutorial
• [ ] Copy JavaScript function from tutorial
• [ ] Click "Save" on the tool
• [ ] Click "Test" button
• [ ] Test with: {"city": "Seattle", "category": "C"}
• [ ] Verify JSON response with charity data

Custom Tool #2: Financial Data

• [ ] Add another "Custom Tool" node
• [ ] Set Tool Name: get_charity_financials
• [ ] Copy tool description from tutorial
• [ ] Copy input schema JSON from tutorial
• [ ] Copy JavaScript function from tutorial
• [ ] Save the tool
• [ ] Test with: {"ein": "94-1156200"}
• [ ] Verify financial data response

Foundation Nodes

• [ ] Add "Start" node
• [ ] Configure Input Type: Chat Input
• [ ] Enable Session Memory: Yes
• [ ] Add "ChatOpenAI" node
• [ ] Set Model: gpt-3.5-turbo or gpt-4
• [ ] Temperature: 0.7
• [ ] Connect to API key from environment
• [ ] Add "Buffer Memory" node
• [ ] Memory Key: chat_history
• [ ] Enable Session ID

Agent Setup

• [ ] Add "OpenAI Function Agent" node
• [ ] Connect Start → Agent
• [ ] Connect ChatOpenAI → Agent (as LLM)
• [ ] Connect Buffer Memory → Agent (as Memory)
• [ ] Connect search_charities Tool → Agent
• [ ] Connect get_charity_financials Tool → Agent
• [ ] Paste system prompt from tutorial
• [ ] Save entire flow

🧪 Testing Phase (15 minutes)

Basic Tests

• [ ] Click chat icon (bottom right)
• [ ] Test 1: "Find environmental charities in Seattle"
• [ ] Verify: Tool is called, results returned, formatted nicely
• [ ] Test 2: "Tell me about the Red Cross finances"
• [ ] Verify: Search called first, then financial tool
• [ ] Test 3: "What's your favorite color?" (should handle gracefully)

Conversation Memory Test

• [ ] Ask: "Find food banks in Austin"
• [ ] Then ask: "Tell me more about the first one"
• [ ] Verify: Agent remembers previous context
• [ ] Ask: "How about the second?"
• [ ] Verify: Still maintaining context

Debug Check

• [ ] Enable "Show Agent Messages"
• [ ] Run a query
• [ ] Review agent's reasoning process
• [ ] Verify tools being called correctly
• [ ] Check parameter passing

🚀 Enhancement Phase (Optional - 30 minutes)

Add Query Classification

• [ ] Add "Condition Agent" node after Start
• [ ] Copy classification instructions from tutorial
• [ ] Create two branches:
  • [ ] Branch 0 (charity queries) → Main agent
  • [ ] Branch 1 (general queries) → Simple LLM redirect
• [ ] Test with both charity and non-charity questions

Add Category Helper

• [ ] Create custom function node
• [ ] Copy category mapping code from tutorial
• [ ] Test: "education" → should return "B"
• [ ] Test: "environment" → should return "C"
• [ ] Integrate into main flow

Add Geolocation Tool (Advanced)

• [ ] Create custom tool: get_nearby_charities
• [ ] Copy JavaScript function from tutorial
• [ ] Add to agent tools
• [ ] Test with latitude/longitude

📊 Production Readiness (15 minutes)

Error Handling

• [ ] Add try-catch blocks to all custom tools
• [ ] Test with invalid inputs
• [ ] Verify graceful error messages
• [ ] Add retry logic for API failures

Performance

• [ ] Add caching to frequently searched queries
• [ ] Limit API result sizes (rows: 10-20)
• [ ] Test response times
• [ ] Optimize if > 3 seconds

Security Check

• [ ] Verify API keys not exposed in client code
• [ ] Check environment variables are secure
• [ ] Add rate limiting if needed
• [ ] Review data privacy compliance

🌐 Deployment Options

Option A: Embed in Website

• [ ] Click "Embed" button in Flowise
• [ ] Copy embed code
• [ ] Customize theme colors/sizing
• [ ] Add to website <body> tag
• [ ] Test on staging site
• [ ] Deploy to production

Option B: API Endpoint

• [ ] Get chatflow ID from URL
• [ ] Test with curl:

curl -X POST http://localhost:3000/api/v1/prediction/CHATFLOW_ID \ -H "Content-Type: application/json" \ -d '{"question": "Find health charities"}'

• [ ] Integrate into your application
• [ ] Add authentication if needed

Option C: Flowise Cloud

• [ ] Export chatflow (JSON)
• [ ] Upload to Flowise Cloud account
• [ ] Configure environment variables in cloud
• [ ] Get production API endpoint
• [ ] Update DNS/routing as needed

✅ Final Verification

Functionality Check

• [ ] All tools working correctly
• [ ] Memory persisting across conversation
• [ ] Error handling graceful
• [ ] Responses accurate and helpful
• [ ] Performance acceptable (< 5 sec)

User Experience

• [ ] Welcome message clear and inviting
• [ ] Responses well-formatted
• [ ] Follow-up suggestions helpful
• [ ] Citations included for charity data
• [ ] Tone appropriate (helpful, not salesy)

Documentation

• [ ] Document your customizations
• [ ] Note any API changes made
• [ ] Create user guide/FAQ
• [ ] Share example queries
• [ ] Document known limitations

📈 Post-Launch Checklist

Week 1

• [ ] Monitor error rates
• [ ] Track most common queries
• [ ] Collect user feedback
• [ ] Fix critical bugs
• [ ] Optimize slow queries

Week 2

• [ ] Analyze conversation patterns
• [ ] Identify missing features
• [ ] Improve system prompts
• [ ] Add popular query shortcuts
• [ ] Update documentation

Month 1

• [ ] Review analytics dashboard
• [ ] A/B test improvements
• [ ] Add requested features
• [ ] Expand tool capabilities
• [ ] Plan next version

🆘 Troubleshooting Quick Reference

Issue Quick Fix "API key not found" Settings → Env Variables → Verify keys Tool not called Improve tool description clarity 401 error Check API key validity at orghunter.3scale.net 429 rate limit Add caching, reduce request frequency Memory not working Enable Session ID in memory config Slow responses Reduce result size, add caching Hallucinations Add "use only tool data" to system prompt Parse errors Add null checks in JavaScript

📚 Resources Quick Links

Documentation

• Flowise: https://docs.flowiseai.com/
• OrgHunter: https://charityapi.com/docs-introduction
• Tutorial: [Reference main tutorial file]

Support

• OrgHunter: support@orghunter.com
• Flowise Discord: discord.gg/flowise
• GitHub: github.com/FlowiseAI/Flowise

API Testing

• OrgHunter Testing: https://orghunter.3scale.net/docs
• Postman Collection: [Create your own]
• curl examples: [In main tutorial]

🎯 Success Metrics

Define your success criteria:

Technical Metrics

• [ ] Uptime > 99%
• [ ] Response time < 3 seconds
• [ ] Error rate < 1%
• [ ] API success rate > 98%

User Metrics

• [ ] User satisfaction score
• [ ] Conversations per user
• [ ] Task completion rate
• [ ] Return user rate

Business Metrics

• [ ] Number of charity searches
• [ ] Successful recommendations
• [ ] Donations facilitated (if tracking)
• [ ] Cost per query

💡 Pro Tips

1. Start Simple: Get basic search working before adding complexity
2. Test Often: Use test button on every node before connecting
3. Document Changes: Note what works and what doesn't
4. Use Examples: Include example queries in welcome message
5. Iterate Based on Real Use: Let actual usage guide improvements
6. Cache Aggressively: Charity data doesn't change hourly
7. Monitor Costs: Track OpenAI API usage and optimize
8. Backup Regularly: Export your chatflow JSON frequently
9. Version Control: Keep previous versions before major changes
10. Get Feedback Early: Launch with beta users first

🎉 You're Ready!

Once you've completed this checklist, you'll have: ✅ A working charity intelligence RAG system ✅ Custom tools integrated with OrgHunter API ✅ Conversational AI with memory ✅ Production-ready deployment ✅ Monitoring and optimization in place

Next Steps:

1. Launch to users
2. Gather feedback
3. Iterate and improve
4. Share your success story!

Questions?

• Review main tutorial for detailed explanations
• Join Flowise Discord community
• Email OrgHunter support
• Check GitHub discussions

Good luck building! 🚀