Dify

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Overview

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Quick Integration

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1. Navigate to Dify Model Provider Settings

1. Log in to the Dify platform, click your username in the top-right corner → Settings
2. Select Model Provider from the left-hand menu
3. Locate the OpenAI-API-compatible plugin in the list and click to install

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2. Add Model Configuration

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3. Configure Context Length and Parameters

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Core Features

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1. Conversational Assistant

1. Create an application → Select the Conversational Assistant template
2. Configure the system prompt:
   You are an intelligent customer service assistant for UToken. Your responsibilities are:
   - Answer user questions encountered during API integration
   - Recommend models suitable for the user's use case
   - Guide users to documentation or contact BD when uncertain
   Maintain a friendly, professional, and concise tone.
   
3. Select gpt-5.5 or claude-opus-4-7 as the model
4. Recommended parameters: temperature = 0.7, max_tokens = 2000

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2. Workflow Application

• Intent classification: gemini-3.5-flash (high throughput, low latency)
• Knowledge base retrieval / embedding: text-embedding-3-large or gemini-embedding-001
• Long-form summarization / reasoning: claude-opus-4-7 (1M context, strong reasoning)
• Code generation: gpt-5.5 or qwen3-coder-plus

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3. Knowledge Base Q&A (RAG)

1. Create a Knowledge Base → Upload documents (PDF / Word / Markdown / TXT, etc.)
2. Select an embedding model: text-embedding-3-large (OpenAI) is recommended
3. Chunking strategy: Automatic paragraph-based splitting, averaging 500 tokens per chunk
4. Reference the knowledge base in your application
5. Configure retrieval parameters:
   • Top-K: 3–5
   • Similarity threshold: 0.7
   • Reranking: enabled (significantly improves retrieval relevance)

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Application Types and Configuration Examples

Application Type: Conversational Assistant
Model: gpt-5.5
System Prompt: |
  You are a professional AI customer service assistant responsible for:
  - Answering user questions
  - Providing product information
  - Handling after-sales support
  Maintain a friendly and professional demeanor.
temperature: 0.7
max_tokens: 2000

Application Type: Workflow
Input: Upload document
Processing Pipeline:
  1. Document parsing (Dify built-in Parser)
  2. Long-form summarization (claude-opus-4-7, 1M context)
  3. Structured key point extraction
  4. Generate analysis report
Output: Markdown report

Application Type: Conversational Assistant
Model: claude-opus-4-7
System Prompt: |
  You are a professional coding assistant with expertise in:
  - Code writing and optimization
  - Debugging
  - Architecture design
  - Best practice recommendations
  Provide clear, executable code solutions with concise explanations.
temperature: 0.3

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Advanced Features

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1. Calling Dify Applications via API

import requests

url = "https://your-dify-instance/v1/chat-messages"
headers = {
    "Authorization": "Bearer YOUR_DIFY_APP_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "inputs": {},
    "query": "Introduce UToken in one sentence",
    "response_mode": "streaming",
    "user": "user_123"
}

resp = requests.post(url, headers=headers, json=payload, stream=True)
for line in resp.iter_lines():
    if line:
        print(line.decode("utf-8"))

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2. Multimodal (Image Input)

{
    "inputs": {
        "image": "data:image/jpeg;base64,...",
        "instruction": "Analyze the key information in this image"
    },
    "query": "Please describe the image in detail and provide business recommendations"
}

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3. Batch Processing

1. Use low-cost, high-speed models (gemini-3.5-flash, gpt-5.4-mini)
2. Set a concurrency limit on the Dify workflow to avoid saturating rate limits at once
3. Enable result caching to avoid redundant calls for identical inputs

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Model Selection Strategy

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Cost Optimization: Development vs. Production

Development Environment:
  Model: gemini-3.5-flash      # Low cost, fast iteration
  max_tokens: 1000
  temperature: 0.7

Production Environment:
  Model: claude-opus-4-7        # Flagship intelligence, stable and reliable
  max_tokens: 2000
  temperature: 0.3
  fallback: gpt-5.5             # Automatic failover via UToken

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Best Practices

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1. Structured Prompting

# Role Definition
You are a professional [specific role]

# Task Description
Please help the user complete [specific task]

# Output Format
1. Summary (<= 100 words)
2. Detailed analysis (itemized)
3. Actionable recommendations

# Constraints
- Be accurate and objective; explicitly state any uncertainty
- Keep within 500 words
- Use English

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2. Workflow Design

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3. Monitoring and Optimization

• ✅ User satisfaction feedback (collect thumbs up/down)
• ⏱️ P95 response time
• 💰 Per-call cost and daily/monthly usage trends
• ❌ Error rate and failure cause distribution

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4. Version Management

• Export Dify application configurations (JSON / YAML) regularly for backup
• Test new versions before publishing; use gradual rollout (canary deployment) to incrementally shift traffic
• Retain at least N-1 versions for rapid rollback

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Troubleshooting

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Common Issues

• Verify the API Key is correct (re-copy from the Console)
• Confirm the account balance is sufficient
• Check that the baseURL is https://utoken.yoostudio.ai/v1 (including the trailing /v1)

• Verify the model name uses the canonical name (e.g., gpt-5.5 not GPT-5.5)
• Confirm that “Model Name” and “Model Name in API Endpoint” are exactly identical

• Prefer Flash / Mini tier models
• Reduce the max_tokens limit
• Enable Dify’s result caching

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Performance Optimization Reference

Cache Configuration:
  Enabled: true
  TTL: 3600s
  Cache Condition: identical input

Concurrency Control:
  Max Concurrency: 10
  Queue Size: 100
  Timeout: 30s

Resource Limits:
  Memory: 2GB
  CPU: 80%

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Deployment Recommendations

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Production Environment (Self-hosted Dify) Docker Compose Example

version: '3.8'
services:
  dify-api:
    image: langgenius/dify-api:latest
    environment:
      - SECRET_KEY=your-secret-key
      - DB_HOST=postgres
      - REDIS_HOST=redis
      - OPENAI_API_KEY=sk-your-UToken-key
      - OPENAI_API_BASE=https://utoken.yoostudio.ai/v1
    depends_on:
      - postgres
      - redis

  dify-web:
    image: langgenius/dify-web:latest
    ports:
      - "3000:3000"
    depends_on:
      - dify-api

  postgres:
    image: postgres:14
    environment:
      - POSTGRES_DB=dify
      - POSTGRES_USER=dify
      - POSTGRES_PASSWORD=password

  redis:
    image: redis:alpine

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Security Configuration

• Store API Keys in environment variables or a Secret Manager — do not hardcode them in the Dify application configuration
• Enable HTTPS with a reverse proxy (Nginx / Caddy / Traefik) in front
• Enable SSO / two-factor authentication for the Dify admin panel
• Regularly update base images and dependencies

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Health Check

import requests, time

def monitor_dify():
    try:
        r = requests.get("http://dify-api:5001/health", timeout=5)
        if r.status_code == 200:
            print("Dify is running normally")
        else:
            print(f"Anomaly detected, status code: {r.status_code}")
    except Exception as e:
        print(f"Monitoring failed: {e}")

while True:
    monitor_dify()
    time.sleep(60)

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Metrics and Reconciliation