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dify-llm-platform

📁 aeonbridge/ab-anthropic-claude-skills 📅 7 days ago
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全站排名
安装命令
npx skills add https://github.com/aeonbridge/ab-anthropic-claude-skills --skill dify-llm-platform

Agent 安装分布

openclaw 2
gemini-cli 2
github-copilot 2
codex 2
kimi-cli 2
cursor 2

Skill 文档

When to Use This Skill

Use this skill when you need to work with Dify, including:

  • Building LLM-powered applications with visual workflows
  • Creating AI chatbots and conversational agents
  • Implementing RAG (Retrieval-Augmented Generation) systems
  • Developing AI agents with tool calling capabilities
  • Deploying production-ready LLM applications
  • Managing knowledge bases and document processing
  • Integrating LLMs with existing tools and data sources
  • Monitoring and optimizing LLM application performance

Overview

Dify (Do It For You) is an open-source platform for building agentic workflows and LLM applications. It provides a visual interface for designing complex AI processes without extensive coding, supporting integration with hundreds of LLM models and existing tools.

Key Resources:

Platform Name: Do It For You – reflecting its purpose of simplifying LLM application development.

Core Capabilities

1. Visual Workflow Engine

  • Node-based interface for building AI workflows
  • Drag-and-drop components for rapid development
  • Real-time preview and testing
  • Version control for workflow iterations

2. Model Support

  • Hundreds of LLMs: Integration with proprietary and open-source models
  • Dozens of providers: OpenAI, Anthropic, Google, Hugging Face, local models, etc.
  • Model comparison: Side-by-side testing in Prompt IDE
  • Flexible switching: Easy model provider changes

3. RAG Pipeline

  • Document processing: PDF, PPT, TXT, Markdown support
  • Knowledge bases: Create and manage multiple knowledge sources
  • Chunking strategies: Configurable text segmentation
  • Retrieval methods: Vector search, keyword search, hybrid retrieval
  • Embedding models: Multiple embedding provider support

4. Agent Capabilities

  • Function calling: ReAct-based agent architecture
  • 50+ built-in tools: Pre-configured integrations
  • Custom tools: Add your own API and function tools
  • Multi-step reasoning: Complex task decomposition

5. LLMOps

  • Application monitoring: Track usage and performance
  • Analytics dashboard: User interactions and metrics
  • Debugging tools: Step-by-step execution inspection
  • Variable tracking: Monitor data flow through workflows

6. Backend-as-a-Service

  • REST APIs: Programmatic access to applications
  • Web applications: Deploy as standalone web apps
  • Embedded components: Integrate into existing applications
  • API documentation: Auto-generated API specs

Installation

Prerequisites

System Requirements:

  • CPU: ≥2 cores
  • RAM: ≥4 GB
  • Docker and Docker Compose installed

Option 1: Docker Compose (Recommended)

# Clone the repository
git clone https://github.com/langgenius/dify.git
cd dify

# Navigate to docker directory
cd docker

# Copy environment example
cp .env.example .env

# Edit .env with your configuration
vim .env

# Start Dify
docker compose up -d

# Access the dashboard
open http://localhost/install

Option 2: Dify Cloud

# Sign up at https://cloud.dify.ai
# Get 200 free GPT-4 calls
# No installation required

Option 3: Deploy to Cloud Platforms

Kubernetes:

helm repo add dify https://langgenius.github.io/dify-helm
helm install dify dify/dify

AWS:

# Using AWS CDK
cdk deploy DifyStack

Azure/Google Cloud/Alibaba Cloud: See deployment guides in official documentation.

Environment Configuration

Key environment variables in .env:

# API Service
API_URL=http://localhost:5001

# Web Service
WEB_URL=http://localhost:3000

# Database
DB_USERNAME=postgres
DB_PASSWORD=your_password
DB_HOST=db
DB_PORT=5432
DB_DATABASE=dify

# Redis
REDIS_HOST=redis
REDIS_PORT=6379
REDIS_PASSWORD=your_redis_password

# Storage (S3, Azure Blob, or local)
STORAGE_TYPE=local
STORAGE_LOCAL_PATH=storage

# Vector Database
VECTOR_STORE=weaviate  # or pgvector, qdrant, milvus

# API Keys for LLM Providers
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key

Quick Start

1. Create Your First Application

# After installation, access http://localhost/install
# Complete initial setup wizard
# Create your first workspace

Application Types:

  1. Chatbot – Conversational AI with memory
  2. Text Generator – Single-turn text generation
  3. Agent – Task-oriented AI with tools
  4. Workflow – Custom multi-step processes

2. Build a Simple Chatbot

Via Web Interface:

  1. Click “Create Application”
  2. Select “Chatbot”
  3. Choose your LLM model (e.g., GPT-4, Claude)
  4. Configure system prompt
  5. Test in playground
  6. Publish as API or web app

Example System Prompt:

You are a helpful customer service assistant for TechCorp.
You can help users with:
- Product information
- Order tracking
- Technical support
- Account management

Be friendly, professional, and concise.

3. Add Knowledge Base (RAG)

Create Knowledge Base:

  1. Go to “Knowledge” section
  2. Click “Create Knowledge Base”
  3. Upload documents (PDF, TXT, Markdown)
  4. Configure chunking settings:
    • Chunk size: 500 tokens (recommended)
    • Overlap: 50 tokens
  5. Select embedding model
  6. Process documents

Connect to Application:

  1. Open your chatbot
  2. Add “Knowledge Retrieval” node
  3. Select your knowledge base
  4. Configure retrieval settings:
    • Top K: 3-5 results
    • Score threshold: 0.7
    • Reranking: Enable for better results

4. Using the API

Python Example:

import requests

API_KEY = "your_dify_api_key"
API_URL = "http://localhost/v1"

# Send a chat message
response = requests.post(
    f"{API_URL}/chat-messages",
    headers={
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    },
    json={
        "inputs": {},
        "query": "What are your business hours?",
        "response_mode": "streaming",
        "conversation_id": "",
        "user": "user-123"
    }
)

for line in response.iter_lines():
    if line:
        print(line.decode('utf-8'))

Node.js Example:

const fetch = require('node-fetch');

const API_KEY = 'your_dify_api_key';
const API_URL = 'http://localhost/v1';

async function sendMessage(query) {
  const response = await fetch(`${API_URL}/chat-messages`, {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      inputs: {},
      query: query,
      response_mode: 'blocking',
      user: 'user-123'
    })
  });

  return await response.json();
}

sendMessage('Hello!').then(console.log);

Workflow Development

Visual Workflow Builder

Core Nodes:

  1. LLM Node – Call language models
  2. Knowledge Retrieval – Query knowledge bases
  3. Tool Node – Execute functions and APIs
  4. Code Node – Run custom Python/JavaScript
  5. Conditional – Branch based on logic
  6. Variable Aggregator – Combine data
  7. HTTP Request – Call external APIs
  8. Template Transform – Format text

Example: Customer Support Workflow

Workflow Steps:
1. Start → User Message
2. Knowledge Retrieval → Search documentation
3. Conditional Branch:
   - If relevant docs found → Use context
   - If not found → Use general knowledge
4. LLM Node → Generate response
5. Tool Node → Create ticket (if needed)
6. End → Return response

Implementation:

  1. Drag “LLM” node to canvas
  2. Connect to “Knowledge Retrieval” node
  3. Add “Conditional” node with logic: 
    {{retrieval.score}} > 0.7
  4. Configure LLM prompt: 
    Context: {{retrieval.context}}
User question: {{user.query}}

Provide a helpful answer based on the context.
If the context doesn't contain the answer, say so politely.

Code Node Examples

Python Code Node:

def main(data: dict) -> dict:
    # Access workflow variables
    user_query = data.get('query', '')

    # Custom processing
    processed = user_query.upper()

    # Return results
    return {
        'result': processed,
        'length': len(user_query)
    }

JavaScript Code Node:

function main(data) {
  const query = data.query || '';

  // Process data
  const words = query.split(' ');

  return {
    word_count: words.length,
    first_word: words[0]
  };
}

Agent Development

Building an Agent with Tools

Built-in Tools:

  • Google Search
  • Wikipedia
  • Weather APIs
  • Calculator
  • Web Scraper
  • Database queries
  • File operations
  • Email sending

Example: Research Agent

Agent Configuration:
  Model: gpt-4
  Reasoning Mode: ReAct
  Tools:
    - Google Search
    - Wikipedia
    - Web Scraper
  System Prompt: |
    You are a research assistant that helps users find
    accurate information from reliable sources.

    Always:
    1. Search for current information
    2. Cite your sources
    3. Verify facts from multiple sources

Custom Tool Definition:

# Define custom tool for Dify
{
  "name": "check_inventory",
  "description": "Check product inventory levels",
  "parameters": {
    "type": "object",
    "properties": {
      "product_id": {
        "type": "string",
        "description": "Product identifier"
      }
    },
    "required": ["product_id"]
  }
}

Knowledge Base Management

Document Processing

Supported Formats:

  • PDF
  • Microsoft Word (.docx)
  • PowerPoint (.pptx)
  • Text files (.txt)
  • Markdown (.md)
  • HTML
  • CSV

Chunking Strategies

1. Fixed Size Chunking:

Strategy: fixed_size
Chunk Size: 500 tokens
Overlap: 50 tokens
Use Case: General documents, articles

2. Paragraph Chunking:

Strategy: paragraph
Min Size: 100 tokens
Max Size: 800 tokens
Use Case: Well-formatted documents

3. Semantic Chunking:

Strategy: semantic
Model: text-embedding-ada-002
Similarity Threshold: 0.8
Use Case: Complex technical documents

Retrieval Configuration

Vector Search:

Type: vector
Top K: 5
Score Threshold: 0.7
Embedding Model: text-embedding-3-large

Hybrid Retrieval:

Type: hybrid
Vector Weight: 0.7
Keyword Weight: 0.3
Reranking: enabled
Reranking Model: cross-encoder/ms-marco-MiniLM-L-12-v2

API Integration

REST API Endpoints

Chat Messages:

POST /v1/chat-messages
Content-Type: application/json
Authorization: Bearer {api_key}

{
  "inputs": {},
  "query": "Your question here",
  "response_mode": "streaming",
  "user": "user-identifier"
}

Completion Messages:

POST /v1/completion-messages
Content-Type: application/json
Authorization: Bearer {api_key}

{
  "inputs": {
    "name": "John",
    "topic": "AI"
  },
  "response_mode": "blocking",
  "user": "user-123"
}

Feedback:

POST /v1/messages/{message_id}/feedbacks
Content-Type: application/json
Authorization: Bearer {api_key}

{
  "rating": "like",
  "user": "user-123"
}

SDK Usage

Python SDK:

from dify_client import DifyClient

client = DifyClient(api_key="your_api_key")

# Chat completion
response = client.chat(
    query="What is Dify?",
    user="user-123",
    conversation_id=None
)

print(response.answer)

Streaming Response:

for chunk in client.chat_stream(
    query="Explain quantum computing",
    user="user-123"
):
    print(chunk.delta, end="", flush=True)

Best Practices

Development

  1. Start Simple: Begin with basic chatbots before complex workflows
  2. Test Incrementally: Validate each node before connecting
  3. Use Variables: Properly scope and name workflow variables
  4. Version Control: Save workflow versions before major changes
  5. Monitor Costs: Track token usage and API costs

Production

  1. Security:

    • Use environment variables for API keys
    • Implement rate limiting
    • Enable authentication on public endpoints
    • Regular security updates

  2. Performance:

    • Cache frequent queries
    • Optimize knowledge base chunking
    • Use streaming for long responses
    • Implement connection pooling

  3. Monitoring:

    Metrics to Track:
  - Response latency
  - Token usage
  - Error rates
  - User satisfaction scores
  - Knowledge retrieval accuracy

  4. Scalability:

    • Use load balancers
    • Scale workers horizontally
    • Separate databases for different services
    • Implement queue systems for async tasks

Knowledge Base Optimization

  1. Document Quality:

    • Clean and format documents before upload
    • Remove duplicates and outdated content
    • Use consistent terminology

  2. Chunking:

    • Start with 500-token chunks
    • Adjust based on document structure
    • Use overlap for context preservation

  3. Retrieval Tuning:

    • Monitor retrieval accuracy
    • Adjust score thresholds
    • Use reranking for critical applications
    • A/B test retrieval configurations

Troubleshooting

Common Issues

Issue 1: Docker Containers Won’t Start

Symptoms:

  • docker compose up fails
  • Services crash on startup

Solutions:

# Check logs
docker compose logs

# Verify environment variables
cat .env

# Ensure ports are available
lsof -i :3000
lsof -i :5001

# Reset and restart
docker compose down -v
docker compose up -d

Issue 2: Knowledge Retrieval Returns No Results

Symptoms:

  • Queries return empty results
  • Low relevance scores

Solutions:

  1. Check document processing status
  2. Verify embedding model configuration
  3. Lower score threshold temporarily
  4. Test with exact document text
  5. Rebuild knowledge base index
# Via API - trigger reindex
POST /v1/datasets/{dataset_id}/documents/{document_id}/processing

Issue 3: High Latency on API Calls

Symptoms:

  • Slow response times
  • Timeout errors

Solutions:

  1. Enable caching:

    cache:
  enabled: true
  ttl: 3600

  2. Use streaming mode:

    response_mode: "streaming"

  3. Optimize LLM settings:

    max_tokens: 500  # Reduce if possible
temperature: 0.7
top_p: 0.9

  4. Check database performance:

    # Monitor PostgreSQL
docker exec -it dify-db psql -U postgres -c "\
  SELECT pid, query, state, wait_event_type \
  FROM pg_stat_activity WHERE state != 'idle';"

Issue 4: Model API Errors

Symptoms:

  • “Invalid API key” errors
  • Rate limit exceeded

Solutions:

# Verify API keys in .env
cat .env | grep API_KEY

# Check provider status
curl https://status.openai.com/api/v2/status.json

# Implement retry logic
max_retries: 3
retry_delay: 1000  # milliseconds

Debugging Tips

  1. Use Workflow Debugger:

    • Step through each node
    • Inspect variable values
    • Check node execution time

  2. Enable Detailed Logging:

    # In .env
LOG_LEVEL=DEBUG

  3. Test Components Individually:

    • Test LLM calls separately
    • Verify knowledge retrieval standalone
    • Test tools in isolation

  4. Monitor System Resources:

    docker stats

Advanced Topics

Custom Model Providers

Add custom LLM providers:

# model_providers/custom_provider.py
from dify.core.model_runtime import ModelProvider

class CustomProvider(ModelProvider):
    def get_models(self):
        return [
            {
                'model': 'custom-gpt',
                'label': 'Custom GPT Model',
                'model_type': 'llm'
            }
        ]

    def invoke(self, model, credentials, prompt, **kwargs):
        # Custom API call logic
        response = your_api_call(prompt)
        return response

Workflow Optimization

Parallel Execution:

Workflow:
  - Node1: LLM Call
  - Parallel:
      - Node2a: Knowledge Retrieval
      - Node2b: External API Call
  - Node3: Combine Results

Conditional Caching:

# Cache expensive operations
if cache.exists(query_hash):
    return cache.get(query_hash)
else:
    result = expensive_operation()
    cache.set(query_hash, result, ttl=3600)
    return result

Enterprise Deployment

High Availability Setup:

Services:
  API:
    replicas: 3
    load_balancer: nginx
  Worker:
    replicas: 5
    queue: redis
  Database:
    primary: postgres-main
    replicas: 2
    backup: daily

Monitoring Stack:

Monitoring:
  - Prometheus: Metrics collection
  - Grafana: Visualization
  - Loki: Log aggregation
  - Alertmanager: Alerts

Resources

Official Documentation

Community

Deployment Guides

Related Tools

  • LangChain: Alternative framework for LLM apps
  • LlamaIndex: Data framework for LLMs
  • Flowise: Visual LLM workflow builder
  • LangFlow: Drag-and-drop LLM pipeline builder

Contributing

Dify is an active open-source project with 8,220+ commits.

Ways to Contribute:

  1. Report bugs via GitHub Issues
  2. Submit pull requests for features
  3. Improve documentation
  4. Share use cases and examples
  5. Help with translations

Development Setup:

# Clone repository
git clone https://github.com/langgenius/dify.git
cd dify

# See deployment guide
# https://docs.dify.ai/development/deploy-from-source

Security Issues: Email: security@dify.ai

Version Information

Last Updated: 2025-12-15 Skill Version: 1.0.0 Dify GitHub: 8,220+ commits, actively maintained

Note: Dify is rapidly evolving. Always check the official documentation for the latest features and best practices. This skill is based on official documentation and repository information as of December 2025.

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