Dify vs FastGPT vs RagFlow：开源 AI 应用平台对比

# Dify 检索配置示例
retrieval_config = {
    "search_method": "hybrid",  # full_text / semantic / hybrid
    "top_k": 5,
    "score_threshold": 0.5,
    "rerank": true,
    "rerank_model": "cohere/rerank-multilingual-v3",
}

# FastGPT 检索配置示例
retrieval_config = {
    "similarity": 0.2,
    "limit": 5,
    "search_mode": "embedding",  # embedding / fullText / mixed
    "rerank": true,
    "rerank_model": "bge-reranker-large",
}

# RagFlow 检索配置示例
retrieval_config = {
    "similarity_threshold": 0.2,
    "vector_similarity_weight": 0.3,
    "top_kw": 10,  # 关键词检索数量
    "top_vec": 10,  # 向量检索数量
    "rerank_id": "BGE-reranker-v2-m3",
    # 独有：引用追踪，可定位到原文档的具体位置
    "show_chunks_in_source": true,
}

┌────────────────────────────────────────────┐
│              Dify 工作流示例                 │
│                                            │
│  [开始] → [意图识别] → [条件分支]           │
│                            │               │
│              ┌─────────────┼─────────────┐ │
│              ↓             ↓             ↓ │
│         [RAG 检索]    [Web 搜索]   [直接回复]│
│              │             │             │ │
│              ↓             ↓             │ │
│         [重排序]      [结果过滤]          │ │
│              │             │             │ │
│              └─────→[LLM 生成]←──────────┘ │
│                         │                   │
│                    [输出结果]               │
└────────────────────────────────────────────┘

# FastGPT 工作流配置（简化版）
nodes:
  - id: start
    type: "questionInput"
  
  - id: classify
    type: "classify"
    model: "gpt-4o"
    categories:
      - "技术问题 → rag_search"
      - "闲聊 → direct_reply"
  
  - id: rag_search
    type: "datasetSearch"
    datasetId: "knowledge_base_1"
    similarity: 0.2
    limit: 5
  
  - id: generate
    type: "chat"
    model: "gpt-4o"
    systemPrompt: "基于检索结果回答问题"
  
  - id: direct_reply
    type: "chat"
    model: "gpt-4o"

# RagFlow Agent 配置
agent_config = {
    "components": {
        "retrieval": {
            "type": "knowledge_retrieval",
            "dataset_id": "financial_reports",
            "top_n": 10,
            "rerank": true,
        },
        "generation": {
            "type": "llm_generation",
            "model": "gpt-4o",
            "system_prompt": "你是金融分析师...",
            "temperature": 0.3,
        }
    },
    "edges": [
        {"from": "retrieval", "to": "generation"}
    ]
}

# docker-compose.yml（简化版）
services:
  dify-api:
    image: langgenius/dify-api:latest
    environment:
      - DB_USERNAME=postgres
      - DB_PASSWORD=password
      - REDIS_HOST=redis
      - VECTOR_STORE=qdrant
      - QDRANT_URL=http://qdrant:6333
    depends_on: [postgres, redis, qdrant]
  
  dify-web:
    image: langgenius/dify-web:latest
    ports: ["3000:3000"]
  
  dify-worker:
    image: langgenius/dify-api:latest
    command: celery worker
    environment:
      - CELERY_BROKER=redis://redis:6379/0
  
  postgres:
    image: postgres:16
  
  redis:
    image: redis:7
  
  qdrant:
    image: qdrant/qdrant:latest
    volumes:
      - qdrant-data:/qdrant/storage

services:
  fastgpt:
    image: registry.cn-hangzhou.aliyuncs.com/fastgpt/fastgpt:latest
    ports: ["3000:3000"]
    environment:
      - MONGODB_URI=mongodb://mongo:27017/fastgpt
      - OPENAI_API_KEY=sk-xxx
    depends_on: [mongo]
  
  mongo:
    image: mongo:7
    volumes:
      - mongo-data:/data/db
  
  pg:
    image: ankane/pgvector:v0.5.1
    environment:
      - POSTGRES_DB=fastgpt
    volumes:
      - pg-data:/var/lib/postgresql/data

services:
  ragflow:
    image: infiniflow/ragflow:latest
    ports: ["9380:9380"]
    environment:
      - MYSQL_HOST=mysql
      - REDIS_HOST=redis
      - ES_HOST=elasticsearch:9200
      - MINIO_HOST=minio:9000
    depends_on: [mysql, redis, elasticsearch, minio]
  
  elasticsearch:
    image: elasticsearch:8.13.0
    environment:
      - discovery.type=single-node
  
  mysql:
    image: mysql:8
  
  minio:
    image: minio/minio
    command: server /data

# Dify API 调用示例
import requests

# 对话型应用
response = requests.post(
    "https://api.dify.ai/v1/chat-messages",
    headers={"Authorization": "Bearer app-xxx"},
    json={
        "query": "公司年假政策是什么？",
        "user": "user-123",
        "response_mode": "streaming",
    }
)

# 工作流应用
response = requests.post(
    "https://api.dify.ai/v1/workflows/run",
    headers={"Authorization": "Bearer app-xxx"},
    json={
        "inputs": {"text": "分析这段文字的情感"},
        "user": "user-123",
        "response_mode": "blocking",
    }
)

# FastGPT API 调用示例
response = requests.post(
    "http://localhost:3000/api/v1/chat/completions",
    headers={"Authorization": "Bearer fastgpt-xxx"},
    json={
        "model": "gpt-4o",
        "messages": [{"role": "user", "content": "你好"}],
        "stream": False,
    }
)

RagFlow（深度文档解析 + 高质量分块）
    ↓ 导出分块数据
FastGPT（知识库管理 + 问答路由）
    ↓ API 调用
Dify（工作流编排 + 多渠道发布）