Dify开源：LLM应用架构

# Dify后端架构简化示意from flask import Flask, request, jsonifyfrom celery import Celery app = Flask(__name__)celery = Celery('dify', broker='redis://localhost:6379/0') @app.route('/v1/chat-messages', methods=['POST'])def chat():    """对话API - 同步处理"""    data = request.json    app_id = data.get('app_id')    query = data.get('query')    # 调用模型生成回答    response = call_model(app_id, query)    return jsonify({"answer": response}) @celery.taskdef index_document(doc_id, content):    """文档索引 - 异步处理"""    chunks = split_document(content)    vectors = embed_chunks(chunks)    save_to_vector_db(doc_id, chunks, vectors)    return {"indexed_chunks": len(chunks)} @app.route('/v1/documents', methods=['POST'])def upload_document():    """上传文档 - 触发异步索引"""    doc = save_document(request.files['file'])    index_document.delay(doc.id, doc.content)    return jsonify({"doc_id": doc.id, "status": "indexing"})

# Dify模型适配层Provider接口设计class ModelProvider:    """模型Provider统一接口"""    def __init__(self, model_name, capabilities):        self.model_name = model_name        self.capabilities = capabilities  # 能力矩阵        def chat(self, messages, **kwargs):        raise NotImplementedError        def completion(self, prompt, **kwargs):        raise NotImplementedError        def embed(self, texts):        raise NotImplementedError class OpenAIProvider(ModelProvider):    def __init__(self, api_key):        super().__init__("gpt-4", {            "function_calling": True,            "vision": True,            "streaming": True        })        self.api_key = api_key        def chat(self, messages, **kwargs):        # 调用OpenAI API        return {"content": "...", "usage": {"tokens": 100}} class CustomPrivateProvider(ModelProvider):    """企业私有模型Provider"""    def __init__(self, endpoint):        super().__init__("private-llm", {            "function_calling": False,            "vision": False,            "streaming": True        })        self.endpoint = endpoint        def chat(self, messages, **kwargs):        # 调用企业内网模型API        return {"content": "...", "usage": {"tokens": 80}}

# Dify平台核心架构模拟器# 模拟API服务、Worker异步任务和模型适配层的工作流程 import jsonfrom collections import defaultdict class ModelProvider:    """模型Provider统一接口"""    def __init__(self, name, capabilities):        self.name = name        self.capabilities = capabilities        def chat(self, messages):        return {"content": f"[{self.name}] 回复: 基于{len(messages)}条消息生成回答", "tokens": 50} class ModelRegistry:    """模型注册中心"""    def __init__(self):        self.providers = {}        def register(self, provider):        self.providers[provider.name] = provider        print(f"注册模型: {provider.name}, 能力: {provider.capabilities}")        def get_provider(self, model_name):        return self.providers.get(model_name)        def get_by_capability(self, capability):        return [n for n, p in self.providers.items() if p.capabilities.get(capability)] class TaskQueue:    """异步任务队列(Celery模拟)"""    def __init__(self):        self.tasks = []        def submit(self, task_name, args):        self.tasks.append({"task": task_name, "args": args, "status": "pending"})        print(f"提交异步任务: {task_name}")        def process_all(self):        results = []        for task in self.tasks:            if task["status"] == "pending":                result = self.execute(task)                task["status"] = "done"                results.append(result)        self.tasks.clear()        return results        def execute(self, task):        if task["task"] == "index_document":            content = task["args"]["content"]            chunks = content.split("。")            return {"indexed": len(chunks), "doc_id": task["args"]["doc_id"]}        return {} class DifyPlatform:    """Dify平台核心"""    def __init__(self):        self.registry = ModelRegistry()        self.queue = TaskQueue()        self.apps = {}        def create_app(self, app_id, app_type, model_name):        self.apps[app_id] = {"type": app_type, "model": model_name}        return self.apps[app_id]        def chat(self, app_id, query):        app = self.apps[app_id]        provider = self.registry.get_provider(app["model"])        if not provider:            return {"error": f"模型 {app['model']} 未注册"}        return provider.chat([{"role": "user", "content": query}])        def upload_document(self, doc_id, content):        self.queue.submit("index_document", {"doc_id": doc_id, "content": content})        return {"doc_id": doc_id, "status": "indexing"} # 运行模拟platform = DifyPlatform() # 注册模型platform.registry.register(ModelProvider("gpt-4", {"function_calling": True, "vision": True}))platform.registry.register(ModelProvider("chatglm3", {"function_calling": False, "vision": False})) # 创建应用platform.create_app("hr-bot", "chat", "chatglm3")platform.create_app("agent-app", "agent", "gpt-4") # 对话print("\n=== 对话测试 ===")print(platform.chat("hr-bot", "请假流程是什么？"))print(platform.chat("agent-app", "帮我查一下工单#1234")) # 文档索引print("\n=== 文档索引 ===")platform.upload_document("doc1", "Dify是一个开源LLM平台。支持多种模型接入。提供可视化编排。")results = platform.queue.process_all()print(f"索引结果: {results}") # 能力查询print(f"\n支持Function Calling的模型: {platform.registry.get_by_capability('function_calling')}")