175 lines
4.5 KiB
Markdown
175 lines
4.5 KiB
Markdown
# 代码示例
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langchain生态
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- Chain
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```python
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async def main():
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prompt = ChatPromptTemplate.from_messages([
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("system", "你是一个友好、专业的AI助手。"),
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("human", "{input}")
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])
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# 2) 定义模型（按你本地 Ollama 配置调整）
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model = ChatOllama(
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model="qwen3:8b", # 或你的实际模型名
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base_url="http://localhost:11434"
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)
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# 3) 定义输出解析器，将 AIMessage 转字符串
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parser = StrOutputParser()
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# 4) 组合为表达式管道
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chain = prompt | model | parser
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# 非流式调用
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text = await chain.ainvoke({"input": "请用一句话做自我介绍"})
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print("非流式：", text)
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# 流式调用
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print("流式：", end="", flush=True)
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async for chunk in chain.astream({"input": "讲一个简短的笑话"}):
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print(chunk, end="", flush=True)
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```
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- Graph
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```python
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# 创建状态图
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async def main():
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workflow = StateGraph(WorkflowState)
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# 添加节点
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workflow.add_node("intent_analysis", self._intent_analysis_wrapper)
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workflow.add_node("order_diagnosis", self._order_diagnosis_wrapper)
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workflow.add_node("natural_chat", self._natural_chat_wrapper)
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# 设置入口点
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workflow.set_entry_point("intent_analysis")
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# 添加条件边
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workflow.add_conditional_edges(
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"intent_analysis",
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self._route_after_intent_analysis,
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{
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"order_diagnosis": "order_diagnosis",
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"natural_chat": "natural_chat",
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"end": END
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}
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)
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# 添加结束边
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workflow.add_edge("order_diagnosis", END)
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workflow.add_edge("natural_chat", END)
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# 编译图
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agent = workflow.compile()
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messages = [HumanMessage(content="Add 3 and 4.")]
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messages = agent.invoke({"messages": messages})
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for m in messages["messages"]:
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print(m.content)
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```
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对应流程图（Mermaid）
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```mermaid
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flowchart TD
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IA[intent_analysis] --> ROUTE{判断意图}
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ROUTE -->|order_diagnosis| OD[order_diagnosis]
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ROUTE -->|natural_chat| NC[natural_chat]
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ROUTE -->|end| END((END))
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OD --> END
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NC --> END
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```
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说明：当意图分析结果为空或不满足条件时，视为“否”分支，进入 `natural_chat`。
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- Agent
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```python
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from langchain.agents import create_agent
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def get_weather(city: str) -> str:
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"""Get weather for a given city."""
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return f"It's always sunny in {city}!"
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agent = create_agent(
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model="claude-sonnet-4-5-20250929",
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tools=[get_weather],
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system_prompt="You are a helpful assistant",
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)
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# Run the agent
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agent.invoke(
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{"messages": [{"role": "user", "content": "what is the weather in sf"}]}
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)
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```
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Go生态
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- chain
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```go
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package main
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import (
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"context"
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"fmt"
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"os"
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"github.com/cloudwego/eino/compose"
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"github.com/cloudwego/eino/components/prompt"
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"github.com/cloudwego/eino/schema"
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"github.com/cloudwego/eino-ext/components/model/openai"
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)
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func main() {
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ctx := context.Background()
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// 1) 定义 ChatTemplate（输入键：query），使用 Jinja2 格式
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pt := prompt.FromMessages(
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schema.Jinja2,
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schema.SystemMessage("你是一个友好的AI助手。请用中文回答。"),
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schema.UserMessage("用户问题：{{query}}"),
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)
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// 2) 创建 ChatModel（以 OpenAI 为例，按需替换为其他提供商）
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cm, err := openai.NewChatModel(ctx, &openai.ChatModelConfig{
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APIKey: os.Getenv("OPENAI_API_KEY"),
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BaseURL: os.Getenv("OPENAI_BASE_URL"), // 可选，自建或代理时设置
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Model: os.Getenv("MODEL_NAME"), // 例如："gpt-4o-mini"
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})
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if err != nil {
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fmt.Println("初始化模型失败:", err)
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return
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}
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// 3) 组合并编译 Chain：模板 -> 模型
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chain, err := compose.NewChain[map[string]any, *schema.Message]().
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AppendChatTemplate(pt). // prompt
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AppendChatModel(cm). // model
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// AppendBranch 分支
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// AppendPassthrough 空透传节点
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// AppendParallel 并行节点
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// AppendGraph 子图节点
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// AppendLambda 自定义函数节点
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Compile(ctx) // 编译链
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if err != nil {
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fmt.Println("编译链失败:", err)
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return
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}
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// 4) 调用链（非流式）
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msg, err := chain.Invoke(ctx, map[string]any{"query": "简要介绍一下 Eino 的 Chain"})
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if err != nil {
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fmt.Println("调用失败:", err)
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return
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}
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fmt.Println("回复：", msg.Content)
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}
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```
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