一.python 包管理器和环境管理器
#初始化项目
uv init ai-agent-test
cd ai-agent-test
#当前项目安装
uv add pyyaml
二.智能体开发流程
三.大模型调用方法
方法1、Ollama本地调用
启动服务
ollama serve
查看本地安装了哪些大模型
ollama ls
安装并运行大模型
ollama run deepseek-r1:70b
3.2调用大模型
使用 langchain-ollama 库：https://docs.langchain.com/oss/python/integrations/providers/ollama
uv add langchain-ollama
from langchain_ollama.chat_models import ChatOllama
if __name__ == "__main__":
llm = ChatOllama(model="llama4:latest")
resp = llm.invoke("你是谁？")
print(resp)
# 上下文回答
messages = [
(
"system",
"You are a helpful assistant that translates English to French. Translate the user sentence.",
),
("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg
3.3 ollama大模型的流式调用
if __name__ == "__main__":
llm = ChatOllama(model="deepseek-r1:7b")
messages = [
(
"system",
"You are a helpful assistant that translates English to French"
),
("human", "I love programming.")
]
resp = llm.stream(messages)
for chunk in resp:
print(chunk.content, end="")
方法2、阿里云百炼API调用
https://bailian.console.aliyun.com/
from openai import OpenAI
client = OpenAI(
# 若没有配置环境变量，请用百炼API Key将下行替换为: api_key="sk-xxx",
api_key="sk-d60f9a55704a46d096c51bc200d2b7fd",
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
# 模型列表: https://help.aliyun.com/zh/model-studio/getting-started/models
model="qwen-plus",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "你是谁？"},
],
# Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
# 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
# extra_body={"enable_thinking": False},
)
print(completion.model_dump_json())
选择推理模型
from openai import OpenAI
import os
# 初始化OpenAI客户端
client = OpenAI(
# 如果没有配置环境变量，请用阿里云百炼API Key替换：api_key="sk-xxx"
# 新加坡和北京地域的API Key不同。获取API Key：https://help.aliyun.com/zh/model-studio/get-api-key
api_key=os.getenv("DASHSCOPE_API_KEY"),
# 以下是北京地域base_url，如果使用新加坡地域的模型，需要将base_url替换为：https://dashscope-intl.aliyuncs.com/compatible-mode/v1
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
messages = [{"role": "user", "content": "你是谁"}]
completion = client.chat.completions.create(
model="qwen-plus", # 您可以按需更换为其它深度思考模型
messages=messages,
extra_body={"enable_thinking": True},
stream=True,
stream_options={
"include_usage": True
},
)
reasoning_content = "" # 完整思考过程
answer_content = "" # 完整回复
is_answering = False # 是否进入回复阶段
print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
for chunk in completion:
if not chunk.choices:
print("\nUsage:")
print(chunk.usage)
continue
delta = chunk.choices[0].delta
# 只收集思考内容
if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:
if not is_answering:
print(delta.reasoning_content, end="", flush=True)
reasoning_content += delta.reasoning_content
# 收到content，开始进行回复
if hasattr(delta, "content") and delta.content:
if not is_answering:
print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
is_answering = True
print(delta.content, end="", flush=True)
answer_content += delta.content
使用LangChain调用通义千问
"""https://www.yuque.com/xiaxiaoshuai/xsc8y4/glv267gwfc5nwxy9"""
# from langchain_core.prompts import PromptTemplate
from langchain_core.prompts import ChatPromptTemplate,ChatMessagePromptTemplate,FewShotPromptTemplate,PromptTemplate
from langchain_openai import ChatOpenAI
from pydantic import SecretStr
# 实例化大模型
llm = ChatOpenAI(
model="qwen-max-latest",
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
api_key=SecretStr("sk-160f9a55704a46d096c51bc200d2b7fd"),
streaming=True,
)
# 消息体的抽象和复用
system_message_template = ChatMessagePromptTemplate.from_template(
template="你是一位{role}专家，擅长回答{domain}领域的问题",
role="system"
)
human_message_template = ChatMessagePromptTemplate.from_template(
template="用户问题：{question}",
role="user",
)
# 1.创建提示词模板
# prompt_template = PromptTemplate.from_messages("今天{something}真不错")
# prompt = prompt_template.format(something="天气")
# print(prompt)
chat_prompt_template = ChatPromptTemplate.from_messages([
# ("system", "你是一位{role}专家，擅长回答{domain}领域的问题"),
# ("user", "用户问题：{question}")
system_message_template,
human_message_template
])
# 2.模板+变量 =》 提示词
# resp = llm.stream(prompt)
prompt = chat_prompt_template.format_messages(
role="编程",
domain="Web开发",
question="如何构建一个基于Vue的前端应用？"
)
example_template = "输入：{input}\n输出：{output}"
examples = [
{"input": "将'Hello'翻译成中文", "output": "你好"},
{"input": "将'Goodbye'翻译成中文", "output": "再见"},
{"input": "将'Pen'翻译成中文", "output": "钢笔"},
]
# FewShotPromptTemplate通过提示词实现大模型少样本学习
few_shot_prompt_template = FewShotPromptTemplate(
examples=examples,
example_prompt=PromptTemplate.from_template(example_template),
prefix="请将以下英文翻译成中文：",
suffix="输入：{text}\n输出：",
input_variables=["text"]
)
prompt = few_shot_prompt_template.format(text="Thank you!")
print(prompt)
resp = llm.stream(prompt)
for chunk in resp:
print(chunk.conten, end="")
# 链式调用大模型
chain = few_shot_prompt_template | llm
resp = chain.stream(input={"text": "Thank you!"})
for chunk in resp:
print(chunk.content, end="")
绑定自定义工具
from pydantic import BaseModel, Field
from langchain_core.tools import tool
from app.bailian.common import chat_prompt_template, llm
class AddInputArgs(BaseModel):
a: int = Field(description="first number")
b: int = Field(description="second number")
@tool(
description="add two numbers",
args_schema =AddInputArgs
)
def add(a, b):
"""add two numbers"""
return a + b
# add_tools = Tool.from_function(
# func=add,
# name="add",
# description="add two numbers",
# )
# llm_with_tools = llm.bind_tools([add_tools])
llm_with_tools = llm.bind_tools([add])
chain = chat_prompt_template | llm_with_tools
resp = chain.stream(input={"role": "计算", "domain": "数学计算", "question": "使用工具计算：100+100=?"})
tool_dict = {
"add": add
}
for tool_calls in resp.tool_calls:
print(tool_calls)
args = tool_calls["args"]
func_name = tool_calls["name"]
print(func_name)
tool_func = tool_dict[func_name]
# tool_content = tool_func(int(args["__arg1"]), int(args["__arg2"]))
tool_content = tool_func.invoke(args)
print(tool_content)
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