python调用本地部署的大模型(llama3.2)

本文档主要解决python调用本地大模型的入门代码，不需要太纠结输出是否ok。

Http调用本地大模型

安装requests库

pip install requests -i https://pypi.tuna.tsinghua.edu.cn/simple

或者在pycharm中安装

安装python插件命令集合_python安装插件命令-CSDN博客

python中引用

import requests

安装本地大模型

自己安装一台DeepSeek的服务器_南宁 deepseek服务器 安装-CSDN博客

只安装 llama3.2即可（比较小）

本地模型的请求地址

默认地址：

base_url = http://localhost:11434/api

由于我安装在虚拟机（192.168.10.106）

base_url = "http://192.168.10.106:11434/api"

列出本地模型信息

api：/tags
列出本地已加载的所有模型
请求方法: GET
参数:无

 代码

def list_models():base_url = "http://192.168.10.106:11434/api"url = f"{base_url}/tags"headers = {"Content-Type": "application/json"}response = requests.get(url,headers=headers)return response.json().get("models",[])print(list_models())

输出

[{'name': 'llama3.2:latest', 'model': 'llama3.2:latest', 'modified_at': '2025-04-10T15:14:24.704912865+08:00', 'size': 2019393189, 'digest': 'a80c4f17acd55265feec403c7aef86be0c25983ab279d83f3bcd3abbcb5b8b72', 'details': {'parent_model': '', 'format': 'gguf', 'family': 'llama', 'families': ['llama'], 'parameter_size': '3.2B', 'quantization_level': 'Q4_K_M'}}]

文本补全接口

API:/generate
功能: 生成指定模型的文本补全。输入提示词后，模型根据提示生成文本结果
请求方法:POST
API参数:
model:  必填 如llama3.2
prompt：必填 生成文本所使用的提示词
suffix: 可选 生成的补全之后附加的文本
stream: 可选  是否流式传输响应，默认为true
system: 可选 覆盖模型系统信息的字段，影响生成文本的风格
temperature: 可选，控制文本生成的随机性 默认值为1

 代码

def generate_completion(prompt,model="llama3.2"):base_url = "http://192.168.10.106:11434/api"url = f"{base_url}/generate"headers = {"Content-Type": "application/json"}data = {"model":model,"prompt":prompt,"stream":False}response= requests.post(url, json=data, headers=headers)print(response)return response.json().get("response","")print(generate_completion("解释一下证券营销"))
# print(generate_completion("窦再兴是谁？"))

输出

证券营销（Securities Marketing）是指通过各种渠道、媒体和策略来推广并出售证券产品的过程。这种营销活动通常涉及金融机构、投资银行、保险公司等与证券市场相关的企业。证券营销的目的是为了提高证券产品的可market化程度，吸引潜在客户购买这些_product。证券营销可以通过各种方式实现，如：1. **传播**: 使用多种媒体形式，如电视广告、在线广告、印刷广告等来推广证券产品。
2. **销售**: 通过与投资者接触、定期报告和教育来促进证券产品的购买。
3. **宣传**: 通过各种事件和活动，例如证券会员日、教育 seminars 等来提高证券产品的知名度。证券营销需要遵守相关法律法规，如《证券交易法》等，而这些法律法规通常针对证券市场的各项行为，包括营销活动。证券营销的好处包括：1. **增加客户信心**: 通过有效的营销活动，可以提高客户对证券产品的信任度。
2. **推动销售额**: 证券营销可以帮助金融机构和投资银行等机构推动证券产品的销售额。
3. **提高竞争力**: 证券营销可以帮助financial机构与其他竞争者保持领先地位。但是，证券营销也需要注意一些重要事项，如：1. **合规性**: 证券营销活动必须遵守相关法律法规和规范。
2. **透明度**: 证券营销活动的内容应该 transparent 和真实，而不会引起误解或欺骗行为。
3. **客户保护**: 证券营销活动应优先考虑客户的利益和保护。总之，证券营销是一个重要的方面，可以帮助金融机构和投资银行等机构推动证券产品的销售，但同时也需要遵守相关法律法规和规范。

文本补全接口流式

代码

def generate_completion_stream(prompt,model="llama3.2",stream=True):base_url = "http://192.168.10.106:11434/api"url = "{base_url}/generate".format(base_url=base_url)headers = {"Content-Type": "application/json"}data = {"model":model,"prompt":prompt,"stream":stream}respose= requests.post(url,json=data,headers=headers,stream=stream)result = ""import jsonfor line in respose.iter_lines():if line:t = json.loads(line.decode("utf-8"))if not t.get("done"):result += t.get("response")return resultprint(generate_completion_stream("解释一下证券营销"))

输出同上

会话补全接口

API：/chat
功能: 模拟对话补全，支持多轮交互，适用于聊天机器人等场景
请求方法:POST
API参数
model： 必填 模型名称
messages：必填 对话的消息列表，按顺序包含历史对话，每条消息包含role 和contentrole: user(用户) assistant（助手） 或system（系统）content：消息内容
stream: 可选 是否流式传输响应 默认true

代码

def generate_chat_completion(messages,model="llama3.2"):base_url = "http://192.168.10.106:11434/api"url = "{base_url}/chat".format(base_url=base_url)headers = {"Content-Type": "application/json"}data = {"model":model,"messages":messages,"stream":False}response = requests.post(url, json=data,headers=headers)return response.json().get("message",{}).get("content","")messages =[{"role": "user", "content": "什么是证券营销"},{"role": "assistant", "content": "证券营销是证券行业的营销"}
]print(generate_chat_completion(messages,"llama3.2"))

输出

活动，旨在推广和宣传证券产品、增强投资者的信心，并吸引更多的投资者参与。证券营销通常包括以下方面：1. **证券信息发布**：提供最新的证券信息，例如公司 news、财务报告、商业发展情况等。
2. **市场分析**：进行市场研究和分析，预测 Securities market trends 和方向，以帮助投资者做出明智的决策。
3. **投资建议**：为投资者提供个人化的投资建议和规划，帮助他们达到其投资目标。
4. **证券产品推广**：宣传和推广不同的证券产品，如股票、债券、期货等，并解释这些产品的优势和风险。
5. **客户关系维护**：建立并维护与投资者的长期关系，从而保持稳定的信心和忠诚度。证券营销通常由证券公司的销售团队或专家负责，包括：1.证券分析师
2.证券销售代表
3.投资顾问
4.市场研究员证券营销的目标是通过合理的信息流、专业的建议和高效的沟通手段，帮助投资者做出明智的决策，并最大化其投资收益。

 会话补全接口流式

代码

def generate_chat_completion_stream(messages,model="llama3.2"):base_url = "http://192.168.10.106:11434/api"url = "{base_url}/chat".format(base_url=base_url)headers = {"Content-Type": "application/json"}data = {"model":model,"messages":messages,"stream":True}response = requests.post(url, json=data,headers=headers,stream=True)result = ""import jsonfor line in response.iter_lines():if line:t = json.loads(line.decode("utf-8"))print(t)if not t.get("done"):result += t.get("message").get("content")return resultmessages =[{"role":"user","content":"什么是证券营销"},{"role":"assistant","content":"证券营销是证券行业的营销"}
]print(generate_chat_completion_stream(messages,"llama3.2"))

输出同上

文本嵌入接口

API:/embed
功能: 为输入的文本生成嵌入向量，常用于语义搜索或分类等任务。
请求方法: POST
API 参数:
model:必填 生成嵌入模型名称
input: 必填 文本或文本列表，用户生成嵌入
truncate: 可选 是否在文本超出上下文长度时进行截断，默认true
stream: 可选 是否流式传输响应，默认为true

代码

def generate_embedding(text,model="llama3.2"):base_url = "http://192.168.10.106:11434/api"url = "{base_url}/embed".format(base_url=base_url)headers = {"Content-Type": "application/json"}data = {"model":model,"input": text}response = requests.post(url, json=data,headers=headers)return response.json()embedding = generate_embedding("什么是证券营销")
print("生成文本嵌入",embedding)

输出

生成文本嵌入 {'model': 'llama3.2', 'embeddings': [[-0.004711413, 0.017777665, 0.028891403, -0.02093706, 0.03231895, 0.00119765, 0.0020432037, -0.009595348, -0.0065270592, 0.0013342365, 0.0019657994, -0.0112751275, 0.018923838, 0.0020954409, -0.030230904, 0.007242399, 0.0006049033, -0.002487322, -0.014934444, -0.0133084785, -0.013938734, -0.0047322935, -0.040972423, -0.0010034348, 0.003972612, -0.0015203115, 0.02269681, 0.01638558, 0.007663827, -0.0052647204, -0.010002679, 0.0050850017, -0.012508803, 0.011598661, -0.008679192, -0.014687568, 0.022961505, -0.010448234, 0.001927346, -0.009071495, -0.005313909, 0.00780027, -0.01564827, -1.50905325e-05, 0.00808506, -0.015049232, 0.00897875, 0.015858114, -0.009375791, 0.0026963316, 0.0061902045, 0.004746499, 0.0035688328, 0.0016032335, 0.004635559, 0.01367854, 0.0075938185, 0.025320228, 0.0047770524, -0.0006245435, -0.004784689, -0.016757106, 0.013876733, -0.013752976, 0.029662913, 0.008028135, 0.0142299365, -0.001098276, 0.009894521, -0.006510229, 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OpenAI调用本地大模型

安装OpenAI

pip install openai

或者pycharm安装

代码

import os
from openai import OpenAIos.environ["OPENAI_BASE_URL"] = "http://192.168.10.106:11434/v1"
os.environ["OPENAI_API_KEY"] = "sk-xxx"client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"),base_url=os.environ.get("OPENAI_API_URL"))response = client.chat.completions.create(messages=[{"role": "user","content": "讲个笑话"}],model="llama3.2"
)print(response)print(response.choices[0].message.content)  # 更具体的的打印

输出

ChatCompletion(id='chatcmpl-685', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='有一位老人，想向年轻人学着如何骑自行 bike。\n\n他对自己说：“首先，要把两个手放在平地上并且向前看!”\n\n然后，他就走到公路的中间，用双手握着一根木头，并且面带严肃的表情看方向牌。', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None))], created=1744335016, model='llama3.2', object='chat.completion', service_tier=None, system_fingerprint='fp_ollama', usage=CompletionUsage(completion_tokens=70, prompt_tokens=29, total_tokens=99, completion_tokens_details=None, prompt_tokens_details=None))有一位老人，想向年轻人学着如何骑自行 bike。他对自己说：“首先，要把两个手放在平地上并且向前看!”然后，他就走到公路的中间，用双手握着一根木头，并且面带严肃的表情看方向牌。