AI大模型应用

• 参考文档

https://creative.chat/

1.调用AI大模型API

1.1文心一言

https://console.bce.baidu.com/qianfan/ais/console/applicationConsole/application

1. 创建应用：https://console.bce.baidu.com/qianfan/ais/console/applicationConsole/application
2. 示例代码：https://console.bce.baidu.com/tools/?_=1692863460488#/api?product=QIANFAN&project=千帆大模型平台&parent=鉴权认证机制&api=oauth%2F2.0%2Ftoken&method=post

import requests
import json

API_KEY = "CUJRwLhCHA8hQapnaAQsIGPR"
SECRET_KEY = "L1yYhbcZCId5iMQQsBnVXkjNOdzBAH2O"


def main():
    url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-speed-128k?access_token=" + get_access_token()

    payload = json.dumps({
        "system":"你是一个生活AI。对人的作息，休闲，工作，学习，娱乐，饮食，运动，健康，心理，社交，教育，娱乐知识很精通",
        "messages": [
            {
                "role": "user",
                "content": "您叫什么名字"
            },
        ]
    })
    headers = {
        'Content-Type': 'application/json'
    }

    response = requests.request("POST", url, headers=headers, data=payload)

    print(response.text)


def get_access_token():
    """
    使用 AK，SK 生成鉴权签名（Access Token）
    :return: access_token，或是None(如果错误)
    """
    url = "https://aip.baidubce.com/oauth/2.0/token"
    params = {"grant_type": "client_credentials", "client_id": API_KEY, "client_secret": SECRET_KEY}
    return str(requests.post(url, params=params).json().get("access_token"))


if __name__ == '__main__':
    main()

2大模型部署fastapi

2.1 文心一言

• app.py

from fastapi import FastAPI
from uvicorn import run
from Config.modules import modules

app = FastAPI()

# 自动加载模块路由
for module_name, module_path in modules.items():
    module = __import__(module_path, fromlist=['router'])
    app.include_router(module.router)

if __name__ == '__main__':
    run(app, host='0.0.0.0', port=1888)

• config/modules.py

modules = {
    'Demo': 'Extension.Demo.route',
    'LLM': 'Extension.LLM.route',
}

• LLM/setting.py

Erniebot_API_KEY = "CUJRwLhCHA8hQapnaAQsIGPR"
Erniebot_SECRET_KEY"L1yYhbcZCId5iMQQsBnVXkjNOdzBAH2"
Qwen_API_KEY = ""
ChatGPT_API_KEY = ""

• LLM/llm_erniebot.py

import requests
import json
from Extension.LLM.setting import Erniebot_API_KEY, Erniebot_SECRET_KEY


def erniebot(text, ai_name, ai_role):
    url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-speed-128k?access_token=" + get_access_token()

    payload = json.dumps({
        "system": ai_name+' '+ai_role,
        "messages": [
            {
                "role": "user",
                "content": text
            }
        ],
        # 如果设置为False，AI可能会使用搜索功能来查找最新的信息或解决特定的查询，从而提供更准确、更及时的信息。
        "disable_search": False,
        # 如果设置为True，AI在提供信息时会尝试包含引用来源，比如网页链接、书籍引用等，这有助于验证信息的准确性和来源。
        "enable_citation": True,
    })
    headers = {
        'Content-Type': 'application/json'
    }

    response = requests.request("POST", url, headers=headers, data=payload)

    data = json.loads(response.text)

    usage = data['usage']

    result = {
        "reply": data['result'],
        "prompt_tokens": usage['prompt_tokens'],
        "completion_tokens": usage['completion_tokens'],
        "total_tokens": usage['total_tokens']
    }

    return result


def get_access_token():
    """
    使用 AK，SK 生成鉴权签名（Access Token）
    :return: access_token，或是None(如果错误)
    """

    API_KEY = Erniebot_API_KEY
    SECRET_KEY = Erniebot_SECRET_KEY

    url = "https://aip.baidubce.com/oauth/2.0/token"
    params = {"grant_type": "client_credentials",
              "client_id": API_KEY, "client_secret": SECRET_KEY}
    return str(requests.post(url, params=params).json().get("access_token"))

2.2通义千问

• API KEY：https://dashscope.console.aliyun.com/apiKey

• 示例代码：https://help.aliyun.com/zh/dashscope/developer-reference/quick-start#014863e0459zq

import random
from http import HTTPStatus
import dashscope
dashscope.api_key = "sk-3f7cd2b89a894c3e9a115f83d37c8afe"


def call_with_messages():
    messages = [{'role': 'system', 'content': '你是一个健康AI。你是一个健康专家，对人的作息，饮食，健身，营养，医学等健康方面的知识，非常精通。'},
                {'role': 'user', 'content': '你叫什么名字'}]
    response = dashscope.Generation.call(
        "qwen-turbo",
        messages=messages,
        # set the random seed, optional, default to 1234 if not set
        seed=random.randint(1, 10000),
        # set the result to be "message" format.
        result_format='message',
    )
    if response.status_code == HTTPStatus.OK:
        print(response)
    else:
        print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
            response.request_id, response.status_code,
            response.code, response.message
        ))


if __name__ == '__main__':
    call_with_messages()

• LLM/llm_qwen.py

import random
import dashscope
from Extension.LLM.setting import Qwen_API_KEY

dashscope.api_key = Qwen_API_KEY


def qwen(text, ai_name, ai_role):
    messages = [
        {'role': 'system', 'content': ai_name+' '+ai_role},
        {'role': 'user', 'content': text}
    ]
    response = dashscope.Generation.call(
        "qwen-turbo",
        messages=messages,
        # set the random seed, optional, default to 1234 if not set
        seed=random.randint(1, 10000),
        # set the result to be "message" format.
        result_format='message',
    )

    usage = response.usage

    data = response.output.choices
    result = {
        "reply": data[0].message.content,
        "prompt_tokens": usage.input_tokens,
        "completion_tokens": usage.output_tokens,
        "total_tokens": usage.total_tokens
    }

    return result

2.3windows部署AI大模型

2.31Windows部署通义千问

• https://lmstudio.ai/

• https://www.modelscope.cn/models/qwen/Qwen2-7B-Instruct-GGUF/summary

2.32Windows部署Llama 3

• https://lmstudio.ai/
• https://www.modelscope.cn/models/ModelM/Llama-3-8b-zh-gguf/summary

2.33Windows部署Stable Diffusion

• https://pan.quark.cn/s/2c832199b09b
• https://www.bilibili.com/video/BV1iM4y1y7oA