dash-fastapi-admin/dash-fastapi-frontend/api/modmag.py

from utils.request_xinf import api_request_xinf
from utils.request import api_request
import json
import re
from collections import defaultdict


def get_model_tree_data(extracted_data):
    tree_data = []

    # 按照 model_type 和 model_family 分组
    grouped_data = defaultdict(lambda: defaultdict(list))
    for item in extracted_data:
        model_type = item['model_type']
        model_family = item['model_family']
        grouped_data[model_type][model_family].append(item)

    # 构建 treeData 结构
    for model_type, families in grouped_data.items():
        model_type_node = {
            'title': model_type,
            'key': model_type,
            # 'tooltipProps': {
            #     'title': '模型类型描述😀'
            # },
            'children': []
        }

        for model_family, models in families.items():
            model_family_node = {
                'title': model_family,
                'key': f"{model_type}/{model_family}",
            }
            model_type_node['children'].append(model_family_node)

        tree_data.append(model_type_node)

    return tree_data

def get_model_flag(item, element):
    if element == "LLM":
        return {
            'model_lang': item.get('model_lang'),
            'model_ability': item.get('model_ability'),
            'context_length': item.get('context_length')
        }
    elif element == "embedding":
        return {
            'model_lang': item.get('language'),
            'model_dims': item.get('dimensions'),
            'model_mtks': item.get('max_tokens')
        }
    elif element == "rerank":
        return {
            'model_lang': item.get('language'),
            'model_tp': item.get('type')
        }
    elif element == "image":
        return {
            'model_ability': item.get('ability'),
            'model_tp': item.get('type')
        }
    elif element == "audio":
        if item.get('multilingual') is True:
            return {'model_lang': "mul"}
        else:
            return {}
    return {}

def clean_model_family(family):
    # 提取路径部分的第一个元素，移除前缀中的数字并转为小写
    family = family.split('/')[0]
    family = re.sub(r'\d+$', '', family)
    family = family.lower()
    
    # 如果识别为 glm，则转换为 chatglm
    if family == 'glm' or family == 'codegeex' or family == 'chatglm':
        family = 'zhipu'
    
    if family == 'starcoderplus':
        family = 'starcoder'
    
    if family == 'wizardlm' or family == 'wizardmath' or family == 'wizardcoder':
        family = 'wizard'
    # 移除尾部的标点符号
    # family = re.sub(r'[^\w\s\d]$', '', family)  # 移除尾部的标点符号和数字
    # family = re.sub(r'[^\w\s\d]$', '', family)  # 移除尾部的标点符号

    return family

def extract_data(element, data):
    extracted_data = []
    for item in data:
        model_flag = get_model_flag(item, element)
        
        # 获取 model_family 的值并进行清洗
        model_family = item.get('model_family')
        if not model_family:
            model_id = item.get('model_id', '')
            model_name = item.get('model_name', '')
            
            if model_id:
                model_family = model_id.split('\/')[0]
            elif model_name:
                # model_family = model_name.split('-')[0]
                model_family = re.split('[-_]', model_name)[0]

        if model_family:
            model_family = clean_model_family(model_family)

        # 检查 model_name 并根据需要更新 model_family
        model_name = item.get('model_name', '').lower()
        if 'llama' in model_name:
            model_family = 'llama'
        elif 'qwen' in model_name:
            model_family = 'qwen'
        
        
        index = len(model_family) - 1

        # 从字符串末尾开始遍历
        while index >= 0 and (model_family[index].isdigit() or re.match(r'[^\w\s]', model_family[index])):
            index -= 1

        # 获取新的字符串
        model_family = model_family[:index + 1]
        # while s and (s[-1].isdigit() or re.match(r'[^\w\s]', s[-1])):
        #     s = s[:-1]
        # return s
        # model_family = re.sub(r'[^\w\s\d]$', '', model_family)  # 移除尾部的标点符号和数字
    
        # 使用get方法获取model_description，确保即使键不存在也不会报错
        model_description = item.get('model_description', '') or item.get('model_id', '')
        
        extracted = {
            'model_type': element,
            'model_family': model_family,
            'model_name': item.get('model_name'),
            'model_flag': model_flag,
            'model_description': model_description,
        }
        extracted_data.append(extracted)
    return extracted_data

def get_models_detail_api(model_type_list: list):
    extracted_data = []
    for element in model_type_list:
        if element in ["LLM", "embedding", "rerank", "image", "audio"]:
            url = f'model_registrations/{element}?detailed=true'
            data = api_request_xinf(method='get', url=url, is_headers=False)
            extracted_data.extend(extract_data(element, data))
                
    # with open('datanew3.txt', 'w', encoding='utf-8') as text_file:
    #     text_file.write(json.dumps(extracted_data, ensure_ascii=False, indent=4))
    tree_data = get_model_tree_data(extracted_data)
    return tree_data,extracted_data

def get_model_info_api(model_name: str):

#从后台服务获得model_info大部分信息

#从xinference获得prompt的信息
    url = f'models/prompts'
    re_data = api_request_xinf(method='get', url=url, is_headers=False)
    # print("prompt信息",re_data,type(re_data))
    print("model_name信息",model_name)
    # re_data_dict = json.loads(re_data)
    prompt_data = re_data.get(model_name, "模型prompt不存在")
    print("prompt_data1",prompt_data)
    model_info = {
        "model_name_cn": "通义千问2-开源版",
        "model_name_en": model_name,
        "model_note": "通义千问2对外开源的模型，模型支持 131,072 tokens上下文，为了保障正常使用和正常输出，API限定用户输入为 128,000 ，输出最大 6,144。",
        "model_fune_flag": "Y",
        "model_is_open_source": "是",
        "model_release_date": "2024-08-08",
        "model_website_url": "https://modelscope.cn/organization/qwen?tab=model",
        "model_prmopt":str(prompt_data)
        }
    return model_info

def get_model_info_extend_api(model_name: str):
    curl_code = '''
curl --location 'https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation' \
--header 'Authorization: Bearer <your-dashscope-api-key>' \
--header 'Content-Type: application/json' \
--header 'X-DashScope-SSE: enable' \
--data '{
    "model": "qwen-max",
    "input":{
        "messages":[      
            {
                "role": "system",
                "content": "You are a helpful assistant."
            },
            {
                "role": "user",
                "content": "Introduce the capital of China"
            }
        ]
    },
    "parameters": {
    }
}'''
    python_code = '''# 
from http import HTTPStatus
import dashscope

def call_with_stream():
    messages = [
        {'role': 'user', 'content': 'Introduce the capital of China'}]
    responses = dashscope.Generation.call("qwen-max",
                                messages=messages,
                                result_format='message',  # set the result to be "message"  format.
                                stream=True, # set streaming output
                                incremental_output=True  # get streaming output incrementally
                                )
    for response in responses:
        if response.status_code == HTTPStatus.OK:
            print(response.output.choices[0]['message']['content'],end='')
        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_stream()'''
    java_code = '''        
// Copyright (c) Alibaba, Inc. and its affiliates.

import java.util.Arrays;
import java.util.concurrent.Semaphore;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.ResultCallback;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.protocol.Protocol;
import com.alibaba.dashscope.utils.JsonUtils;
import io.reactivex.Flowable;

public class Main {
    public static void streamCallWithMessage()
            throws NoApiKeyException, ApiException, InputRequiredException {
        Generation gen = new Generation();
        Message userMsg =
                Message.builder().role(Role.USER.getValue()).content("Introduce the capital of China").build();
        GenerationParam param = GenerationParam.builder()
        .model("qwen-max")
        .messages(Arrays.asList(userMsg))
        .resultFormat(GenerationParam.ResultFormat.MESSAGE) // the result if message format.
        .topP(0.8).enableSearch(true) // set streaming output
        .incrementalOutput(true) // get streaming output incrementally
        .build();
        Flowable<GenerationResult> result = gen.streamCall(param);
        StringBuilder fullContent = new StringBuilder();
        result.blockingForEach(message -> {
            fullContent.append(message.getOutput().getChoices().get(0).getMessage().getContent());
            System.out.println(JsonUtils.toJson(message));
        });
        System.out.println("Full content: \n" + fullContent.toString());
    }

    public static void streamCallWithCallback()
            throws NoApiKeyException, ApiException, InputRequiredException, InterruptedException {
        Generation gen = new Generation();
        Message userMsg =
                Message.builder().role(Role.USER.getValue()).content("Introduce the capital of China").build();
        GenerationParam param = GenerationParam.builder()
        .model("${modelCode}")
        .resultFormat(GenerationParam.ResultFormat.MESSAGE)  //set result format message
        .messages(Arrays.asList(userMsg)) // set messages
        .topP(0.8)
        .incrementalOutput(true) // set streaming output incrementally
        .build();
        Semaphore semaphore = new Semaphore(0);
        StringBuilder fullContent = new StringBuilder();
        gen.streamCall(param, new ResultCallback<GenerationResult>() {

            @Override
            public void onEvent(GenerationResult message) {
                fullContent
                        .append(message.getOutput().getChoices().get(0).getMessage().getContent());
                System.out.println(message);
            }

            @Override
            public void onError(Exception err) {
                System.out.println(String.format("Exception: %s", err.getMessage()));
                semaphore.release();
            }

            @Override
            public void onComplete() {
                System.out.println("Completed");
                semaphore.release();
            }

        });
        semaphore.acquire();
        System.out.println("Full content: \n" + fullContent.toString());
    }

    public static void main(String[] args) {
        try {
            streamCallWithMessage();
        } catch (ApiException | NoApiKeyException | InputRequiredException e) {
            System.out.println(e.getMessage());
        }
        try {
            streamCallWithCallback();
        } catch (ApiException | NoApiKeyException | InputRequiredException
                | InterruptedException e) {
            System.out.println(e.getMessage());
        }
        System.exit(0);
    }
}
'''
    mode_info_extend = {
        # "model_name_cn": "通义千问2-开源版",
        "model_oneid":"001",
        "model_name_en": model_name,
        "curl_code": curl_code,
        "python_code": python_code,
        "java_code": java_code,
        "model_update_date": "2024-08-08",
        }
    return mode_info_extend
    # return api_request(method='get', url=f'/modmag/model/{model_name}', is_headers=True)

#从后台服务获得model_info大部分信息
def get_model_engine_api(model_name: str):
    url = f'engines/{model_name}'
    re_data = api_request_xinf(method='get', url=url, is_headers=False)
    print("model_name信息",model_name)
    return re_data

#从后台服务获得cluster大部分信息
def get_cluster_info_api( ):
    url = f'cluster/info?detailed=true'
    re_data = api_request_xinf(method='get', url=url, is_headers=False)
    print("cluster信息",re_data)
    return re_data

def launch_model_api(params: dict):
    url = f'models'
    re = api_request_xinf(method='post', url=url, is_headers=True, json=params)
    return re

def get_model_run_api( ):
    url = f'models/instances'
    data = api_request_xinf(method='get', url=url, is_headers=False)
    print("data:",data)
    mode_llm_run_data = []
    mode_embed_run_data = []
    mode_rerank_run_data = []
    for item in data:
        url = f'models/{item["model_uid"]}'
        print(item['model_uid'])
        detail_data = api_request_xinf(method='get', url=url, is_headers=False)
        print("detail_data为：",detail_data)
        if detail_data["model_type"] == "LLM":
            mode_llm_run_data.append({
                'model_uid': item["model_uid"],
                'model_name': detail_data["model_name"],
                'worker_address': detail_data["address"],
                'GPU_indexs': str(detail_data["accelerators"]),
                'model_size': detail_data["model_size_in_billions"],
                'model_quant': detail_data["quantization"],
                'model_replica': detail_data["replica"],

            })
        if detail_data["model_type"] == "embedding":
            mode_embed_run_data.append({
                'model_uid': item["model_uid"],
                'model_name': detail_data["model_name"],
                'worker_address': detail_data["address"],
                'GPU_indexs': str(detail_data["accelerators"]),
                # 'model_size': detail_data["model_size_in_billions"],
                # 'model_quant': detail_data["quantization"],
                'model_replica': detail_data["replica"],
            })
        if detail_data["model_type"] == "rerank":
            mode_rerank_run_data.append({
                'model_uid': item["model_uid"],
                'model_name': detail_data["model_name"],
                'worker_address': detail_data["address"],
                'GPU_indexs': str(detail_data["accelerators"]),
                # 'model_size': detail_data["model_size_in_billions"],
                # 'model_quant': detail_data["quantization"],
                'model_replica': detail_data["replica"],

            })
    print("mode_llm_run_data:",mode_llm_run_data)
    print("mode_embed_run_data:",mode_embed_run_data)
    print("mode_rerank_run_data:",mode_rerank_run_data)

    return mode_llm_run_data,mode_embed_run_data,mode_rerank_run_data
    # model_run_data = []
    # for element in model_type_list:
    #     if element in ["LLM", "embedding", "rerank", "image", "audio"]:
    #         url = f'models/instances'
    #         data = api_request_xinf(method='get', url=url, is_headers=False)
    #         extracted_data.extend(extract_data(element, data))
                
    # with open('datanew3.txt', 'w', encoding='utf-8') as text_file:
    #     text_file.write(json.dumps(extracted_data, ensure_ascii=False, indent=4))
    # tree_data = get_model_tree_data(extracted_data)
    # return tree_data,extracted_data