Add .ipynb_checkpoints to .gitignore and remove from tracking

.gitignore

@@ -1,3 +1,4 @@
 __pycache__/
 *.pyc
-
+.ipynb_checkpoints
+*/.ipynb_checkpoints/*

.ipynb_checkpoints/app-checkpoint.py

@@ -1,644 +0,0 @@
-import argparse
-import json
-import time
-import os
-import glob
-import random
-import shutil
-from enum import Enum
-from threading import Thread
-from multiprocessing import Process, Value
-
-import gradio as gr
-import pytoml
-from loguru import logger
-import spaces
-
-from huixiangdou.service import Worker, llm_serve, ArticleRetrieval, CacheRetriever, FeatureStore, FileOperation
-
-class PARAM_CODE(Enum):
-    """Parameter code."""
-    SUCCESS = 0
-    FAILED = 1
-    ERROR = 2
-
-def parse_args():
-    """Parse args."""
-    parser = argparse.ArgumentParser(description='Worker.')
-    parser.add_argument('--work_dir',
-                        type=str,
-                        default='workdir',
-                        help='Working directory.')
-    parser.add_argument('--repo_dir',
-                        type=str,
-                        default='repodir',
-                        help='Repository directory.')
-    parser.add_argument(
-        '--config_path',
-        default='config.ini',
-        type=str,
-        help='Worker configuration path. Default value is config.ini')
-    parser.add_argument('--standalone',
-                        action='store_true',
-                        default=True,
-                        help='Auto deploy required Hybrid LLM Service.')
-    args = parser.parse_args()
-    return args
-
-def update_remote_buttons(remote):
-    if remote:
-        return [
-                gr.Markdown("[如何配置API]('https://github.com/jabberwockyang/MedicalReviewAgent/blob/main/README.md')",
-                            visible=True),
-                gr.Dropdown(["kimi", "deepseek", "zhipuai",'gpt'],
-                                                label="选择大模型提供商",
-                                                interactive=True,visible=True),
-                gr.Textbox(label="您的API",lines = 1,
-                        interactive=True,visible=True),
-                gr.Dropdown([],label="选择模型",
-                            interactive=True,visible=True)
-        ]
-    else:
-        return [
-                gr.Markdown("[如何配置API]('https://github.com/jabberwockyang/MedicalReviewAgent/blob/main/README.md')",
-                            visible=False),
-                gr.Dropdown(["kimi", "deepseek", "zhipuai",'gpt'],
-                                                label="选择大模型提供商",
-                                                interactive=False,visible=False),
-                gr.Textbox(label="您的API",lines = 1,
-                        interactive=False,visible=False),
-                gr.Dropdown([],label="选择模型",
-                            interactive=False,visible=False)
-        ]
-
-def udate_model_dropdown(remote_company):
-    model_choices = {
-        'kimi': ['moonshot-v1-128k'],
-        'deepseek': ['deepseek-chat'],
-        'zhipuai': ['glm-4'],
-        'gpt': ['gpt-4-32k-0613','gpt-3.5-turbo']
-    }
-    return gr.Dropdown(choices= model_choices[remote_company])
-
-def update_remote_config(remote_ornot,remote_company = None,api = None,model = None):
-    with open(CONFIG_PATH, encoding='utf8') as f:
-        config = pytoml.load(f)
-         
-        if remote_ornot:
-            if remote_company == None or api == None or model == None:
-                raise ValueError('remote_company, api, model not provided')
-            config['llm']['enable_local'] = 0
-            config['llm']['enable_remote'] = 1
-            config['llm']['server']['remote_type'] = remote_company
-            config['llm']['server']['remote_api_key'] = api
-            config['llm']['server']['remote_llm_model'] = model
-        else:
-            config['llm']['enable_local'] = 1
-            config['llm']['enable_remote'] = 0
-    with open(CONFIG_PATH, 'w') as f:
-        pytoml.dump(config, f)
-    return gr.Button("配置已保存")
-
-@spaces.GPU
-def get_ready(query:str,chunksize=None,k=None):
-    
-    with open(CONFIG_PATH, encoding='utf8') as f:
-        config = pytoml.load(f)
-    workdir = config['feature_store']['work_dir']
-    repodir = config['feature_store']['repo_dir']
-
-    if query == 'repo_work': # no need to return assistant
-        return repodir, workdir, config
-    theme = ''
-    try:
-        with open(os.path.join(config['feature_store']['repo_dir'],'config.json'), 'r') as f:
-            repo_config = json.load(f)
-        theme = ' '.join(repo_config['keywords'])
-    except:
-        pass
-
-    if query == 'annotation':
-        if not chunksize or not k:
-            raise ValueError('chunksize or k not provided')
-        chunkdir = os.path.join(workdir, f'chunksize_{chunksize}')
-        clusterdir = os.path.join(chunkdir, 'cluster_features', f'cluster_features_{k}')
-        assistant = Worker(work_dir=chunkdir, config_path=CONFIG_PATH,language='en')
-        samples_json = os.path.join(clusterdir,'samples.json')
-        with open(samples_json, 'r') as f:
-            samples = json.load(f)
-            f.close()
-        return clusterdir, samples, assistant, theme
-    
-    elif query == 'inspiration':
-        if not chunksize or not k:
-            raise ValueError('chunksize or k not provided')
-        
-        chunkdir = os.path.join(workdir, f'chunksize_{chunksize}')
-        clusterdir = os.path.join(chunkdir, 'cluster_features', f'cluster_features_{k}')
-        assistant = Worker(work_dir=chunkdir, config_path=CONFIG_PATH,language='en')
-        annofile = os.path.join(clusterdir,'annotation.jsonl')
-        with open(annofile, 'r') as f:
-            annoresult = f.readlines()
-
-            f.close()
-        annoresult = [json.loads(obj) for obj in annoresult]
-        return clusterdir, annoresult, assistant, theme
-    elif query == 'summarize': # no need for params k
-        if not chunksize:
-            raise ValueError('chunksize not provided')
-        chunkdir = os.path.join(workdir, f'chunksize_{chunksize}')
-        assistant = Worker(work_dir=chunkdir, config_path=CONFIG_PATH,language='en')
-        return assistant,theme
-
-    else:
-        raise ValueError('query not recognized')
-            
-def update_repo_info():
-    with open(CONFIG_PATH, encoding='utf8') as f:
-        config = pytoml.load(f)
-    repodir = config['feature_store']['repo_dir']
-    if os.path.exists(repodir):
-        pdffiles = glob.glob(os.path.join(repodir, '*.pdf'))
-        number_of_pdf = len(pdffiles)
-        if os.path.exists(os.path.join(repodir,'config.json')):
-                
-            with open(os.path.join(repodir,'config.json'), 'r') as f:
-                repo_config = json.load(f)
-
-            keywords = repo_config['keywords']
-            length = repo_config['len']
-            retmax = repo_config['retmax']
-
-            return keywords,length,retmax,number_of_pdf
-        else:
-            return None,None,None,number_of_pdf
-    else:
-        return None,None,None,None
-               
-def upload_file(files):
-    repodir, workdir, _ = get_ready('repo_work')
-    if not os.path.exists(repodir):
-        os.makedirs(repodir)
-
-    for file in files:
-        destination_path = os.path.join(repodir, os.path.basename(file.name))
-
-        shutil.copy(file.name, destination_path)
-    
-
-    return files
-
-def generate_articles_repo(keywords:str,retmax:int):
-    keys= [k.strip() for k in keywords.split('\n')]
-    repodir, _, _ = get_ready('repo_work')
-
-    articelfinder = ArticleRetrieval(keywords = keys,
-                                     repo_dir = repodir,
-                                     retmax = retmax)
-    articelfinder.initiallize()
-    return update_repo()
-
-def delete_articles_repo():
-    # 在这里运行生成数据库的函数
-    repodir, workdir, _ = get_ready('repo_work')
-    if os.path.exists(repodir):
-        shutil.rmtree(repodir)
-    if os.path.exists(workdir):
-        shutil.rmtree(workdir)
-
-    return gr.Textbox(label="文献库概况",lines =3,
-                      value = '文献库和相关数据库已删除',
-                      visible = True)
-
-def update_repo():
-    keys,len,retmax,pdflen = update_repo_info()
-    if keys:
-        newinfo = f"搜索得到文献：\n    关键词：{keys}\n    文献数量：{len}\n    获取上限：{retmax}\n\n上传文献：\n    数量：{pdflen}"
-    else:
-        if pdflen:
-            newinfo = f'搜索得到文献：无\n上传文献：\n    数量：{pdflen}'
-        else:
-            newinfo = '目前还没有文献库'
-
-    return gr.Textbox(label="文献库概况",lines =1,
-                      value = newinfo,
-                      visible = True)
-
-def update_database_info():
-    with open(CONFIG_PATH, encoding='utf8') as f:
-        config = pytoml.load(f)
-    workdir = config['feature_store']['work_dir']
-    chunkdirs = glob.glob(os.path.join(workdir, 'chunksize_*'))
-    chunkdirs.sort()
-    list_of_chunksize = [int(chunkdir.split('_')[-1]) for chunkdir in chunkdirs]
-    # print(list_of_chunksize)
-    jsonobj = {}
-    for chunkdir in chunkdirs:
-        k_dir = glob.glob(os.path.join(chunkdir, 'cluster_features','cluster_features_*'))
-        k_dir.sort()
-        list_of_k = [int(k.split('_')[-1]) for k in k_dir]
-        jsonobj[int(chunkdir.split('_')[-1])] = list_of_k
-        
-
-    new_options = [f"chunksize:{chunksize}, k:{k}" for chunksize in list_of_chunksize for k in jsonobj[chunksize]]
-    
-    return new_options, jsonobj
-
-@spaces.GPU
-def generate_database(chunksize:int,nclusters:str|list[str]):
-    # 在这里运行生成数据库的函数
-    repodir, workdir, _ = get_ready('repo_work')
-    if not os.path.exists(repodir):
-        return gr.Textbox(label="数据库已生成",value = '请先生成文献库',visible = True)
-    nclusters = [int(i) for i in nclusters]
-    # 文献库和数据库的覆盖删除逻辑待定 
-    # 理论上 文献库只能生成一次 所以每次生成文献库都要删除之前的文献库和数据库
-    # 数据库可以根据文献库多次生成 暂不做删除 目前没有节省算力的逻辑 重复计算后覆盖 以后优化 
-    # 不同的chunksize和nclusters会放在不同的文件夹下 不会互相覆盖
-    # if os.path.exists(workdir):
-    #     shutil.rmtree(workdir)
-
-    cache = CacheRetriever(config_path=CONFIG_PATH)
-    fs_init = FeatureStore(embeddings=cache.embeddings,
-                           reranker=cache.reranker,
-                            chunk_size=chunksize,
-                            n_clusters=nclusters,
-                           config_path=CONFIG_PATH)
-
-    # walk all files in repo dir
-    file_opr = FileOperation()
-    files = file_opr.scan_dir(repo_dir=repodir)
-    fs_init.initialize(files=files, work_dir=workdir,file_opr=file_opr)
-    file_opr.summarize(files)
-    del fs_init
-    cache.pop('default')
-    texts, _ = update_database_info()
-    return gr.Textbox(label="数据库概况",value = '\n'.join(texts) ,visible = True)
-
-def delete_database():
-    _, workdir, _ = get_ready('repo_work')
-    if os.path.exists(workdir):
-        shutil.rmtree(workdir)
-    return  gr.Textbox(label="数据库概况",lines =3,value = '数据库已删除',visible = True)
-
-def update_database_textbox():
-    texts, _ = update_database_info()
-    if texts == []:
-        return gr.Textbox(label="数据库概况",value = '目前还没有数据库',visible = True)
-    else:
-        return gr.Textbox(label="数据库概况",value = '\n'.join(texts),visible = True)
-
-def update_chunksize_dropdown():
-    _, jsonobj = update_database_info()
-    return gr.Dropdown(choices= jsonobj.keys())
-
-def update_ncluster_dropdown(chunksize:int):
-    _, jsonobj = update_database_info()
-    nclusters = jsonobj[chunksize]
-    return gr.Dropdown(choices= nclusters)
-
-@spaces.GPU
-def annotation(n,chunksize:int,nclusters:int,remote_ornot:bool):
-    '''
-    use llm to annotate cluster
-    n: percentage of clusters to annotate
-    '''
-    query = 'annotation'
-    if remote_ornot:
-        backend = 'remote'
-    else:
-        backend = 'local'
-
-    clusterdir, samples, assistant, theme = get_ready('annotation',chunksize,nclusters)
-    new_obj_list = []
-    n = round(n * len(samples.keys()))
-    for cluster_no in random.sample(samples.keys(), n):
-        chunk = '\n'.join(samples[cluster_no]['samples'][:10])
-
-        code, reply, cluster_no = assistant.annotate_cluster(
-                                                theme = theme,
-                                                cluster_no=cluster_no,
-                                                chunk=chunk,
-                                                history=[],
-                                                groupname='',
-                                                backend=backend)
-        references = f"cluster_no: {cluster_no}"
-        new_obj = {
-            'cluster_no': cluster_no,
-            'chunk': chunk,
-            'annotation': reply
-        }
-        new_obj_list.append(new_obj)
-        logger.info(f'{code}, {query}, {reply}, {references}')
-
-        with open(os.path.join(clusterdir, 'annotation.jsonl'), 'a') as f:
-            json.dump(new_obj, f, ensure_ascii=False)
-            f.write('\n')
-            
-    return '\n\n'.join([obj['annotation'] for obj in new_obj_list])
-
-@spaces.GPU
-def inspiration(annotation:str,chunksize:int,nclusters:int,remote_ornot:bool):
-    query = 'inspiration'
-    if remote_ornot:
-        backend = 'remote'
-    else:
-        backend = 'local'
-        
-    clusterdir, annoresult, assistant, theme = get_ready('inspiration',chunksize,nclusters)
-    new_obj_list = []
-
-    if annotation is not None: # if the user wants to get inspiration from specific clusters only  
-        annoresult = [obj for obj in annoresult if obj['annotation'] in [txt.strip() for txt in annotation.split('\n')]]
-    
-    for index in random.sample(range(len(annoresult)), min(5, len(annoresult))):
-        cluster_no = annoresult[index]['cluster_no']
-        chunks = annoresult[index]['annotation']
-        
-        code, reply = assistant.getinspiration(
-                                                theme = theme,
-                                                annotations = chunks,
-                                                history=[], 
-                                                groupname='',backend=backend)
-        new_obj = {
-            'inspiration': reply,
-            'cluster_no': cluster_no
-        }
-        new_obj_list.append(new_obj)
-        logger.info(f'{code}, {query}, {cluster_no},{reply}')
-
-        with open(os.path.join(clusterdir, 'inspiration.jsonl'), 'a') as f:
-            json.dump(new_obj, f, ensure_ascii=False)
-        with open(os.path.join(clusterdir, 'inspiration.txt'), 'a') as f:
-            f.write(f'{reply}\n')
-            
-    return '\n\n'.join(list(set([obj['inspiration'] for obj in new_obj_list])))
-
-
-def getpmcurls(references):
-    urls = []
-    for ref in references:
-        if ref.startswith('PMC'):
-            
-            refid = ref.replace('.txt','')
-            urls.append(f'https://www.ncbi.nlm.nih.gov/pmc/articles/{refid}/')
-        else:
-            urls.append(ref)
-    return urls
-    
-@spaces.GPU
-def summarize_text(query,chunksize:int,remote_ornot:bool):
-    if remote_ornot:
-        backend = 'remote'
-    else:
-        backend = 'local'
-        
-    assistant,_ = get_ready('summarize',chunksize=chunksize,k=None)
-    code, reply, references = assistant.generate(query=query,
-                                                history=[],
-                                                groupname='',backend = backend)
-      
-    logger.info(f'{code}, {query}, {reply}, {references}')
-    urls = getpmcurls(references)
-    mds = '\n'.join([f'[{ref}]({url})' for ref,url in zip(references,urls)])
-    return reply, gr.Markdown(label="参考文献",value = mds) 
-
-def main_interface():   
-    with gr.Blocks() as demo:
-        with gr.Row():
-            gr.Markdown(
-                """
-                # 医学文献综述助手 (又名 不想看文献)
-                """
-            )
-
-        with gr.Tab("模型服务配置"):
-            gr.Markdown("""
-            #### 配置模型服务 🛠️
-
-            1. **是否使用远程大模型**
-            - 勾选此项，如果你想使用远程的大模型服务。
-            - 如果不勾选，将默认使用本地模型服务。
-
-            2. **API配置**
-            - 配置大模型提供商和API，确保模型服务能够正常运行。
-            - 提供商选择：kimi、deepseek、zhipuai、gpt。
-            - 输入您的API密钥和选择对应模型。
-            - 点击“保存配置”按钮以保存您的设置。
-
-            📝 **备注**：请参考[如何使用]('https://github.com/jabberwockyang/MedicalReviewAgent/blob/main/README.md')获取更多信息。
-
-            """)
-
-            remote_ornot = gr.Checkbox(label="是否使用远程大模型")
-            with gr.Accordion("API配置", open=True):
-                apimd = gr.Markdown("[如何配置API]('https://github.com/jabberwockyang/MedicalReviewAgent/blob/main/README.md')",visible=False)
-                remote_company = gr.Dropdown(["kimi", "deepseek", "zhipuai",'gpt'],
-                                            label="选择大模型提供商",interactive=False,visible=False)
-                api = gr.Textbox(label="您的API",lines = 1,interactive=False,visible=False)
-                model = gr.Dropdown([],label="选择模型",interactive=False,visible=False)
-            
-            confirm_button = gr.Button("保存配置")
-
-            remote_ornot.change(update_remote_buttons, inputs=[remote_ornot],outputs=[apimd,remote_company,api,model])
-            remote_company.change(udate_model_dropdown, inputs=[remote_company],outputs=[model])
-            confirm_button.click(update_remote_config, inputs=[remote_ornot,remote_company,api,model],outputs=[confirm_button])
-
-
-        with gr.Tab("文献查找+数据库生成"):
-            gr.Markdown("""
-#### 查找文献 📚
-
-1. **输入关键词批量PubMed PMC文献**
-   - 在“感兴趣的关键词”框中输入您感兴趣的关键词，每行一个。
-   - 设置查找数量（0-1000）。
-   - 点击“搜索PubMed PMC”按钮进行文献查找。
-
-2. **上传PDF**
-   - 通过“上传PDF”按钮上传您已有的PDF文献文件。
-
-3. **更新文献库情况 删除文献库**
-   - 点击“更新文献库情况”按钮，查看当前文献库的概况。
-   - 如果需要重置或删除现有文献库，点击“删除文献库”按钮。
-
-
-#### 生成数据库 🗂️
-
-1. **设置数据库构建参数 生成数据库**
-   - 选择块大小（Chunk Size）和聚类数（Number of Clusters）。
-   - 提供选项用于选择合适的块大小和聚类数。
-   - 点击“生成数据库”按钮开始数据库生成过程。
-
-2. **更新数据库情况 删除数据库**
-   - 点击“更新数据库情况”按钮，查看当前数据库的概况。
-   - 点击“删除数据库”按钮移除现有数据库。
-
-📝 **备注**：请参考[如何选择数据库构建参数]('https://github.com/jabberwockyang/MedicalReviewAgent/tree/main')获取更多信息。
-""")
-            with gr.Row(equal_height=True):
-                with gr.Column(scale=1):
-                    input_keys = gr.Textbox(label="感兴趣的关键词",
-                                                    lines = 5)
-                    retmax = gr.Slider(
-                            minimum=0,
-                            maximum=1000,
-                            value=500,
-                            interactive=True,
-                            label="查多少",
-                        )
-                    generate_repo_button = gr.Button("搜索PubMed PMC")
-                with gr.Column(scale=2):
-                    file_output = gr.File(scale=2)
-                    upload_button = gr.UploadButton("上传PDF", 
-                                    file_types=[".pdf",".csv",".doc"], 
-                                    file_count="multiple",scale=0)
-                    
-            with gr.Row(equal_height=True):
-                with gr.Column(scale=0):
-                    delete_repo_button = gr.Button("删除文献库")
-                    update_repo_button = gr.Button("更新文献库情况")
-                with gr.Column(scale=2):
-
-                    repo_summary =gr.Textbox(label= '文献库概况', value="目前还没有文献库")
-
-            generate_repo_button.click(generate_articles_repo, 
-                                inputs=[input_keys,retmax],
-                                outputs = [repo_summary])
-            
-            
-            delete_repo_button.click(delete_articles_repo, inputs=None,
-                                outputs = repo_summary)
-            update_repo_button.click(update_repo, inputs=None,
-                                outputs = repo_summary)
-            upload_button.upload(upload_file, upload_button, file_output)
-            
-            with gr.Accordion("数据库构建参数", open=True):
-                gr.Markdown("[如何选择数据库构建参数]('https://github.com/jabberwockyang/MedicalReviewAgent/tree/main')")
-                chunksize = gr.Slider(label="Chunk Size",
-                                      info= 'How long you want the chunk to be?',
-                                        minimum=128, maximum=4096,value=1024,step=1,
-                                        interactive=True)
-                ncluster = gr.CheckboxGroup(["10", "20", "50", '100','200','500','1000'], 
-                                            # default=["20", "50", '100'],
-                                            label="Number of Clusters", 
-                                            info="How many Clusters you want to generate")
-
-            with gr.Row():
-                gene_database_button = gr.Button("生成数据库")
-                delete_database_button = gr.Button("删除数据库")
-                update_database_button = gr.Button("更新数据库情况")
-
-            database_summary = gr.Textbox(label="数据库概况",lines = 1,value="目前还没有数据库")
-            
-
-            gene_database_button.click(generate_database, inputs=[chunksize,ncluster],
-                                outputs = database_summary)
-            
-            update_database_button.click(update_database_textbox,inputs=None,
-                                outputs = [database_summary])
-                                         
-            delete_database_button.click(delete_database, inputs=None,
-                                outputs = database_summary)
-        with gr.Tab("写综述"):
-            gr.Markdown("""
-#### 写综述 ✍️
-
-1. **更新数据库情况**
-   - 点击“更新数据库情况”按钮，确保使用最新的数据库信息。
-
-2. **选择块大小和聚类数**
-   - 从下拉菜单中选择合适的块大小和聚类数。
-
-3. **抽样标注文章聚类**
-   - 设置抽样标注比例（0-1）。
-   - 点击“抽样标注文章聚类”按钮开始标注过程。
-
-4. **获取灵感**
-   - 如果不知道写什么，点击“获取灵感”按钮。
-   - 系统将基于标注的文章聚类提供相应的综述子问题。
-
-5. **写综述**
-   - 输入您想写的内容或主题。
-   - 点击“写综述”按钮，生成综述文本。
-
-6. **查看生成结果**
-   - 生成的综述文本将显示在“看看”文本框中。
-   - 参考文献将显示在“参考文献”框中。
-
-📝 **备注**：可以尝试不同的参数进行标注和灵感获取，有助于提高综述的质量和相关性。
-            """)
-
-            with gr.Accordion("聚类标注相关参数", open=True):
-                with gr.Row():
-                    update_options = gr.Button("更新数据库情况", scale=0)
-                    chunksize = gr.Dropdown([], label="选择块大小", scale=0)
-                    nclusters = gr.Dropdown([], label="选择聚类数", scale=0)
-                    ntoread = gr.Slider(
-                            minimum=0,maximum=1,value=0.5,
-                            interactive=True,
-                            label="抽样标注比例",
-                        )
-
-            annotation_button = gr.Button("抽样标注文章聚类")
-            annotation_output =  gr.Textbox(label="文章聚类标注/片段摘要",
-                                            lines = 5, 
-                                            interactive= True,
-                                            show_copy_button=True)
-            inspiration_button = gr.Button("获取灵感")
-            inspiration_output = gr.Textbox(label="灵光一现",
-                                            lines = 5,
-                                            show_copy_button=True)
-
-
-            query = gr.Textbox(label="想写什么")
-            
-            write_button = gr.Button("写综述")
-            output_text = gr.Textbox(label="看看",lines=10)
-            output_references = gr.Markdown(label="参考文献")
-            
-            update_options.click(update_chunksize_dropdown,
-                                outputs=[chunksize])
-            
-            chunksize.change(update_ncluster_dropdown, 
-                             inputs=[chunksize],
-                             outputs= [nclusters])
-            
-            annotation_button.click(annotation, 
-                                    inputs = [ntoread, chunksize, nclusters,remote_ornot],
-                                    outputs=[annotation_output])
-            
-            inspiration_button.click(inspiration, 
-                                     inputs= [annotation_output, chunksize, nclusters,remote_ornot],
-                                     outputs=[inspiration_output])
-            
-            write_button.click(summarize_text,
-                                inputs=[query, chunksize,remote_ornot],
-                                outputs =[output_text,output_references])
-
-    demo.launch(share=False, server_name='0.0.0.0', debug=True,show_error=True,allowed_paths=['img_0.jpg']) 
- 
-# start service
-if __name__ == '__main__':
-    args = parse_args()
-    # copy config from config-bak
-    shutil.copy('config-bak.ini', args.config_path) # yyj
-    CONFIG_PATH = args.config_path
-
-    if args.standalone is True:
-        # hybrid llm serve
-        server_ready = Value('i', 0)
-        server_process = Process(target=llm_serve,
-                                args=(args.config_path, server_ready))
-        server_process.start()
-        while True:
-            if server_ready.value == 0:
-                logger.info('waiting for server to be ready..')
-                time.sleep(3)
-            elif server_ready.value == 1:
-                break
-            else:
-                logger.error('start local LLM server failed, quit.')
-                raise Exception('local LLM path')
-        logger.info('Hybrid LLM Server start.')
-
-    main_interface()

.ipynb_checkpoints/config-bak-checkpoint.ini

@@ -1,63 +0,0 @@
-[feature_store]
-reject_throttle = 0
-embedding_model_path = "maidalun1020/bce-embedding-base_v1"
-reranker_model_path = "maidalun1020/bce-reranker-base_v1"
-repo_dir = "repodir"
-work_dir = "workdir"
-n_clusters = [20, 50]
-chunk_size = 1024
-
-[web_search]
-x_api_key = "${YOUR-API-KEY}"
-domain_partial_order = ["openai.com", "pytorch.org", "readthedocs.io", "nvidia.com", "stackoverflow.com", "juejin.cn", "zhuanlan.zhihu.com", "www.cnblogs.com"]
-save_dir = "logs/web_search_result"
-
-[llm]
-enable_local = 1
-enable_remote = 1
-client_url = "http://127.0.0.1:8888/inference"
-
-[llm.server]
-local_llm_path = "Qwen/Qwen1.5-7B-Chat"
-local_llm_max_text_length = 32000
-local_llm_bind_port = 8888
-remote_type = ""
-remote_api_key = ""
-remote_llm_max_text_length = 32000
-remote_llm_model = ""
-rpm = 500
-
-[worker]
-enable_sg_search = 0
-save_path = "logs/work.txt"
-
-[worker.time]
-start = "00:00:00"
-end = "23:59:59"
-has_weekday = 1
-
-[sg_search]
-binary_src_path = "/usr/local/bin/src"
-src_access_token = "${YOUR-SRC-ACCESS-TOKEN}"
-
-[sg_search.opencompass]
-github_repo_id = "open-compass/opencompass"
-introduction = "用于评测大型语言模型（LLM）. 它提供了完整的开源可复现的评测框架，支持大语言模型、多模态模型的一站式评测，基于分布式技术，对大参数量模型亦能实现高效评测。评测方向汇总为知识、语言、理解、推理、考试五大能力维度，整合集纳了超过70个评测数据集，合计提供了超过40万个模型评测问题，并提供长文本、安全、代码3类大模型特色技术能力评测。"
-
-[sg_search.lmdeploy]
-github_repo_id = "internlm/lmdeploy"
-introduction = "lmdeploy 是一个用于压缩、部署和服务 LLM（Large Language Model）的工具包。是一个服务端场景下，transformer 结构 LLM 部署工具，支持 GPU 服务端部署，速度有保障，支持 Tensor Parallel，多并发优化，功能全面，包括模型转换、缓存历史会话的 cache feature 等. 它还提供了 WebUI、命令行和 gRPC 客户端接入。"
-
-[frontend]
-type = "none"
-webhook_url = "https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxxxxxxx"
-message_process_policy = "immediate"
-
-[frontend.lark_group]
-app_id = "cli_a53a34dcb778500e"
-app_secret = "2ajhg1ixSvlNm1bJkH4tJhPfTCsGGHT1"
-encrypt_key = "abc"
-verification_token = "def"
-
-[frontend.wechat_personal]
-bind_port = 9527

.ipynb_checkpoints/packages-checkpoint.txt

@@ -1,8 +0,0 @@
-apt-get install libgl1-mesa-glx
-cd /root && mkdir models
-cd /root/models
-
-# login required
-huggingface-cli download Qwen/Qwen1.5-7B-Chat --local-dir /root/models/Qwen1.5-7B-Chat
-huggingface-cli download maidalun1020/bce-embedding-base_v1 --local-dir /root/models/bce-embedding-base_v1
-huggingface-cli download maidalun1020/bce-reranker-base_v1 --local-dir /root/models/bce-reranker-base_v1