pythonversion

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,644 @@
+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

@@ -0,0 +1,63 @@
+[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