20251025

.gitignore

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+.idea
+knightutils.egg-info

knightutils/utils/proxy_example.py

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+"""
+Python中设置代理的完整示例
+"""
+
+import os
+import requests
+import urllib.request
+from urllib.parse import urlparse
+
+
+def set_global_proxy(proxy_url):
+    """
+    为整个Python进程设置全局代理
+    
+    Args:
+        proxy_url (str): 代理地址，例如 "http://proxy.example.com:8080"
+                        或 "socks5://127.0.0.1:1080"
+    """
+    # 解析代理URL
+    parsed = urlparse(proxy_url)
+    proxy_address = f"{parsed.scheme}://{parsed.hostname}:{parsed.port}"
+    
+    # 设置环境变量（影响urllib、requests等库）
+    os.environ['http_proxy'] = proxy_address
+    os.environ['https_proxy'] = proxy_address
+    
+    # 有些库可能使用大写环境变量
+    os.environ['HTTP_PROXY'] = proxy_address
+    os.environ['HTTPS_PROXY'] = proxy_address
+    
+    print(f"已设置全局代理: {proxy_address}")
+
+
+def set_requests_proxy(proxy_url, username=None, password=None):
+    """
+    仅为requests库设置代理
+    
+    Args:
+        proxy_url (str): 代理地址
+        username (str, optional): 代理用户名
+        password (str, optional): 代理密码
+        
+    Returns:
+        dict: 可直接用于requests的代理字典
+    """
+    if username and password:
+        # 添加认证信息
+        parsed = urlparse(proxy_url)
+        proxy_with_auth = f"{parsed.scheme}://{username}:{password}@{parsed.hostname}:{parsed.port}"
+        proxies = {
+            'http': proxy_with_auth,
+            'https': proxy_with_auth
+        }
+    else:
+        proxies = {
+            'http': proxy_url,
+            'https': proxy_url
+        }
+    
+    print(f"已为requests设置代理: {proxy_url}")
+    return proxies
+
+
+def test_proxy_connection(test_url="https://httpbin.org/ip", proxies=None):
+    """
+    测试代理连接
+    
+    Args:
+        test_url (str): 测试URL
+        proxies (dict, optional): 代理设置
+    """
+    try:
+        if proxies:
+            response = requests.get(test_url, proxies=proxies, timeout=10)
+        else:
+            response = requests.get(test_url, timeout=10)
+        
+        print(f"代理测试成功，IP信息: {response.json()}")
+        return True
+    except Exception as e:
+        print(f"代理测试失败: {e}")
+        return False
+
+
+def clear_proxy():
+    """
+    清除代理设置
+    """
+    for var in ['http_proxy', 'https_proxy', 'HTTP_PROXY', 'HTTPS_PROXY']:
+        if var in os.environ:
+            del os.environ[var]
+    
+    print("已清除代理设置")
+
+
+def example_with_requests():
+    """
+    使用requests库通过代理访问网络的示例
+    """
+    print("\n=== Requests代理示例 ===")
+    
+    # 不使用代理
+    print("不使用代理:")
+    test_proxy_connection()
+    
+    # 使用代理（示例地址，请替换为实际代理地址）
+    print("\n使用代理:")
+    proxies = set_requests_proxy("http://proxy.example.com:8080")
+    # test_proxy_connection(proxies=proxies)  # 取消注释并替换为实际代理地址进行测试
+
+
+def example_with_global_proxy():
+    """
+    设置全局代理的示例
+    """
+    print("\n=== 全局代理示例 ===")
+    
+    # 设置全局代理
+    set_global_proxy("http://proxy.example.com:8080")
+    
+    # 测试连接
+    test_proxy_connection()
+    
+    # 清除代理
+    clear_proxy()
+
+
+if __name__ == "__main__":
+    print("Python代理设置完整示例")
+    print("=" * 40)
+    
+    # 示例1: 使用requests设置代理
+    example_with_requests()
+    
+    # 示例2: 设置全局代理
+    example_with_global_proxy()
+    
+    print("\n代理设置说明:")
+    print("1. 环境变量方法影响整个Python进程")
+    print("2. requests代理方法仅影响使用requests的请求")
+    print("3. 实际使用时请替换示例代理地址为真实地址")
+    print("4. SOCKS代理需要安装: pip install requests[socks]")

knightutils/utils/set_proxy.py

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+"""
+演示在Python中设置代理的几种方法
+"""
+
+import os
+import requests
+from urllib.request import ProxyHandler, build_opener
+
+fiddler_proxy = 'http://localhost:8866'
+fiddlers_proxy = 'http://localhost:8866'
+
+proxy = fiddler_proxy
+proxys = fiddlers_proxy
+
+
+def set_proxy_with_os():
+    """
+    方法1: 使用环境变量设置代理
+    这种方法会影响整个Python进程的网络请求
+    """
+    # 设置HTTP代理
+    os.environ['http_proxy'] = proxy
+    os.environ['https_proxy'] = proxys
+    
+    # 如果需要身份验证
+    # os.environ['http_proxy'] = 'http://username:password@proxy.example.com:8080'
+    # os.environ['https_proxy'] = 'http://username:password@proxy.example.com:8080'
+    
+    print("已通过环境变量设置代理")
+
+
+def set_proxy_with_requests():
+    """
+    方法2: 使用requests库单独设置代理
+    这种方法只影响使用requests的请求
+    """
+    proxies = {
+        'http': proxy,
+        'https': proxys
+    }
+    
+    # 如果需要身份验证
+    # proxies = {
+    #     'http': 'http://username:password@proxy.example.com:8080',
+    #     'https': 'http://username:password@proxy.example.com:8080'
+    # }
+    
+    try:
+        response = requests.get('https://httpbin.org/ip', proxies=proxies, timeout=10)
+        print(f"通过代理访问的结果: {response.json()}")
+    except Exception as e:
+        print(f"使用requests通过代理访问出错: {e}")
+
+
+def set_proxy_with_urllib():
+    """
+    方法3: 使用urllib设置代理
+    """
+    proxy_handler = ProxyHandler({
+        'http': proxy,
+        'https': proxys
+    })
+    
+    opener = build_opener(proxy_handler)
+    
+    try:
+        response = opener.open('https://httpbin.org/ip', timeout=10)
+        print(f"通过urllib代理访问的结果: {response.read().decode()}")
+    except Exception as e:
+        print(f"使用urllib通过代理访问出错: {e}")
+
+
+def unset_proxy():
+    """
+    取消代理设置
+    """
+    if 'http_proxy' in os.environ:
+        del os.environ['http_proxy']
+    if 'https_proxy' in os.environ:
+        del os.environ['https_proxy']
+    if 'HTTP_PROXY' in os.environ:
+        del os.environ['HTTP_PROXY']
+    if 'HTTPS_PROXY' in os.environ:
+        del os.environ['HTTPS_PROXY']
+    
+    print("已取消代理设置")
+
+
+if __name__ == "__main__":
+    print("Python代理设置演示")
+    print("=" * 30)
+    
+    # 演示设置代理
+    set_proxy_with_os()
+    
+    # 演示使用requests通过代理访问
+    set_proxy_with_requests()
+    
+    # 演示使用urllib通过代理访问
+    set_proxy_with_urllib()
+    
+    # 取消代理设置
+    unset_proxy()

llamaindex_cloud_rag/create_cloud_index.py

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+from llama_index.core import SimpleDirectoryReader
+from llama_index.readers.dashscope.base import DashScopeParse
+from llama_index.readers.dashscope.utils import ResultType
+from llama_index.indices.managed.dashscope import DashScopeCloudIndex
+
+
+def read_parse_upload_local_documents(dir, num_workers=1):
+    """读取、解析、上传本地文件到百炼数据管理平台。
+
+    Args:
+        dir (str): 本地文件存储的路径。
+        num_workers (int, optional): 执行的并发数。
+
+    Returns:
+        已上传到云端的文件列表
+    """
+    parse = DashScopeParse(result_type=ResultType.DASHSCOPE_DOCMIND)
+    file_extractor = {'.txt': parse, '.docx': parse, ".pdf": parse}  # 设置需要读取解析的文件格式，请根据实际需求调整
+    documents = SimpleDirectoryReader(input_dir=dir, file_extractor=file_extractor).load_data(num_workers=num_workers)
+    
+    return documents
+
+
+if __name__ == '__main__':
+    dir = "./docs/" # 本例中，业务相关文件存储在当前路径下的docs文件夹，请根据实际情况调整。
+    documents = read_parse_upload_local_documents(dir)
+    cloud_index_name = "my_first_index"  # 设置云端知识库索引名称
+    index = DashScopeCloudIndex.from_documents(documents, cloud_index_name, verbose=True) # 创建云端知识库索引

llamaindex_cloud_rag/docs/百炼系列智能音箱产品介绍.txt

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+百炼系列智能音箱产品介绍
+
+踏入智能家居的核心地带，百炼智能音箱系列以其创新设计与智能交互技术，重新定义家庭生活的每一个角落。
+
+百炼EchoSphere S1 —— 智慧家居的指挥官： 设计简约而不失时尚，采用360度全向发声技术，配备高端音频单元，提供深沉低音与清澈高音的完美平衡。支持Wi-Fi与蓝牙5.0双连接，轻松接入您的智能家居生态。内置智能语音助手，只需一声令下，即可控制家中电器、查询天气、播放音乐等。长达20小时的续航能力，让音乐与便利伴您一整天。参考售价：999 - 1299。
+通义VoiceBox V7 —— 生活小秘书： 采用8个远场麦克风阵列，即使在嘈杂环境中也能精准识别您的指令。拥有7英寸触摸屏，不仅能听更能看，查看菜谱、视频通话、浏览新闻一应俱全。4GB内存与64GB存储空间，存储您喜爱的歌曲与节目。内置电池支持8小时连续使用，从厨房到卧室，随心移动。参考售价：1499 - 1799。
+星尘SoundWave SW Pro —— 音乐发烧友的选择： 采用HiFi级音频解码芯片，支持aptX HD与LDAC高品质音频传输，还原录音室级别的音质。独特的声学结构设计，带来震撼的3D环绕声效果。12GB RAM与256GB存储，存储海量音乐资源。AI音效调节，根据环境自动优化音质。内置电池续航高达12小时，户外派对也能尽兴。参考售价：2999 - 3499。
+百炼SmartHalo H1 —— 家庭互动的中心： 独特的环形灯效设计，不仅是一款音箱，更是家居装饰的艺术品。支持多种语音助手，兼容iOS与Android系统，家庭成员均可个性化设置。内置丰富的儿童教育资源，成为孩子成长的好伙伴。5000mAh电池，满足全家人的日常使用需求。参考售价：1299 - 1599。
+百炼MiniBoom MB Plus —— 便携式智能乐趣： 极致小巧，仅手掌大小，却拥有令人惊喜的音质表现。IP67防水防尘设计，无论户外探险还是浴室放松，都能陪伴左右。支持语音控制与触控操作，内置电池续航达10小时。无线串联功能，轻松构建环绕立体声场。参考售价：299 - 399。
+
+每一款百炼智能音箱都是对智能生活美学的诠释，旨在为您的家庭带来前所未有的便捷与愉悦。选择百炼，让智能生活的声音更加悦耳动听。

llamaindex_cloud_rag/rag.py

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+from llama_index.core import Settings
+from llama_index.llms.dashscope import DashScope
+from llama_index.indices.managed.dashscope import DashScopeCloudIndex
+from llama_index.core.postprocessor import SimilarityPostprocessor
+from llama_index.postprocessor.dashscope_rerank import DashScopeRerank
+
+'''
+本例中构建检索引擎时，需要手动设置下列参数，请根据实际效果调整。
+'''
+Settings.llm = DashScope(model_name="qwen-max") # 设置检索引擎生成回答时调用的大模型。
+similarity_top_k = 5  # 检索引擎找到的相似度最高的结果数
+similarity_cutoff = 0.4  # 过滤检索结果时使用的最低相似度阈值
+top_n = 1  # 进行重排后返回语义上相关度最高的结果数
+
+'''
+本例中构建RAG应用时，设置如下问答模板，请根据实际需求调整。
+'''
+init_chat = "\n您好，我是AI助手，可以回答关于百炼系列产品的提问。有什么可以帮您的？（请输入问题，退出请输入'q'）\n> "
+resp_with_no_answer = "很抱歉，知识库未提供相关信息。" + "\n"
+prompt_template = "回答如下问题: {0}\n如果根据提供的信息无法回答，请返回：{1}"
+
+
+'''
+格式化输出。
+'''
+def prettify_rag(resp):
+    output = ""
+    output += "\n回答：{0}\n".format(resp.response)
+    for j in range(len(resp.source_nodes)):
+        output += "\n产品知识库中的相关文本：\n{0}\n".format(resp.source_nodes[j].text)
+    return output
+
+
+'''
+基于云端知识库的向量索引，构建检索引擎，能够接收终端用户的提问，从云端知识库中检索相关的文本片段，再将提问和检索结果合并后输入到大模型，并生成回答。
+RAG应用提供与终端用户的交互界面，如果无法检索到相关的文本片段，或根据检索到的文本片段无法回答终端用户的提问，则返回适当的错误信息。
+'''
+if __name__ == '__main__':
+    index = DashScopeCloudIndex("my_first_index")  # 读取百炼平台上已创建的知识库索引
+    query_engine = index.as_query_engine(  # 构建检索引擎
+        similarity_top_k=similarity_top_k,
+        node_postprocessors=[  # 默认检索结果可能不满足需求，本例中通过加入node_postprocessors对检索结果进行后处理。
+            SimilarityPostprocessor(similarity_cutoff=similarity_cutoff),  # 过滤不满足最低相似度阈值的检索结果。
+            DashScopeRerank(top_n=top_n, model="gte-rerank")  # 对检索结果进行重排，返回语义上相关度最高的结果。
+        ],
+        response_mode="tree_summarize"
+    )
+    while True:
+        user_prompt = input(init_chat)
+        if user_prompt in ['q','Q']:
+            break
+        resp = query_engine.query(prompt_template.format(user_prompt, resp_with_no_answer))
+        if len(resp.source_nodes) == 0:
+            output = resp_with_no_answer  # 如果未找到相关上下文信息，则返回适当的报错信息。
+        else:
+            output = prettify_rag(resp)
+        print(output)

llamaindex_cloud_rag/requirements.txt

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+llama-index==0.10.65
+dashscope==1.20.4
+llama-index-embeddings-dashscope==0.1.4
+llama-index-indices-managed-llama-cloud==0.2.7
+llama-index-llms-dashscope==0.1.2
+llama-index-node-parser-dashscope==0.1.2
+llama-index-postprocessor-dashscope-rerank==0.1.4
+llama-index-readers-dashscope==0.1.2
+llama-index-readers-file==0.1.33
+llama-index-readers-llama-parse==0.1.6
+llama-parse==0.4.9
+llama-index-indices-managed-dashscope==0.1.2

local_rag/README.md

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+Run: uvicorn main:app --port 7866
+Then visit 127.0.0.1:7866

local_rag/VectorStore/tmp_abcd/graph_store.json

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+{"graph_dict": {}}

local_rag/VectorStore/tmp_abcd/image__vector_store.json

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+{"embedding_dict": {}, "text_id_to_ref_doc_id": {}, "metadata_dict": {}}

local_rag/VectorStore/tmp_abcd/index_store.json

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+{"index_store/data": {"ab9a86f4-0029-48e5-b823-eccfc6f58622": {"__type__": "vector_store", "__data__": "{\"index_id\": \"ab9a86f4-0029-48e5-b823-eccfc6f58622\", \"summary\": null, \"nodes_dict\": {\"c7e14d46-b930-4a98-acaf-2854ac14c1de\": \"c7e14d46-b930-4a98-acaf-2854ac14c1de\", \"fb1f2a38-146e-4840-8f0d-221033dbc849\": \"fb1f2a38-146e-4840-8f0d-221033dbc849\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}

local_rag/chat.py

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+import os
+from openai import OpenAI
+from llama_index.core import StorageContext,load_index_from_storage,Settings
+from llama_index.embeddings.dashscope import (
+    DashScopeEmbedding,
+    DashScopeTextEmbeddingModels,
+    DashScopeTextEmbeddingType,
+)
+from llama_index.postprocessor.dashscope_rerank import DashScopeRerank
+from create_kb import *
+DB_PATH = "VectorStore"
+TMP_NAME = "tmp_abcd"
+EMBED_MODEL = DashScopeEmbedding(
+    model_name=DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2,
+    text_type=DashScopeTextEmbeddingType.TEXT_TYPE_DOCUMENT,
+)
+# 若使用本地嵌入模型，请取消以下注释：
+# from langchain_community.embeddings import ModelScopeEmbeddings
+# from llama_index.embeddings.langchain import LangchainEmbedding
+# embeddings = ModelScopeEmbeddings(model_id="modelscope/iic/nlp_gte_sentence-embedding_chinese-large")
+# EMBED_MODEL = LangchainEmbedding(embeddings)
+
+# 设置嵌入模型
+Settings.embed_model = EMBED_MODEL
+
+def get_model_response(multi_modal_input,history,model,temperature,max_tokens,history_round,db_name,similarity_threshold,chunk_cnt):
+    # prompt = multi_modal_input['text']
+    prompt = history[-1][0]
+    tmp_files = multi_modal_input['files']
+    if os.path.exists(os.path.join("File",TMP_NAME)):
+        db_name = TMP_NAME
+    else:
+        if tmp_files:
+            create_tmp_kb(tmp_files)
+            db_name = TMP_NAME
+    # 获取index
+    print(f"prompt:{prompt},tmp_files:{tmp_files},db_name:{db_name}")
+    try:
+        dashscope_rerank = DashScopeRerank(top_n=chunk_cnt,return_documents=True)
+        storage_context = StorageContext.from_defaults(
+            persist_dir=os.path.join(DB_PATH,db_name)
+        )
+        index = load_index_from_storage(storage_context)
+        print("index获取完成")
+        retriever_engine = index.as_retriever(
+            similarity_top_k=20,
+        )
+        # 获取chunk
+        retrieve_chunk = retriever_engine.retrieve(prompt)
+        print(f"原始chunk为：{retrieve_chunk}")
+        try:
+            results = dashscope_rerank.postprocess_nodes(retrieve_chunk, query_str=prompt)
+            print(f"rerank成功，重排后的chunk为：{results}")
+        except:
+            results = retrieve_chunk[:chunk_cnt]
+            print(f"rerank失败，chunk为：{results}")
+        chunk_text = ""
+        chunk_show = ""
+        for i in range(len(results)):
+            if results[i].score >= similarity_threshold:
+                chunk_text = chunk_text + f"## {i+1}:\n {results[i].text}\n"
+                chunk_show = chunk_show + f"## {i+1}:\n {results[i].text}\nscore: {round(results[i].score,2)}\n"
+        print(f"已获取chunk：{chunk_text}")
+        prompt_template = f"请参考以下内容：{chunk_text}，以合适的语气回答用户的问题：{prompt}。如果参考内容中有图片链接也请直接返回。"
+    except Exception as e:
+        print(f"异常信息：{e}")
+        prompt_template = prompt
+        chunk_show = ""
+    history[-1][-1] = ""
+    client = OpenAI(
+        api_key=os.getenv("DASHSCOPE_API_KEY"),
+        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    )                
+    system_message = {'role': 'system', 'content': 'You are a helpful assistant.'}
+    messages = []
+    history_round = min(len(history),history_round)
+    for i in range(history_round):
+        messages.append({'role': 'user', 'content': history[-history_round+i][0]})
+        messages.append({'role': 'assistant', 'content': history[-history_round+i][1]})
+    messages.append({'role': 'user', 'content': prompt_template})
+    messages = [system_message] + messages
+    completion = client.chat.completions.create(
+        model=model,
+        messages=messages,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        stream=True
+        )
+    assistant_response = ""
+    for chunk in completion:
+        assistant_response += chunk.choices[0].delta.content
+        history[-1][-1] = assistant_response
+        yield history,chunk_show

local_rag/create_kb.py

@@ -0,0 +1,134 @@
+#####################################
+######       创建知识库         #######
+#####################################
+import gradio as gr
+import os
+import shutil
+from llama_index.core import VectorStoreIndex,Settings,SimpleDirectoryReader
+from llama_index.embeddings.dashscope import (
+    DashScopeEmbedding,
+    DashScopeTextEmbeddingModels,
+    DashScopeTextEmbeddingType,
+)
+from llama_index.core.schema import TextNode
+from upload_file import *
+DB_PATH = "VectorStore"
+STRUCTURED_FILE_PATH = "File/Structured"
+UNSTRUCTURED_FILE_PATH = "File/Unstructured"
+TMP_NAME = "tmp_abcd"
+EMBED_MODEL = DashScopeEmbedding(
+    model_name=DashScopeTextEmbeddingModels.TEXT_EMBEDDING_V2,
+    text_type=DashScopeTextEmbeddingType.TEXT_TYPE_DOCUMENT,
+)
+# 若使用本地嵌入模型，请取消以下注释：
+# from langchain_community.embeddings import ModelScopeEmbeddings
+# from llama_index.embeddings.langchain import LangchainEmbedding
+# embeddings = ModelScopeEmbeddings(model_id="modelscope/iic/nlp_gte_sentence-embedding_chinese-large")
+# EMBED_MODEL = LangchainEmbedding(embeddings)
+
+
+# 设置嵌入模型
+Settings.embed_model = EMBED_MODEL
+# 刷新知识库
+def refresh_knowledge_base():
+    return os.listdir(DB_PATH)
+
+# 创建非结构化向量数据库
+def create_unstructured_db(db_name:str,label_name:list):
+    print(f"知识库名称为：{db_name}，类目名称为：{label_name}")
+    if label_name is None:
+        gr.Info("没有选择类目")
+    elif len(db_name) == 0:
+        gr.Info("没有命名知识库")
+    # 判断是否存在同名向量数据库
+    elif db_name in os.listdir(DB_PATH):
+        gr.Info("知识库已存在，请换个名字或删除原来知识库再创建")
+    else:
+        gr.Info("正在创建知识库，请等待知识库创建成功信息显示后前往RAG问答")
+        documents = []
+        for label in label_name:
+            label_path = os.path.join(UNSTRUCTURED_FILE_PATH,label)
+            documents.extend(SimpleDirectoryReader(label_path).load_data())
+        index = VectorStoreIndex.from_documents(
+            documents
+        )
+        db_path = os.path.join(DB_PATH,db_name)
+        if not os.path.exists(db_path):
+            os.mkdir(db_path)
+            index.storage_context.persist(db_path)
+        elif os.path.exists(db_path):
+            pass
+        gr.Info("知识库创建成功，可前往RAG问答进行提问")
+    
+# 创建结构化向量数据库
+def create_structured_db(db_name:str,data_table:list):
+    print(f"知识库名称为：{db_name}，数据表名称为：{data_table}")
+    if data_table is None:
+        gr.Info("没有选择数据表")
+    elif len(db_name) == 0:
+        gr.Info("没有命名知识库")
+    # 判断是否存在同名向量数据库
+    elif db_name in os.listdir(DB_PATH):
+        gr.Info("知识库已存在，请换个名字或删除原来知识库再创建")
+    else:
+        gr.Info("正在创建知识库，请等待知识库创建成功信息显示后前往RAG问答")
+        documents = []
+        for label in data_table:
+            label_path = os.path.join(STRUCTURED_FILE_PATH,label)
+            documents.extend(SimpleDirectoryReader(label_path).load_data())
+        # index = VectorStoreIndex.from_documents(
+        #     documents
+        # )
+        nodes = []
+        for doc in documents:
+            doc_content = doc.get_content().split('\n')
+            for chunk in doc_content:
+                node = TextNode(text=chunk)
+                node.metadata = {'source': doc.get_doc_id(),'file_name':doc.metadata['file_name']}
+                nodes = nodes + [node]
+        index = VectorStoreIndex(nodes)
+        db_path = os.path.join(DB_PATH,db_name)
+        if not os.path.exists(db_path):
+            os.mkdir(db_path)
+        index.storage_context.persist(db_path)
+        gr.Info("知识库创建成功，可前往RAG问答进行提问")
+
+
+# 删除指定名称知识库
+def delete_db(db_name:str):
+    if db_name is not None:
+        folder_path = os.path.join(DB_PATH, db_name)
+        if os.path.exists(folder_path):
+            shutil.rmtree(folder_path)
+            gr.Info(f"已成功删除{db_name}知识库")
+            print(f"已成功删除{db_name}知识库")
+        else:
+            gr.Info(f"{db_name}知识库不存在")
+            print(f"{db_name}知识库不存在")
+
+# 实时更新知识库列表
+def update_knowledge_base():
+    return gr.update(choices=os.listdir(DB_PATH))
+
+# 临时文件创建知识库
+def create_tmp_kb(files):
+    if not os.path.exists(os.path.join("File",TMP_NAME)):
+        os.mkdir(os.path.join("File",TMP_NAME))
+    for file in files:
+        file_name = os.path.basename(file)
+        shutil.move(file,os.path.join("File",TMP_NAME,file_name))
+    documents = SimpleDirectoryReader(os.path.join("File",TMP_NAME)).load_data()
+    index = VectorStoreIndex.from_documents(
+        documents
+    )
+    db_path = os.path.join(DB_PATH,TMP_NAME)
+    if not os.path.exists(db_path):
+        os.mkdir(db_path)
+    index.storage_context.persist(db_path)
+
+# 清除tmp文件夹下内容 
+def clear_tmp():
+    if os.path.exists(os.path.join("File",TMP_NAME)):
+        shutil.rmtree(os.path.join("File",TMP_NAME))
+    if os.path.exists(os.path.join(DB_PATH,TMP_NAME)):
+        shutil.rmtree(os.path.join(DB_PATH,TMP_NAME))

local_rag/html_string.py

@@ -0,0 +1,150 @@
+main_html = """<!DOCTYPE html>
+<html lang="zh">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>阿里云本地RAG解决方案</title>
+    <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons">
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background-color: #f5f5f5;
+            margin: 0;
+            padding: 0;
+            display: flex;
+            flex-direction: column;
+            align-items: center;
+        }
+        header {
+            background-color: #2196f3;
+            color: white;
+            width: 100%;
+            padding: 1.5em;
+            text-align: center;
+            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+        }
+        main {
+            margin: 2em;
+            box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+            background-color: white;
+            border-radius: 8px;
+            overflow: hidden;
+            width: 90%;
+            max-width: 800px;
+            padding: 2em;
+        }
+        h1 {
+            color: #333;
+        }
+        p {
+            color: #666;
+            font-size: 1.1em;
+        }
+        ul {
+            list-style-type: none;
+            padding: 0;
+        }
+        ul li {
+            background-color: #2196f3;
+            margin: 0.5em 0;
+            padding: 1em;
+            border-radius: 4px;
+            transition: background-color 0.3s;
+        }
+        ul li a {
+            color: white;
+            text-decoration: none;
+            display: flex;
+            align-items: center;
+        }
+        ul li:hover {
+            background-color: #1976d2;
+        }
+        .material-icons {
+            margin-right: 0.5em;
+        }
+    </style>
+</head>
+<body>
+    <header>
+        <h1>阿里云本地RAG解决方案</h1>
+    </header>
+    <main>
+        <p>如果您需要基于上传的文档与模型直接对话，请直接访问<a href="/chat">RAG问答</a>，并在输入框位置上传文件，就可以开始对话了。（此次上传的数据在页面刷新后无法保留，若您希望可以持久使用、维护知识库，请创建知识库）。</p>
+        <p>如果您需要创建或更新知识库，请按照<a href="/upload_data">上传数据</a>、<a href="/create_knowledge_base">创建知识库</a>操作，在<a href="/chat">RAG问答</a>中的“知识库选择”位置选择您需要使用的知识库。</p>
+        <p>如果您需要基于已创建好的知识库进行问答，请直接访问<a href="/chat">RAG问答</a>，在“加载知识库”处选择您已创建的知识库。</p>
+        <ul>
+            <li><a href="/upload_data"><span class="material-icons"></span> 1. 上传数据</a></li>
+            <li><a href="/create_knowledge_base"><span class="material-icons"></span> 2. 创建知识库</a></li>
+            <li><a href="/chat"><span class="material-icons"></span> 3. RAG问答</a></li>
+        </ul>
+    </main>
+</body>
+</html>"""
+
+plain_html = """<!DOCTYPE html>
+<html lang="zh">
+    <head>
+        <title>RAG问答</title>
+        <link href="https://fonts.googleapis.com/icon?family=Material+Icons" rel="stylesheet">
+        <style>
+        .links-container {
+            display: flex;
+            justify-content: center; /* 在容器中居中分布子元素 */
+            list-style-type: none; /* 去掉ul默认的列表样式 */
+            padding: 0; /* 去掉ul默认的内边距 */
+            margin: 0; /* 去掉ul默认的外边距 */
+        }
+        .links-container li {
+            margin: 0 5px; /* 每个li元素的左右留出一些空间 */
+            padding: 10px 15px; /* 添加内边距 */
+            border: 1px solid #ccc; /* 添加边框 */
+            border-radius: 5px; /* 添加圆角 */
+            background-color: #f9f9f9; /* 背景颜色 */
+            transition: background-color 0.3s; /* 背景颜色变化的过渡效果 */
+            display: flex; /* 使用flex布局 */
+            align-items: center; /* 垂直居中对齐 */
+            height: 50px; /* 设置固定高度，确保一致 */
+        }
+        .links-container li:hover {
+            background-color: #e0e0e0; /* 悬停时的背景颜色 */
+        }
+        .links-container a {
+            text-decoration: none !important; /* 去掉链接的下划线 */
+            color: #333; /* 链接颜色 */
+            font-family: Arial, sans-serif; /* 字体 */
+            font-size: 14px; /* 字体大小 */
+            display: flex; /* 使用flex布局 */
+            align-items: center; /* 垂直居中对齐 */
+            height: 100%; /* 确保链接高度与父元素一致 */
+        }
+        .material-icons {
+            font-size: 20px; /* 图标大小 */
+            margin-right: 8px; /* 图标和文字间的间距 */
+            text-decoration: none; /* 确保图标没有下划线 */
+        }
+
+        /* 深色模式样式 */
+        @media (prefers-color-scheme: dark) {
+            .links-container li {
+                background-color: #333; /* 深色模式下的背景颜色 */
+                border-color: #555; /* 深色模式下的边框颜色 */
+            }
+            .links-container li:hover {
+                background-color: #555; /* 深色模式下悬停时的背景颜色 */
+            }
+            .links-container a {
+                color: #f9f9f9; /* 深色模式下的文字颜色 */
+            }
+        }
+        </style>
+    </head>
+    <body>
+        <ul class="links-container">
+            <li><a href="/"><span class="material-icons">home</span> 主页</a></li>
+            <li><a href="/upload_data"><span class="material-icons">cloud_upload</span> 上传数据</a></li>
+            <li><a href="/create_knowledge_base"><span class="material-icons">library_add</span> 创建知识库</a></li>
+            <li><a href="/chat"><span class="material-icons">question_answer</span> RAG问答</a></li>
+        </ul>
+    </body>
+</html>"""

local_rag/main.py

@@ -0,0 +1,116 @@
+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse
+import gradio as gr
+import os
+from html_string import main_html,plain_html
+from upload_file import *
+from create_kb import *
+from chat import get_model_response
+def user(user_message, history):
+    print(user_message)
+    return {'text': '','files': user_message['files']}, history + [[user_message['text'], None]]
+
+#####################################
+######       gradio界面       #######
+#####################################
+
+def get_chat_block():
+    with gr.Blocks(theme=gr.themes.Base(),css=".gradio_container { background-color: #f0f0f0; }") as chat:
+        gr.HTML(plain_html)
+        with gr.Row():     
+            with gr.Column(scale=10):
+                chatbot = gr.Chatbot(label="Chatbot",height=750,avatar_images=("images/user.jpeg","images/tongyi.png"))
+                with gr.Row():
+                    # 
+                    input_message = gr.MultimodalTextbox(label="请输入",file_types=[".xlsx",".csv",".docx",".pdf",".txt"],scale=7)
+                    clear_btn = gr.ClearButton(chatbot,input_message,scale=1)
+            # 模型与知识库参数
+            with gr.Column(scale=5):
+                knowledge_base =gr.Dropdown(choices=os.listdir(DB_PATH),label="加载知识库",interactive=True,scale=2)
+                with gr.Accordion(label="召回文本段",open=False):
+                    chunk_text = gr.Textbox(label="召回文本段",interactive=False,scale=5,lines=10)
+                with gr.Accordion(label="模型设置",open=True):
+                    model =gr.Dropdown(choices=['qwen-max','qwen-plus','qwen-turbo'],label="选择模型",interactive=True,value="qwen-max",scale=2)
+                    temperature = gr.Slider(maximum=2,minimum=0,interactive=True,label="温度参数",step=0.01,value=0.85,scale=2)
+                    max_tokens = gr.Slider(maximum=8000,minimum=0,interactive=True,label="最大回复长度",step=50,value=1024,scale=2)
+                    history_round = gr.Slider(maximum=30,minimum=1,interactive=True,label="携带上下文轮数",step=1,value=3,scale=2)
+                with gr.Accordion(label="RAG参数设置",open=True):
+                    chunk_cnt = gr.Slider(maximum=20,minimum=1,interactive=True,label="选择召回片段数",step=1,value=5,scale=2)
+                    similarity_threshold = gr.Slider(maximum=1,minimum=0,interactive=True,label="相似度阈值",step=0.01,value=0.2,scale=2)
+        input_message.submit(fn=user,inputs=[input_message,chatbot],outputs=[input_message,chatbot],queue=False).then(
+            fn=get_model_response,inputs=[input_message,chatbot,model,temperature,max_tokens,history_round,knowledge_base,similarity_threshold,chunk_cnt],outputs=[chatbot,chunk_text]
+            )
+        chat.load(update_knowledge_base,[],knowledge_base)
+        chat.load(clear_tmp)
+    return chat
+
+
+def get_upload_block():
+    with gr.Blocks(theme=gr.themes.Base()) as upload:
+        gr.HTML(plain_html)
+        with gr.Tab("非结构化数据"):
+            with gr.Accordion(label="新建类目",open=True):
+                with gr.Column(scale=2):
+                    unstructured_file = gr.Files(file_types=["pdf","docx","txt"])
+                    with gr.Row():
+                        new_label = gr.Textbox(label="类目名称",placeholder="请输入类目名称",scale=5)
+                        create_label_btn = gr.Button("新建类目",variant="primary",scale=1)
+            with gr.Accordion(label="管理类目",open=False):
+                with gr.Row():
+                    data_label =gr.Dropdown(choices=os.listdir(UNSTRUCTURED_FILE_PATH),label="管理类目",interactive=True,scale=8,multiselect=True)
+                    delete_label_btn = gr.Button("删除类目",variant="stop",scale=1)
+        with gr.Tab("结构化数据"):
+            with gr.Accordion(label="新建数据表",open=True):
+                with gr.Column(scale=2):
+                    structured_file = gr.Files(file_types=["xlsx","csv"])
+                    with gr.Row():
+                        new_label_1 = gr.Textbox(label="数据表名称",placeholder="请输入数据表名称",scale=5)
+                        create_label_btn_1 = gr.Button("新建数据表",variant="primary",scale=1)
+            with gr.Accordion(label="管理数据表",open=False):
+                with gr.Row():
+                    data_label_1 =gr.Dropdown(choices=os.listdir(STRUCTURED_FILE_PATH),label="管理数据表",interactive=True,scale=8,multiselect=True)
+                    delete_data_table_btn = gr.Button("删除数据表",variant="stop",scale=1)
+        delete_label_btn.click(delete_label,inputs=[data_label]).then(fn=update_label,outputs=[data_label])
+        create_label_btn.click(fn=upload_unstructured_file,inputs=[unstructured_file,new_label]).then(fn=update_label,outputs=[data_label])
+        delete_data_table_btn.click(delete_data_table,inputs=[data_label_1]).then(fn=update_datatable,outputs=[data_label_1])
+        create_label_btn_1.click(fn=upload_structured_file,inputs=[structured_file,new_label_1]).then(fn=update_datatable,outputs=[data_label_1])
+        upload.load(update_label,[],data_label)
+        upload.load(update_datatable,[],data_label_1)
+    return upload
+
+def get_knowledge_base_block():
+    with gr.Blocks(theme=gr.themes.Base()) as knowledge:
+        gr.HTML(plain_html)
+        # 非结构化数据知识库
+        with gr.Tab("非结构化数据"):
+            with gr.Row():
+                data_label_2 =gr.Dropdown(choices=os.listdir(UNSTRUCTURED_FILE_PATH),label="选择类目",interactive=True,scale=2,multiselect=True)
+                knowledge_base_name = gr.Textbox(label="知识库名称",placeholder="请输入知识库名称",scale=2)
+                create_knowledge_base_btn = gr.Button("确认创建知识库",variant="primary",scale=1)
+        # 结构化数据知识库
+        with gr.Tab("结构化数据"):
+            with gr.Row():
+                data_label_3 =gr.Dropdown(choices=os.listdir(STRUCTURED_FILE_PATH),label="选择数据表",interactive=True,scale=2,multiselect=True)
+                knowledge_base_name_1 = gr.Textbox(label="知识库名称",placeholder="请输入知识库名称",scale=2)
+                create_knowledge_base_btn_1 = gr.Button("确认创建知识库",variant="primary",scale=1)
+        with gr.Row():
+            knowledge_base =gr.Dropdown(choices=os.listdir(DB_PATH),label="管理知识库",interactive=True,scale=4)
+            delete_db_btn = gr.Button("删除知识库",variant="stop",scale=1)
+        create_knowledge_base_btn.click(fn=create_unstructured_db,inputs=[knowledge_base_name,data_label_2]).then(update_knowledge_base,outputs=[knowledge_base])
+        delete_db_btn.click(delete_db,inputs=[knowledge_base]).then(update_knowledge_base,outputs=[knowledge_base])
+        create_knowledge_base_btn_1.click(fn=create_structured_db,inputs=[knowledge_base_name_1,data_label_3]).then(update_knowledge_base,outputs=[knowledge_base])
+        knowledge.load(update_knowledge_base,[],knowledge_base)
+        knowledge.load(update_label,[],data_label_2)
+        knowledge.load(update_datatable,[],data_label_3)
+    return knowledge
+
+app = FastAPI()
+@app.get("/", response_class=HTMLResponse)
+def read_main():
+    html_content = main_html
+    return HTMLResponse(content=html_content)
+
+
+app = gr.mount_gradio_app(app, get_chat_block(), path="/chat")
+app = gr.mount_gradio_app(app, get_upload_block(), path="/upload_data")
+app = gr.mount_gradio_app(app, get_knowledge_base_block(), path="/create_knowledge_base")

local_rag/requirements.txt

@@ -0,0 +1,26 @@
+gradio==4.32.0
+faiss-cpu==1.8.0.post1
+dashscope==1.20.4
+openai==1.55.3
+httpx==0.27.0
+llama-index-vector-stores-faiss==0.1.2
+llama-index-embeddings-dashscope==0.1.4
+llama-index-readers-file==0.1.33
+matplotlib==3.9.3
+docx2txt==0.8
+openpyxl==3.1.5
+llama-index-core==0.10.67
+uvicorn==0.30.6
+fastapi==0.112.0
+llama-index-postprocessor-dashscope-rerank-custom==0.1.0
+simplejson==3.19.3
+pydantic==2.10.6
+# modelscope==1.18.0
+# langchain_community==0.2.16
+# transformers==4.44.2
+# llama_index.embeddings.huggingface==0.2.3
+# llama-index-embeddings-langchain==0.1.2
+# datasets==2.21.0
+# oss2==2.19.0
+# sortedcontainers==2.4.0
+# addict==2.4.0s

local_rag/upload_file.py

@@ -0,0 +1,107 @@
+#####################################
+#######       上传文件         #######
+#####################################
+import gradio as gr
+import os
+import shutil
+import pandas as pd
+STRUCTURED_FILE_PATH = "File/Structured"
+UNSTRUCTURED_FILE_PATH = "File/Unstructured"
+# 刷新非结构化类目
+def refresh_label():
+    return os.listdir(UNSTRUCTURED_FILE_PATH)
+
+# 刷新结构化数据表
+def refresh_data_table():
+    return os.listdir(STRUCTURED_FILE_PATH)
+
+# 上传非结构化数据
+def upload_unstructured_file(files,label_name):
+    if files is None:
+        gr.Info("请上传文件")
+    elif len(label_name) == 0:
+        gr.Info("请输入类目名称")
+    # 判断类目是否存在
+    elif label_name in os.listdir(UNSTRUCTURED_FILE_PATH):
+        gr.Info(f"{label_name}类目已存在")
+    else:
+        try:
+            if not os.path.exists(os.path.join(UNSTRUCTURED_FILE_PATH,label_name)):
+                os.mkdir(os.path.join(UNSTRUCTURED_FILE_PATH,label_name))
+            for file in files:
+                print(file)
+                file_path = file.name
+                file_name = os.path.basename(file_path)
+                destination_file_path = os.path.join(UNSTRUCTURED_FILE_PATH,label_name,file_name)
+                shutil.move(file_path,destination_file_path)
+            gr.Info(f"文件已上传至{label_name}类目中，请前往创建知识库")
+        except:
+            gr.Info(f"请勿重复上传")
+
+# 上传结构化数据
+def upload_structured_file(files,label_name):
+    if files is None:
+        gr.Info("请上传文件")
+    elif len(label_name) == 0:
+        gr.Info("请输入数据表名称")
+    # 判断数据表是否存在
+    elif label_name in os.listdir(STRUCTURED_FILE_PATH):
+        gr.Info(f"{label_name}数据表已存在")
+    else:
+        try:
+            if not os.path.exists(os.path.join(STRUCTURED_FILE_PATH,label_name)):
+                os.mkdir(os.path.join(STRUCTURED_FILE_PATH,label_name))
+            for file in files:
+                file_path = file.name
+                file_name = os.path.basename(file_path)
+                destination_file_path = os.path.join(STRUCTURED_FILE_PATH,label_name,file_name)
+                shutil.move(file_path,destination_file_path)
+                if os.path.splitext(destination_file_path)[1] == ".xlsx":
+                    df = pd.read_excel(destination_file_path)
+                elif os.path.splitext(destination_file_path)[1] == ".csv":
+                    df = pd.read_csv(destination_file_path)
+                txt_file_name = os.path.splitext(file_name)[0]+'.txt'
+                columns = df.columns
+                with open(os.path.join(STRUCTURED_FILE_PATH,label_name,txt_file_name),"w") as file:
+                    for idx,row in df.iterrows():
+                        file.write("【")
+                        info = []
+                        for col in columns:
+                            info.append(f"{col}:{row[col]}")
+                        infos = ",".join(info)
+                        file.write(infos)
+                        if idx != len(df)-1:
+                            file.write("】\n")
+                        else:
+                            file.write("】")
+                os.remove(destination_file_path)
+            gr.Info(f"文件已上传至{label_name}数据表中，请前往创建知识库")
+        except:
+            gr.Info(f"请勿重复上传")
+
+# 实时更新结构化数据表
+def update_datatable():
+    return gr.update(choices=os.listdir(STRUCTURED_FILE_PATH))
+
+
+# 实时更新非结构化类目
+def update_label():
+    return gr.update(choices=os.listdir(UNSTRUCTURED_FILE_PATH))
+
+# 删除类目
+def delete_label(label_name):
+    if label_name is not None:
+        for label in label_name:
+            folder_path = os.path.join(UNSTRUCTURED_FILE_PATH,label)
+            if os.path.exists(folder_path):
+                shutil.rmtree(folder_path)
+                gr.Info(f"{label}类目已删除")
+
+# 删除数据表
+def delete_data_table(table_name):
+    if table_name is not None:
+        for table in table_name:
+            folder_path = os.path.join(STRUCTURED_FILE_PATH,table)
+            if os.path.exists(folder_path):
+                shutil.rmtree(folder_path)
+                gr.Info(f"{table}数据表已删除")

requirements.txt

@@ -0,0 +1,12 @@
+openai
+dashscope
+langchain
+langchain-deepseek
+langchain-community
+
+llama-index
+llama-index-core
+llama-index-llms-dashscope
+llama-index-indices-managed-dashscope
+
+qwen-agent

setup.py

@@ -0,0 +1,38 @@
+from setuptools import setup, find_packages
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+with open("requirements.txt", "r", encoding="utf-8") as fh:
+    requirements = [line.strip() for line in fh if line.strip() and not line.startswith("#")]
+
+setup(
+    name="knightutils",
+    version="0.1.0",
+    author="Unknown",
+    author_email="unknown@example.com",
+    description="A utility package for sharing code",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/yourusername/knightutils",
+    packages=find_packages(),
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "License :: OSI Approved :: MIT License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+    ],
+    python_requires=">=3.7",
+    install_requires=requirements,
+    extras_require={
+        "dev": [
+            "pytest>=6.0",
+            "pytest-cov>=2.0",
+        ],
+    },
+)

test/compatible/deepseek.py

@@ -0,0 +1,23 @@
+import os
+from openai import OpenAI
+
+client = OpenAI(
+    # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：api_key="sk-xxx",
+    api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如何获取API Key：https://help.aliyun.com/zh/model-studio/developer-reference/get-api-key
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
+)
+
+completion = client.chat.completions.create(
+    model="deepseek-r1",  # 此处以 deepseek-r1 为例，可按需更换模型名称。
+    messages=[
+        {'role': 'user', 'content': '9.9和9.11谁大'}
+    ]
+)
+
+# 通过reasoning_content字段打印思考过程
+print("思考过程：")
+print(completion.choices[0].message.reasoning_content)
+
+# 通过content字段打印最终答案
+print("最终答案：")
+print(completion.choices[0].message.content)

test/compatible/qwen.py

@@ -0,0 +1,21 @@
+import os
+from openai import OpenAI
+
+client = OpenAI(
+    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
+    api_key=os.getenv("DASHSCOPE_API_KEY"),
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+)
+
+completion = client.chat.completions.create(
+    # 模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
+    model="qwen-plus",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "你是谁？"},
+    ],
+    # Qwen3模型通过enable_thinking参数控制思考过程（开源版默认True，商业版默认False）
+    # 使用Qwen3开源版模型时，若未启用流式输出，请将下行取消注释，否则会报错
+    # extra_body={"enable_thinking": False},
+)
+print(completion.model_dump_json())

test/dashscope/deepseek.py

@@ -0,0 +1,20 @@
+import os
+import dashscope
+
+messages = [
+    {'role': 'user', 'content': '你是谁？'}
+]
+
+response = dashscope.Generation.call(
+    # 若没有配置环境变量，请用阿里云百炼API Key将下行替换为：api_key="sk-xxx",
+    api_key=os.getenv('DASHSCOPE_API_KEY'),
+    model="deepseek-r1",  # 此处以 deepseek-r1 为例，可按需更换模型名称。
+    messages=messages,
+    # result_format参数不可以设置为"text"。
+    result_format='message'
+)
+
+print("=" * 20 + "思考过程" + "=" * 20)
+print(response.output.choices[0].message.reasoning_content)
+print("=" * 20 + "最终答案" + "=" * 20)
+print(response.output.choices[0].message.content)

test/dashscope/qwen.py

@@ -0,0 +1,15 @@
+import os
+import dashscope
+
+messages = [
+    {'role': 'system', 'content': 'You are a helpful assistant.'},
+    {'role': 'user', 'content': '你是谁？'}
+]
+response = dashscope.Generation.call(
+    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key="sk-xxx",
+    api_key=os.getenv('DASHSCOPE_API_KEY'),
+    model="qwen-plus", # 此处以qwen-plus为例，可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
+    messages=messages,
+    result_format='message'
+    )
+print(response)

test/langchain/compatible/deepseek.py

@@ -0,0 +1,15 @@
+from langchain_openai import ChatOpenAI
+from langchain_deepseek import ChatDeepSeek
+import os
+
+chatLLM = ChatDeepSeek(
+    api_key=os.getenv("DASHSCOPE_API_KEY"),
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    model="deepseek-chat",  # 此处以qwen-plus为例，您可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
+    # other params...
+)
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "你是谁？"}]
+response = chatLLM.invoke(messages)
+print(response.model_dump_json())

test/langchain/compatible/qwen.py

@@ -0,0 +1,14 @@
+from langchain_openai import ChatOpenAI
+import os
+
+chatLLM = ChatOpenAI(
+    api_key=os.getenv("DASHSCOPE_API_KEY"),
+    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+    model="qwen-plus",  # 此处以qwen-plus为例，您可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
+    # other params...
+)
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "你是谁？"}]
+response = chatLLM.invoke(messages)
+print(response.model_dump_json())

test/langchain/dashscope/qwen.py

@@ -0,0 +1,11 @@
+from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_core.messages import HumanMessage
+
+chatLLM = ChatTongyi(
+    model="qwen-max",   # 此处以qwen-max为例，您可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
+    streaming=True,
+    # other params...
+)
+res = chatLLM.stream([HumanMessage(content="hi")], streaming=True)
+for r in res:
+    print("chat resp:", r.content)

test/langchain/deepseek.py

@@ -0,0 +1,15 @@
+import getpass
+import os
+
+if not os.environ.get("DEEPSEEK_API_KEY"):
+  os.environ["DEEPSEEK_API_KEY"] = os.environ['DASHSCOPE_API_KEY']
+
+from langchain.chat_models import init_chat_model
+
+model = init_chat_model("deepseek-chat", model_provider="deepseek")
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "你是谁？"}]
+response = model.invoke(messages)
+print(response.model_dump_json())

test/langchain/qwen.py

@@ -0,0 +1,15 @@
+import getpass
+import os
+
+if not os.environ.get("DEEPSEEK_API_KEY"):
+  os.environ["DEEPSEEK_API_KEY"] = os.environ['DASHSCOPE_API_KEY']
+
+from langchain.chat_models import init_chat_model
+
+model = init_chat_model("qwen-max", model_provider="deepseek")
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "你是谁？"}]
+response = model.invoke(messages)
+print(response.model_dump_json())

test/langgraph/quickstart_graph_api.py

@@ -0,0 +1,144 @@
+# Step 0: Define tools and model
+
+from langchain_core.tools import tool
+from langchain_community.chat_models.tongyi import ChatTongyi
+
+llm = ChatTongyi(
+    model="qwen-max",   # 此处以qwen-max为例，您可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
+    streaming=True,
+    # other params...
+)
+
+# Define tools
+@tool
+def multiply(a: int, b: int) -> int:
+    """Multiply a and b.
+
+    Args:
+        a: first int
+        b: second int
+    """
+    return a * b
+
+
+@tool
+def add(a: int, b: int) -> int:
+    """Adds a and b.
+
+    Args:
+        a: first int
+        b: second int
+    """
+    return a + b
+
+
+@tool
+def divide(a: int, b: int) -> float:
+    """Divide a and b.
+
+    Args:
+        a: first int
+        b: second int
+    """
+    return a / b
+
+
+# Augment the LLM with tools
+tools = [add, multiply, divide]
+tools_by_name = {tool.name: tool for tool in tools}
+llm_with_tools = llm.bind_tools(tools)
+
+# Step 1: Define state
+
+from langchain_core.messages import AnyMessage
+from typing_extensions import TypedDict, Annotated
+import operator
+
+class MessagesState(TypedDict):
+    messages: Annotated[list[AnyMessage], operator.add]
+    llm_calls: int
+
+# Step 2: Define model node
+from langchain_core.messages import SystemMessage
+def llm_call(state: dict):
+    """LLM decides whether to call a tool or not"""
+
+    return {
+        "messages": [
+            llm_with_tools.invoke(
+                [
+                    SystemMessage(
+                        content="You are a helpful assistant tasked with performing arithmetic on a set of inputs."
+                    )
+                ]
+                + state["messages"]
+            )
+        ],
+        "llm_calls": state.get('llm_calls', 0) + 1
+    }
+
+
+# Step 3: Define tool node
+
+
+from langchain_core.messages import ToolMessage
+
+def tool_node(state: dict):
+    """Performs the tool call"""
+
+    result = []
+    for tool_call in state["messages"][-1].tool_calls:
+        tool = tools_by_name[tool_call["name"]]
+        observation = tool.invoke(tool_call["args"])
+        result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
+    return {"messages": result}
+
+# Step 4: Define logic to determine whether to end
+
+from typing import Literal
+from langgraph.graph import StateGraph, START, END
+
+# Conditional edge function to route to the tool node or end based upon whether the LLM made a tool call
+def should_continue(state: MessagesState) -> Literal["tool_node", END]:
+    """Decide if we should continue the loop or stop based upon whether the LLM made a tool call"""
+
+    messages = state["messages"]
+    last_message = messages[-1]
+    # If the LLM makes a tool call, then perform an action
+    if last_message.tool_calls:
+        return "tool_node"
+    # Otherwise, we stop (reply to the user)
+    return END
+
+# Step 5: Build agent
+
+# Build workflow
+agent_builder = StateGraph(MessagesState)
+
+# Add nodes
+agent_builder.add_node("llm_call", llm_call)
+agent_builder.add_node("tool_node", tool_node)
+
+# Add edges to connect nodes
+agent_builder.add_edge(START, "llm_call")
+agent_builder.add_conditional_edges(
+    "llm_call",
+    should_continue,
+    ["tool_node", END]
+)
+agent_builder.add_edge("tool_node", "llm_call")
+
+# Compile the agent
+agent = agent_builder.compile()
+
+
+from IPython.display import Image, display
+# Show the agent
+display(Image(agent.get_graph(xray=True).draw_mermaid_png()))
+
+# Invoke
+# from langchain_core.messages import HumanMessage
+# messages = [HumanMessage(content="Add 3 and 4.")]
+# messages = agent.invoke({"messages": messages})
+# for m in messages["messages"]:
+#     m.pretty_print()

test/langgraph/quistart_functional_api.ipynb

@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-12T15:52:56.909385Z",
+     "start_time": "2025-09-12T15:52:56.417088Z"
+    }
+   },
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ['DASHSCOPE_API_KEY'] = 'sk-e2a05bbcfac84e53b73f98acef15a009'\n",
+    "\n",
+    "# Step 0: Define tools and model\n",
+    "\n",
+    "from langchain_core.tools import tool\n",
+    "from langchain_community.chat_models.tongyi import ChatTongyi\n",
+    "\n",
+    "llm = ChatTongyi(\n",
+    "    model=\"qwen-max\",   # 此处以qwen-max为例，您可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models\n",
+    "    streaming=True,\n",
+    "    # other params...\n",
+    ")"
+   ],
+   "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-12T15:54:08.425580Z",
+     "start_time": "2025-09-12T15:54:08.374623Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "\n",
+    "# Define tools\n",
+    "@tool\n",
+    "def multiply(a: int, b: int) -> int:\n",
+    "    \"\"\"Multiply a and b.\n",
+    "\n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a * b\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def add(a: int, b: int) -> int:\n",
+    "    \"\"\"Adds a and b.\n",
+    "\n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a + b\n",
+    "\n",
+    "\n",
+    "@tool\n",
+    "def divide(a: int, b: int) -> float:\n",
+    "    \"\"\"Divide a and b.\n",
+    "\n",
+    "    Args:\n",
+    "        a: first int\n",
+    "        b: second int\n",
+    "    \"\"\"\n",
+    "    return a / b\n",
+    "\n",
+    "\n",
+    "# Augment the LLM with tools\n",
+    "tools = [add, multiply, divide]\n",
+    "tools_by_name = {tool.name: tool for tool in tools}\n",
+    "llm_with_tools = llm.bind_tools(tools)\n",
+    "\n",
+    "from langgraph.graph import add_messages\n",
+    "from langchain_core.messages import (\n",
+    "    SystemMessage,\n",
+    "    HumanMessage,\n",
+    "    BaseMessage,\n",
+    "    ToolCall,\n",
+    ")\n",
+    "from langgraph.func import entrypoint, task\n",
+    "\n",
+    "# Step 1: define model node\n",
+    "@task\n",
+    "def call_llm(messages: list[BaseMessage]):\n",
+    "    \"\"\"LLM decides whether to call a tool or not\"\"\"\n",
+    "    return llm_with_tools.invoke(\n",
+    "        [\n",
+    "            SystemMessage(\n",
+    "                content=\"You are a helpful assistant tasked with performing arithmetic on a set of inputs.\"\n",
+    "            )\n",
+    "        ]\n",
+    "        + messages\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Step 2: define tool node\n",
+    "@task\n",
+    "def call_tool(tool_call: ToolCall):\n",
+    "    \"\"\"Performs the tool call\"\"\"\n",
+    "    tool = tools_by_name[tool_call[\"name\"]]\n",
+    "    return tool.invoke(tool_call)\n",
+    "\n",
+    "\n",
+    "# Step 3: define agent\n",
+    "@entrypoint()\n",
+    "def agent(messages: list[BaseMessage]):\n",
+    "    llm_response = call_llm(messages).result()\n",
+    "\n",
+    "    while True:\n",
+    "        if not llm_response.tool_calls:\n",
+    "            break\n",
+    "\n",
+    "        # Execute tools\n",
+    "        tool_result_futures = [\n",
+    "            call_tool(tool_call) for tool_call in llm_response.tool_calls\n",
+    "        ]\n",
+    "        tool_results = [fut.result() for fut in tool_result_futures]\n",
+    "        messages = add_messages(messages, [llm_response, *tool_results])\n",
+    "        llm_response = call_llm(messages).result()\n",
+    "\n",
+    "    messages = add_messages(messages, llm_response)\n",
+    "    return messages"
+   ],
+   "id": "8a77a9b24ee9616d",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-09-12T15:54:11.693756Z",
+     "start_time": "2025-09-12T15:54:10.101700Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "\n",
+    "# Invoke\n",
+    "messages = [HumanMessage(content=\"Add 3 and 4.\")]\n",
+    "for chunk in agent.stream(messages, stream_mode=\"updates\"):\n",
+    "    print(chunk)\n",
+    "    print(\"\\n\")"
+   ],
+   "id": "7c4b06da8200b106",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'call_llm': AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_ef8c897dd4f84afbbf0927', 'type': 'function', 'function': {'name': 'add', 'arguments': '{\"a\": 3, \"b\": 4}'}}]}, response_metadata={'model_name': 'qwen-max', 'finish_reason': 'tool_calls', 'request_id': '6d8c6555-1a67-4cc9-a93f-57e94bc20842', 'token_usage': {'input_tokens': 354, 'output_tokens': 22, 'total_tokens': 376, 'prompt_tokens_details': {'cached_tokens': 0}}}, id='lc_run--afcea3de-940e-45c6-ba96-bbd7e41fa115-0', tool_calls=[{'name': 'add', 'args': {'a': 3, 'b': 4}, 'id': 'call_ef8c897dd4f84afbbf0927', 'type': 'tool_call'}], chunk_position=None)}\n",
+      "\n",
+      "\n",
+      "{'call_tool': ToolMessage(content='7', name='add', id='aeaf3d29-254b-48ab-a933-814e9ea72394', tool_call_id='call_ef8c897dd4f84afbbf0927')}\n",
+      "\n",
+      "\n",
+      "{'call_llm': AIMessage(content='The sum of 3 and 4 is 7.', additional_kwargs={}, response_metadata={'model_name': 'qwen-max', 'finish_reason': 'stop', 'request_id': '310102ab-48dc-4e80-bc57-ca8814239a65', 'token_usage': {'input_tokens': 386, 'output_tokens': 13, 'total_tokens': 399, 'prompt_tokens_details': {'cached_tokens': 0}}}, id='lc_run--b3dffae8-42c2-492e-a1f4-e659eba6a879-0', chunk_position=None)}\n",
+      "\n",
+      "\n",
+      "{'agent': [HumanMessage(content='Add 3 and 4.', additional_kwargs={}, response_metadata={}, id='40fc3758-a8ab-4302-aff9-8dfbdec16fa0'), AIMessage(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_ef8c897dd4f84afbbf0927', 'type': 'function', 'function': {'name': 'add', 'arguments': '{\"a\": 3, \"b\": 4}'}}]}, response_metadata={'model_name': 'qwen-max', 'finish_reason': 'tool_calls', 'request_id': '6d8c6555-1a67-4cc9-a93f-57e94bc20842', 'token_usage': {'input_tokens': 354, 'output_tokens': 22, 'total_tokens': 376, 'prompt_tokens_details': {'cached_tokens': 0}}}, id='lc_run--afcea3de-940e-45c6-ba96-bbd7e41fa115-0', tool_calls=[{'name': 'add', 'args': {'a': 3, 'b': 4}, 'id': 'call_ef8c897dd4f84afbbf0927', 'type': 'tool_call'}], chunk_position=None), ToolMessage(content='7', name='add', id='aeaf3d29-254b-48ab-a933-814e9ea72394', tool_call_id='call_ef8c897dd4f84afbbf0927'), AIMessage(content='The sum of 3 and 4 is 7.', additional_kwargs={}, response_metadata={'model_name': 'qwen-max', 'finish_reason': 'stop', 'request_id': '310102ab-48dc-4e80-bc57-ca8814239a65', 'token_usage': {'input_tokens': 386, 'output_tokens': 13, 'total_tokens': 399, 'prompt_tokens_details': {'cached_tokens': 0}}}, id='lc_run--b3dffae8-42c2-492e-a1f4-e659eba6a879-0', chunk_position=None)]}\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "execution_count": 3
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "7e55492ae0289f06"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/llamaindex/dashscope_parse.py

@@ -0,0 +1,25 @@
+import os
+
+from llama_index.readers.dashscope.base import DashScopeParse
+from llama_index.readers.dashscope.utils import ResultType
+
+# 设置业务空间 ID 将决定文档解析结果在”创建知识库“步骤中上传到哪个业务空间
+os.environ['DASHSCOPE_WORKSPACE_ID'] = "<Your Workspace id, Default workspace is empty.>"
+
+# 第一种方式：使用文档解析器解析一个或多个文件
+file = [
+    # 需要解析的文件，支持pdf,doc,docx
+]
+# 解析文件
+parse = DashScopeParse(result_type=ResultType.DASHSCOPE_DOCMIND)
+documents = parse.load_data(file_path=file)
+
+# 第二种方式：使用文档解析器解析一个文件夹内指定类型的文件
+from llama_index.core import SimpleDirectoryReader
+parse = DashScopeParse(result_type=ResultType.DASHSCOPE_DOCMIND)
+# 定义不同文档类型的解析器
+file_extractor = {".pdf": parse, '.doc': parse, '.docx': parse}
+# 读取文件夹，提取和解析文件信息
+documents = SimpleDirectoryReader(
+    "your_folder", file_extractor=file_extractor
+).load_data(num_workers=1)

test/llamaindex/data/test1.py

@@ -0,0 +1,7 @@
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+
+documents = SimpleDirectoryReader("data").load_data()
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+response = query_engine.query("Some question about the data should go here")
+print(response)

test/llamaindex/env

@@ -0,0 +1,2 @@
+# MacOS/Linux
+export OPENAI_API_KEY=sk-e2a05bbcfac84e53b73f98acef15a009

test/llamaindex/prompt.markdown

@@ -0,0 +1,51 @@
+You are designed to help with a variety of tasks, from answering questions to providing summaries to other types of analyses.
+## Tools
+
+You have access to a wide variety of tools. You are responsible for using the tools in any sequence you deem appropriate to complete the task at hand.
+This may require breaking the task into subtasks and using different tools to complete each subtask.
+
+You have access to the following tools:
+> Tool Name: multiply
+Tool Description: multiply(a: float, b: float) -> float
+Useful for multiplying two numbers.
+Tool Args: {\"type\": \"object\", \"properties\": {\"a\": {\"title\": \"A\", \"type\": \"number\"}, \"b\": {\"title\": \"B\", \"type\": \"number\"}}, \"required\": [\"a\", \"b\"]}
+
+
+
+## Output Format
+
+Please answer in the same language as the question and use the following format:
+
+```
+Thought: The current language of the user is: (user's language). I need to use a tool to help me answer the question.
+Action: tool name (one of multiply) if using a tool.
+Action Input: the input to the tool, in a JSON format representing the kwargs (e.g. {\"input\": \"hello world\", \"num_beams\": 5})
+```
+
+Please ALWAYS start with a Thought.
+
+NEVER surround your response with markdown code markers. You may use code markers within your response if you need to.
+
+Please use a valid JSON format for the Action Input. Do NOT do this {'input': 'hello world', 'num_beams': 5}.
+
+If this format is used, the user will respond in the following format:
+
+```
+Observation: tool response
+```
+
+You should keep repeating the above format till you have enough information to answer the question without using any more tools. At that point, you MUST respond in the one of the following two formats:
+
+```
+Thought: I can answer without using any more tools. I'll use the user's language to answer
+Answer: [your answer here (In the same language as the user's question)]
+```
+
+```
+Thought: I cannot answer the question with the provided tools.
+Answer: [your answer here (In the same language as the user's question)]
+```
+
+## Current Conversation
+
+Below is the current conversation consisting of interleaving human and assistant messages.

test/llamaindex/starter.py

@@ -0,0 +1,30 @@
+import asyncio
+from llama_index.core.agent import ReActAgent
+from llama_index.core.tools import FunctionTool
+from llama_index.llms.dashscope import DashScope
+
+
+# Define a simple calculator tool
+def multiply(a: float, b: float) -> float:
+    """Useful for multiplying two numbers."""
+    return a * b
+
+
+# Create an agent workflow with our calculator tool
+llm = DashScope(model_name="qwen-max")
+agent = ReActAgent.from_tools(
+    tools=[FunctionTool.from_defaults(fn=multiply)],
+    llm=llm,
+    verbose=True,
+)
+
+
+async def main():
+    # Run the agent
+    response = await agent.achat("What is 1234 * 4567?")
+    print(str(response))
+
+
+# Run the agent
+if __name__ == "__main__":
+    asyncio.run(main())

test/llamaindex/test1.py

@@ -0,0 +1,7 @@
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+
+documents = SimpleDirectoryReader("data").load_data()
+index = VectorStoreIndex.from_documents(documents)
+query_engine = index.as_query_engine()
+response = query_engine.query("Some question about the data should go here")
+print(response)

test/llamaindex/test_20250826_1.ipynb

@@ -0,0 +1,117 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "2eeef59eecfab41a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-27T16:02:52.719750Z",
+     "start_time": "2025-08-27T16:02:51.725308Z"
+    }
+   },
+   "source": [
+    "import os\n",
+    "\n",
+    "from llama_index.llms.dashscope import DashScope\n",
+    "from llama_index.llms.openai import OpenAI\n",
+    "from knightutils.utils.set_proxy import set_proxy_with_os\n",
+    "\n",
+    "set_proxy_with_os()\n",
+    "\n",
+    "os.environ[\"DASHSCOPE_API_KEY\"] = \"sk-e2a05bbcfac84e53b73f98acef15a009\"\n",
+    "# llm = DashScope(model_name=\"qwen-max\", verify=False) # 设置检索引擎生成回答时调用的大模型。\n",
+    "llm = DashScope(model_name=\"qwen-max\") # 设置检索引擎生成回答时调用的大模型。"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "已通过环境变量设置代理\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2025-08-27T16:02:58.073124Z",
+     "start_time": "2025-08-27T16:02:57.170496Z"
+    }
+   },
+   "source": [
+    "response = llm.complete(\"William Shakespeare is \")\n",
+    "print(response)"
+   ],
+   "outputs": [
+    {
+     "ename": "SSLError",
+     "evalue": "HTTPSConnectionPool(host='dashscope.aliyuncs.com', port=443): Max retries exceeded with url: /api/v1/services/aigc/text-generation/generation (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1007)')))",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mSSLError\u001B[0m                                  Traceback (most recent call last)",
+      "\u001B[0;31mSSLError\u001B[0m: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1007)",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001B[0;31mMaxRetryError\u001B[0m                             Traceback (most recent call last)",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/requests/adapters.py:644\u001B[0m, in \u001B[0;36mHTTPAdapter.send\u001B[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001B[0m\n\u001B[1;32m    643\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 644\u001B[0m     resp \u001B[38;5;241m=\u001B[39m \u001B[43mconn\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murlopen\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    645\u001B[0m \u001B[43m        \u001B[49m\u001B[43mmethod\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    646\u001B[0m \u001B[43m        \u001B[49m\u001B[43murl\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    647\u001B[0m \u001B[43m        \u001B[49m\u001B[43mbody\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbody\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    648\u001B[0m \u001B[43m        \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    649\u001B[0m \u001B[43m        \u001B[49m\u001B[43mredirect\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    650\u001B[0m \u001B[43m        \u001B[49m\u001B[43massert_same_host\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    651\u001B[0m \u001B[43m        \u001B[49m\u001B[43mpreload_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    652\u001B[0m \u001B[43m        \u001B[49m\u001B[43mdecode_content\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m,\u001B[49m\n\u001B[1;32m    653\u001B[0m \u001B[43m        \u001B[49m\u001B[43mretries\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmax_retries\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    654\u001B[0m \u001B[43m        \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    655\u001B[0m \u001B[43m        \u001B[49m\u001B[43mchunked\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mchunked\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    656\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    658\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (ProtocolError, \u001B[38;5;167;01mOSError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m err:\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/urllib3/connectionpool.py:841\u001B[0m, in \u001B[0;36mHTTPConnectionPool.urlopen\u001B[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001B[0m\n\u001B[1;32m    839\u001B[0m     new_e \u001B[38;5;241m=\u001B[39m ProtocolError(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mConnection aborted.\u001B[39m\u001B[38;5;124m\"\u001B[39m, new_e)\n\u001B[0;32m--> 841\u001B[0m retries \u001B[38;5;241m=\u001B[39m \u001B[43mretries\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mincrement\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    842\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmethod\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43merror\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnew_e\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_pool\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m_stacktrace\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msys\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexc_info\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[1;32m    843\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    844\u001B[0m retries\u001B[38;5;241m.\u001B[39msleep()\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/urllib3/util/retry.py:519\u001B[0m, in \u001B[0;36mRetry.increment\u001B[0;34m(self, method, url, response, error, _pool, _stacktrace)\u001B[0m\n\u001B[1;32m    518\u001B[0m     reason \u001B[38;5;241m=\u001B[39m error \u001B[38;5;129;01mor\u001B[39;00m ResponseError(cause)\n\u001B[0;32m--> 519\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m MaxRetryError(_pool, url, reason) \u001B[38;5;28;01mfrom\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21;01mreason\u001B[39;00m  \u001B[38;5;66;03m# type: ignore[arg-type]\u001B[39;00m\n\u001B[1;32m    521\u001B[0m log\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mIncremented Retry for (url=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m): \u001B[39m\u001B[38;5;132;01m%r\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, url, new_retry)\n",
+      "\u001B[0;31mMaxRetryError\u001B[0m: HTTPSConnectionPool(host='dashscope.aliyuncs.com', port=443): Max retries exceeded with url: /api/v1/services/aigc/text-generation/generation (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1007)')))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001B[0;31mSSLError\u001B[0m                                  Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[2], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[43mllm\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcomplete\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mWilliam Shakespeare is \u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m      2\u001B[0m \u001B[38;5;28mprint\u001B[39m(response)\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/llama_index/core/instrumentation/dispatcher.py:260\u001B[0m, in \u001B[0;36mDispatcher.span.<locals>.wrapper\u001B[0;34m(func, instance, args, kwargs)\u001B[0m\n\u001B[1;32m    252\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mspan_enter(\n\u001B[1;32m    253\u001B[0m     id_\u001B[38;5;241m=\u001B[39mid_,\n\u001B[1;32m    254\u001B[0m     bound_args\u001B[38;5;241m=\u001B[39mbound_args,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    257\u001B[0m     tags\u001B[38;5;241m=\u001B[39mtags,\n\u001B[1;32m    258\u001B[0m )\n\u001B[1;32m    259\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 260\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    261\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m    262\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mevent(SpanDropEvent(span_id\u001B[38;5;241m=\u001B[39mid_, err_str\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mstr\u001B[39m(e)))\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/llama_index/core/llms/callbacks.py:429\u001B[0m, in \u001B[0;36mllm_completion_callback.<locals>.wrap.<locals>.wrapped_llm_predict\u001B[0;34m(_self, *args, **kwargs)\u001B[0m\n\u001B[1;32m    420\u001B[0m event_id \u001B[38;5;241m=\u001B[39m callback_manager\u001B[38;5;241m.\u001B[39mon_event_start(\n\u001B[1;32m    421\u001B[0m     CBEventType\u001B[38;5;241m.\u001B[39mLLM,\n\u001B[1;32m    422\u001B[0m     payload\u001B[38;5;241m=\u001B[39m{\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    426\u001B[0m     },\n\u001B[1;32m    427\u001B[0m )\n\u001B[1;32m    428\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 429\u001B[0m     f_return_val \u001B[38;5;241m=\u001B[39m \u001B[43mf\u001B[49m\u001B[43m(\u001B[49m\u001B[43m_self\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    430\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m    431\u001B[0m     callback_manager\u001B[38;5;241m.\u001B[39mon_event_end(\n\u001B[1;32m    432\u001B[0m         CBEventType\u001B[38;5;241m.\u001B[39mLLM,\n\u001B[1;32m    433\u001B[0m         payload\u001B[38;5;241m=\u001B[39m{EventPayload\u001B[38;5;241m.\u001B[39mEXCEPTION: e},\n\u001B[1;32m    434\u001B[0m         event_id\u001B[38;5;241m=\u001B[39mevent_id,\n\u001B[1;32m    435\u001B[0m     )\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/llama_index/llms/dashscope/base.py:228\u001B[0m, in \u001B[0;36mDashScope.complete\u001B[0;34m(self, prompt, **kwargs)\u001B[0m\n\u001B[1;32m    226\u001B[0m parameters\u001B[38;5;241m.\u001B[39mpop(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstream\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;28;01mNone\u001B[39;00m)\n\u001B[1;32m    227\u001B[0m messages \u001B[38;5;241m=\u001B[39m chat_message_to_dashscope_messages([message])\n\u001B[0;32m--> 228\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[43mcall_with_messages\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    229\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmodel_name\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    230\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmessages\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmessages\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    231\u001B[0m \u001B[43m    \u001B[49m\u001B[43mapi_key\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mapi_key\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    232\u001B[0m \u001B[43m    \u001B[49m\u001B[43mparameters\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mparameters\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    233\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    234\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m dashscope_response_to_completion_response(response)\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/llama_index/llms/dashscope/base.py:83\u001B[0m, in \u001B[0;36mcall_with_messages\u001B[0;34m(model, messages, parameters, api_key, **kwargs)\u001B[0m\n\u001B[1;32m     78\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mImportError\u001B[39;00m:\n\u001B[1;32m     79\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m     80\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mDashScope is not installed. Please install it with \u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m     81\u001B[0m         \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m`pip install dashscope`.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m     82\u001B[0m     )\n\u001B[0;32m---> 83\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mGeneration\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m     84\u001B[0m \u001B[43m    \u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmessages\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmessages\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mapi_key\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mapi_key\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mparameters\u001B[49m\n\u001B[1;32m     85\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/dashscope/aigc/generation.py:138\u001B[0m, in \u001B[0;36mGeneration.call\u001B[0;34m(cls, model, prompt, history, api_key, messages, plugins, workspace, **kwargs)\u001B[0m\n\u001B[1;32m    135\u001B[0m     kwargs[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mheaders\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m headers\n\u001B[1;32m    136\u001B[0m \u001B[38;5;28minput\u001B[39m, parameters \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mcls\u001B[39m\u001B[38;5;241m.\u001B[39m_build_input_parameters(\n\u001B[1;32m    137\u001B[0m     model, prompt, history, messages, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m--> 138\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmodel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    139\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mtask_group\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtask_group\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    140\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mtask\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mGeneration\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtask\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    141\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mfunction\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfunction\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    142\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mapi_key\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mapi_key\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    143\u001B[0m \u001B[43m                        \u001B[49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43minput\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m    144\u001B[0m \u001B[43m                        \u001B[49m\u001B[43mworkspace\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mworkspace\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    145\u001B[0m \u001B[43m                        \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mparameters\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    146\u001B[0m is_stream \u001B[38;5;241m=\u001B[39m kwargs\u001B[38;5;241m.\u001B[39mget(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mstream\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[1;32m    147\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m is_stream:\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/dashscope/client/base_api.py:146\u001B[0m, in \u001B[0;36mBaseApi.call\u001B[0;34m(cls, model, input, task_group, task, function, api_key, workspace, **kwargs)\u001B[0m\n\u001B[1;32m    138\u001B[0m request \u001B[38;5;241m=\u001B[39m _build_api_request(model\u001B[38;5;241m=\u001B[39mmodel,\n\u001B[1;32m    139\u001B[0m                              \u001B[38;5;28minput\u001B[39m\u001B[38;5;241m=\u001B[39m\u001B[38;5;28minput\u001B[39m,\n\u001B[1;32m    140\u001B[0m                              task_group\u001B[38;5;241m=\u001B[39mtask_group,\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    143\u001B[0m                              api_key\u001B[38;5;241m=\u001B[39mapi_key,\n\u001B[1;32m    144\u001B[0m                              \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m    145\u001B[0m \u001B[38;5;66;03m# call request service.\u001B[39;00m\n\u001B[0;32m--> 146\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mrequest\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcall\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/dashscope/api_entities/http_request.py:84\u001B[0m, in \u001B[0;36mHttpRequest.call\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m     82\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m (item \u001B[38;5;28;01mfor\u001B[39;00m item \u001B[38;5;129;01min\u001B[39;00m response)\n\u001B[1;32m     83\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m---> 84\u001B[0m     output \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mnext\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mresponse\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     85\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m     86\u001B[0m         \u001B[38;5;28mnext\u001B[39m(response)\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/dashscope/api_entities/http_request.py:303\u001B[0m, in \u001B[0;36mHttpRequest._handle_request\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    301\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mBaseException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m    302\u001B[0m     logger\u001B[38;5;241m.\u001B[39merror(e)\n\u001B[0;32m--> 303\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m e\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/dashscope/api_entities/http_request.py:286\u001B[0m, in \u001B[0;36mHttpRequest._handle_request\u001B[0;34m(self)\u001B[0m\n\u001B[1;32m    284\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    285\u001B[0m         logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mRequest body: \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m'\u001B[39m \u001B[38;5;241m%\u001B[39m obj)\n\u001B[0;32m--> 286\u001B[0m         response \u001B[38;5;241m=\u001B[39m \u001B[43msession\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpost\u001B[49m\u001B[43m(\u001B[49m\u001B[43murl\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    287\u001B[0m \u001B[43m                                \u001B[49m\u001B[43mstream\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mstream\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    288\u001B[0m \u001B[43m                                \u001B[49m\u001B[43mjson\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    289\u001B[0m \u001B[43m                                \u001B[49m\u001B[43mheaders\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m{\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mheaders\u001B[49m\u001B[43m}\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m    290\u001B[0m \u001B[43m                                \u001B[49m\u001B[43mtimeout\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtimeout\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    291\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmethod \u001B[38;5;241m==\u001B[39m HTTPMethod\u001B[38;5;241m.\u001B[39mGET:\n\u001B[1;32m    292\u001B[0m     response \u001B[38;5;241m=\u001B[39m session\u001B[38;5;241m.\u001B[39mget(url\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39murl,\n\u001B[1;32m    293\u001B[0m                            params\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mdata\u001B[38;5;241m.\u001B[39mparameters,\n\u001B[1;32m    294\u001B[0m                            headers\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mheaders,\n\u001B[1;32m    295\u001B[0m                            timeout\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtimeout)\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/requests/sessions.py:637\u001B[0m, in \u001B[0;36mSession.post\u001B[0;34m(self, url, data, json, **kwargs)\u001B[0m\n\u001B[1;32m    626\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[38;5;21mpost\u001B[39m(\u001B[38;5;28mself\u001B[39m, url, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, json\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m    627\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"Sends a POST request. Returns :class:`Response` object.\u001B[39;00m\n\u001B[1;32m    628\u001B[0m \n\u001B[1;32m    629\u001B[0m \u001B[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001B[39;00m\n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    634\u001B[0m \u001B[38;5;124;03m    :rtype: requests.Response\u001B[39;00m\n\u001B[1;32m    635\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 637\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mrequest\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mPOST\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mjson\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mjson\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/requests/sessions.py:589\u001B[0m, in \u001B[0;36mSession.request\u001B[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001B[0m\n\u001B[1;32m    584\u001B[0m send_kwargs \u001B[38;5;241m=\u001B[39m {\n\u001B[1;32m    585\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtimeout\u001B[39m\u001B[38;5;124m\"\u001B[39m: timeout,\n\u001B[1;32m    586\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mallow_redirects\u001B[39m\u001B[38;5;124m\"\u001B[39m: allow_redirects,\n\u001B[1;32m    587\u001B[0m }\n\u001B[1;32m    588\u001B[0m send_kwargs\u001B[38;5;241m.\u001B[39mupdate(settings)\n\u001B[0;32m--> 589\u001B[0m resp \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mprep\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43msend_kwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    591\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m resp\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/requests/sessions.py:703\u001B[0m, in \u001B[0;36mSession.send\u001B[0;34m(self, request, **kwargs)\u001B[0m\n\u001B[1;32m    700\u001B[0m start \u001B[38;5;241m=\u001B[39m preferred_clock()\n\u001B[1;32m    702\u001B[0m \u001B[38;5;66;03m# Send the request\u001B[39;00m\n\u001B[0;32m--> 703\u001B[0m r \u001B[38;5;241m=\u001B[39m \u001B[43madapter\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrequest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    705\u001B[0m \u001B[38;5;66;03m# Total elapsed time of the request (approximately)\u001B[39;00m\n\u001B[1;32m    706\u001B[0m elapsed \u001B[38;5;241m=\u001B[39m preferred_clock() \u001B[38;5;241m-\u001B[39m start\n",
+      "File \u001B[0;32m~/src/knightutils/.venv/lib/python3.10/site-packages/requests/adapters.py:675\u001B[0m, in \u001B[0;36mHTTPAdapter.send\u001B[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001B[0m\n\u001B[1;32m    671\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m ProxyError(e, request\u001B[38;5;241m=\u001B[39mrequest)\n\u001B[1;32m    673\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(e\u001B[38;5;241m.\u001B[39mreason, _SSLError):\n\u001B[1;32m    674\u001B[0m         \u001B[38;5;66;03m# This branch is for urllib3 v1.22 and later.\u001B[39;00m\n\u001B[0;32m--> 675\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m SSLError(e, request\u001B[38;5;241m=\u001B[39mrequest)\n\u001B[1;32m    677\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m(e, request\u001B[38;5;241m=\u001B[39mrequest)\n\u001B[1;32m    679\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m ClosedPoolError \u001B[38;5;28;01mas\u001B[39;00m e:\n",
+      "\u001B[0;31mSSLError\u001B[0m: HTTPSConnectionPool(host='dashscope.aliyuncs.com', port=443): Max retries exceeded with url: /api/v1/services/aigc/text-generation/generation (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: unable to get local issuer certificate (_ssl.c:1007)')))"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e8170348d3bad72",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/llamaindex/test_20250826_1.py

@@ -0,0 +1,41 @@
+from llama_index.core.base.llms.types import ChatMessage
+from llama_index.llms.dashscope import DashScope
+import asyncio
+
+llm = DashScope(model_name="qwen-max") # 设置检索引擎生成回答时调用的大模型。
+
+def test1():
+    response = llm.complete("William Shakespeare is ")
+    print(response)
+
+async def test2():
+    response = await llm.acomplete("William Shakespeare is ")
+    print(response)
+
+
+def test3():
+    response = llm.stream_complete("William Shakespeare is ")
+    for chunk in response:
+        print(chunk)
+
+def test4():
+    handle = llm.stream_complete("William Shakespeare is ")
+
+    for token in handle:
+        print(token.delta, end="", flush=True)
+
+
+def test5():
+    messages = [
+        ChatMessage(role="system", content="You are a helpful assistant."),
+        ChatMessage(role="user", content="Tell me a joke."),
+    ]
+    chat_response = llm.chat(messages)
+    print(chat_response)
+
+if __name__ == '__main__':
+    # test1()
+    # asyncio.run(test2())
+    # test3()
+    # test4()
+    test5()

test/llamaindex/test_20250826_2.ipynb

@@ -0,0 +1,61 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "48072a19c44a9164",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-08-27T16:02:46.279589Z",
+     "start_time": "2025-08-27T16:02:46.277036Z"
+    }
+   },
+   "source": [
+    "print(\"hello\")"
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello\n"
+     ]
+    }
+   ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "is_executing": true
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

test/mcp/math_server.py

@@ -0,0 +1,16 @@
+from mcp.server.fastmcp import FastMCP
+
+mcp = FastMCP("Math")
+
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+@mcp.tool()
+def multiply(a: int, b: int) -> int:
+    """Multiply two numbers"""
+    return a * b
+
+if __name__ == "__main__":
+    mcp.run(transport="stdio")

test/qwenagent/start.py

@@ -0,0 +1,77 @@
+import pprint
+import urllib.parse
+import json5
+from qwen_agent.agents import Assistant
+from qwen_agent.tools.base import BaseTool, register_tool
+from qwen_agent.utils.output_beautify import typewriter_print
+
+
+# Step 1 (Optional): Add a custom tool named `my_image_gen`.
+@register_tool('my_image_gen')
+class MyImageGen(BaseTool):
+    # The `description` tells the agent the functionality of this tool.
+    description = 'AI painting (image generation) service, input text description, and return the image URL drawn based on text information.'
+    # The `parameters` tell the agent what input parameters the tool has.
+    parameters = [{
+        'name': 'prompt',
+        'type': 'string',
+        'description': 'Detailed description of the desired image content, in English',
+        'required': True
+    }]
+
+    def call(self, params: str, **kwargs) -> str:
+        # `params` are the arguments generated by the LLM agent.
+        prompt = json5.loads(params)['prompt']
+        prompt = urllib.parse.quote(prompt)
+        return json5.dumps(
+            {'image_url': f'https://image.pollinations.ai/prompt/{prompt}'},
+            ensure_ascii=False)
+
+
+# Step 2: Configure the LLM you are using.
+llm_cfg = {
+    # Use the model service provided by DashScope:
+    'model': 'qwen-max-latest',
+    'model_type': 'qwen_dashscope',
+    # 'api_key': 'YOUR_DASHSCOPE_API_KEY',
+    # It will use the `DASHSCOPE_API_KEY' environment variable if 'api_key' is not set here.
+
+    # Use a model service compatible with the OpenAI API, such as vLLM or Ollama:
+    # 'model': 'Qwen2.5-7B-Instruct',
+    # 'model_server': 'http://localhost:8000/v1',  # base_url, also known as api_base
+    # 'api_key': 'EMPTY',
+
+    # (Optional) LLM hyperparameters for generation:
+    'generate_cfg': {
+        'top_p': 0.8
+    }
+}
+
+# Step 3: Create an agent. Here we use the `Assistant` agent as an example, which is capable of using tools and reading files.
+system_instruction = '''After receiving the user's request, you should:
+- first draw an image and obtain the image url,
+- then run code `request.get(image_url)` to download the image,
+- and finally select an image operation from the given document to process the image.
+Please show the image using `plt.show()`.'''
+tools = ['my_image_gen', 'code_interpreter']  # `code_interpreter` is a built-in tool for executing code.
+files = ['./examples/resource/doc.pdf']  # Give the bot a PDF file to read.
+bot = Assistant(llm=llm_cfg,
+                system_message=system_instruction,
+                function_list=tools,
+                files=files)
+
+# Step 4: Run the agent as a chatbot.
+messages = []  # This stores the chat history.
+while True:
+    # For example, enter the query "draw a dog and rotate it 90 degrees".
+    query = input('\nuser query: ')
+    # Append the user query to the chat history.
+    messages.append({'role': 'user', 'content': query})
+    response = []
+    response_plain_text = ''
+    print('bot response:')
+    for response in bot.run(messages=messages):
+        # Streaming output.
+        response_plain_text = typewriter_print(response, response_plain_text)
+    # Append the bot responses to the chat history.
+    messages.extend(response)

test/test_pydantic/test.py

@@ -0,0 +1,34 @@
+# 正确示例：显式类型注解
+import os
+
+from pydantic import BaseModel
+
+class User(BaseModel):
+    id: int  # 显式指定int类型
+    name: str  # 显式指定str类型
+    is_active: bool = True  # 带默认值的类型注解
+
+# 错误示例：缺少类型注解
+# class BadUser(BaseModel):
+#     id = 1  # 缺少类型注解，Pydantic 2.9+将报错
+#     name = "John"  # 缺少类型注解，Pydantic 2.9+将报错
+
+os.environ['app_port'] = '8888'
+
+# 环境变量自动映射示例
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class AppConfig(BaseSettings):
+    host: str = "localhost"
+    port: int = 8000
+
+    model_config = SettingsConfigDict(
+        env_prefix="APP_",  # 环境变量前缀
+        case_sensitive=False  # 不区分大小写
+    )
+
+
+# 当环境变量存在APP_PORT=8080时
+config = AppConfig()
+print(config.port)  # 输出: 8080 (而非默认的8000)