save all

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/dashscope/deepseek.py

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+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

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+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/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

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+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

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+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/llamaindex/dashscope_parse.py

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+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

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+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

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+# MacOS/Linux
+export OPENAI_API_KEY=sk-e2a05bbcfac84e53b73f98acef15a009

test/llamaindex/test1.py

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+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/test_pydantic/test.py

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+# 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)