文章目录 11.1 xml输出 11.2 json输出 11.3 支持图片输入 11.4 配合工具输出 11.1 xml输出 from langchain_core. output_parsersimport JsonOutputParser, XMLOutputParserfrom langchain_core. promptsimport ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, PromptTemplatefrom langchain_community. chat_modelsimport ChatTongyifrom dotenvimport load_dotenvfrom pydanticimport BaseModel, Field load_dotenv( ) llm= ChatTongyi( model= "qwen-plus" ) joke_query= "生成周杰伦的歌曲，按照年份升序排列" parser= XMLOutputParser( ) prompt= PromptTemplate( template= "回答用户的查询。\n{format_instructions}\n{query}\n" , input_variables= [ "query" ] , partial_variables= { "format_instructions" : parser. get_format_instructions( ) } , ) chain= prompt| llm response= chain. invoke( { "query" : joke_query} ) print ( response. content) #输出结果 < songs> < song> < title> 可爱女人< / title> < year> 2000 < / year> < album> Jay< / album> < / song> < song> < title> 星晴< / title> < year> 2000 < / year> < album> Jay< / album> < / song> < song> < title> 龙卷风< / title> < year> 2000 < / year> < album> Jay< / album> < / song> < song> < title> 简单爱< / title> < year> 2001 < / year> < album> Fantasy< / album> < / song> < song> < title> 开不了口< / title> < year> 2001 < / year> < album> Fantasy< / album> < / song> < song> < title> 双截棍< / title> < year> 2001 < / year> < album> Fantasy< / album> < / song> < song> < title> 爱在西元前< / title> < year> 2001 < / year> < album> Fantasy< / album> < / song> < song> < title> 娘子< / title> < year> 2000 < / year> < album> Jay< / album> < / song> < song> < title> 安静< / title> < year> 2000 < / year> < album> Jay< / album> < / song> < song> < title> 半岛铁盒< / title> < year> 2002 < / year> < album> The Eight Dimensions< / album> < / song> < song> < title> 东风破< / title> < year> 2003 < / year> < album> Yeh Hui- Mei< / album> < / song> < song> < title> 晴天< / title> < year> 2003 < / year> < album> Yeh Hui- Mei< / album> < / song> < song> < title> 七里香< / title> < year> 2004 < / year> < album> Common Jasmine Orange< / album> < / song> < song> < title> 发如雪< / title> < year> 2005 < / year> < album> November's Chopin< / album> < / song> < song> < title> 千里之外< / title> < year> 2006 < / year> < album> Still Fantasy< / album> < / song> < song> < title> 青花瓷< / title> < year> 2007 < / year> < album> On the Run!< / album> < / song> < song> < title> 稻香< / title> < year> 2008 < / year> < album> Capricorn< / album> < / song> < song> < title> 说好不哭< / title> < year> 2019 < / year> < album> 未发行专辑< / album> < / song> < song> < title> Mojito< / title> < year> 2020 < / year> < album> Mojito< / album> < / song> < song> < title> 最伟大的作品< / title> < year> 2022 < / year> < album> 最伟大的作品< / album> < / song> < / songs> 11.2 json输出 from langchain_core. output_parsersimport JsonOutputParserfrom langchain_core. promptsimport ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder, PromptTemplatefrom langchain_community. chat_modelsimport ChatTongyifrom dotenvimport load_dotenvfrom pydanticimport BaseModel, Field load_dotenv( ) llm= ChatTongyi( model= "qwen-plus" ) class Joke ( BaseModel) : setup: str = Field( description= "设置笑话的问题" ) punchline: str = Field( description= "解决笑话的答案" ) joke_query= "告诉我一个笑话" parser= JsonOutputParser( pydantic_object= Joke) prompt= PromptTemplate( template= "回答用户的查询。\n{format_instructions}\n{query}\n" , input_variables= [ "query" ] , partial_variables= { "format_instructions" : parser. get_format_instructions( ) } , ) chain= prompt| llm| parser##print(chain.invoke({"query": joke_query})) for chunkin chain. stream( { "query" : joke_query} ) : print ( chunk) #输出 { } { 'setup' : '' } { 'setup' : '为什么' } { 'setup' : '为什么程序员总是喜欢用黑暗' } { 'setup' : '为什么程序员总是喜欢用黑暗模式？' } { 'setup' : '为什么程序员总是喜欢用黑暗模式？' , 'punchline' : '因为他们' } { 'setup' : '为什么程序员总是喜欢用黑暗模式？' , 'punchline' : '因为他们不喜欢光明。' } 11.3 支持图片输入 import base64import httpxfrom langchain_core. messagesimport HumanMessagefrom langchain_community. chat_modelsimport ChatTongyifrom dotenvimport load_dotenv load_dotenv( ) image_url= "https://img1.baidu.com/it/u=352739982,3234821554&fm=253&app=138&f=JPEG?w=500&h=857" image_data= base64. b64encode( httpx. get( image_url) . content) . decode( "utf-8" ) llm= ChatTongyi( model= "qwen-plus" ) # 使用支持图像识别的模型 message= HumanMessage( content= [ { "type" : "text" , "text" : "用中文描述这张图片的内容" } , { "type" : "image_url" , "image_url" : { "url" : { "url" : f"data:image/jpeg;base64, { image_data} " } } } ] ) response= llm. invoke( [ message] ) print ( response. content) #输出 这张图片展示了一个现代风格的客厅，整体色调以白色和浅木色为主，营造出明亮、宽敞的空间感。以下是图片中各个元素的详细描述：1 . ** 地板** ： 地板采用浅色木地板，表面光滑且有自然的木纹纹理，增加了空间的温馨感。2 . ** 沙发** ： 客厅中央摆放着一组灰色布艺沙发，包括一个三人座沙发和两个单人座沙发。沙发的设计简洁，线条流畅，坐垫柔软，给人一种舒适的感觉。沙发上没有多余的装饰，保持了极简主义的风格。3 . ** 茶几** ： 沙发前有一张圆形玻璃茶几，透明的桌面让空间显得更加通透。茶几上放置了一本打开的书和一个小型植物盆栽，增添了一丝生活气息。. . . . . . . . . . . . . . . 11.4 配合工具输出 import base64import httpxfrom langchain_core. messagesimport HumanMessage, ToolMessagefrom langchain_community. chat_modelsimport ChatTongyifrom langchain_core. toolsimport tool# 补充导入tool装饰器 from typingimport Literal# 添加此行以解决 Literal 未定义问题 from dotenvimport load_dotenv load_dotenv( ) image_url= "https://copyright.bdstatic.com/vcg/creative/cae2f3d12fc98f2eb94d625dad4f42a3.jpg@wm_1,k_cGljX2JqaHdhdGVyLmpwZw==" image_data= base64. b64encode( httpx. get( image_url) . content) . decode( "utf-8" ) llm= ChatTongyi( model= "qwen-plus" ) # 使用支持图像识别的模型 @tool def weather_tool ( weather: Literal[ "晴朗的" , "多云的" , "多雨的" , "下雪的" ] ) - > str : """根据天气类型返回描述""" weather_descriptions= { "晴朗的" : "今天阳光明媚，是个好天气。" , "多云的" : "今天多云，可能会有些阴沉。" , "多雨的" : "今天有雨，记得带伞。" , "下雪的" : "今天下雪，注意保暖和防滑。" } return weather_descriptions. get( weather, "无法识别的天气类型。" ) model_with_tools= llm. bind_tools( tools= [ weather_tool] ) message= HumanMessage( content= [ { "type" : "text" , "text" : "用中文描述这张图片的天气" } , { "type" : "image_url" , "image_url" : f"data:image/jpeg;base64, { image_data} " # 直接使用字符串 } ] ) response= model_with_tools. invoke( [ message] ) print ( response. content) # 创建包含工具调用的完整链路 # 修改工具调用处理部分 # 修改工具调用处理部分，确保消息格式正确 # def call_model_with_tools(messages): # response = model_with_tools.invoke(messages) # # # 检查是否有工具调用 # if hasattr(response, 'tool_calls') and response.tool_calls: # tool_results = [] # for tool_call in response.tool_calls: # # 访问字典中的参数（如果tool_call是字典） # if isinstance(tool_call, dict): # tool_name = tool_call.get('name') # tool_args = tool_call.get('args') # tool_call_id = tool_call.get('id') # else: # # 如果tool_call是对象 # tool_name = getattr(tool_call, 'name', None) # tool_args = getattr(tool_call, 'args', {}) # tool_call_id = getattr(tool_call, 'id', None) # # # 根据工具名称执行相应工具 # if tool_name == 'weather_tool': # try: # result = weather_tool.invoke(tool_args) # tool_results.append( # ToolMessage( # content=str(result), # tool_call_id=tool_call_id # ) # ) # except Exception as e: # tool_results.append( # ToolMessage( # content=f"工具执行错误: {str(e)}", # tool_call_id=tool_call_id # ) # ) # # # 将工具结果添加到消息历史中，并重新调用模型 # new_messages = messages + [response] + tool_results # return call_model_with_tools(new_messages) # # return response # # # 使用完整工具调用链路 # final_response = call_model_with_tools([message]) # print(final_response.content) 
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