"), ("ai", "I'm doing well, thanks!"), ("human", "{user_input}"),])messages = template.format_messages( name="Bob", user_input="What is your name?")Retrieval -检索而非查询 传统的应用开发通常需要定义好查询（Query）的格式和规范，例如关键词、标签、分类等。这样做的好处...
messages = chat_template.format_messages(name="Bob", user_input="What is your name?") from langchain.chat_models import ChatOpenAI from langchain.prompts import HumanMessagePromptTemplate from langchain.schema.messages import SystemMessage chat_template = ChatPromptTemplate.from_messages( [ SystemMe...
assistant_sys_msg = assistant_sys_template.format_messages( assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task, )[0] user_sys_template = SystemMessagePromptTemplate.from_template( template=user_inception_prompt ) user_sys_msg = user_sys_template.format_messages( ...
human_template ="{text}"human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt]) chat_prompt.format_messages(input_language="中文",output_language="英文",text="坤坤会去踩缝纫机吗?"...
# 导入LangChain中的ChatOpenAI模型接口fromlangchain_openaiimportChatOpenAI# 创建模型实例model=ChatOpenAI(model_name='gpt-3.5-turbo')# 输入提示messages=chat_prompt.format_messages(input_language="英文",output_language="中文",text="I love programming.")# 得到模型的输出output=model.invoke(messages)# ...
chat_prompt.format_messages(input_language="English", output_language="French", text="I love programming.") 3.3 Output parsers OutputParsers 将LLM的原始输出转换为可在下游使用的格式。其中主要的OutputParsers类型包括: 将LLM的文本转换为结构化信息(例如 JSON) ...
format_prompt(user_prompt="I really like So Young by Portugal. The Man") print(artist_query.messages[0].content) 代码语言:javascript 代码运行次数:0 复制Cloud Studio 代码运行 Given a command from the user, extract the artist and song names The output should be a markdown code snippet ...
(human_template)chat_prompt=ChatPromptTemplate.from_messages([system_message_prompt,human_message_prompt])#geta chat completion from the formatted messageschat(chat_prompt.format_prompt(input_language="English",output_language="Chinese",text="I love programming.").to_messages())#->AIMessage(...
{context} \n</context>\n", ), ("human","你好!"), ("ai","你好"), ("human","{question}"), ] prompt = ChatPromptTemplate.from_messages(template) messages = prompt.format_messages(context=_content, question=question) response = model.invoke(messages) output_parser = StrOutputParser()...
longString = "big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else" print(dynamic_prompt.format(input=longString)) 另外官方也提供了根据最大边际相关性、文法重叠、语义相似性来选择示例。