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标题: Langchain Chat Model 和 Chat Prompt Template [打印本页]
作者: 农民    时间: 2024-12-30 22:13
标题: Langchain Chat Model 和 Chat Prompt Template
0. 简介

Chat Model 不止是一个用于谈天对话的模子抽象，更紧张的是提供了多角色提示能力（System,AI,Human,Function)。
Chat Prompt Template 则为开发者提供了便捷维护不同角色的提示模板与消息记录的接口。
1. 构造 ChatPromptTemplate

  1. from langchain.prompts.chat import (
  2.     ChatPromptTemplate,
  3.     SystemMessagePromptTemplate,
  4.     AIMessagePromptTemplate,
  5.    HumanMessagePromptTemplate,
  6. )
  8. import os
  9. from dotenv import load_dotenv, find_dotenv
  11. # 删除all_proxy环境变量
  12. if 'all_proxy' in os.environ:
  13.     del os.environ['all_proxy']
  15. # 删除ALL_PROXY环境变量
  16. if 'ALL_PROXY' in os.environ:
  17.     del os.environ['ALL_PROXY']
  19. _ = load_dotenv(find_dotenv())
  21. template = (
  22.     """You are a translation expert, proficient in various languages. \n
  23.     Translates English to Chinese."""
  24. )
  25. system_message_prompt = SystemMessagePromptTemplate.from_template(template)
  26. print(type(system_message_prompt))
  27. print(system_message_prompt)
  29. human_template = "{text}"
  30. human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
  31. print(type(human_message_prompt))
  32. print(human_message_prompt)
  34. print("*"*40)
  35. # 使用 System 和 Human 角色的提示模板构造 ChatPromptTemplate
  36. chat_prompt_template = ChatPromptTemplate.from_messages(
  37.     [system_message_prompt, human_message_prompt]
  38. )
  39. print(type(chat_prompt_template))
  40. print(chat_prompt_template)
  42. print("*"*50)
  43. chat_prompt_prompt_value = chat_prompt_template.format_prompt(text="I love python.")
  44. print(type(chat_prompt_prompt_value))
  45. print(chat_prompt_prompt_value)
  47. print("*"*60)
  48. chat_prompt_list = chat_prompt_template.format_prompt(text="I love python.").to_messages()
  49. print(type(chat_prompt_list))
  50. print(chat_prompt_list)
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输出：
  1. <class 'langchain_core.prompts.chat.SystemMessagePromptTemplate'>
  2. prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='You are a translation expert, proficient in various languages. \n\n    Translates English to Chinese.') additional_kwargs={}
  3. <class 'langchain_core.prompts.chat.HumanMessagePromptTemplate'>
  4. prompt=PromptTemplate(input_variables=['text'], input_types={}, partial_variables={}, template='{text}') additional_kwargs={}
  5. ****************************************
  6. <class 'langchain_core.prompts.chat.ChatPromptTemplate'>
  7. input_variables=['text'] input_types={} partial_variables={} messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], input_types={}, partial_variables={}, template='You are a translation expert, proficient in various languages. \n\n    Translates English to Chinese.'), additional_kwargs={}), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['text'], input_types={}, partial_variables={}, template='{text}'), additional_kwargs={})]
  8. **************************************************
  9. <class 'langchain_core.prompt_values.ChatPromptValue'>
  10. messages=[SystemMessage(content='You are a translation expert, proficient in various languages. \n\n    Translates English to Chinese.', additional_kwargs={}, response_metadata={}), HumanMessage(content='I love python.', additional_kwargs={}, response_metadata={})]
  11. ************************************************************
  12. <class 'list'>
  13. [SystemMessage(content='You are a translation expert, proficient in various languages. \n\n    Translates English to Chinese.', additional_kwargs={}, response_metadata={}), HumanMessage(content='I love python.', additional_kwargs={}, response_metadata={})]
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2. LCEL 执行

  1. from langchain_openai import ChatOpenAI
  2. from langchain_core.output_parsers import StrOutputParser
  3. from langchain.prompts.chat import (
  4.     ChatPromptTemplate,
  5.     SystemMessagePromptTemplate,
  6.     AIMessagePromptTemplate,
  7.    HumanMessagePromptTemplate,
  8. )
  9. import os
  10. from dotenv import load_dotenv, find_dotenv
  12. # 删除all_proxy环境变量
  13. if 'all_proxy' in os.environ:
  14.     del os.environ['all_proxy']
  16. # 删除ALL_PROXY环境变量
  17. if 'ALL_PROXY' in os.environ:
  18.     del os.environ['ALL_PROXY']
  20. _ = load_dotenv(find_dotenv())
  22. # 为了结果的稳定性，将 temperature 设置为 0
  23. translation_model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
  25. template = (
  26.     """You are a translation expert, proficient in various languages. \n
  27.     Translates {source_language} to {target_language} in the style of {name}."""
  28. )
  29. system_message_prompt = SystemMessagePromptTemplate.from_template(template)
  31. human_template = "{text}"
  32. human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
  34. # 使用 System 和 Human 角色的提示模板构造 ChatPromptTemplate
  35. m_chat_prompt_template = ChatPromptTemplate.from_messages(
  36.     [system_message_prompt, human_message_prompt]
  37. )
  38. output_parser = StrOutputParser()
  40. m_translation_chain = m_chat_prompt_template| translation_model |  StrOutputParser(callbacks=[callback_handler])
  42. # Prepare input data
  43. input_data = {
  44.     "source_language": "English",
  45.     "target_language": "Chinese",
  46.     "name": "严复",
  47.     "text": "Life is full of regrets. All we can do is to minimize them.",
  48. }
  49. input_data1 = {
  50.     "source_language": "English",
  51.     "target_language": "Chinese",
  52.     "name": "李白",
  53.     "text": "Life is full of regrets. All we can do is to minimize them.",
  54. }
  57. # Format the prompt
  58. prompt_value = m_chat_prompt_template.format_prompt(**input_data)
  59. print(type(prompt_value))
  60. print(prompt_value)
  62. print(type(prompt_value.to_messages()))
  63. print(prompt_value.to_messages())
  65. result = translation_model.invoke(prompt_value)
  66. print(result)
  67. result = m_translation_chain.invoke(input_data)
  68. print(result)
  69. result = m_translation_chain.invoke(input_data1)
  70. print(result)
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输出：
  1. <class 'langchain_core.prompt_values.ChatPromptValue'>
  2. messages=[SystemMessage(content='You are a translation expert, proficient in various languages. \n\n    Translates English to Chinese in the style of 严复.', additional_kwargs={}, response_metadata={}), HumanMessage(content='Life is full of regrets. All we can do is to minimize them.', additional_kwargs={}, response_metadata={})]
  3. <class 'list'>
  4. [SystemMessage(content='You are a translation expert, proficient in various languages. \n\n    Translates English to Chinese in the style of 严复.', additional_kwargs={}, response_metadata={}), HumanMessage(content='Life is full of regrets. All we can do is to minimize them.', additional_kwargs={}, response_metadata={})]
  5. content='人生充满了遗憾。我们所能做的就是尽量减少它们。' additional_kwargs={'refusal': None} response_metadata={'token_usage': {'completion_tokens': 31, 'prompt_tokens': 53, 'total_tokens': 84, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None} id='run-676a9818-bbbc-44f7-a30e-0ec065aa502f-0' usage_metadata={'input_tokens': 53, 'output_tokens': 31, 'total_tokens': 84, 'input_token_details': {}, 'output_token_details': {}}
  6. 人生充满了遗憾。我们所能做的就是尽量减少它们。
  7. 人生充滿遺憾，唯有盡量減少。
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当 translation_model.invoke(input_data) 输入参数是字典时报错
  1. result = translation_model.invoke(input_data)
  2. print(result)
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报错：
  1. ValueError: Invalid input type <class 'dict'>. Must be a PromptValue, str, or list of BaseMessages.
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当 m_translation_chain.invoke(prompt_value) 输入参数是prompt_value时报错
  1. result = m_translation_chain.invoke(prompt_value)
  2. print(result)
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报错：
  1. TypeError: Expected mapping type as input to ChatPromptTemplate. Received <class 'langchain_core.prompt_values.ChatPromptValue'>.
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检察属性：
  1. input_schema = m_translation_chain.input_schema.model_json_schema()
  2. print(input_schema)
  3. output_schema = m_translation_chain.output_schema.model_json_schema()
  4. print(output_schema)
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输出：
  1. {'properties': {'name': {'title': 'Name', 'type': 'string'}, 'source_language': {'title': 'Source Language', 'type': 'string'}, 'target_language': {'title': 'Target Language', 'type': 'string'}, 'text': {'title': 'Text', 'type': 'string'}}, 'required': ['name', 'source_language', 'target_language', 'text'], 'title': 'PromptInput', 'type': 'object'}
  3. {'title': 'StrOutputParserOutput', 'type': 'string'}
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