Formatting Outputs for ChatPrompt Templates(one)

https://python.langchain.com.cn/docs/modules/model_io/prompts/prompt_templates/format_output

The chat_prompt variable in LangChain is built by combining message templates (system messages, human messages, etc.) into a structured ChatPromptTemplate. Let's break down how it's constructed, using the exact example from the original source (translating English to French).

Step 1: Import Required Tools

First, import the necessary classes from LangChain to create chat prompts:

python 复制代码
from langchain.prompts.chat import (
    ChatPromptTemplate,          # To combine message templates
    SystemMessagePromptTemplate, # For system messages (AI's role)
    HumanMessagePromptTemplate   # For human/user messages (input)
)

Step 2: Define Message Templates

A chat_prompt typically includes two key parts:

  • A system message: Tells the AI its role/instructions.
  • A human message: The user's input (with placeholders for dynamic content).
Create the System Message Template

This defines the AI's task (e.g., "translate English to French"):

python 复制代码
# Template string for the system message
system_template = "You are a helpful assistant that translates {input_language} to {output_language}."

# Convert the string to a SystemMessagePromptTemplate
system_message_prompt = SystemMessagePromptTemplate.from_template(system_template)
  • {input_language} and {output_language} are placeholders (we'll fill them later).
Create the Human Message Template

This defines the user's input (the text to translate):

python 复制代码
# Template string for the human message
human_template = "{text}"  # {text} is a placeholder for the user's text

# Convert the string to a HumanMessagePromptTemplate
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)

Step 3: Combine Templates into chat_prompt

Use ChatPromptTemplate.from_messages() to merge the system and human message templates into a single chat_prompt:

python 复制代码
# Combine the two message templates into a ChatPromptTemplate
chat_prompt = ChatPromptTemplate.from_messages([
    system_message_prompt,  # First: system instructions
    human_message_prompt    # Second: user input
])

Final Result: What chat_prompt Contains

The chat_prompt variable now holds a structured prompt that:

  1. Includes the system's role (translation task).
  2. Includes a placeholder for the user's text.
  3. Can be filled with actual values (e.g., input_language="English", text="I love programming") later using .format() or .format_prompt().

This exact structure matches the original source---no changes to code or logic. The chat_prompt is simply a container for combining message templates to guide the AI's behavior.

相关推荐
北斗落凡尘6 小时前
LangGraph 入门实战(9)--中断
后端·langchain
赵广陆9 小时前
企业实战:主体识别
langchain·pdf·langgraph
李妍.15 小时前
DeepSeek Harness 从安装到使用:一站式 AI 开发工具指南
chatgpt·prompt·aigc·agi
小马过河R15 小时前
Graph Engineering 深度解析:模型越强,越需要给它画好“地图”
人工智能·langchain·graph·ai工程化·harness·驾驭工程
jyOverQ15 小时前
LangGraph 记忆管理详解:短期记忆、长期记忆与 Runtime Context
python·langchain
做前端的娜娜子16 小时前
文本切片与召回(Chunk、Overlap 到混合检索)学习笔记
langchain·openai·掘金·金石计划
赵广陆17 小时前
企业实战:Milvues向量数据库实践
数据库·pycharm·langchain
Patrick在香港18 小时前
Claude API 成本直降90%:Prompt Caching 提示词缓存 Python 实战
python·缓存·prompt
tachibana218 小时前
怎么让大模型同时给意图节点打分
大数据·人工智能·ai·大模型·llm·prompt