ChatPromptTemplate和AI Message的用法

ChatPromptTemplate的用法

用法1:

python 复制代码
from langchain.chains import LLMChain
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain.chains import LLMMathChain

prompt= ChatPromptTemplate.from_template("tell me the weather of {topic}")
str = prompt.format(topic="shenzhen")
print(str)

打印出:

bash 复制代码
Human: tell me the weather of shenzhen

最终和llm一起使用:

python 复制代码
import ChatGLM
from langchain.chains import LLMChain
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

from langchain_community.tools.tavily_search import TavilySearchResults
from langchain.chains import LLMMathChain


prompt = ChatPromptTemplate.from_template("who is {name}")
# str = prompt.format(name="Bill Gates")
# print(str)
llm = ChatGLM.ChatGLM_LLM()
output_parser = StrOutputParser()
chain05 = prompt| llm | output_parser
print(chain05.invoke({"name": "Bill Gates"}))

用法2:

python 复制代码
import ChatGLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
                ("system", "You are a helpful AI bot. Your name is {name}."),
                ("human", "Hello, how are you doing?"),
                ("ai", "I'm doing well, thanks!"),
                ("human", "{user_input}"),
            ])

llm = ChatGLM.ChatGLM_LLM()
output_parser = StrOutputParser()
chain05 = prompt| llm | output_parser
print(chain05.invoke({"name": "Bob","user_input": "What is your name"}))

也可以这样:

python 复制代码
import ChatGLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

llm = ChatGLM.ChatGLM_LLM()

prompt = ChatPromptTemplate.from_messages([
                ("system", "You are a helpful AI bot. Your name is {name}."),
                ("human", "Hello, how are you doing?"),
                ("ai", "I'm doing well, thanks!"),
                ("human", "{user_input}"),
            ])


# a = prompt.format_prompt({name="Bob"})

a = prompt.format_prompt(name="Bob",user_input="What is your name") 
print(a)
print(llm.invoke(a))

参考: https://python.langchain.com/docs/modules/model_io/prompts/quick_start

https://python.langchain.com/docs/modules/model_io/prompts/composition

相关推荐
bqq198610264 分钟前
MySQL 8与MySQL 5.7的主要区别
数据库·mysql
yaoxin5211237 分钟前
406. Java 文件操作基础 - 字符与二进制流
java·开发语言·python
happymaker062614 分钟前
SpringBoot学习日记——DAY02(SpringBoot整合Swagger3)
java·spring boot·学习
阿坤带你走近大数据25 分钟前
Java中的JVM、类加载记住、多线程、性能优化的概念
java·jvm·性能优化
chushiyunen34 分钟前
r树索引、mysql对r树的支持
数据库·mysql
会编程的土豆34 分钟前
Redis Sorted Set(有序集合)详解
数据库·redis·bootstrap
鱼鳞_36 分钟前
苍穹外卖-Day04
java
未若君雅裁39 分钟前
Spring Boot 自动配置原理与常用注解
java·spring boot·后端
Xiacqi11 小时前
Java数据库连接--JDBC--DRUID
数据库·后端
Yushan Bai1 小时前
ORACLE Enterprise Manager Cloud Control 系列测试3-Data Masking
数据库·oracle