ChatPromptTemplate和AI Message的用法

ChatPromptTemplate的用法

用法1：


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

打印出：

Human: tell me the weather of shenzhen

最终和llm一起使用：

import ChatGLM
from langchain.chains import LLMChain
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplatefrom langchain_community.tools.tavily_search import TavilySearchResults
from langchain.chains import LLMMathChainprompt = 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：

import ChatGLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplateprompt = 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：

import ChatGLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplatellm = 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))