{context} Question: {question} Helpful Answer:""" QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template,) # Run chain from langchain.chains import RetrievalQA llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) qa_chain = RetrievalQA.from_chain...
from langchain.chains import LLMChain template = "我的邻居姓{lastname},他生了个儿子,给他儿子起个名字" prompt = PromptTemplate( input_variables=["lastname"], template=template, ) llm = OpenAI(temperature=0.9) chain = LLMChain(llm = llm, prompt = prompt) # 执行链 chain.run("王") chai...
llms import OpenAI from langchain import PromptTemplate llm = OpenAI(temperature=0, model_name = 'gpt-3.5-turbo', openai_api_key=openai_api_key) # 初始化LLM模型# 创建模板 template = """ %INSTRUCTIONS: Please summarize the following piece of text. Respond in a manner that a 5 year old...
与模型交互的，基本上是通过给与 Prompt 的方式，LangChain 通过 PromptTemplate 的方式方便我们构建以及复...
The PromptTemplate class in LangChain formalizes the composition of prompts without the need to manually hard code context and queries. Important elements of a prompt are likewise entered as formal classes, like input_variables. A prompt template can thus contain and reproduce context, instructions ...
StrOutputParser() llm = Ollama(model="llama2") prompt = ChatPromptTemplate.from_messages([ ("system","You are world class technical documentation writer."), ("user","{input}") ]) chain = prompt | llm | output_parserprint(chain.invoke({"input":"how can langsmith help with testing?
{openAIApiKey:"YOUR_OPENAI_KEY",model:"gpt-3.5-turbo",temperature:0});constprompt=PromptTemplate.fromTemplate(`You are a poetic assistant that always answers in rhymes: {question}`);construnnable=prompt.pipe(chat);constresponse=awaitrunnable.invoke({question:"Who is better, Djokovic, Federer ...
passing in a factory function that returns a new chain for each row. def create_agent(prompt,...
("./dbs/documentation/faiss_index", embeddings) #use the faiss vector store we saved to search the local document retriever = vectorStore.as_retriever(search_type="similarity", search_kwargs={"k":2}) QUESTION_PROMPT = PromptTemplate.from_template("""Given the fo...
Let’s create a business name generator using prompt templates that will return five to seven relevant business names: from langchain_openai.chat_models import ChatOpenAI from langchain_core.prompts import (SystemMessagePromptTemplate, ChatPromptTemplate) template = """ You are a creative consultant...