Quick Start | Документация для разработчиков

Quick Start

Обновлено 24 мая 2024

Prompt templates are predefined recipes for generating prompts for language models.

A template may include instructions, few-shot examples, and specific context and questions appropriate for a given task.

LangChain provides tooling to create and work with prompt templates.

LangChain strives to create model agnostic templates to make it easy to reuse existing templates across different language models.

Typically, language models expect the prompt to either be a string or else a list of chat messages.


Use PromptTemplate to create a template for a string prompt.

By default, PromptTemplate uses Python's str.format syntax for templating.

from langchain.prompts import PromptTemplate

prompt_template = PromptTemplate.from_template(
"Tell me a {adjective} joke about {content}."
prompt_template.format(adjective="funny", content="chickens")
    'Tell me a funny joke about chickens.'

The template supports any number of variables, including no variables:

from langchain.prompts import PromptTemplate

prompt_template = PromptTemplate.from_template("Tell me a joke")
    'Tell me a joke'

You can create custom prompt templates that format the prompt in any way you want. For more information, see Prompt Template Composition.


The prompt to chat models is a list of chat messages.

Each chat message is associated with content, and an additional parameter called role. For example, in the OpenAI Chat Completions API, a chat message can be associated with an AI assistant, a human or a system role.

Create a chat prompt template like this:

from langchain_core.prompts import ChatPromptTemplate

chat_template = 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}"),

messages = chat_template.format_messages(name="Bob", user_input="What is your name?")

ChatPromptTemplate.from_messages accepts a variety of message representations.

For example, in addition to using the 2-tuple representation of (type, content) used above, you could pass in an instance of MessagePromptTemplate or BaseMessage.

from langchain.prompts import HumanMessagePromptTemplate
from langchain_core.messages import SystemMessage
from langchain_openai import ChatOpenAI

chat_template = ChatPromptTemplate.from_messages(
"You are a helpful assistant that re-writes the user's text to "
"sound more upbeat."
messages = chat_template.format_messages(text="I don't like eating tasty things")
    [SystemMessage(content="You are a helpful assistant that re-writes the user's text to sound more upbeat."), HumanMessage(content="I don't like eating tasty things")]

This provides you with a lot of flexibility in how you construct your chat prompts.


PromptTemplate and ChatPromptTemplate implement the Runnable interface, the basic building block of the LangChain Expression Language (LCEL). This means they support invoke, ainvoke, stream, astream, batch, abatch, astream_log calls.

PromptTemplate accepts a dictionary (of the prompt variables) and returns a StringPromptValue. A ChatPromptTemplate accepts a dictionary and returns a ChatPromptValue.

prompt_val = prompt_template.invoke({"adjective": "funny", "content": "chickens"})
    StringPromptValue(text='Tell me a joke')
    'Tell me a joke'
    [HumanMessage(content='Tell me a joke')]
chat_val = chat_template.invoke({"text": "i dont like eating tasty things."})
    [SystemMessage(content="You are a helpful assistant that re-writes the user's text to sound more upbeat."),
HumanMessage(content='i dont like eating tasty things.')]
    "System: You are a helpful assistant that re-writes the user's text to sound more upbeat.\nHuman: i dont like eating tasty things."
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