Chat prompt template.
A type representing any defined Message or MessageChunk type.
Type variable for message prompt templates.
Decorator to mark a function, a class, or a property as deprecated.
When deprecating a classmethod, a staticmethod, or a property, the @deprecated
decorator should go under @classmethod and @staticmethod (i.e., deprecated
should directly decorate the underlying callable), but over @property.
When deprecating a class C intended to be used as a base class in a multiple
inheritance hierarchy, C must define an __init__ method (if C instead
inherited its __init__ from its own base class, then @deprecated would mess up
__init__ inheritance when installing its own (deprecation-emitting) C.__init__).
Parameters are the same as for warn_deprecated, except that obj_type defaults to
'class' if decorating a class, 'attribute' if decorating a property, and 'function'
otherwise.
Convert a sequence of messages to a list of messages.
Get a title representation for a message.
Get the variables from the template.
Get colored text.
Determine if running within IPython or Jupyter.
Message from an AI.
An AIMessage is returned from a chat model as a response to a prompt.
This message represents the output of the model and consists of both the raw output as returned by the model and standardized fields (e.g., tool calls, usage metadata) added by the LangChain framework.
Base abstract message class.
Messages are the inputs and outputs of a chat model.
Examples include HumanMessage,
AIMessage, and
SystemMessage.
Message that can be assigned an arbitrary speaker (i.e. role).
Message from the user.
A HumanMessage is a message that is passed in from a user to the model.
Message for priming AI behavior.
The system message is usually passed in as the first of a sequence of input messages.
Chat prompt value.
A type of a prompt value that is built from messages.
Image URL for multimodal model inputs (OpenAI format).
Represents the inner image_url object in OpenAI's Chat Completion API format. This
is used by ImagePromptTemplate and ChatPromptTemplate.
Base class for all prompt templates, returning a prompt.
Template represented by a dictionary.
Recognizes variables in f-string or mustache formatted string dict values.
Does NOT recognize variables in dict keys. Applies recursively.
Image prompt template for a multimodal model.
Base class for message prompt templates.
Prompt template for a language model.
A prompt template consists of a string template. It accepts a set of parameters from the user that can be used to generate a prompt for a language model.
The template can be formatted using either f-strings (default), jinja2, or mustache syntax.
Prefer using template_format='f-string' instead of template_format='jinja2',
or make sure to NEVER accept jinja2 templates from untrusted sources as they may
lead to arbitrary Python code execution.
As of LangChain 0.0.329, Jinja2 templates will be rendered using Jinja2's SandboxedEnvironment by default. This sand-boxing should be treated as a best-effort approach rather than a guarantee of security, as it is an opt-out rather than opt-in approach.
Despite the sandboxing, we recommend to never use jinja2 templates from untrusted sources.
String prompt that exposes the format method, returning a prompt.
Base class for message prompt templates that use a string prompt template.
Chat message prompt template.
Human message prompt template.
This is a message sent from the user.
AI message prompt template.
This is a message sent from the AI.
System message prompt template.
This is a message that is not sent to the user.
Base class for chat prompt templates.
Prompt template that assumes variable is already list of messages.
A placeholder which can be used to pass in a list of messages.
from langchain_core.prompts import MessagesPlaceholder
prompt = MessagesPlaceholder("history")
prompt.format_messages() # raises KeyError
prompt = MessagesPlaceholder("history", optional=True)
prompt.format_messages() # returns empty list []
prompt.format_messages(
history=[
("system", "You are an AI assistant."),
("human", "Hello!"),
]
)
# -> [
# SystemMessage(content="You are an AI assistant."),
# HumanMessage(content="Hello!"),
# ]from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant."),
MessagesPlaceholder("history"),
("human", "{question}"),
]
)
prompt.invoke(
{
"history": [("human", "what's 5 + 2"), ("ai", "5 + 2 is 7")],
"question": "now multiply that by 4",
}
)
# -> ChatPromptValue(messages=[
# SystemMessage(content="You are a helpful assistant."),
# HumanMessage(content="what's 5 + 2"),
# AIMessage(content="5 + 2 is 7"),
# HumanMessage(content="now multiply that by 4"),
# ])from langchain_core.prompts import MessagesPlaceholder
prompt = MessagesPlaceholder("history", n_messages=1)
prompt.format_messages(
history=[
("system", "You are an AI assistant."),
("human", "Hello!"),
]
)
# -> [
# HumanMessage(content="Hello!"),
# ]Prompt template for chat models.
Use to create flexible templated prompts for chat models.
from langchain_core.prompts import ChatPromptTemplate
template = ChatPromptTemplate(
[
("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}"),
]
)
prompt_value = template.invoke(
{
"name": "Bob",
"user_input": "What is your name?",
}
)
# Output:
# ChatPromptValue(
# messages=[
# SystemMessage(content='You are a helpful AI bot. Your name is Bob.'),
# HumanMessage(content='Hello, how are you doing?'),
# AIMessage(content="I'm doing well, thanks!"),
# HumanMessage(content='What is your name?')
# ]
# )# In addition to Human/AI/Tool/Function messages,
# you can initialize the template with a MessagesPlaceholder
# either using the class directly or with the shorthand tuple syntax:
template = ChatPromptTemplate(
[
("system", "You are a helpful AI bot."),
# Means the template will receive an optional list of messages under
# the "conversation" key
("placeholder", "{conversation}"),
# Equivalently:
# MessagesPlaceholder(variable_name="conversation", optional=True)
]
)
prompt_value = template.invoke(
{
"conversation": [
("human", "Hi!"),
("ai", "How can I assist you today?"),
("human", "Can you make me an ice cream sundae?"),
("ai", "No."),
]
}
)
# Output:
# ChatPromptValue(
# messages=[
# SystemMessage(content='You are a helpful AI bot.'),
# HumanMessage(content='Hi!'),
# AIMessage(content='How can I assist you today?'),
# HumanMessage(content='Can you make me an ice cream sundae?'),
# AIMessage(content='No.'),
# ]
# )If your prompt has only a single input variable (i.e., one instance of
'{variable_nams}'), and you invoke the template with a non-dict object, the
prompt template will inject the provided argument into that variable location.
from langchain_core.prompts import ChatPromptTemplate
template = ChatPromptTemplate(
[
("system", "You are a helpful AI bot. Your name is Carl."),
("human", "{user_input}"),
]
)
prompt_value = template.invoke("Hello, there!")
# Equivalent to
# prompt_value = template.invoke({"user_input": "Hello, there!"})
# Output:
# ChatPromptValue(
# messages=[
# SystemMessage(content='You are a helpful AI bot. Your name is Carl.'),
# HumanMessage(content='Hello, there!'),
# ]
# )