Load prompts.
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.
Load prompt from config dict.
Unified method for loading a prompt from LangChainHub or local filesystem.
Extract text content from model outputs as a string.
Converts model outputs (such as AIMessage or AIMessageChunk objects) into plain
text strings. It's the simplest output parser and is useful when you need string
responses for downstream processing, display, or storage.
Supports streaming, yielding text chunks as they're generated by the model.
Base class for all prompt templates, returning a prompt.
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!'),
# ]
# )Prompt template that contains few shot examples.
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.