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.
Example:
from langchain_core.prompts import PromptTemplate
# Instantiation using from_template (recommended)
prompt = PromptTemplate.from_template("Say {foo}")
prompt.format(foo="bar")
# Instantiation using initializer
prompt = PromptTemplate(template="Say {foo}")Get the namespace of the LangChain object.
Check that template and input variables are consistent.
Get the input schema for the prompt.
Format the prompt with the inputs.
Take examples in list format with prefix and suffix to create a prompt.
Intended to be used as a way to dynamically create a prompt from examples.
Load a prompt from a file.
Load a prompt template from a template.
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.
Template input variables.
A list of the names of the variables for placeholder or MessagePlaceholder that
A dictionary of the types of the variables the prompt template expects.
How to parse the output of calling an LLM on this formatted prompt.
A dictionary of the partial variables the prompt template carries.
Optional metadata associated with the retriever.
Optional list of tags associated with the retriever.
Validate variable names do not include restricted names.
Return True as this class is serializable.
Invoke the retriever to get relevant documents.
Asynchronously invoke the retriever to get relevant documents.
Format prompt.
Async format prompt.
Get a new ChatPromptTemplate with some input variables already filled in.
Format the prompt with the inputs.
Return dictionary representation of output parser.
Save prompt to file.
Get a JSON schema that represents the input to the Runnable.
Get a JSON schema that represents the output of the Runnable.
The type of config this Runnable accepts specified as a Pydantic model.
Get a JSON schema that represents the config of the Runnable.
Return a list of prompts used by this Runnable.
Pipe Runnable objects.
Pick keys from the output dict of this Runnable.
Merge the Dict input with the output produced by the mapping argument.
Invoke the retriever to get relevant documents.
Asynchronously invoke the retriever to get relevant documents.
Run invoke in parallel on a list of inputs.
Run ainvoke in parallel on a list of inputs.
Stream all output from a Runnable, as reported to the callback system.
Generate a stream of events.
Bind arguments to a Runnable, returning a new Runnable.
Bind lifecycle listeners to a Runnable, returning a new Runnable.
Bind async lifecycle listeners to a Runnable.
Bind input and output types to a Runnable, returning a new Runnable.
Create a new Runnable that retries the original Runnable on exceptions.
Map a function to multiple iterables.
Add fallbacks to a Runnable, returning a new Runnable.
Create a BaseTool from a Runnable.