ChatPromptTemplate#
- class langchain_core.prompts.chat.ChatPromptTemplate[source]#
Bases:
BaseChatPromptTemplate
Prompt template for chat models.
Use to create flexible templated prompts for chat models.
Examples
Changed in version 0.2.24: You can pass any Message-like formats supported by
ChatPromptTemplate.from_messages()
directly toChatPromptTemplate()
init.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?') # ] #)
Messages Placeholder:
# 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.'), # ] #)
Single-variable template:
If your prompt has only a single input variable (i.e., 1 instance of β{variable_nams}β), 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!'), # ] # )
Create a chat prompt template from a variety of message formats.
- Parameters:
messages β sequence of message representations. A message can be represented using the following formats: (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of (message type, template); e.g., (βhumanβ, β{user_input}β), (4) 2-tuple of (message class, template), (5) a string which is shorthand for (βhumanβ, template); e.g., β{user_input}β.
template_format β format of the template. Defaults to βf-stringβ.
input_variables β A list of the names of the variables whose values are required as inputs to the prompt.
optional_variables β A list of the names of the variables for placeholder
inferred (or MessagePlaceholder that are optional. These variables are auto)
them. (from the prompt and user need not provide)
partial_variables β A dictionary of the partial variables the prompt template carries. Partial variables populate the template so that you donβt need to pass them in every time you call the prompt.
validate_template β Whether to validate the template.
input_types β A dictionary of the types of the variables the prompt template expects. If not provided, all variables are assumed to be strings.
- Returns:
A chat prompt template.
Examples
Instantiation from a list of message templates:
template = ChatPromptTemplate([ ("human", "Hello, how are you?"), ("ai", "I'm doing well, thanks!"), ("human", "That's good to hear."), ])
Instantiation from mixed message formats:
template = ChatPromptTemplate([ SystemMessage(content="hello"), ("human", "Hello, how are you?"), ])
Note
ChatPromptTemplate implements the standard
Runnable Interface
. πThe
Runnable Interface
has additional methods that are available on runnables, such aswith_types
,with_retry
,assign
,bind
,get_graph
, and more.- param input_types: Dict[str, Any] [Optional]#
A dictionary of the types of the variables the prompt template expects. If not provided, all variables are assumed to be strings.
- param input_variables: list[str] [Required]#
A list of the names of the variables whose values are required as inputs to the prompt.
- param messages: Annotated[list[MessageLike], SkipValidation()] [Required]#
List of messages consisting of either message prompt templates or messages.
- param metadata: Dict[str, Any] | None = None#
Metadata to be used for tracing.
- param optional_variables: list[str] = []#
optional_variables: A list of the names of the variables for placeholder or MessagePlaceholder that are optional. These variables are auto inferred from the prompt and user need not provide them.
- param output_parser: BaseOutputParser | None = None#
How to parse the output of calling an LLM on this formatted prompt.
- param partial_variables: Mapping[str, Any] [Optional]#
A dictionary of the partial variables the prompt template carries.
Partial variables populate the template so that you donβt need to pass them in every time you call the prompt.
- param tags: list[str] | None = None#
Tags to be used for tracing.
- param validate_template: bool = False#
Whether or not to try validating the template.
- async abatch(inputs: list[Input], config: RunnableConfig | list[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) list[Output] #
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.
- Parameters:
inputs (list[Input]) β A list of inputs to the Runnable.
config (RunnableConfig | list[RunnableConfig] | None) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None.
return_exceptions (bool) β Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
- Returns:
A list of outputs from the Runnable.
- Return type:
list[Output]
- async abatch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) AsyncIterator[tuple[int, Output | Exception]] #
Run ainvoke in parallel on a list of inputs, yielding results as they complete.
- Parameters:
inputs (Sequence[Input]) β A list of inputs to the Runnable.
config (RunnableConfig | Sequence[RunnableConfig] | None) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None. Defaults to None.
return_exceptions (bool) β Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
- Yields:
A tuple of the index of the input and the output from the Runnable.
- Return type:
AsyncIterator[tuple[int, Output | Exception]]
- async aformat(**kwargs: Any) str #
Async format the chat template into a string.
- Parameters:
**kwargs (Any) β keyword arguments to use for filling in template variables in all the template messages in this chat template.
- Returns:
formatted string.
- Return type:
str
- async aformat_messages(**kwargs: Any) list[BaseMessage] [source]#
Async format the chat template into a list of finalized messages.
- Parameters:
**kwargs (Any) β keyword arguments to use for filling in template variables in all the template messages in this chat template.
- Returns:
list of formatted messages.
- Raises:
ValueError β If unexpected input.
- Return type:
list[BaseMessage]
- async aformat_prompt(**kwargs: Any) PromptValue #
Async format prompt. Should return a PromptValue.
- Parameters:
**kwargs (Any) β Keyword arguments to use for formatting.
- Returns:
PromptValue.
- Return type:
- async ainvoke(input: dict, config: RunnableConfig | None = None, **kwargs: Any) PromptValue #
Async invoke the prompt.
- Parameters:
input (dict) β Dict, input to the prompt.
config (RunnableConfig | None) β RunnableConfig, configuration for the prompt.
kwargs (Any)
- Returns:
The output of the prompt.
- Return type:
- append(message: BaseMessagePromptTemplate | BaseMessage | BaseChatPromptTemplate | tuple[str | type, str | list[dict] | list[object]] | str) None [source]#
Append a message to the end of the chat template.
- Parameters:
message (BaseMessagePromptTemplate | BaseMessage | BaseChatPromptTemplate | tuple[str | type, str | list[dict] | list[object]] | str) β representation of a message to append.
- Return type:
None
- async astream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) AsyncIterator[Output] #
Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.
- Parameters:
input (Input) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable. Defaults to None.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
- Yields:
The output of the Runnable.
- Return type:
AsyncIterator[Output]
- async astream_events(input: Any, config: RunnableConfig | None = None, *, version: Literal['v1', 'v2'], include_names: Sequence[str] | None = None, include_types: Sequence[str] | None = None, include_tags: Sequence[str] | None = None, exclude_names: Sequence[str] | None = None, exclude_types: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, **kwargs: Any) AsyncIterator[StandardStreamEvent | CustomStreamEvent] #
Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.
A StreamEvent is a dictionary with the following schema:
event
: str - Event names are of theformat: on_[runnable_type]_(start|stream|end).
name
: str - The name of the Runnable that generated the event.run_id
: str - randomly generated ID associated with the given execution ofthe Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.
parent_ids
: List[str] - The IDs of the parent runnables thatgenerated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
tags
: Optional[List[str]] - The tags of the Runnable that generatedthe event.
metadata
: Optional[Dict[str, Any]] - The metadata of the Runnablethat generated the event.
data
: Dict[str, Any]
Below is a table that illustrates some evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.
ATTENTION This reference table is for the V2 version of the schema.
event
name
chunk
input
output
on_chat_model_start
[model name]
{βmessagesβ: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=βhelloβ)
on_chat_model_end
[model name]
{βmessagesβ: [[SystemMessage, HumanMessage]]}
AIMessageChunk(content=βhello worldβ)
on_llm_start
[model name]
{βinputβ: βhelloβ}
on_llm_stream
[model name]
βHelloβ
on_llm_end
[model name]
βHello human!β
on_chain_start
format_docs
on_chain_stream
format_docs
βhello world!, goodbye world!β
on_chain_end
format_docs
[Document(β¦)]
βhello world!, goodbye world!β
on_tool_start
some_tool
{βxβ: 1, βyβ: β2β}
on_tool_end
some_tool
{βxβ: 1, βyβ: β2β}
on_retriever_start
[retriever name]
{βqueryβ: βhelloβ}
on_retriever_end
[retriever name]
{βqueryβ: βhelloβ}
[Document(β¦), ..]
on_prompt_start
[template_name]
{βquestionβ: βhelloβ}
on_prompt_end
[template_name]
{βquestionβ: βhelloβ}
ChatPromptValue(messages: [SystemMessage, β¦])
In addition to the standard events, users can also dispatch custom events (see example below).
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
Attribute
Type
Description
name
str
A user defined name for the event.
data
Any
The data associated with the event. This can be anything, though we suggest making it JSON serializable.
Here are declarations associated with the standard events shown above:
format_docs:
def format_docs(docs: List[Document]) -> str: '''Format the docs.''' return ", ".join([doc.page_content for doc in docs]) format_docs = RunnableLambda(format_docs)
some_tool:
@tool def some_tool(x: int, y: str) -> dict: '''Some_tool.''' return {"x": x, "y": y}
prompt:
template = ChatPromptTemplate.from_messages( [("system", "You are Cat Agent 007"), ("human", "{question}")] ).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
from langchain_core.runnables import RunnableLambda async def reverse(s: str) -> str: return s[::-1] chain = RunnableLambda(func=reverse) events = [ event async for event in chain.astream_events("hello", version="v2") ] # will produce the following events (run_id, and parent_ids # has been omitted for brevity): [ { "data": {"input": "hello"}, "event": "on_chain_start", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"chunk": "olleh"}, "event": "on_chain_stream", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"output": "olleh"}, "event": "on_chain_end", "metadata": {}, "name": "reverse", "tags": [], }, ]
Example: Dispatch Custom Event
from langchain_core.callbacks.manager import ( adispatch_custom_event, ) from langchain_core.runnables import RunnableLambda, RunnableConfig import asyncio async def slow_thing(some_input: str, config: RunnableConfig) -> str: """Do something that takes a long time.""" await asyncio.sleep(1) # Placeholder for some slow operation await adispatch_custom_event( "progress_event", {"message": "Finished step 1 of 3"}, config=config # Must be included for python < 3.10 ) await asyncio.sleep(1) # Placeholder for some slow operation await adispatch_custom_event( "progress_event", {"message": "Finished step 2 of 3"}, config=config # Must be included for python < 3.10 ) await asyncio.sleep(1) # Placeholder for some slow operation return "Done" slow_thing = RunnableLambda(slow_thing) async for event in slow_thing.astream_events("some_input", version="v2"): print(event)
- Parameters:
input (Any) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable.
version (Literal['v1', 'v2']) β The version of the schema to use either v2 or v1. Users should use v2. v1 is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in v2.
include_names (Sequence[str] | None) β Only include events from runnables with matching names.
include_types (Sequence[str] | None) β Only include events from runnables with matching types.
include_tags (Sequence[str] | None) β Only include events from runnables with matching tags.
exclude_names (Sequence[str] | None) β Exclude events from runnables with matching names.
exclude_types (Sequence[str] | None) β Exclude events from runnables with matching types.
exclude_tags (Sequence[str] | None) β Exclude events from runnables with matching tags.
kwargs (Any) β Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.
- Yields:
An async stream of StreamEvents.
- Raises:
NotImplementedError β If the version is not v1 or v2.
- Return type:
AsyncIterator[StandardStreamEvent | CustomStreamEvent]
- batch(inputs: list[Input], config: RunnableConfig | list[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) list[Output] #
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.
- Parameters:
inputs (list[Input])
config (RunnableConfig | list[RunnableConfig] | None)
return_exceptions (bool)
kwargs (Any | None)
- Return type:
list[Output]
- batch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) Iterator[tuple[int, Output | Exception]] #
Run invoke in parallel on a list of inputs, yielding results as they complete.
- Parameters:
inputs (Sequence[Input])
config (RunnableConfig | Sequence[RunnableConfig] | None)
return_exceptions (bool)
kwargs (Any | None)
- Return type:
Iterator[tuple[int, Output | Exception]]
- bind(**kwargs: Any) Runnable[Input, Output] #
Bind arguments to a Runnable, returning a new Runnable.
Useful when a Runnable in a chain requires an argument that is not in the output of the previous Runnable or included in the user input.
- Parameters:
kwargs (Any) β The arguments to bind to the Runnable.
- Returns:
A new Runnable with the arguments bound.
- Return type:
Runnable[Input, Output]
Example:
from langchain_community.chat_models import ChatOllama from langchain_core.output_parsers import StrOutputParser llm = ChatOllama(model='llama2') # Without bind. chain = ( llm | StrOutputParser() ) chain.invoke("Repeat quoted words exactly: 'One two three four five.'") # Output is 'One two three four five.' # With bind. chain = ( llm.bind(stop=["three"]) | StrOutputParser() ) chain.invoke("Repeat quoted words exactly: 'One two three four five.'") # Output is 'One two'
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) RunnableSerializable #
Configure alternatives for Runnables that can be set at runtime.
- Parameters:
which (ConfigurableField) β The ConfigurableField instance that will be used to select the alternative.
default_key (str) β The default key to use if no alternative is selected. Defaults to βdefaultβ.
prefix_keys (bool) β Whether to prefix the keys with the ConfigurableField id. Defaults to False.
**kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) β A dictionary of keys to Runnable instances or callables that return Runnable instances.
- Returns:
A new Runnable with the alternatives configured.
- Return type:
from langchain_anthropic import ChatAnthropic from langchain_core.runnables.utils import ConfigurableField from langchain_openai import ChatOpenAI model = ChatAnthropic( model_name="claude-3-sonnet-20240229" ).configurable_alternatives( ConfigurableField(id="llm"), default_key="anthropic", openai=ChatOpenAI() ) # uses the default model ChatAnthropic print(model.invoke("which organization created you?").content) # uses ChatOpenAI print( model.with_config( configurable={"llm": "openai"} ).invoke("which organization created you?").content )
- configurable_fields(**kwargs: ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) RunnableSerializable #
Configure particular Runnable fields at runtime.
- Parameters:
**kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) β A dictionary of ConfigurableField instances to configure.
- Returns:
A new Runnable with the fields configured.
- Return type:
from langchain_core.runnables import ConfigurableField from langchain_openai import ChatOpenAI model = ChatOpenAI(max_tokens=20).configurable_fields( max_tokens=ConfigurableField( id="output_token_number", name="Max tokens in the output", description="The maximum number of tokens in the output", ) ) # max_tokens = 20 print( "max_tokens_20: ", model.invoke("tell me something about chess").content ) # max_tokens = 200 print("max_tokens_200: ", model.with_config( configurable={"output_token_number": 200} ).invoke("tell me something about chess").content )
- extend(messages: Sequence[BaseMessagePromptTemplate | BaseMessage | BaseChatPromptTemplate | tuple[str | type, str | list[dict] | list[object]] | str]) None [source]#
Extend the chat template with a sequence of messages.
- Parameters:
messages (Sequence[BaseMessagePromptTemplate | BaseMessage | BaseChatPromptTemplate | tuple[str | type, str | list[dict] | list[object]] | str]) β sequence of message representations to append.
- Return type:
None
- format(**kwargs: Any) str #
Format the chat template into a string.
- Parameters:
**kwargs (Any) β keyword arguments to use for filling in template variables in all the template messages in this chat template.
- Returns:
formatted string.
- Return type:
str
- format_messages(**kwargs: Any) list[BaseMessage] [source]#
Format the chat template into a list of finalized messages.
- Parameters:
**kwargs (Any) β keyword arguments to use for filling in template variables in all the template messages in this chat template.
- Returns:
list of formatted messages.
- Return type:
list[BaseMessage]
- format_prompt(**kwargs: Any) PromptValue #
Format prompt. Should return a PromptValue.
- Parameters:
**kwargs (Any) β Keyword arguments to use for formatting.
- Returns:
PromptValue.
- Return type:
- classmethod from_messages(messages: Sequence[BaseMessagePromptTemplate | BaseMessage | BaseChatPromptTemplate | tuple[str | type, str | list[dict] | list[object]] | str], template_format: Literal['f-string', 'mustache', 'jinja2'] = 'f-string') ChatPromptTemplate [source]#
Create a chat prompt template from a variety of message formats.
Examples
Instantiation from a list of message templates:
template = ChatPromptTemplate.from_messages([ ("human", "Hello, how are you?"), ("ai", "I'm doing well, thanks!"), ("human", "That's good to hear."), ])
Instantiation from mixed message formats:
template = ChatPromptTemplate.from_messages([ SystemMessage(content="hello"), ("human", "Hello, how are you?"), ])
- Parameters:
messages (Sequence[BaseMessagePromptTemplate | BaseMessage | BaseChatPromptTemplate | tuple[str | type, str | list[dict] | list[object]] | str]) β sequence of message representations. A message can be represented using the following formats: (1) BaseMessagePromptTemplate, (2) BaseMessage, (3) 2-tuple of (message type, template); e.g., (βhumanβ, β{user_input}β), (4) 2-tuple of (message class, template), (5) a string which is shorthand for (βhumanβ, template); e.g., β{user_input}β.
template_format (Literal['f-string', 'mustache', 'jinja2']) β format of the template. Defaults to βf-stringβ.
- Returns:
a chat prompt template.
- Return type:
- classmethod from_role_strings(string_messages: list[tuple[str, str]]) ChatPromptTemplate [source]#
Deprecated since version langchain-core==0.0.1: Use
from_messages classmethod()
instead.Create a chat prompt template from a list of (role, template) tuples.
- Parameters:
string_messages (list[tuple[str, str]]) β list of (role, template) tuples.
- Returns:
a chat prompt template.
- Return type:
- classmethod from_strings(string_messages: list[tuple[type[BaseMessagePromptTemplate], str]]) ChatPromptTemplate [source]#
Deprecated since version langchain-core==0.0.1: Use
from_messages classmethod()
instead.Create a chat prompt template from a list of (role class, template) tuples.
- Parameters:
string_messages (list[tuple[type[BaseMessagePromptTemplate], str]]) β list of (role class, template) tuples.
- Returns:
a chat prompt template.
- Return type:
- classmethod from_template(template: str, **kwargs: Any) ChatPromptTemplate [source]#
Create a chat prompt template from a template string.
Creates a chat template consisting of a single message assumed to be from the human.
- Parameters:
template (str) β template string
**kwargs (Any) β keyword arguments to pass to the constructor.
- Returns:
A new instance of this class.
- Return type:
- invoke(input: dict, config: RunnableConfig | None = None, **kwargs: Any) PromptValue #
Invoke the prompt.
- Parameters:
input (dict) β Dict, input to the prompt.
config (RunnableConfig | None) β RunnableConfig, configuration for the prompt.
kwargs (Any)
- Returns:
The output of the prompt.
- Return type:
- partial(**kwargs: Any) ChatPromptTemplate [source]#
Get a new ChatPromptTemplate with some input variables already filled in.
- Parameters:
**kwargs (Any) β keyword arguments to use for filling in template variables. Ought to be a subset of the input variables.
- Returns:
A new ChatPromptTemplate.
- Return type:
Example
from langchain_core.prompts import ChatPromptTemplate template = ChatPromptTemplate.from_messages( [ ("system", "You are an AI assistant named {name}."), ("human", "Hi I'm {user}"), ("ai", "Hi there, {user}, I'm {name}."), ("human", "{input}"), ] ) template2 = template.partial(user="Lucy", name="R2D2") template2.format_messages(input="hello")
- pretty_print() None #
Print a human-readable representation.
- Return type:
None
- pretty_repr(html: bool = False) str [source]#
Human-readable representation.
- Parameters:
html (bool) β Whether to format as HTML. Defaults to False.
- Returns:
Human-readable representation.
- Return type:
str
- save(file_path: Path | str) None [source]#
Save prompt to file.
- Parameters:
file_path (Path | str) β path to file.
- Return type:
None
- stream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) Iterator[Output] #
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.
- Parameters:
input (Input) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable. Defaults to None.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
- Yields:
The output of the Runnable.
- Return type:
Iterator[Output]
- with_alisteners(*, on_start: AsyncListener | None = None, on_end: AsyncListener | None = None, on_error: AsyncListener | None = None) Runnable[Input, Output] #
Bind asynchronous lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Asynchronously called before the Runnable starts running. on_end: Asynchronously called after the Runnable finishes running. on_error: Asynchronously called if the Runnable throws an error.
The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.
- Parameters:
on_start (Optional[AsyncListener]) β Asynchronously called before the Runnable starts running. Defaults to None.
on_end (Optional[AsyncListener]) β Asynchronously called after the Runnable finishes running. Defaults to None.
on_error (Optional[AsyncListener]) β Asynchronously called if the Runnable throws an error. Defaults to None.
- Returns:
A new Runnable with the listeners bound.
- Return type:
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda import time async def test_runnable(time_to_sleep : int): print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}") await asyncio.sleep(time_to_sleep) print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}") async def fn_start(run_obj : Runnable): print(f"on start callback starts at {format_t(time.time())} await asyncio.sleep(3) print(f"on start callback ends at {format_t(time.time())}") async def fn_end(run_obj : Runnable): print(f"on end callback starts at {format_t(time.time())} await asyncio.sleep(2) print(f"on end callback ends at {format_t(time.time())}") runnable = RunnableLambda(test_runnable).with_alisteners( on_start=fn_start, on_end=fn_end ) async def concurrent_runs(): await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3)) asyncio.run(concurrent_runs()) Result: on start callback starts at 2024-05-16T14:20:29.637053+00:00 on start callback starts at 2024-05-16T14:20:29.637150+00:00 on start callback ends at 2024-05-16T14:20:32.638305+00:00 on start callback ends at 2024-05-16T14:20:32.638383+00:00 Runnable[3s]: starts at 2024-05-16T14:20:32.638849+00:00 Runnable[5s]: starts at 2024-05-16T14:20:32.638999+00:00 Runnable[3s]: ends at 2024-05-16T14:20:35.640016+00:00 on end callback starts at 2024-05-16T14:20:35.640534+00:00 Runnable[5s]: ends at 2024-05-16T14:20:37.640169+00:00 on end callback starts at 2024-05-16T14:20:37.640574+00:00 on end callback ends at 2024-05-16T14:20:37.640654+00:00 on end callback ends at 2024-05-16T14:20:39.641751+00:00
- with_config(config: RunnableConfig | None = None, **kwargs: Any) Runnable[Input, Output] #
Bind config to a Runnable, returning a new Runnable.
- Parameters:
config (RunnableConfig | None) β The config to bind to the Runnable.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
- Returns:
A new Runnable with the config bound.
- Return type:
Runnable[Input, Output]
- with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: tuple[type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) RunnableWithFallbacksT[Input, Output] #
Add fallbacks to a Runnable, returning a new Runnable.
The new Runnable will try the original Runnable, and then each fallback in order, upon failures.
- Parameters:
fallbacks (Sequence[Runnable[Input, Output]]) β A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) β A tuple of exception types to handle. Defaults to (Exception,).
exception_key (Optional[str]) β If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input. Defaults to None.
- Returns:
A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.
- Return type:
RunnableWithFallbacksT[Input, Output]
Example
from typing import Iterator from langchain_core.runnables import RunnableGenerator def _generate_immediate_error(input: Iterator) -> Iterator[str]: raise ValueError() yield "" def _generate(input: Iterator) -> Iterator[str]: yield from "foo bar" runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks( [RunnableGenerator(_generate)] ) print(''.join(runnable.stream({}))) #foo bar
- Parameters:
fallbacks (Sequence[Runnable[Input, Output]]) β A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) β A tuple of exception types to handle.
exception_key (Optional[str]) β If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input.
- Returns:
A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.
- Return type:
RunnableWithFallbacksT[Input, Output]
- with_listeners(*, on_start: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, on_end: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, on_error: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None) Runnable[Input, Output] #
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the Runnable starts running, with the Run object. on_end: Called after the Runnable finishes running, with the Run object. on_error: Called if the Runnable throws an error, with the Run object.
The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.
- Parameters:
on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called before the Runnable starts running. Defaults to None.
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called after the Runnable finishes running. Defaults to None.
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called if the Runnable throws an error. Defaults to None.
- Returns:
A new Runnable with the listeners bound.
- Return type:
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda from langchain_core.tracers.schemas import Run import time def test_runnable(time_to_sleep : int): time.sleep(time_to_sleep) def fn_start(run_obj: Run): print("start_time:", run_obj.start_time) def fn_end(run_obj: Run): print("end_time:", run_obj.end_time) chain = RunnableLambda(test_runnable).with_listeners( on_start=fn_start, on_end=fn_end ) chain.invoke(2)
- with_retry(*, retry_if_exception_type: tuple[type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) Runnable[Input, Output] #
Create a new Runnable that retries the original Runnable on exceptions.
- Parameters:
retry_if_exception_type (tuple[type[BaseException], ...]) β A tuple of exception types to retry on. Defaults to (Exception,).
wait_exponential_jitter (bool) β Whether to add jitter to the wait time between retries. Defaults to True.
stop_after_attempt (int) β The maximum number of attempts to make before giving up. Defaults to 3.
- Returns:
A new Runnable that retries the original Runnable on exceptions.
- Return type:
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda count = 0 def _lambda(x: int) -> None: global count count = count + 1 if x == 1: raise ValueError("x is 1") else: pass runnable = RunnableLambda(_lambda) try: runnable.with_retry( stop_after_attempt=2, retry_if_exception_type=(ValueError,), ).invoke(1) except ValueError: pass assert (count == 2)
- Parameters:
retry_if_exception_type (tuple[type[BaseException], ...]) β A tuple of exception types to retry on
wait_exponential_jitter (bool) β Whether to add jitter to the wait time between retries
stop_after_attempt (int) β The maximum number of attempts to make before giving up
- Returns:
A new Runnable that retries the original Runnable on exceptions.
- Return type:
Runnable[Input, Output]
- with_types(*, input_type: type[Input] | None = None, output_type: type[Output] | None = None) Runnable[Input, Output] #
Bind input and output types to a Runnable, returning a new Runnable.
- Parameters:
input_type (type[Input] | None) β The input type to bind to the Runnable. Defaults to None.
output_type (type[Output] | None) β The output type to bind to the Runnable. Defaults to None.
- Returns:
A new Runnable with the types bound.
- Return type:
Runnable[Input, Output]
Examples using ChatPromptTemplate
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