langchain.agents.schema.AgentScratchPadChatPromptTemplate

class langchain.agents.schema.AgentScratchPadChatPromptTemplate[source]

Bases: ChatPromptTemplate

Chat prompt template for the agent scratchpad.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

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]

List of input variables in template messages. Used for validation.

param messages: List[MessageLike] [Required]

List of messages consisting of either message prompt templates or messages.

param output_parser: Optional[BaseOutputParser] = None

How to parse the output of calling an LLM on this formatted prompt.

param partial_variables: Mapping[str, Union[str, Callable[[], str]]] [Optional]
param validate_template: bool = False

Whether or not to try validating the template.

async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → 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.

async ainvoke(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any) → Output

Default implementation of ainvoke, calls invoke from a thread.

The default implementation allows usage of async code even if the runnable did not implement a native async version of invoke.

Subclasses should override this method if they can run asynchronously.

append(message: Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate, Tuple[str, str], Tuple[Type, str], str]) → None

Append message to the end of the chat template.

Parameters

message – representation of a message to append.

assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) → RunnableSerializable[Any, Any]

Assigns new fields to the dict output of this runnable. Returns a new runnable.

async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]

Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → AsyncIterator[StreamEvent]

[Beta] Generate a stream of events.

Use to create an iterator ove 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 the

    format: 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 of

    the 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.

  • tags: Optional[List[str]] - The tags of the runnable that generated

    the event.

  • metadata: Optional[Dict[str, Any]] - The metadata of the runnable

    that 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.

event | name | chunk | input | output |

|----------------------|——————|---------------------------------|———————————————–|-------------------------------------------------| | on_chat_model_start | [model name] | | {“messages”: [[SystemMessage, HumanMessage]]} | | | on_chat_model_stream | [model name] | AIMessageChunk(content=”hello”) | | | | on_chat_model_end | [model name] | | {“messages”: [[SystemMessage, HumanMessage]]} | {“generations”: […], “llm_output”: None, …} | | on_llm_start | [model name] | | {‘input’: ‘hello’} | | | on_llm_stream | [model name] | ‘Hello’ | | | | on_llm_end | [model name] | | ‘Hello human!’ | | on_chain_start | format_docs | | | | | on_chain_stream | format_docs | “hello world!, goodbye world!” | | | | on_chain_end | format_docs | | [Document(…)] | “hello world!, goodbye world!” | | on_tool_start | some_tool | | {“x”: 1, “y”: “2”} | | | on_tool_stream | some_tool | {“x”: 1, “y”: “2”} | | | | on_tool_end | some_tool | | | {“x”: 1, “y”: “2”} | | on_retriever_start | [retriever name] | | {“query”: “hello”} | | | on_retriever_chunk | [retriever name] | {documents: […]} | | | | on_retriever_end | [retriever name] | | {“query”: “hello”} | {documents: […]} | | on_prompt_start | [template_name] | | {“question”: “hello”} | | | on_prompt_end | [template_name] | | {“question”: “hello”} | ChatPromptValue(messages: [SystemMessage, …]) |

Here are declarations associated with the events shown above:

format_docs:

```python 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:

```python @tool def some_tool(x: int, y: str) -> dict:

‘’’Some_tool.’’’ return {“x”: x, “y”: y}

```

prompt:

```python 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="v1")
]

# will produce the following events (run_id 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": [],
    },
]
Parameters

  • input – The input to the runnable.

  • config – The config to use for the runnable.

  • version – The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.

  • include_names – Only include events from runnables with matching names.

  • include_types – Only include events from runnables with matching types.

  • include_tags – Only include events from runnables with matching tags.

  • exclude_names – Exclude events from runnables with matching names.

  • exclude_types – Exclude events from runnables with matching types.

  • exclude_tags – Exclude events from runnables with matching tags.

  • kwargs – 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.

Returns

An async stream of StreamEvents.[Beta] Generate a stream of events.

Use to create an iterator ove 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 the

    format: 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 of

    the 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.

  • tags: Optional[List[str]] - The tags of the runnable that generated

    the event.

  • metadata: Optional[Dict[str, Any]] - The metadata of the runnable

    that 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.

event | name | chunk | input | output |

|----------------------|——————|---------------------------------|———————————————–|-------------------------------------------------| | on_chat_model_start | [model name] | | {“messages”: [[SystemMessage, HumanMessage]]} | | | on_chat_model_stream | [model name] | AIMessageChunk(content=”hello”) | | | | on_chat_model_end | [model name] | | {“messages”: [[SystemMessage, HumanMessage]]} | {“generations”: […], “llm_output”: None, …} | | on_llm_start | [model name] | | {‘input’: ‘hello’} | | | on_llm_stream | [model name] | ‘Hello’ | | | | on_llm_end | [model name] | | ‘Hello human!’ | | on_chain_start | format_docs | | | | | on_chain_stream | format_docs | “hello world!, goodbye world!” | | | | on_chain_end | format_docs | | [Document(…)] | “hello world!, goodbye world!” | | on_tool_start | some_tool | | {“x”: 1, “y”: “2”} | | | on_tool_stream | some_tool | {“x”: 1, “y”: “2”} | | | | on_tool_end | some_tool | | | {“x”: 1, “y”: “2”} | | on_retriever_start | [retriever name] | | {“query”: “hello”} | | | on_retriever_chunk | [retriever name] | {documents: […]} | | | | on_retriever_end | [retriever name] | | {“query”: “hello”} | {documents: […]} | | on_prompt_start | [template_name] | | {“question”: “hello”} | | | on_prompt_end | [template_name] | | {“question”: “hello”} | ChatPromptValue(messages: [SystemMessage, …]) |

Here are declarations associated with the events shown above:

format_docs:

```python 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:

```python @tool def some_tool(x: int, y: str) -> dict:

‘’’Some_tool.’’’ return {“x”: x, “y”: y}

```

prompt:

```python 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="v1")
]

# will produce the following events (run_id 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": [],
    },
]
Parameters

  • input – The input to the runnable.

  • config – The config to use for the runnable.

  • version – The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.

  • include_names – Only include events from runnables with matching names.

  • include_types – Only include events from runnables with matching types.

  • include_tags – Only include events from runnables with matching tags.

  • exclude_names – Exclude events from runnables with matching names.

  • exclude_types – Exclude events from runnables with matching types.

  • exclude_tags – Exclude events from runnables with matching tags.

  • kwargs – 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.

Returns

An async stream of StreamEvents.

Notes

async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]

Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc.

Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.

The jsonpatch ops can be applied in order to construct state.

Parameters

  • input – The input to the runnable.

  • config – The config to use for the runnable.

  • diff – Whether to yield diffs between each step, or the current state.

  • with_streamed_output_list – Whether to yield the streamed_output list.

  • include_names – Only include logs with these names.

  • include_types – Only include logs with these types.

  • include_tags – Only include logs with these tags.

  • exclude_names – Exclude logs with these names.

  • exclude_types – Exclude logs with these types.

  • exclude_tags – Exclude logs with these tags.

async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]

Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.

batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → 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.

bind(**kwargs: Any) → Runnable[Input, Output]

Bind arguments to a Runnable, returning a new Runnable.

config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]

The type of config this runnable accepts specified as a pydantic model.

To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.

Parameters

include – A list of fields to include in the config schema.

Returns

A pydantic model that can be used to validate config.

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]
configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

  • include – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

  • deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return dictionary representation of prompt.

extend(messages: Sequence[Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate, Tuple[str, str], Tuple[Type, str], str]]) → None

Extend the chat template with a sequence of messages.

format(**kwargs: Any) → str

Format the chat template into a string.

Parameters

**kwargs – keyword arguments to use for filling in template variables in all the template messages in this chat template.

Returns

formatted string

format_messages(**kwargs: Any) → List[BaseMessage]

Format the chat template into a list of finalized messages.

Parameters

**kwargs – keyword arguments to use for filling in template variables in all the template messages in this chat template.

Returns

list of formatted messages

format_prompt(**kwargs: Any) → PromptValue

Format prompt. Should return a PromptValue. :param **kwargs: Keyword arguments to use for formatting.

Returns

PromptValue.

classmethod from_messages(messages: Sequence[Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate, Tuple[str, str], Tuple[Type, str], str]]) → ChatPromptTemplate

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 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., (“human”, “{user_input}”), (4) 2-tuple of (message class, template), (4) a string which is shorthand for (“human”, template); e.g., “{user_input}”

Returns

a chat prompt template

classmethod from_orm(obj: Any) → Model
classmethod from_role_strings(string_messages: List[Tuple[str, str]]) → ChatPromptTemplate

[Deprecated] Create a chat prompt template from a list of (role, template) tuples.

Parameters

  • string_messages – list of (role, template) tuples.

  • string_messages – list of (role, template) tuples.

Returns

a chat prompt template[Deprecated] Create a chat prompt template from a list of (role, template) tuples.

Returns

a chat prompt template

Notes

Deprecated since version 0.0.260: Use from_messages classmethod instead.

classmethod from_strings(string_messages: List[Tuple[Type[BaseMessagePromptTemplate], str]]) → ChatPromptTemplate

[Deprecated] Create a chat prompt template from a list of (role class, template) tuples.

Parameters

  • string_messages – list of (role class, template) tuples.

  • string_messages – list of (role class, template) tuples.

Returns

a chat prompt template[Deprecated] Create a chat prompt template from a list of (role class, template) tuples.

Returns

a chat prompt template

Notes

Deprecated since version 0.0.260: Use from_messages classmethod instead.

classmethod from_template(template: str, **kwargs: Any) → ChatPromptTemplate

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 – template string

  • **kwargs – keyword arguments to pass to the constructor.

Returns

A new instance of this class.

get_graph(config: Optional[RunnableConfig] = None) → Graph

Return a graph representation of this runnable.

get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]

Get a pydantic model that can be used to validate input to the runnable.

Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the runnable is invoked with.

This method allows to get an input schema for a specific configuration.

Parameters

config – A config to use when generating the schema.

Returns

A pydantic model that can be used to validate input.

classmethod get_lc_namespace() → List[str]

Get the namespace of the langchain object.

get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str

Get the name of the runnable.

get_output_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]

Get a pydantic model that can be used to validate output to the runnable.

Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with.

This method allows to get an output schema for a specific configuration.

Parameters

config – A config to use when generating the schema.

Returns

A pydantic model that can be used to validate output.

get_prompts(config: Optional[RunnableConfig] = None) → List[BasePromptTemplate]
invoke(input: Dict, config: Optional[RunnableConfig] = None) → PromptValue

Transform a single input into an output. Override to implement.

Parameters

  • input – The input to the runnable.

  • config – A config to use when invoking the runnable. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details.

Returns

The output of the runnable.

classmethod is_lc_serializable() → bool[source]

Return whether this class is serializable.

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod lc_id() → List[str]

A unique identifier for this class for serialization purposes.

The unique identifier is a list of strings that describes the path to the object.

map() → Runnable[List[Input], List[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
classmethod parse_obj(obj: Any) → Model
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model
partial(**kwargs: Union[str, Callable[[], str]]) → ChatPromptTemplate

Get a new ChatPromptTemplate with some input variables already filled in.

Parameters

**kwargs – keyword arguments to use for filling in template variables. Ought to be a subset of the input variables.

Returns

A new ChatPromptTemplate.

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")
pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]

Pick keys from the dict output of this runnable. Returns a new runnable.

pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) → RunnableSerializable[Input, Other]

Compose this runnable with another object to create a RunnableSequence.

pretty_print() → None
pretty_repr(html: bool = False) → str

Human-readable representation.

save(file_path: Union[Path, str]) → None

Save prompt to file.

Parameters

file_path – path to file.

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]

Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

to_json() → Union[SerializedConstructor, SerializedNotImplemented]
to_json_not_implemented() → SerializedNotImplemented
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]

Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

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.

Parameters

  • fallbacks – A sequence of runnables to try if the original runnable fails.

  • exceptions_to_handle – A tuple of exception types to handle.

  • exception_key – 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.

with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = 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.

with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.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 – A tuple of exception types to retry on

  • wait_exponential_jitter – Whether to add jitter to the wait time between retries

  • stop_after_attempt – The maximum number of attempts to make before giving up

Returns

A new Runnable that retries the original runnable on exceptions.

with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) → Runnable[Input, Output]

Bind input and output types to a Runnable, returning a new Runnable.

property InputType: Type[Input]

The type of input this runnable accepts specified as a type annotation.

property OutputType: Any

The type of output this runnable produces specified as a type annotation.

property config_specs: List[ConfigurableFieldSpec]

List configurable fields for this runnable.

property input_schema: Type[BaseModel]

The type of input this runnable accepts specified as a pydantic model.

property lc_attributes: Dict

Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor.

property lc_secrets: Dict[str, str]

A map of constructor argument names to secret ids.

For example,

{“openai_api_key”: “OPENAI_API_KEY”}

name: Optional[str] = None

The name of the runnable. Used for debugging and tracing.

property output_schema: Type[BaseModel]

The type of output this runnable produces specified as a pydantic model.