FetchDataTool

class hana_ai.tools.df_tools.fetch_tools.FetchDataTool(connection_context: ConnectionContext, return_direct: bool = False, is_transform: bool = False)

This tool fetches data from a given table.

Parameters:

connection_contextConnectionContext: Connection context to the HANA database.

Attributes:

InputType: The type of input this Runnable accepts specified as a type annotation.
OutputType: The type of output this Runnable produces specified as a type annotation.
args: Get the tool's input arguments schema.
config_specs: List configurable fields for this Runnable.
input_schema: The type of input this Runnable accepts specified as a pydantic model.
is_single_input: Check if the tool accepts only a single input argument.
lc_attributes: List of attribute names that should be included in the serialized kwargs.
lc_secrets: A map of constructor argument names to secret ids.
model_extra: Get extra fields set during validation.
model_fields_set: Returns the set of fields that have been explicitly set on this model instance.
output_schema: The type of output this Runnable produces specified as a pydantic model.
tool_call_schema: Get the schema for tool calls, excluding injected arguments.

Methods

`__call__`(tool_input[, callbacks])
`abatch`(inputs[, config, return_exceptions])	Default implementation runs ainvoke in parallel using asyncio.gather.
`abatch_as_completed`(inputs[, config, ...])	Run ainvoke in parallel on a list of inputs.
`ainvoke`(input[, config])	Default implementation of ainvoke, calls invoke from a thread.
`arun`(tool_input[, verbose, start_color, ...])	Run the tool asynchronously.
`as_tool`([args_schema, name, description, ...])
`assign`(**kwargs)	Assigns new fields to the dict output of this Runnable.
`astream`(input[, config])	Default implementation of astream, which calls ainvoke.
`astream_events`(input[, config, version, ...])	Generate a stream of events.
`astream_log`(input[, config, diff, ...])	Stream all output from a Runnable, as reported to the callback system.
`atransform`(input[, config])	Default implementation of atransform, which buffers input and calls astream.
`batch`(inputs[, config, return_exceptions])	Default implementation runs invoke in parallel using a thread pool executor.
`batch_as_completed`(inputs[, config, ...])	Run invoke in parallel on a list of inputs.
`bind`(**kwargs)	Bind arguments to a Runnable, returning a new Runnable.
`config_schema`(*[, include])	The type of config this Runnable accepts specified as a pydantic model.
`configurable_alternatives`(which, *[, ...])	Configure alternatives for Runnables that can be set at runtime.
`configurable_fields`(**kwargs)	Configure particular Runnable fields at runtime.
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`get_config_jsonschema`(*[, include])	Get a JSON schema that represents the config of the Runnable.
`get_graph`([config])	Return a graph representation of this Runnable.
`get_input_jsonschema`([config])	Get a JSON schema that represents the input to the Runnable.
`get_input_schema`([config])	The tool's input schema.
`get_lc_namespace`()	Get the namespace of the langchain object.
`get_name`([suffix, name])	Get the name of the Runnable.
`get_output_jsonschema`([config])	Get a JSON schema that represents the output of the Runnable.
`get_output_schema`([config])	Get a pydantic model that can be used to validate output to the Runnable.
`invoke`(input[, config])	Transform a single input into an output.
`is_lc_serializable`()	Is this class serializable?
`lc_id`()	A unique identifier for this class for serialization purposes.
`map`()	Return a new Runnable that maps a list of inputs to a list of outputs.
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`model_dump`(*[, mode, include, exclude, ...])	!!! abstract "Usage Documentation"
`model_dump_json`(*[, indent, include, ...])	!!! abstract "Usage Documentation"
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(context, /)	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	!!! abstract "Usage Documentation"
`model_validate_strings`(obj, *[, strict, ...])	Validate the given object with string data against the Pydantic model.
`pick`(keys)	Pick keys from the output dict of this Runnable.
`pipe`(*others[, name])	Compose this Runnable with Runnable-like objects to make a RunnableSequence.
`raise_deprecation`(values)	Raise deprecation warning if callback_manager is used.
`run`(tool_input[, verbose, start_color, ...])	Run the tool.
`set_transform`(is_transform)	Return a copy of the tool with the is_transform flag set.
`stream`(input[, config])	Default implementation of stream, which calls invoke.
`to_json`()	Serialize the Runnable to JSON.
`transform`(input[, config])	Default implementation of transform, which buffers input and calls astream.
`with_alisteners`(*[, on_start, on_end, on_error])	Bind async lifecycle listeners to a Runnable, returning a new Runnable.
`with_config`([config])	Bind config to a Runnable, returning a new Runnable.
`with_fallbacks`(fallbacks, *[, ...])	Add fallbacks to a Runnable, returning a new Runnable.
`with_listeners`(*[, on_start, on_end, on_error])	Bind lifecycle listeners to a Runnable, returning a new Runnable.
`with_retry`(*[, retry_if_exception_type, ...])	Create a new Runnable that retries the original Runnable on exceptions.
`with_types`(*[, input_type, output_type])	Bind input and output types to a Runnable, returning a new Runnable.

construct
dict
json
update_forward_refs

Returns:

pandas.DataFrame

The fetched data.

Note

args_schema is used to define the schema of the inputs as follows:

Field	Description
select_statement	The select_statement of dataframe. If not provided, ask the user. Do not guess.
top_n	The number of rows to fetch, it is optional
last_n	The number of rows to fetch from the end of the table, it is optional

name: str: Name of the tool.

description: str: Description of the tool.

connection_context: ConnectionContext: Connection context to the HANA database.

args_schema: Type[BaseModel]: Input schema of the tool.

return_direct: bool: Used for transform

property InputType: type[Input]: The type of input this Runnable accepts specified as a type annotation.

property OutputType: type[Output]: The type of output this Runnable produces specified as a type annotation.

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.

Args:

inputs: A list of inputs 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. Defaults to None.

return_exceptions: Whether to return exceptions instead of raising them.: Defaults to False.

kwargs: Additional keyword arguments to pass to the Runnable.

Returns:

A list of outputs from the Runnable.

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.

Yields results as they complete.

Args:

inputs: A list of inputs 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. Defaults to None. Defaults to None.

return_exceptions: Whether to return exceptions instead of raising them.: Defaults to False.

kwargs: Additional keyword arguments to pass to the Runnable.

Yields:

A tuple of the index of the input and the output from the Runnable.

as_tool(args_schema: type[BaseModel] | None = None, *, name: str | None = None, description: str | None = None, arg_types: dict[str, type] | None = None) → BaseTool

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema. Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. You can also pass arg_types to just specify the required arguments and their types.

Args:: args_schema: The schema for the tool. Defaults to None. name: The name of the tool. Defaults to None. description: The description of the tool. Defaults to None. arg_types: A dictionary of argument names to types. Defaults to None.
Returns:: A BaseTool instance.

Typed dict input:

from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda

class Args(TypedDict):
    a: int
    b: list[int]

def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))

runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via args_schema:

from typing import Any
from pydantic import BaseModel, Field
from langchain_core.runnables import RunnableLambda

def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

class FSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: list[int] = Field(..., description="List of ints")

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via arg_types:

from typing import Any
from langchain_core.runnables import RunnableLambda

def f(x: dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})

String input:

from langchain_core.runnables import RunnableLambda

def f(x: str) -> str:
    return x + "a"

def g(x: str) -> str:
    return x + "z"

runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")

Added in version 0.2.14.

assign(**kwargs: Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any] | Mapping[str, 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.

from langchain_community.llms.fake import FakeStreamingListLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import SystemMessagePromptTemplate
from langchain_core.runnables import Runnable
from operator import itemgetter

prompt = (
    SystemMessagePromptTemplate.from_template("You are a nice assistant.")
    + "{question}"
)
llm = FakeStreamingListLLM(responses=["foo-lish"])

chain: Runnable = prompt | llm | {"str": StrOutputParser()}

chain_with_assign = chain.assign(hello=itemgetter("str") | llm)

print(chain_with_assign.input_schema.model_json_schema())
# {'title': 'PromptInput', 'type': 'object', 'properties':
{'question': {'title': 'Question', 'type': 'string'}}}
print(chain_with_assign.output_schema.model_json_schema())
# {'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
{'str': {'title': 'Str',
'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}

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.

Args:: input: The input to the Runnable. config: The config to use for the Runnable. Defaults to None. kwargs: Additional keyword arguments to pass to the Runnable.
Yields:: The output of the Runnable.

async astream_events(input: Any, config: RunnableConfig | None = None, *, version: Literal['v1', 'v2'] = '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[StreamEvent]

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 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.
parent_ids: list[str] - The IDs of the parent runnables that
generated 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 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 events 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]		{"messages": [[SystemMessage, HumanMessage]]}
on_chat_model_stream	[model name]	AIMessageChunk(content="hello")
on_chat_model_end	[model name]		{"messages": [[SystemMessage, HumanMessage]]}	AIMessageChunk(content="hello world")
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_end	some_tool			{"x": 1, "y": "2"}
on_retriever_start	[retriever name]		{"query": "hello"}
on_retriever_end	[retriever name]		{"query": "hello"}	[Document(...), ..]
on_prompt_start	[template_name]		{"question": "hello"}
on_prompt_end	[template_name]		{"question": "hello"}	ChatPromptValue(messages: [SystemMessage, ...])

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)

Args:

input: The input to the Runnable. config: The config to use for the Runnable. version: 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: 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.

Yields:

An async stream of StreamEvents.

Raises:

NotImplementedError: If the version is not v1 or v2.

async astream_log(input: Any, config: RunnableConfig | None = None, *, diff: bool = True, with_streamed_output_list: bool = True, 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[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.

Args:: 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. kwargs: Additional keyword arguments to pass to the Runnable.
Yields:: A RunLogPatch or RunLog object.

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

Args:: input: An async iterator of inputs to the Runnable. config: The config to use for the Runnable. Defaults to None. kwargs: Additional keyword arguments to pass to the Runnable.
Yields:: The output of the Runnable.

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.

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.

Yields results as they complete.

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.

Args:: kwargs: The arguments to bind to the Runnable.
Returns:: A new Runnable with the arguments bound.

Example:

from langchain_ollama 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'

config_schema(*, include: Sequence[str] | None = 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.

Args:: include: A list of fields to include in the config schema.
Returns:: A pydantic model that can be used to validate config.

property config_specs: list[ConfigurableFieldSpec]: List configurable fields for this Runnable.

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.

Args:

which: The ConfigurableField instance that will be used to select the: alternative.
default_key: The default key to use if no alternative is selected.: Defaults to "default".
prefix_keys: Whether to prefix the keys with the ConfigurableField id.: Defaults to False.
**kwargs: A dictionary of keys to Runnable instances or callables that: return Runnable instances.

Returns:

A new Runnable with the alternatives configured.

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.

Args:: **kwargs: A dictionary of ConfigurableField instances to configure.
Returns:: A new Runnable with the fields configured.

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
)

get_config_jsonschema(*, include: Sequence[str] | None = None) → dict[str, Any]

Get a JSON schema that represents the config of the Runnable.

Args:: include: A list of fields to include in the config schema.
Returns:: A JSON schema that represents the config of the Runnable.

Added in version 0.3.0.

get_graph(config: RunnableConfig | None = None) → Graph: Return a graph representation of this Runnable.

get_input_jsonschema(config: RunnableConfig | None = None) → dict[str, Any]

Get a JSON schema that represents the input to the Runnable.

Args:: config: A config to use when generating the schema.
Returns:: A JSON schema that represents the input to the Runnable.

Example:

from langchain_core.runnables import RunnableLambda

def add_one(x: int) -> int:
    return x + 1

runnable = RunnableLambda(add_one)

print(runnable.get_input_jsonschema())

Added in version 0.3.0.

classmethod get_lc_namespace() → list[str]

Get the namespace of the langchain object.

For example, if the class is langchain.llms.openai.OpenAI, then the namespace is ["langchain", "llms", "openai"]

get_name(suffix: str | None = None, *, name: str | None = None) → str: Get the name of the Runnable.

get_output_jsonschema(config: RunnableConfig | None = None) → dict[str, Any]

Get a JSON schema that represents the output of the Runnable.

Args:: config: A config to use when generating the schema.
Returns:: A JSON schema that represents the output of the Runnable.

Example:

from langchain_core.runnables import RunnableLambda

def add_one(x: int) -> int:
    return x + 1

runnable = RunnableLambda(add_one)

print(runnable.get_output_jsonschema())

Added in version 0.3.0.

get_output_schema(config: RunnableConfig | None = 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.

Args:: config: A config to use when generating the schema.
Returns:: A pydantic model that can be used to validate output.

property input_schema: type[BaseModel]: The type of input this Runnable accepts specified as a pydantic model.

classmethod is_lc_serializable() → bool

Is this class serializable?

By design, even if a class inherits from Serializable, it is not serializable by default. This is to prevent accidental serialization of objects that should not be serialized.

Returns:: Whether the class is serializable. Default is False.

property lc_attributes: dict

List of attribute names that should be included in the serialized kwargs.

These attributes must be accepted by the constructor. Default is an empty dictionary.

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. For example, for the class langchain.llms.openai.OpenAI, the id is ["langchain", "llms", "openai", "OpenAI"].

property lc_secrets: dict[str, str]

A map of constructor argument names to secret ids.

For example,: {"openai_api_key": "OPENAI_API_KEY"}

map() → Runnable[list[Input], list[Output]]

Return a new Runnable that maps a list of inputs to a list of outputs.

Calls invoke() with each input.

Returns:: A new Runnable that maps a list of inputs to a list of outputs.

Example:

from langchain_core.runnables import RunnableLambda

def _lambda(x: int) -> int:
    return x + 1

runnable = RunnableLambda(_lambda)
print(runnable.map().invoke([1, 2, 3])) # [2, 3, 4]

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'protected_namespaces': ()}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

property output_schema: type[BaseModel]: The type of output this Runnable produces specified as a pydantic model.

pick(keys: str | list[str]) → RunnableSerializable[Any, Any]

Pick keys from the output dict of this Runnable.

Pick single key:

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
chain = RunnableMap(str=as_str, json=as_json)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}

json_only_chain = chain.pick("json")
json_only_chain.invoke("[1, 2, 3]")
# -> [1, 2, 3]

Pick list of keys:

from typing import Any

import json

from langchain_core.runnables import RunnableLambda, RunnableMap

as_str = RunnableLambda(str)
as_json = RunnableLambda(json.loads)
def as_bytes(x: Any) -> bytes:
    return bytes(x, "utf-8")

chain = RunnableMap(
    str=as_str,
    json=as_json,
    bytes=RunnableLambda(as_bytes)
)

chain.invoke("[1, 2, 3]")
# -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

json_and_bytes_chain = chain.pick(["json", "bytes"])
json_and_bytes_chain.invoke("[1, 2, 3]")
# -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

pipe(*others: Runnable[Any, Other] | Callable[[Any], Other], name: str | None = None) → RunnableSerializable[TypeVar, TypeVar]

Compose this Runnable with Runnable-like objects to make a RunnableSequence.

Equivalent to RunnableSequence(self, *others) or self | others[0] | ...

Example:

from langchain_core.runnables import RunnableLambda

def add_one(x: int) -> int:
    return x + 1

def mul_two(x: int) -> int:
    return x * 2

runnable_1 = RunnableLambda(add_one)
runnable_2 = RunnableLambda(mul_two)
sequence = runnable_1.pipe(runnable_2)
# Or equivalently:
# sequence = runnable_1 | runnable_2
# sequence = RunnableSequence(first=runnable_1, last=runnable_2)
sequence.invoke(1)
await sequence.ainvoke(1)
# -> 4

sequence.batch([1, 2, 3])
await sequence.abatch([1, 2, 3])
# -> [4, 6, 8]

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.

Args:: input: The input to the Runnable. config: The config to use for the Runnable. Defaults to None. kwargs: Additional keyword arguments to pass to the Runnable.
Yields:: The output of the Runnable.

to_json() → SerializedConstructor | SerializedNotImplemented

Serialize the Runnable to JSON.

Returns:: A JSON-serializable representation of the Runnable.

transform(input: Iterator[Input], config: RunnableConfig | None = None, **kwargs: Any | None) → Iterator[Output]

Default implementation of transform, which buffers input and calls astream.

Subclasses should override this method if they can start producing output while input is still being generated.

Args:: input: An iterator of inputs to the Runnable. config: The config to use for the Runnable. Defaults to None. kwargs: Additional keyword arguments to pass to the Runnable.
Yields:: The output of the Runnable.

with_alisteners(*, on_start: AsyncListener | None = None, on_end: AsyncListener | None = None, on_error: AsyncListener | None = None) → Runnable[Input, Output]

Bind async 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.

Args:

on_start: Asynchronously called before the Runnable starts running.: Defaults to None.
on_end: Asynchronously called after the Runnable finishes running.: Defaults to None.
on_error: Asynchronously called if the Runnable throws an error.: Defaults to None.

Returns:

A new Runnable with the listeners bound.

Example:

from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio

def format_t(timestamp: float) -> str:
    return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()

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 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00

with_config(config: RunnableConfig | None = None, **kwargs: Any) → Runnable[Input, Output]

Bind config to a Runnable, returning a new Runnable.

Args:: config: The config to bind to the Runnable. kwargs: Additional keyword arguments to pass to the Runnable.
Returns:: A new Runnable with the config bound.

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.

Args:

fallbacks: A sequence of runnables to try if the original Runnable fails. exceptions_to_handle: A tuple of exception types to handle.

Defaults to (Exception,).

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. Defaults to None.

Returns:

A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.

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

Args:: 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: 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.

Args:: on_start: Called before the Runnable starts running. Defaults to None. on_end: Called after the Runnable finishes running. Defaults to None. on_error: Called if the Runnable throws an error. Defaults to None.
Returns:: A new Runnable with the listeners bound.

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, exponential_jitter_params: Optional[ExponentialJitterParams] = None, stop_after_attempt: int = 3) → Runnable[Input, Output]

Create a new Runnable that retries the original Runnable on exceptions.

Args:

retry_if_exception_type: A tuple of exception types to retry on.: Defaults to (Exception,).
wait_exponential_jitter: Whether to add jitter to the wait: time between retries. Defaults to True.
stop_after_attempt: The maximum number of attempts to make before: giving up. Defaults to 3.
exponential_jitter_params: Parameters for: tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).

Returns:

A new Runnable that retries the original Runnable on exceptions.

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)

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.

Args:: input_type: The input type to bind to the Runnable. Defaults to None. output_type: The output type to bind to the Runnable. Defaults to None.
Returns:: A new Runnable with the types bound.

verbose: bool: Whether to log the tool's progress.

callbacks: Callbacks: Callbacks to be called during tool execution.

tags: list[str] | None: Optional list of tags associated with the tool. Defaults to None. These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

metadata: dict[str, Any] | None: Optional metadata associated with the tool. Defaults to None. This metadata will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.

handle_tool_error: bool | str | Callable[[ToolException], str] | None: Handle the content of the ToolException thrown.

handle_validation_error: bool | str | Callable[[ValidationError | ValidationErrorV1], str] | None: Handle the content of the ValidationError thrown.

response_format: Literal['content', 'content_and_artifact']

The tool response format. Defaults to 'content'.

If "content" then the output of the tool is interpreted as the contents of a ToolMessage. If "content_and_artifact" then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage.

set_transform(is_transform: bool)

Return a copy of the tool with the is_transform flag set.

Parameters:

is_transformbool: Whether to set the tool to transform mode.