ChatSambaStudio#

class langchain_community.chat_models.sambanova.ChatSambaStudio[source]#

Bases: BaseChatModel

Deprecated since version 0.3.16: Use :class:`~langchain_sambanova.ChatSambaStudio` instead. It will not be removed until langchain-community==1.0.

SambaStudio chat model.

Setup:

To use, you should have the environment variables: SAMBASTUDIO_URL set with your SambaStudio deployed endpoint URL. SAMBASTUDIO_API_KEY set with your SambaStudio deployed endpoint Key. https://docs.sambanova.ai/sambastudio/latest/index.html Example:

ChatSambaStudio(
    sambastudio_url = set with your SambaStudio deployed endpoint URL,
    sambastudio_api_key = set with your SambaStudio deployed endpoint Key.
    model = model or expert name (set for Bundle endpoints),
    max_tokens = max number of tokens to generate,
    temperature = model temperature,
    top_p = model top p,
    top_k = model top k,
    do_sample = wether to do sample
    process_prompt = wether to process prompt
        (set for Bundle generic v1 and v2 endpoints)
    stream_options = include usage to get generation metrics
    special_tokens = start, start_role, end_role, end special tokens
        (set for Bundle generic v1 and v2 endpoints when process prompt
         set to false or for StandAlone v1 and v2 endpoints)
    model_kwargs: Optional = Extra Key word arguments to pass to the model.
)

Key init args — completion params:

model: str: The name of the model to use, e.g., Meta-Llama-3-70B-Instruct-4096 (set for Bundle endpoints).
streaming: bool: Whether to use streaming
max_tokens: inthandler when using non streaming methods: max tokens to generate
temperature: float: model temperature
top_p: float: model top p
top_k: int: model top k
do_sample: bool: wether to do sample
process_prompt:: wether to process prompt (set for Bundle generic v1 and v2 endpoints)
stream_options: dict: stream options, include usage to get generation metrics
special_tokens: dict: start, start_role, end_role and end special tokens (set for Bundle generic v1 and v2 endpoints when process prompt set to false

or for StandAlone v1 and v2 endpoints) default to llama3 special tokens
model_kwargs: dict: Extra Key word arguments to pass to the model.

Key init args — client params:

sambastudio_url: str: SambaStudio endpoint Url
sambastudio_api_key: str: SambaStudio endpoint api key

Instantiate:

from langchain_community.chat_models import ChatSambaStudio

chat = ChatSambaStudio=(
    sambastudio_url = set with your SambaStudio deployed endpoint URL,
    sambastudio_api_key = set with your SambaStudio deployed endpoint Key.
    model = model or expert name (set for Bundle endpoints),
    max_tokens = max number of tokens to generate,
    temperature = model temperature,
    top_p = model top p,
    top_k = model top k,
    do_sample = wether to do sample
    process_prompt = wether to process prompt
        (set for Bundle generic v1 and v2 endpoints)
    stream_options = include usage to get generation metrics
    special_tokens = start, start_role, end_role, and special tokens
        (set for Bundle generic v1 and v2 endpoints when process prompt
         set to false or for StandAlone v1 and v2 endpoints)
    model_kwargs: Optional = Extra Key word arguments to pass to the model.
)

Invoke:

messages = [
    SystemMessage(content="your are an AI assistant."),
    HumanMessage(content="tell me a joke."),
]
response = chat.invoke(messages)

Stream:

for chunk in chat.stream(messages):
    print(chunk.content, end="", flush=True)

Async:

response = chat.ainvoke(messages)
await response

Tool calling:

from pydantic import BaseModel, Field

class GetWeather(BaseModel):
    '''Get the current weather in a given location'''

    location: str = Field(
        ...,
        description="The city and state, e.g. Los Angeles, CA"
    )

llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke("Should I bring my umbrella today in LA?")
ai_msg.tool_calls

[
    {
        'name': 'GetWeather',
        'args': {'location': 'Los Angeles, CA'},
        'id': 'call_adf61180ea2b4d228a'
    }
]

Structured output:

from typing import Optional

from pydantic import BaseModel, Field

class Joke(BaseModel):
    '''Joke to tell user.'''

    setup: str = Field(description="The setup of the joke")
    punchline: str = Field(description="The punchline to the joke")

structured_model = llm.with_structured_output(Joke)
structured_model.invoke("Tell me a joke about cats")

Joke(setup="Why did the cat join a band?",
punchline="Because it wanted to be the purr-cussionist!")

See ChatSambaStudio.with_structured_output() for more.

Token usage:

response = chat.invoke(messages)
print(response.response_metadata["usage"]["prompt_tokens"]
print(response.response_metadata["usage"]["total_tokens"]

Response metadata

response = chat.invoke(messages)
print(response.response_metadata)

init and validate environment variables

Note

ChatSambaStudio implements the standard Runnable Interface. 🏃

The Runnable Interface has additional methods that are available on runnables, such as with_config, with_types, with_retry, assign, bind, get_graph, and more.

param additional_headers: Dict[str, Any] = {}#: Additional headers to send in request

param base_url: str = ''#: SambaStudio non streaming Url

param cache: BaseCache | bool | None = None#

Whether to cache the response.

If true, will use the global cache.
If false, will not use a cache
If None, will use the global cache if it’s set, otherwise no cache.
If instance of BaseCache, will use the provided cache.

Caching is not currently supported for streaming methods of models.

param callback_manager: BaseCallbackManager | None = None#: Deprecated since version 0.1.7: Use callbacks() instead. It will be removed in pydantic==1.0.

Callback manager to add to the run trace.

param callbacks: Callbacks = None#: Callbacks to add to the run trace.

param custom_get_token_ids: Callable[[str], list[int]] | None = None#: Optional encoder to use for counting tokens.

param disable_streaming: bool | Literal['tool_calling'] = False#

Whether to disable streaming for this model.

If streaming is bypassed, then stream()/astream()/astream_events() will defer to invoke()/ainvoke().

If True, will always bypass streaming case.
If 'tool_calling', will bypass streaming case only when the model is called with a tools keyword argument. In other words, LangChain will automatically switch to non-streaming behavior (invoke()) only when the tools argument is provided. This offers the best of both worlds.
If False (default), will always use streaming case if available.

The main reason for this flag is that code might be written using stream() and a user may want to swap out a given model for another model whose the implementation does not properly support streaming.

param do_sample: bool | None = None#: whether to do sampling

param max_tokens: int = 1024#: max tokens to generate

param metadata: dict[str, Any] | None = None#: Metadata to add to the run trace.

param model: str | None = None#: The name of the model or expert to use (for Bundle endpoints)

param model_kwargs: Dict[str, Any] | None = None#: Key word arguments to pass to the model.

param process_prompt: bool | None = True#: whether process prompt (for Bundle generic v1 and v2 endpoints)

param rate_limiter: BaseRateLimiter | None = None#: An optional rate limiter to use for limiting the number of requests.

param sambastudio_api_key: SecretStr = SecretStr('')#: SambaStudio api key

param sambastudio_url: str = ''#: SambaStudio Url

param special_tokens: Dict[str, Any] = {'end': '<|start_header_id|>assistant<|end_header_id|>\n', 'end_role': '<|eot_id|>', 'start': '<|begin_of_text|>', 'start_role': '<|begin_of_text|><|start_header_id|>{role}<|end_header_id|>'}#

start, start_role, end_role and end special tokens (set for Bundle generic v1 and v2 endpoints when process prompt set to false

or for StandAlone v1 and v2 endpoints)

default to llama3 special tokens

param stream_options: Dict[str, Any] = {'include_usage': True}#: stream options, include usage to get generation metrics

param streaming: bool = False#: Whether to use streaming handler when using non streaming methods

param streaming_url: str = ''#: SambaStudio streaming Url

param tags: list[str] | None = None#: Tags to add to the run trace.

param temperature: float | None = 0.7#: model temperature

param top_k: int | None = None#: model top k

param top_p: float | None = None#: model top p

param verbose: bool [Optional]#: Whether to print out response text.

class Config[source]#

Bases: object

populate_by_name = True#

__call__(

messages: list[BaseMessage],

stop: list[str] | None = None,

callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None,

**kwargs: Any,

) → BaseMessage#

Deprecated since version 0.1.7: Use invoke() instead. It will not be removed until langchain-core==1.0.

Call the model.

Parameters:

messages (list[BaseMessage]) – List of messages.
stop (list[str] | None) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns:

The model output message.

Return type:

BaseMessage

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]) – A list of inputs to the Runnable.
config (RunnableConfig | list[RunnableConfig] | None) – 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 (bool) – Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) – 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.

Yields results as they complete.

Parameters:

inputs (Sequence[Input]) – A list of inputs to the Runnable.
config (RunnableConfig | Sequence[RunnableConfig] | None) – 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 (bool) – Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) – 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 ainvoke(

input: LanguageModelInput,

config: RunnableConfig | None = None,

*,

stop: list[str] | None = None,

**kwargs: Any,

) → BaseMessage#

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.

Parameters:

input (LanguageModelInput)
config (Optional[RunnableConfig])
stop (Optional[list[str]])
kwargs (Any)

Return type:

BaseMessage

async astream(

input: LanguageModelInput,

config: RunnableConfig | None = None,

*,

stop: list[str] | None = None,

**kwargs: Any,

) → AsyncIterator[BaseMessageChunk]#

Default implementation of astream, which calls ainvoke.

Subclasses should override this method if they support streaming output.

Parameters:

input (LanguageModelInput) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable. Defaults to None.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.
stop (Optional[list[str]])

Yields:

The output of the Runnable.

Return type:

AsyncIterator[BaseMessageChunk]

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.

Note

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)

Parameters:

input (Any) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable.
version (Literal['v1', 'v2']) – 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 (Optional[Sequence[str]]) – Only include events from Runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from Runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from Runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from Runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from Runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – Exclude events from Runnables with matching tags.
kwargs (Any) – 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'.

Return type:

AsyncIterator[StreamEvent]

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.

Yields 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) – The arguments to bind to the Runnable.
Returns:: A new Runnable with the arguments bound.
Return type:: Runnable[Input, Output]

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'

Bind tool-like objects to this chat model

tool_choice: does not currently support “any”, choice like should be one of [“auto”, “none”, “required”]

Parameters:

tools (Sequence[Dict[str, Any] | Type[Any] | Callable[[...], Any] | BaseTool])
tool_choice (Dict[str, Any] | bool | str | None)
parallel_tool_calls (bool | None)
kwargs (Any)

Return type:

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) – The ConfigurableField instance that will be used to select the alternative.
default_key (str) – The default key to use if no alternative is selected. Defaults to 'default'.
prefix_keys (bool) – Whether to prefix the keys with the ConfigurableField id. Defaults to False.
**kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) – A dictionary of keys to Runnable instances or callables that return Runnable instances.

Returns:

A new Runnable with the alternatives configured.

Return type:

RunnableSerializable

from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI

model = ChatAnthropic(
    model_name="claude-3-7-sonnet-20250219"
).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) – A dictionary of ConfigurableField instances to configure.
Returns:: A new Runnable with the fields configured.
Return type:: RunnableSerializable

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_num_tokens(text: str) → int#

Get the number of tokens present in the text.

Useful for checking if an input fits in a model’s context window.

Parameters:: text (str) – The string input to tokenize.
Returns:: The integer number of tokens in the text.
Return type:: int

get_num_tokens_from_messages( messages: list[BaseMessage], tools: Sequence | None = None, ) → int#

Get the number of tokens in the messages.

Useful for checking if an input fits in a model’s context window.

Note

The base implementation of get_num_tokens_from_messages ignores tool schemas.

Parameters:

messages (list[BaseMessage]) – The message inputs to tokenize.
tools (Sequence | None) – If provided, sequence of dict, BaseModel, function, or BaseTools to be converted to tool schemas.

Returns:

The sum of the number of tokens across the messages.

Return type:

int

get_token_ids(text: str) → list[int]#

Return the ordered ids of the tokens in a text.

Parameters:: text (str) – The string input to tokenize.
Returns:: A list of ids corresponding to the tokens in the text, in order they occur in the text.
Return type:: list[int]

invoke(

input: LanguageModelInput,

config: RunnableConfig | None = None,

*,

stop: list[str] | None = None,

**kwargs: Any,

) → BaseMessage#

Transform a single input into an output.

Parameters:

input (LanguageModelInput) – The input to the Runnable.
config (Optional[RunnableConfig]) – 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.
stop (Optional[list[str]])
kwargs (Any)

Returns:

The output of the Runnable.

Return type:

BaseMessage

stream(

input: LanguageModelInput,

config: RunnableConfig | None = None,

*,

stop: list[str] | None = None,

**kwargs: Any,

) → Iterator[BaseMessageChunk]#

Default implementation of stream, which calls invoke.

Subclasses should override this method if they support streaming output.

Parameters:

input (LanguageModelInput) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable. Defaults to None.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.
stop (Optional[list[str]])

Yields:

The output of the Runnable.

Return type:

Iterator[BaseMessageChunk]

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.

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]) – Called asynchronously before the Runnable starts running, with the Run object. Defaults to None.
on_end (Optional[AsyncListener]) – Called asynchronously after the Runnable finishes running, with the Run object. Defaults to None.
on_error (Optional[AsyncListener]) – Called asynchronously if the Runnable throws an error, with the Run object. Defaults to None.

Returns:

A new Runnable with the listeners bound.

Return type:

Runnable[Input, Output]

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.

Parameters:

config (RunnableConfig | None) – The config to bind to the Runnable.
kwargs (Any) – 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]]) – A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) – A tuple of exception types to handle. Defaults to (Exception,).
exception_key (Optional[str]) – 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]]) – A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) – A tuple of exception types to handle.
exception_key (Optional[str]) – 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.

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]]]) – Called before the Runnable starts running, with the Run object. Defaults to None.
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called after the Runnable finishes running, with the Run object. Defaults to None.
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called if the Runnable throws an error, with the Run object. 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, 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.

Parameters:

retry_if_exception_type (tuple[type[BaseException], ...]) – A tuple of exception types to retry on. Defaults to (Exception,).
wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries. Defaults to True.
stop_after_attempt (int) – The maximum number of attempts to make before giving up. Defaults to 3.
exponential_jitter_params (Optional[ExponentialJitterParams]) – 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.

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)

Model wrapper that returns outputs formatted to match the given schema.

Args:

schema:

The output schema. Can be passed in as:

an OpenAI function/tool schema,
a JSON Schema,
a TypedDict class,
or a Pydantic class.

If schema is a Pydantic class then the model output will be a Pydantic instance of that class, and the model-generated fields will be validated by the Pydantic class. Otherwise the model output will be a dict and will not be validated. See langchain_core.utils.function_calling.convert_to_openai_tool() for more on how to properly specify types and descriptions of schema fields when specifying a Pydantic or TypedDict class.

method:

The method for steering model generation, either “function_calling” “json_mode” or “json_schema”. If “function_calling” then the schema will be converted to an OpenAI function and the returned model will make use of the function-calling API. If “json_mode” or “json_schema” then OpenAI’s JSON mode will be used. Note that if using “json_mode” or “json_schema” then you must include instructions for formatting the output into the desired schema into the model call.

include_raw:

If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys “raw”, “parsed”, and “parsing_error”.

Returns:

A Runnable that takes same inputs as a langchain_core.language_models.chat.BaseChatModel.

If include_raw is False and schema is a Pydantic class, Runnable outputs an instance of schema (i.e., a Pydantic object).

Otherwise, if include_raw is False then Runnable outputs a dict.

If include_raw is True, then Runnable outputs a dict with keys:

“raw”: BaseMessage
“parsed”: None if there was a parsing error, otherwise the type depends on the schema as described above.
“parsing_error”: Optional[BaseException]

Example: schema=Pydantic class, method=”function_calling”, include_raw=False:

from typing import Optional

from langchain_community.chat_models import ChatSambaStudio
from pydantic import BaseModel, Field


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str = Field(
        description="A justification for the answer."
    )


llm = ChatSambaStudio(model="Meta-Llama-3.1-70B-Instruct", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)

structured_llm.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)

# -> AnswerWithJustification(
#     answer='They weigh the same',
#     justification='A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same.'
# )

Example: schema=Pydantic class, method=”function_calling”, include_raw=True:

from langchain_community.chat_models import ChatSambaStudio
from pydantic import BaseModel


class AnswerWithJustification(BaseModel):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: str


llm = ChatSambaStudio(model="Meta-Llama-3.1-70B-Instruct", temperature=0)
structured_llm = llm.with_structured_output(
    AnswerWithJustification, include_raw=True
)

structured_llm.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)
# -> {
#     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'function': {'arguments': '{"answer": "They weigh the same.", "justification": "A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount."}', 'name': 'AnswerWithJustification'}, 'id': 'call_17a431fc6a4240e1bd', 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'usage': {'acceptance_rate': 5, 'completion_tokens': 53, 'completion_tokens_after_first_per_sec': 343.7964936837758, 'completion_tokens_after_first_per_sec_first_ten': 439.1205661878638, 'completion_tokens_per_sec': 162.8511306784833, 'end_time': 1731527851.0698032, 'is_last_response': True, 'prompt_tokens': 213, 'start_time': 1731527850.7137961, 'time_to_first_token': 0.20475482940673828, 'total_latency': 0.32545061111450196, 'total_tokens': 266, 'total_tokens_per_sec': 817.3283162354066}, 'model_name': 'Meta-Llama-3.1-70B-Instruct', 'system_fingerprint': 'fastcoe', 'created': 1731527850}, id='95667eaf-447f-4b53-bb6e-b6e1094ded88', tool_calls=[{'name': 'AnswerWithJustification', 'args': {'answer': 'They weigh the same.', 'justification': 'A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount.'}, 'id': 'call_17a431fc6a4240e1bd', 'type': 'tool_call'}]),
#     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount.'),
#     'parsing_error': None
# }

Example: schema=TypedDict class, method=”function_calling”, include_raw=False:

# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
# from typing_extensions, not from typing.
from typing_extensions import Annotated, TypedDict

from langchain_community.chat_models import ChatSambaStudio


class AnswerWithJustification(TypedDict):
    '''An answer to the user question along with justification for the answer.'''

    answer: str
    justification: Annotated[
        Optional[str], None, "A justification for the answer."
    ]


llm = ChatSambaStudio(model="Meta-Llama-3.1-70B-Instruct", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)

structured_llm.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount.'
# }

Example: schema=OpenAI function schema, method=”function_calling”, include_raw=False:

from langchain_community.chat_models import ChatSambaStudio

oai_schema = {
    'name': 'AnswerWithJustification',
    'description': 'An answer to the user question along with justification for the answer.',
    'parameters': {
        'type': 'object',
        'properties': {
            'answer': {'type': 'string'},
            'justification': {'description': 'A justification for the answer.', 'type': 'string'}
        },
       'required': ['answer']
   }
}

llm = ChatSambaStudio(model="Meta-Llama-3.1-70B-Instruct", temperature=0)
structured_llm = llm.with_structured_output(oai_schema)

structured_llm.invoke(
    "What weighs more a pound of bricks or a pound of feathers"
)
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'A pound is a unit of weight or mass, so one pound of bricks and one pound of feathers both weigh the same amount.'
# }

Example: schema=Pydantic class, method=”json_mode”, include_raw=True:

from langchain_community.chat_models import ChatSambaStudio
from pydantic import BaseModel

class AnswerWithJustification(BaseModel):
    answer: str
    justification: str

llm = ChatSambaStudio(model="Meta-Llama-3.1-70B-Instruct", temperature=0)
structured_llm = llm.with_structured_output(
    AnswerWithJustification,
    method="json_mode",
    include_raw=True
)

structured_llm.invoke(
    "Answer the following question. "
    "Make sure to return a JSON blob with keys 'answer' and 'justification'.

“

“What’s heavier a pound of bricks or a pound of feathers?”

) # -> { # ‘raw’: AIMessage(content=’{

“answer”: “They are the same weight”, “justification”: “A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.”

}’, additional_kwargs={}, response_metadata={‘finish_reason’: ‘stop’, ‘usage’: {‘acceptance_rate’: 5.3125, ‘completion_tokens’: 79, ‘completion_tokens_after_first_per_sec’: 292.65701089829776, ‘completion_tokens_after_first_per_sec_first_ten’: 346.43324678555325, ‘completion_tokens_per_sec’: 200.012158915008, ‘end_time’: 1731528071.1708555, ‘is_last_response’: True, ‘prompt_tokens’: 70, ‘start_time’: 1731528070.737394, ‘time_to_first_token’: 0.16693782806396484, ‘total_latency’: 0.3949759876026827, ‘total_tokens’: 149, ‘total_tokens_per_sec’: 377.2381225105847}, ‘model_name’: ‘Meta-Llama-3.1-70B-Instruct’, ‘system_fingerprint’: ‘fastcoe’, ‘created’: 1731528070}, id=’83208297-3eb9-4021-a856-ca78a15758df’),

# ‘parsed’: AnswerWithJustification(answer=’They are the same weight’, justification=’A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.’), # ‘parsing_error’: None # }

Example: schema=None, method=”json_mode”, include_raw=True:

from langchain_community.chat_models import ChatSambaStudio

llm = ChatSambaStudio(model="Meta-Llama-3.1-70B-Instruct", temperature=0)
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)

structured_llm.invoke(
    "Answer the following question. "
    "Make sure to return a JSON blob with keys 'answer' and 'justification'.

“

“What’s heavier a pound of bricks or a pound of feathers?”

) # -> { # ‘raw’: AIMessage(content=’{

“answer”: “They are the same weight”, “justification”: “A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.”

}’, additional_kwargs={}, response_metadata={‘finish_reason’: ‘stop’, ‘usage’: {‘acceptance_rate’: 4.722222222222222, ‘completion_tokens’: 79, ‘completion_tokens_after_first_per_sec’: 357.1315485254867, ‘completion_tokens_after_first_per_sec_first_ten’: 416.83279609305305, ‘completion_tokens_per_sec’: 240.92819585198137, ‘end_time’: 1731528164.8474727, ‘is_last_response’: True, ‘prompt_tokens’: 70, ‘start_time’: 1731528164.4906917, ‘time_to_first_token’: 0.13837409019470215, ‘total_latency’: 0.3278985247892492, ‘total_tokens’: 149, ‘total_tokens_per_sec’: 454.4088757208256}, ‘model_name’: ‘Meta-Llama-3.1-70B-Instruct’, ‘system_fingerprint’: ‘fastcoe’, ‘created’: 1731528164}, id=’15261eaf-8a25-42ef-8ed5-f63d8bf5b1b0’),

# ‘parsed’: { # ‘answer’: ‘They are the same weight’, # ‘justification’: ‘A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.’}, # }, # ‘parsing_error’: None # }

Example: schema=None, method=”json_schema”, include_raw=True:

from langchain_community.chat_models import ChatSambaStudio

class AnswerWithJustification(BaseModel):
    answer: str
    justification: str

llm = ChatSambaStudio(model="Meta-Llama-3.1-70B-Instruct", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification, method="json_schema", include_raw=True)

structured_llm.invoke(
    "Answer the following question. "
    "Make sure to return a JSON blob with keys 'answer' and 'justification'.

“: “What’s heavier a pound of bricks or a pound of feathers?”

) # -> { # ‘raw’: AIMessage(content=’{

“answer”: “They are the same weight”, “justification”: “A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.”
}’, additional_kwargs={}, response_metadata={‘finish_reason’: ‘stop’, ‘usage’: {‘acceptance_rate’: 5.3125, ‘completion_tokens’: 79, ‘completion_tokens_after_first_per_sec’: 292.65701089829776, ‘completion_tokens_after_first_per_sec_first_ten’: 346.43324678555325, ‘completion_tokens_per_sec’: 200.012158915008, ‘end_time’: 1731528071.1708555, ‘is_last_response’: True, ‘prompt_tokens’: 70, ‘start_time’: 1731528070.737394, ‘time_to_first_token’: 0.16693782806396484, ‘total_latency’: 0.3949759876026827, ‘total_tokens’: 149, ‘total_tokens_per_sec’: 377.2381225105847}, ‘model_name’: ‘Meta-Llama-3.1-70B-Instruct’, ‘system_fingerprint’: ‘fastcoe’, ‘created’: 1731528070}, id=’83208297-3eb9-4021-a856-ca78a15758df’),: # ‘parsed’: AnswerWithJustification(answer=’They are the same weight’, justification=’A pound is a unit of weight or mass, so a pound of bricks and a pound of feathers both weigh the same amount, one pound. The difference is in their density and volume. A pound of feathers would take up more space than a pound of bricks due to the difference in their densities.’), # ‘parsing_error’: None # }

Parameters:

schema (Dict[str, Any] | Type[BaseModel] | None)
method (Literal['function_calling', 'json_mode', 'json_schema'])
include_raw (bool)
kwargs (Any)

Return type:

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) – The input type to bind to the Runnable. Defaults to None.
output_type (type[Output] | None) – The output type to bind to the Runnable. Defaults to None.

Returns:

A new Runnable with the types bound.

Return type:

Runnable[Input, Output]