langchain_mistralai.chat_models.ChatMistralAI

class langchain_mistralai.chat_models.ChatMistralAI[source]

Bases: BaseChatModel

A chat model that uses the MistralAI API.

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 cache: Union[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: Optional[BaseCallbackManager] = None

[DEPRECATED] Callback manager to add to the run trace.

param callbacks: Callbacks = None

Callbacks to add to the run trace.

param endpoint: str = 'https://api.mistral.ai/v1'
param max_concurrent_requests: int = 64
param max_retries: int = 5
param max_tokens: Optional[int] = None
param metadata: Optional[Dict[str, Any]] = None

Metadata to add to the run trace.

param mistral_api_key: Optional[SecretStr] = None (alias 'api_key')
Constraints
  • type = string

  • writeOnly = True

  • format = password

param model: str = 'mistral-small' (alias 'model_name')
param random_seed: Optional[int] = None
param safe_mode: bool = False
param streaming: bool = False
param tags: Optional[List[str]] = None

Tags to add to the run trace.

param temperature: float = 0.7
param timeout: int = 120
param top_p: float = 1

Decode using nucleus sampling: consider the smallest set of tokens whose probability sum is at least top_p. Must be in the closed interval [0.0, 1.0].

param verbose: bool [Optional]

Whether to print out response text.

__call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) BaseMessage

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
Return type

BaseMessage

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.

Parameters
  • inputs (List[Input]) –

  • config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

  • return_exceptions (bool) –

  • kwargs (Optional[Any]) –

Return type

List[Output]

async abatch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) AsyncIterator[Tuple[int, Union[Output, Exception]]]

Run ainvoke in parallel on a list of inputs, yielding results as they complete.

Parameters
  • inputs (List[Input]) –

  • config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

  • return_exceptions (bool) –

  • kwargs (Optional[Any]) –

Return type

AsyncIterator[Tuple[int, Union[Output, Exception]]]

async agenerate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, run_id: Optional[UUID] = None, **kwargs: Any) LLMResult

Asynchronously pass a sequence of prompts to a model and return generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • messages (List[List[BaseMessage]]) – List of list of messages.

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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.

  • tags (Optional[List[str]]) –

  • metadata (Optional[Dict[str, Any]]) –

  • run_name (Optional[str]) –

  • run_id (Optional[UUID]) –

  • **kwargs

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) LLMResult

Asynchronously pass a sequence of prompts and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

async ainvoke(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = 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 apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use ainvoke instead.

Parameters
  • text (str) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

str

async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use ainvoke instead.

Parameters
  • messages (List[BaseMessage]) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

BaseMessage

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.

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

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]]]]) –

Return type

RunnableSerializable[Any, Any]

async astream(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = 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) –

  • config (Optional[RunnableConfig]) –

  • stop (Optional[List[str]]) –

  • kwargs (Any) –

Return type

AsyncIterator[BaseMessageChunk]

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 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.

  • 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:

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="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 (Any) – The input to the runnable.

  • config (Optional[RunnableConfig]) – The config to use for the runnable.

  • version (Literal['v1']) – 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 (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.

Returns

An async stream of StreamEvents.

Return type

AsyncIterator[StreamEvent]

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 (Any) – The input to the runnable.

  • config (Optional[RunnableConfig]) – The config to use for the runnable.

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

  • with_streamed_output_list (bool) – Whether to yield the streamed_output list.

  • include_names (Optional[Sequence[str]]) – Only include logs with these names.

  • include_types (Optional[Sequence[str]]) – Only include logs with these types.

  • include_tags (Optional[Sequence[str]]) – Only include logs with these tags.

  • exclude_names (Optional[Sequence[str]]) – Exclude logs with these names.

  • exclude_types (Optional[Sequence[str]]) – Exclude logs with these types.

  • exclude_tags (Optional[Sequence[str]]) – Exclude logs with these tags.

  • kwargs (Any) –

Return type

Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]

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.

Parameters
  • input (AsyncIterator[Input]) –

  • config (Optional[RunnableConfig]) –

  • kwargs (Optional[Any]) –

Return type

AsyncIterator[Output]

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.

Parameters
  • inputs (List[Input]) –

  • config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

  • return_exceptions (bool) –

  • kwargs (Optional[Any]) –

Return type

List[Output]

batch_as_completed(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) Iterator[Tuple[int, Union[Output, Exception]]]

Run invoke in parallel on a list of inputs, yielding results as they complete.

Parameters
  • inputs (List[Input]) –

  • config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –

  • return_exceptions (bool) –

  • kwargs (Optional[Any]) –

Return type

Iterator[Tuple[int, Union[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.

Example:

from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser

llm = ChatOllama(model='llama2')

# Without bind.
chain = (
    llm
    | StrOutputParser()
)

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'

# With bind.
chain = (
    llm.bind(stop=["three"])
    | StrOutputParser()
)

chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
Parameters

kwargs (Any) –

Return type

Runnable[Input, Output]

bind_tools(tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], **kwargs: Any) Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], BaseMessage][source]

Bind tool-like objects to this chat model.

Assumes model is compatible with OpenAI tool-calling API.

Parameters
  • tools (Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]]) – A list of tool definitions to bind to this chat model. Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic models, callables, and BaseTools will be automatically converted to their schema dictionary representation.

  • tool_choice – Which tool to require the model to call. Must be the name of the single provided function or “auto” to automatically determine which function to call (if any), or a dict of the form: {“type”: “function”, “function”: {“name”: <<tool_name>>}}.

  • **kwargs (Any) – Any additional parameters to pass to the Runnable constructor.

Return type

Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]

call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) str

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • message (str) –

  • stop (Optional[List[str]]) –

  • kwargs (Any) –

Return type

str

completion_with_retry(run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) Any[source]

Use tenacity to retry the completion call.

Parameters
Return type

Any

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 (Optional[Sequence[str]]) – A list of fields to include in the config schema.

Returns

A pydantic model that can be used to validate config.

Return type

Type[BaseModel]

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) RunnableSerializable[Input, Output]

Configure alternatives for runnables that can be set at runtime.

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 ChatOpenaAI
print(
    model.with_config(
        configurable={"llm": "openai"}
    ).invoke("which organization created you?").content
)
Parameters
Return type

RunnableSerializable[Input, Output]

configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) RunnableSerializable[Input, Output]

Configure particular runnable fields at runtime.

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
)
Parameters

kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) –

Return type

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

Parameters
  • _fields_set (Optional[SetStr]) –

  • values (Any) –

Return type

Model

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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

  • update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model

  • self (Model) –

Returns

new model instance

Return type

Model

dict(**kwargs: Any) Dict

Return a dictionary of the LLM.

Parameters

kwargs (Any) –

Return type

Dict

classmethod from_orm(obj: Any) Model
Parameters

obj (Any) –

Return type

Model

generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, run_id: Optional[UUID] = None, **kwargs: Any) LLMResult

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • messages (List[List[BaseMessage]]) – List of list of messages.

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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.

  • tags (Optional[List[str]]) –

  • metadata (Optional[Dict[str, Any]]) –

  • run_name (Optional[str]) –

  • run_id (Optional[UUID]) –

  • **kwargs

Returns

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) LLMResult

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:
  1. take advantage of batched calls,

  2. need more output from the model than just the top generated value,

  3. are building chains that are agnostic to the underlying language model

    type (e.g., pure text completion models vs chat models).

Parameters
  • prompts (List[PromptValue]) – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).

  • stop (Optional[List[str]]) – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

  • callbacks (Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]) – 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

An LLMResult, which contains a list of candidate Generations for each input

prompt and additional model provider-specific output.

Return type

LLMResult

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

Return a graph representation of this runnable.

Parameters

config (Optional[RunnableConfig]) –

Return type

Graph

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 (Optional[RunnableConfig]) – A config to use when generating the schema.

Returns

A pydantic model that can be used to validate input.

Return type

Type[BaseModel]

classmethod get_lc_namespace() List[str][source]

Get the namespace of the langchain object.

Return type

List[str]

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

Get the name of the runnable.

Parameters
  • suffix (Optional[str]) –

  • name (Optional[str]) –

Return type

str

get_num_tokens(text: str) int

Get the number of tokens present in the text.

Useful for checking if an input will fit 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]) int

Get the number of tokens in the messages.

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

Parameters

messages (List[BaseMessage]) – The message inputs to tokenize.

Returns

The sum of the number of tokens across the messages.

Return type

int

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 (Optional[RunnableConfig]) – A config to use when generating the schema.

Returns

A pydantic model that can be used to validate output.

Return type

Type[BaseModel]

get_prompts(config: Optional[RunnableConfig] = None) List[BasePromptTemplate]
Parameters

config (Optional[RunnableConfig]) –

Return type

List[BasePromptTemplate]

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: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) BaseMessage

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

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.

  • stop (Optional[List[str]]) –

  • kwargs (Any) –

Returns

The output of the runnable.

Return type

BaseMessage

classmethod is_lc_serializable() bool[source]

Return whether this model can be serialized by Langchain.

Return type

bool

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().

Parameters
  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

  • encoder (Optional[Callable[[Any], Any]]) –

  • models_as_dict (bool) –

  • dumps_kwargs (Any) –

Return type

unicode

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.

Return type

List[str]

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.

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]
Return type

Runnable[List[Input], List[Output]]

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
Parameters
  • path (Union[str, Path]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) Model
Parameters

obj (Any) –

Return type

Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model
Parameters
  • b (Union[str, bytes]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

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

Pick keys from the dict output 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]"}
Parameters

keys (Union[str, List[str]]) –

Return type

RunnableSerializable[Any, Any]

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

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]
Parameters
  • others (Union[Runnable[Any, Other], Callable[[Any], Other]]) –

  • name (Optional[str]) –

Return type

RunnableSerializable[Input, Other]

predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • text (str) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

str

predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage

[Deprecated]

Notes

Deprecated since version langchain-core==0.1.7: Use invoke instead.

Parameters
  • messages (List[BaseMessage]) –

  • stop (Optional[Sequence[str]]) –

  • kwargs (Any) –

Return type

BaseMessage

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny
Parameters
  • by_alias (bool) –

  • ref_template (unicode) –

Return type

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode
Parameters
  • by_alias (bool) –

  • ref_template (unicode) –

  • dumps_kwargs (Any) –

Return type

unicode

stream(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = 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) –

  • config (Optional[RunnableConfig]) –

  • stop (Optional[List[str]]) –

  • kwargs (Any) –

Return type

Iterator[BaseMessageChunk]

to_json() Union[SerializedConstructor, SerializedNotImplemented]

Serialize the runnable to JSON.

Return type

Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() SerializedNotImplemented
Return type

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.

Parameters
  • input (Iterator[Input]) –

  • config (Optional[RunnableConfig]) –

  • kwargs (Optional[Any]) –

Return type

Iterator[Output]

classmethod update_forward_refs(**localns: Any) None

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

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) Model
Parameters

value (Any) –

Return type

Model

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

Bind config to a Runnable, returning a new Runnable.

Parameters
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.

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: 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.

Example:

Parameters
  • on_start (Optional[Listener]) –

  • on_end (Optional[Listener]) –

  • on_error (Optional[Listener]) –

Return type

Runnable[Input, Output]

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.

Example:

Parameters
  • retry_if_exception_type (Tuple[Type[BaseException], ...]) – A tuple of exception types to retry on

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

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

Returns

A new Runnable that retries the original runnable on exceptions.

Return type

Runnable[Input, Output]

with_structured_output(schema: Union[Dict, Type[BaseModel]], *, include_raw: bool = False, **kwargs: Any) Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]][source]

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

Parameters
  • schema (Union[Dict, Type[BaseModel]]) – The output schema as a dict or a Pydantic class. If a Pydantic class then the model output will be an object of that class. If a dict then the model output will be a dict. With a Pydantic class the returned attributes will be validated, whereas with a dict they will not be. If method is “function_calling” and schema is a dict, then the dict must match the OpenAI function-calling spec.

  • include_raw (bool) – 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”.

  • kwargs (Any) –

Returns

If include_raw is True then a dict with keys:

raw: BaseMessage parsed: Optional[_DictOrPydantic] parsing_error: Optional[BaseException]

If include_raw is False then just _DictOrPydantic is returned, where _DictOrPydantic depends on the schema:

If schema is a Pydantic class then _DictOrPydantic is the Pydantic

class.

If schema is a dict then _DictOrPydantic is a dict.

Return type

A Runnable that takes any ChatModel input and returns as output

Example: Function-calling, Pydantic schema (method=”function_calling”, include_raw=False):
from langchain_mistralai import ChatMistralAI
from langchain_core.pydantic_v1 import BaseModel

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

llm = ChatMistralAI(model="mistral-large-latest", 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='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: Function-calling, Pydantic schema (method=”function_calling”, include_raw=True):
from langchain_mistralai import ChatMistralAI
from langchain_core.pydantic_v1 import BaseModel

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

llm = ChatMistralAI(model="mistral-large-latest", 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': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
#     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
#     'parsing_error': None
# }
Example: Function-calling, dict schema (method=”function_calling”, include_raw=False):
from langchain_mistralai import ChatMistralAI
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.utils.function_calling import convert_to_openai_tool

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

dict_schema = convert_to_openai_tool(AnswerWithJustification)
llm = ChatMistralAI(model="mistral-large-latest", temperature=0)
structured_llm = llm.with_structured_output(dict_schema)

structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
# -> {
#     'answer': 'They weigh the same',
#     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }

Notes

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.

Parameters
  • input_type (Optional[Type[Input]]) –

  • output_type (Optional[Type[Output]]) –

Return type

Runnable[Input, Output]

property InputType: TypeAlias

Get the input type for this runnable.

property OutputType: Any

Get the output type for this runnable.

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

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.