langchain_fireworks.chat_models.ChatFireworks

Note

ChatFireworks implements the standard Runnable Interface. 🏃

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

class langchain_fireworks.chat_models.ChatFireworks

Bases: BaseChatModel

Fireworks Chat large language models API.

To use, you should have the environment variable FIREWORKS_API_KEY set with your API key.

Any parameters that are valid to be passed to the fireworks.create call can be passed in, even if not explicitly saved on this class.

Example

from langchain_fireworks.chat_models import ChatFireworks
fireworks = ChatFireworks(
    model_name="accounts/fireworks/models/mixtral-8x7b-instruct")

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 custom_get_token_ids: Optional[Callable[[str], List[int]]] = None

Optional encoder to use for counting tokens.

param fireworks_api_base: Optional[str] = None (alias 'base_url')

Base URL path for API requests, leave blank if not using a proxy or service emulator.

param fireworks_api_key: SecretStr = None (alias 'api_key')

Automatically inferred from env var FIREWORKS_API_KEY if not provided.

Constraints

• type = string

• writeOnly = True

• format = password

param max_retries: Optional[int] = None

Maximum number of retries to make when generating.

param max_tokens: Optional[int] = None

Maximum number of tokens to generate.

param metadata: Optional[Dict[str, Any]] = None

Metadata to add to the run trace.

param model_kwargs: Dict[str, Any] [Optional]

Holds any model parameters valid for create call not explicitly specified.

param model_name: str = 'accounts/fireworks/models/mixtral-8x7b-instruct' (alias 'model')

Model name to use.

param n: int = 1

Number of chat completions to generate for each prompt.

param rate_limiter: Optional[BaseRateLimiter] = None

An optional rate limiter to use for limiting the number of requests.

param request_timeout: Union[float, Tuple[float, float], Any, None] = None (alias 'timeout')

Timeout for requests to Fireworks completion API. Can be float, httpx.Timeout or None.

param stop: Optional[Union[str, List[str]]] = None (alias 'stop_sequences')

Default stop sequences.

param streaming: bool = False

Whether to stream the results or not.

param tags: Optional[List[str]] = None

Tags to add to the run trace.

param temperature: float = 0.0

What sampling temperature to use.

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

Parameters

• messages (List[BaseMessage]) –

• stop (Optional[List[str]]) –

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

• kwargs (Any) –

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]) – A list of inputs to the Runnable.

• config (Optional[Union[RunnableConfig, List[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.

• return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.

• kwargs (Optional[Any]) – 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: Optional[Union[RunnableConfig, Sequence[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 (Sequence[Input]) – A list of inputs to the Runnable.

• config (Optional[Union[RunnableConfig, Sequence[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. Defaults to None.

• return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.

• kwargs (Optional[Any]) – 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, 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 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 since version langchain-core==0.1.7: Use ainvoke instead.

Parameters

• messages (List[BaseMessage]) –

• stop (Optional[Sequence[str]]) –

• kwargs (Any) –

Return type

BaseMessage

as_tool(args_schema: Optional[Type[BaseModel]] = None, *, name: Optional[str] = None, description: Optional[str] = None, arg_types: Optional[Dict[str, Type]] = None) → BaseTool

Beta

This API is in beta and may change in the future.

Create a BaseTool from a Runnable.

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

Parameters

• args_schema (Optional[Type[BaseModel]]) – The schema for the tool. Defaults to None.

• name (Optional[str]) – The name of the tool. Defaults to None.

• description (Optional[str]) – The description of the tool. Defaults to None.

• arg_types (Optional[Dict[str, Type]]) – A dictionary of argument names to types. Defaults to None.

Returns

A BaseTool instance.

Return type

BaseTool

Typed dict input:

from typing import List
from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda

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

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

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

dict input, specifying schema via args_schema:

from typing import Any, Dict, List
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.runnables import RunnableLambda

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

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

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

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

dict input, specifying schema via arg_types:

from typing import Any, Dict, List
from langchain_core.runnables import RunnableLambda

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

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

String input:

from langchain_core.runnables import RunnableLambda

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

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

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

New in version 0.2.14.

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) – 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]

astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1', 'v2'], 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[Union[StandardStreamEvent, CustomStreamEvent]]

Beta

This API is in beta and may change in the future.

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 evens that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.

ATTENTION This reference table is for the V2 version of the schema.

event	name	chunk	input	output
on_chat_model_start	[model name]		{“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[Union[StandardStreamEvent, CustomStreamEvent]]

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: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[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 (Sequence[Input]) –

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

• return_exceptions (bool) –

• kwargs (Optional[Any]) –

Return type

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

bind_functions(functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], function_call: Optional[Union[_FunctionCall, str, Literal['auto', 'none']]] = None, **kwargs: Any) → Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]

Bind functions (and other objects) to this chat model.

Assumes model is compatible with Fireworks function-calling API.

NOTE: Using bind_tools is recommended instead, as the functions and

function_call request parameters are officially marked as deprecated by Fireworks.

Parameters

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

• function_call (Optional[Union[_FunctionCall, str, Literal['auto', 'none']]]) – Which function 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).

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

Return type

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

bind_tools(tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], *, tool_choice: Optional[Union[dict, str, Literal['auto', 'any', 'none'], bool]] = None, **kwargs: Any) → Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]

Bind tool-like objects to this chat model.

Assumes model is compatible with Fireworks 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. Supports any tool definition handled by langchain_core.utils.function_calling.convert_to_openai_tool().

• tool_choice (Optional[Union[dict, str, Literal['auto', 'any', 'none'], bool]]) – Which tool to require the model to call. Must be the name of the single provided function, “auto” to automatically determine which function to call with the option to not call any function, “any” to enforce that some function is called, 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, List[str], Tuple[str, str], str, Dict[str, Any]]]], BaseMessage]

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

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

Parameters

• message (str) –

• stop (Optional[List[str]]) –

• kwargs (Any) –

Return type

str

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.

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 (Union[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[Input, Output]

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

model = ChatAnthropic(
    model_name="claude-3-sonnet-20240229"
).configurable_alternatives(
    ConfigurableField(id="llm"),
    default_key="anthropic",
    openai=ChatOpenAI()
)

# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)

# uses ChatOpenAI
print(
    model.with_config(
        configurable={"llm": "openai"}
    ).invoke("which organization created you?").content
)

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

Configure particular Runnable fields at runtime.

Parameters

**kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) – A dictionary of ConfigurableField instances to configure.

Returns

A new Runnable with the fields configured.

Return type

RunnableSerializable[Input, Output]

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
)

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_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]) → int

Get the number of tokens in the messages.

Useful for checking if an input fits 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_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

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

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

Parameters

• messages (List[BaseMessage]) –

• stop (Optional[Sequence[str]]) –

• kwargs (Any) –

Return type

BaseMessage

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) – 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]

to_json() → Union[SerializedConstructor, SerializedNotImplemented]

Serialize the Runnable to JSON.

Returns

A JSON-serializable representation of the Runnable.

Return type

Union[SerializedConstructor, SerializedNotImplemented]

with_structured_output(schema: Optional[Union[Dict, Type[BaseModel]]] = None, *, method: Literal['function_calling', 'json_mode'] = 'function_calling', include_raw: bool = False, **kwargs: Any) → Runnable[Union[PromptValue, str, Sequence[Union[BaseMessage, List[str], Tuple[str, str], str, Dict[str, Any]]]], Union[Dict, BaseModel]]

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 (support added in 0.1.7),

• 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.

Changed in version 0.1.7: Added support for TypedDict class.

method:

The method for steering model generation, either “function_calling” or “json_mode”. 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” then OpenAI’s JSON mode will be used. Note that if using “json_mode” 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_fireworks import ChatFireworks
from langchain_core.pydantic_v1 import BaseModel, Field


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

    answer: str
    # If we provide default values and/or descriptions for fields, these will be passed
    # to the model. This is an important part of improving a model's ability to
    # correctly return structured outputs.
    justification: Optional[str] = Field(
        default=None, description="A justification for the answer."
    )


llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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: schema=Pydantic class, method=”function_calling”, include_raw=True:

from langchain_fireworks import ChatFireworks
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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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: 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_fireworks import ChatFireworks


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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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': '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.'
# }

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

 from langchain_fireworks import ChatFireworks

 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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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': '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.'
 # }

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

from langchain_fireworks import ChatFireworks
from langchain_core.pydantic_v1 import BaseModel

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

llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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 both the same weight.”, “justification”: “Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.”

}’),

# ‘parsed’: AnswerWithJustification(answer=’They are both the same weight.’, justification=’Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.’), # ‘parsing_error’: None # }

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

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 both the same weight.”, “justification”: “Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.”

}’),

# ‘parsed’: { # ‘answer’: ‘They are both the same weight.’, # ‘justification’: ‘Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.’ # }, # ‘parsing_error’: None # }

Parameters

• schema (Optional[Union[Dict, Type[BaseModel]]]) –

• method (Literal['function_calling', 'json_mode']) –

• include_raw (bool) –

• kwargs (Any) –

Return type

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

Examples using ChatFireworks

• ChatFireworks

• Response metadata