ChatFireworks#
- class langchain_fireworks.chat_models.ChatFireworks[source]#
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")
Note
ChatFireworks implements the standard
Runnable Interface
. 🏃The
Runnable Interface
has additional methods that are available on runnables, such aswith_types
,with_retry
,assign
,bind
,get_graph
, and more.- 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.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()
will defer toinvoke()/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.If False (default), will always use streaming case if available.
- param fireworks_api_base: str | None [Optional] (alias 'base_url')#
Base URL path for API requests, leave blank if not using a proxy or service emulator.
- param fireworks_api_key: SecretStr [Optional] (alias 'api_key')#
Fireworks API key.
Automatically read from env variable FIREWORKS_API_KEY if not provided.
- param max_retries: int | None = None#
Maximum number of retries to make when generating.
- param max_tokens: int | None = None#
Maximum number of tokens to generate.
- param metadata: dict[str, Any] | None = 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: BaseRateLimiter | None = None#
An optional rate limiter to use for limiting the number of requests.
- param request_timeout: 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: str | List[str] | None = None (alias 'stop_sequences')#
Default stop sequences.
- param streaming: bool = False#
Whether to stream the results or not.
- param tags: list[str] | None = 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: list[str] | None = None, callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None, **kwargs: Any) BaseMessage #
Deprecated since version langchain-core==0.1.7: Use
invoke()
instead.- Parameters:
messages (list[BaseMessage])
stop (list[str] | None)
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None)
kwargs (Any)
- Return type:
- 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, yielding 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. 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:
- 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'], 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[StandardStreamEvent | CustomStreamEvent] #
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 theformat: 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 ofthe 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 thatgenerated 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 generatedthe event.
metadata
: Optional[Dict[str, Any]] - The metadata of the Runnablethat 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 (RunnableConfig | None) – 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 (Sequence[str] | None) – Only include events from runnables with matching names.
include_types (Sequence[str] | None) – Only include events from runnables with matching types.
include_tags (Sequence[str] | None) – Only include events from runnables with matching tags.
exclude_names (Sequence[str] | None) – Exclude events from runnables with matching names.
exclude_types (Sequence[str] | None) – Exclude events from runnables with matching types.
exclude_tags (Sequence[str] | None) – 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[StandardStreamEvent | CustomStreamEvent]
- 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, yielding 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_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'
- bind_functions(functions: Sequence[Dict[str, Any] | Type[BaseModel] | Callable | BaseTool], function_call: _FunctionCall | str | Literal['auto', 'none'] | None = None, **kwargs: Any) Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], BaseMessage] [source]#
Deprecated since version langchain-fireworks==0.2.1: Use
bind_tools()
instead.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[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 (_FunctionCall | str | Literal['auto', 'none'] | 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[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], BaseMessage]
- bind_tools(tools: Sequence[Dict[str, Any] | Type[BaseModel] | Callable | BaseTool], *, tool_choice: dict | str | Literal['auto', 'any', 'none'] | bool | None = None, **kwargs: Any) Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], BaseMessage] [source]#
Bind tool-like objects to this chat model.
Assumes model is compatible with Fireworks tool-calling API.
- Parameters:
tools (Sequence[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 (dict | str | Literal['auto', 'any', 'none'] | bool | None) – 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
bind()
- Return type:
Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], BaseMessage]
- 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:
from langchain_anthropic import ChatAnthropic from langchain_core.runnables.utils import ConfigurableField from langchain_openai import ChatOpenAI model = ChatAnthropic( model_name="claude-3-sonnet-20240229" ).configurable_alternatives( ConfigurableField(id="llm"), default_key="anthropic", openai=ChatOpenAI() ) # uses the default model ChatAnthropic print(model.invoke("which organization created you?").content) # uses ChatOpenAI print( model.with_config( configurable={"llm": "openai"} ).invoke("which organization created you?").content )
- configurable_fields(**kwargs: ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) RunnableSerializable #
Configure particular Runnable fields at runtime.
- Parameters:
**kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) – A dictionary of ConfigurableField instances to configure.
- Returns:
A new Runnable with the fields configured.
- Return type:
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]) 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: RunnableConfig | None = None, *, stop: list[str] | None = 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:
- 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 asynchronous lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Asynchronously called before the Runnable starts running. on_end: Asynchronously called after the Runnable finishes running. on_error: Asynchronously called if the Runnable throws an error.
The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.
- Parameters:
on_start (Optional[AsyncListener]) – Asynchronously called before the Runnable starts running. Defaults to None.
on_end (Optional[AsyncListener]) – Asynchronously called after the Runnable finishes running. Defaults to None.
on_error (Optional[AsyncListener]) – Asynchronously called if the Runnable throws an error. Defaults to None.
- Returns:
A new Runnable with the listeners bound.
- Return type:
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda import time 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 2024-05-16T14:20:29.637053+00:00 on start callback starts at 2024-05-16T14:20:29.637150+00:00 on start callback ends at 2024-05-16T14:20:32.638305+00:00 on start callback ends at 2024-05-16T14:20:32.638383+00:00 Runnable[3s]: starts at 2024-05-16T14:20:32.638849+00:00 Runnable[5s]: starts at 2024-05-16T14:20:32.638999+00:00 Runnable[3s]: ends at 2024-05-16T14:20:35.640016+00:00 on end callback starts at 2024-05-16T14:20:35.640534+00:00 Runnable[5s]: ends at 2024-05-16T14:20:37.640169+00:00 on end callback starts at 2024-05-16T14:20:37.640574+00:00 on end callback ends at 2024-05-16T14:20:37.640654+00:00 on end callback ends at 2024-05-16T14:20:39.641751+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.
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.
- Parameters:
on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called before the Runnable starts running. Defaults to None.
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called after the Runnable finishes running. Defaults to None.
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called if the Runnable throws an error. 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, 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.
- 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)
- 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: Dict | Type[BaseModel] | None = None, *, method: Literal['function_calling', 'json_mode'] = 'function_calling', include_raw: bool = False, **kwargs: Any) Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], Dict | BaseModel] [source]#
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. Seelangchain_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 andschema
is a Pydantic class, Runnable outputs an instance ofschema
(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 theschema
as described above."parsing_error"
: Optional[BaseException]
- If
- Example: schema=Pydantic class, method=”function_calling”, include_raw=False:
from typing import Optional from langchain_fireworks import ChatFireworks from pydantic 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 pydantic 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 pydantic 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 (Dict | Type[BaseModel] | None)
method (Literal['function_calling', 'json_mode'])
include_raw (bool)
kwargs (Any)
- Return type:
Runnable[PromptValue | str | Sequence[BaseMessage | list[str] | tuple[str, str] | str | dict[str, Any]], Dict | BaseModel]
- 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]