OpenAI #

classlangchain_openai.llms.base.OpenAI[source]#

Bases:BaseOpenAI

OpenAI completion model integration.

Setup:

Installlangchain-openai and set environment variableOPENAI_API_KEY.

pipinstall-Ulangchain-openaiexportOPENAI_API_KEY="your-api-key"

Key init args — completion params:

model: str: Name of OpenAI model to use.
temperature: float: Sampling temperature.
max_tokens: Optional[int]: Max number of tokens to generate.
logprobs: Optional[bool]: Whether to return logprobs.
stream_options: Dict: Configure streaming outputs, like whether to return token usage whenstreaming ({"include_usage":True}).

Key init args — client params:

timeout: Union[float, Tuple[float, float], Any, None]: Timeout for requests.
max_retries: int: Max number of retries.
api_key: Optional[str]: OpenAI API key. If not passed in will be read from env varOPENAI_API_KEY.
base_url: Optional[str]: Base URL for API requests. Only specify if using a proxy or serviceemulator.
organization: Optional[str]: OpenAI organization ID. If not passed in will be read from envvarOPENAI_ORG_ID.

See full list of supported init args and their descriptions in the params section.

Instantiate:

fromlangchain_openaiimportOpenAIllm=OpenAI(model="gpt-3.5-turbo-instruct",temperature=0,max_retries=2,# api_key="...",# base_url="...",# organization="...",# other params...)

Invoke:

input_text="The meaning of life is "llm.invoke(input_text)

"a philosophical question that has been debated by thinkers and scholars for centuries."

Stream:

forchunkinllm.stream(input_text):print(chunk,end="|")

a| philosophical| question| that| has| been| debated| by| thinkers| and| scholars| for| centuries|.

"".join(llm.stream(input_text))

"a philosophical question that has been debated by thinkers and scholars for centuries."

Async:

awaitllm.ainvoke(input_text)# stream:# async for chunk in (await llm.astream(input_text)):#    print(chunk)# batch:# await llm.abatch([input_text])

"a philosophical question that has been debated by thinkers and scholars for centuries."

Note

OpenAI implements the standardRunnableInterface. 🏃

TheRunnableInterface has additional methods that are available on runnables, such aswith_config,with_types,with_retry,assign,bind,get_graph, and more.

paramallowed_special:Literal['all']|set[str]={}#: Set of special tokens that are allowed。

parambatch_size:int=20#: Batch size to use when passing multiple documents to generate.

parambest_of:int=1#: Generates best_of completions server-side and returns the “best”.

paramcache: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.

paramcallback_manager:BaseCallbackManager|None=None#: [DEPRECATED]

paramcallbacks:Callbacks=None#: Callbacks to add to the run trace.

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

paramdefault_headers:Mapping[str,str]|None=None#

paramdefault_query:Mapping[str,object]|None=None#

paramdisallowed_special:Literal['all']|Collection[str]='all'#: Set of special tokens that are not allowed。

paramextra_body:Mapping[str,Any]|None=None#: Optional additional JSON properties to include in the request parameters whenmaking requests to OpenAI compatible APIs, such as vLLM.

paramfrequency_penalty:float=0#: Penalizes repeated tokens according to frequency.

paramhttp_async_client:Any|None=None#: Optionalhttpx.AsyncClient. Only used for async invocations. Must specifyhttp_client as well if you’d like a custom client for sync invocations.

paramhttp_client:Any|None=None#: Optionalhttpx.Client. Only used for sync invocations. Must specifyhttp_async_client as well if you’d like a custom client for asyncinvocations.

paramlogit_bias:dict[str,float]|None=None#: Adjust the probability of specific tokens being generated.

paramlogprobs:int|None=None#: Include the log probabilities on the logprobs most likely output tokens,as well the chosen tokens.

parammax_retries:int=2#: Maximum number of retries to make when generating.

parammax_tokens:int=256#: The maximum number of tokens to generate in the completion.-1 returns as many tokens as possible given the prompt andthe models maximal context size.

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

parammodel_kwargs:dict[str,Any][Optional]#: Holds any model parameters valid forcreate call not explicitly specified.

parammodel_name:str='gpt-3.5-turbo-instruct'(alias'model')#: Model name to use.

paramn:int=1#: How many completions to generate for each prompt.

paramopenai_api_base:str|None[Optional](alias'base_url')#: Base URL path for API requests, leave blank if not using a proxy or serviceemulator.

paramopenai_api_key:SecretStr|None[Optional](alias'api_key')#: Automatically inferred from env varOPENAI_API_KEY if not provided.

paramopenai_organization:str|None[Optional](alias'organization')#: Automatically inferred from env varOPENAI_ORG_ID if not provided.

paramopenai_proxy:str|None[Optional]#

parampresence_penalty:float=0#: Penalizes repeated tokens.

paramrequest_timeout:float|tuple[float,float]|Any|None=None(alias'timeout')#: Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout orNone.

paramseed:int|None=None#: Seed for generation

paramstreaming:bool=False#: Whether to stream the results or not.

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

paramtemperature:float=0.7#: What sampling temperature to use.

paramtiktoken_model_name:str|None=None#: The model name to pass to tiktoken when using this class.Tiktoken is used to count the number of tokens in documents to constrainthem to be under a certain limit. By default, when set to None, this willbe the same as the embedding model name. However, there are some caseswhere you may want to use this Embedding class with a model name notsupported by tiktoken. This can include when using Azure embeddings orwhen using one of the many model providers that expose an OpenAI-likeAPI but with different models. In those cases, in order to avoid erroringwhen tiktoken is called, you can specify a model name to use here.

paramtop_p:float=1#: Total probability mass of tokens to consider at each step.

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

staticmodelname_to_contextsize(modelname:str)→int#

Calculate the maximum number of tokens possible to generate for a model.

Parameters:: modelname (str) – The modelname we want to know the context size for.
Returns:: The maximum context size
Return type:: int

Example

max_tokens=openai.modelname_to_contextsize("gpt-3.5-turbo-instruct")

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

Check Cache and run the LLM on the given prompt and input.

Parameters:

prompt (str) – The prompt to generate from.
stop (list[str]|None) – Stop words to use when generating. Model output is cut off at thefirst occurrence of any of these substrings.
callbacks (list[BaseCallbackHandler]|BaseCallbackManager |None) – Callbacks to pass through. Used for executing additionalfunctionality, such as logging or streaming, throughout generation.
tags (list[str]|None) – List of tags to associate with the prompt.
metadata (dict[str,Any]|None) – Metadata to associate with the prompt.
**kwargs (Any) – Arbitrary additional keyword arguments. These are usually passedto the model provider API call.

Returns:

The generated text.

Raises:

ValueError – If the prompt is not a string.

Return type:

str

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[PromptValue |str |Sequence[BaseMessage |list[str]|tuple[str,str]|str |dict[str,Any]]]) – 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 tracingpurposes, ‘max_concurrency’ for controlling how much work to doin parallel, and other keys. Please refer to the RunnableConfigfor more details. Defaults to None.
return_exceptions (bool) – Whether to return exceptions instead of raising them.Defaults to False.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.

Returns:

A list of outputs from the Runnable.

Return type:

list[str]

asyncabatch_as_completed(
inputs:Sequence[Input],
config:RunnableConfig|Sequence[RunnableConfig]|None=None,
*,
return_exceptions:bool=False,
**kwargs:Any|None,
)→AsyncIterator[tuple[int,Output|Exception]]#

Run ainvoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

inputs (Sequence[Input]) – A list of inputs to the Runnable.
config (RunnableConfig |Sequence[RunnableConfig]|None) – A config to use when invoking the Runnable.The config supports standard keys like ‘tags’, ‘metadata’ for tracingpurposes, ‘max_concurrency’ for controlling how much work to doin parallel, and other keys. Please refer to the RunnableConfigfor 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]]

Default implementation of ainvoke, calls invoke from a thread.

The default implementation allows usage of async code even ifthe Runnable did not implement a native async version of invoke.

Subclasses should override this method if they can run asynchronously.

Parameters:

input (PromptValue |str |Sequence[BaseMessage |list[str]|tuple[str,str]|str |dict[str,Any]])
config (RunnableConfig |None)
stop (list[str]|None)
kwargs (Any)

Return type:

str

Default implementation of astream, which calls ainvoke.

Subclasses should override this method if they support streaming output.

Parameters:

input (PromptValue |str |Sequence[BaseMessage |list[str]|tuple[str,str]|str |dict[str,Any]]) – The input to the Runnable.
config (RunnableConfig |None) – The config to use for the Runnable. Defaults to None.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.
stop (list[str]|None)

Yields:

The output of the Runnable.

Return type:

AsyncIterator[str]

asyncastream_events(
input:Any,
config:RunnableConfig|None=None,
*,
version:Literal['v1','v2']='v2',
include_names:Sequence[str]|None=None,
include_types:Sequence[str]|None=None,
include_tags:Sequence[str]|None=None,
exclude_names:Sequence[str]|None=None,
exclude_types:Sequence[str]|None=None,
exclude_tags:Sequence[str]|None=None,
**kwargs:Any,
)→AsyncIterator[StreamEvent]#

Generate a stream of events.

Use to create an iterator over StreamEvents that provide real-time informationabout the progress of the Runnable, including StreamEvents from intermediateresults.

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 givenexecution of the Runnable that emitted the event. A child Runnable that getsinvoked as part of the execution of a parent Runnable is assigned its ownunique ID.
parent_ids:list[str] - The IDs of the parent runnables that generatedthe event. The root Runnable will have an empty list. The order of the parentIDs is from the root to the immediate parent. Only available for v2 version ofthe 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 Runnable thatgenerated the event.
data:dict[str, Any]

Below is a table that illustrates some events that might be emitted by variouschains. Metadata fields have been omitted from the table for brevity.Chain definitions have been included after the table.

Note

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

event	name	chunk	input	output
on_chat_model_start	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}
on_chat_model_stream	[model name]	AIMessageChunk(content=”hello”)
on_chat_model_end	[model name]		{“messages”: [[SystemMessage, HumanMessage]]}	AIMessageChunk(content=”hello world”)
on_llm_start	[model name]		{‘input’: ‘hello’}
on_llm_stream	[model name]	‘Hello’
on_llm_end	[model name]		‘Hello human!’
on_chain_start	format_docs
on_chain_stream	format_docs	“hello world!, goodbye world!”
on_chain_end	format_docs		[Document(…)]	“hello world!, goodbye world!”
on_tool_start	some_tool		{“x”: 1, “y”: “2”}
on_tool_end	some_tool			{“x”: 1, “y”: “2”}
on_retriever_start	[retriever name]		{“query”: “hello”}
on_retriever_end	[retriever name]		{“query”: “hello”}	[Document(…), ..]
on_prompt_start	[template_name]		{“question”: “hello”}
on_prompt_end	[template_name]		{“question”: “hello”}	ChatPromptValue(messages: [SystemMessage, …])

In addition to the standard events, users can also dispatch custom events (see example below).

Custom events will be only be surfaced with in thev2 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:

defformat_docs(docs:list[Document])->str:'''Format the docs.'''return", ".join([doc.page_contentfordocindocs])format_docs=RunnableLambda(format_docs)

some_tool:

@tooldefsome_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:

fromlangchain_core.runnablesimportRunnableLambdaasyncdefreverse(s:str)->str:returns[::-1]chain=RunnableLambda(func=reverse)events=[eventasyncforeventinchain.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

fromlangchain_core.callbacks.managerimport(adispatch_custom_event,)fromlangchain_core.runnablesimportRunnableLambda,RunnableConfigimportasyncioasyncdefslow_thing(some_input:str,config:RunnableConfig)->str:"""Do something that takes a long time."""awaitasyncio.sleep(1)# Placeholder for some slow operationawaitadispatch_custom_event("progress_event",{"message":"Finished step 1 of 3"},config=config# Must be included for python < 3.10)awaitasyncio.sleep(1)# Placeholder for some slow operationawaitadispatch_custom_event("progress_event",{"message":"Finished step 2 of 3"},config=config# Must be included for python < 3.10)awaitasyncio.sleep(1)# Placeholder for some slow operationreturn"Done"slow_thing=RunnableLambda(slow_thing)asyncforeventinslow_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 eitherv2 orv1.Users should usev2.v1 is for backwards compatibility and will be deprecatedin 0.4.0.No default will be assigned until the API is stabilized.custom events will only be surfaced inv2.
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 implementationof astream_events is built on top of astream_log.

Yields:

An async stream of StreamEvents.

Raises:

NotImplementedError – If the version is notv1 orv2.

Return type:

AsyncIterator[StreamEvent]

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[PromptValue |str |Sequence[BaseMessage |list[str]|tuple[str,str]|str |dict[str,Any]]])
config (RunnableConfig |list[RunnableConfig]|None)
return_exceptions (bool)
kwargs (Any)

Return type:

list[str]

batch_as_completed(
inputs:Sequence[Input],
config:RunnableConfig|Sequence[RunnableConfig]|None=None,
*,
return_exceptions:bool=False,
**kwargs:Any|None,
)→Iterator[tuple[int,Output|Exception]]#

Run invoke in parallel on a list of inputs.

Yields results as they complete.

Parameters:

inputs (Sequence[Input])
config (RunnableConfig |Sequence[RunnableConfig]|None)
return_exceptions (bool)
kwargs (Any |None)

Return type:

Iterator[tuple[int,Output | Exception]]

bind(
**kwargs:Any,
)→Runnable[Input,Output]#

Bind arguments to a Runnable, returning a new Runnable.

Useful when a Runnable in a chain requires an argument that is notin 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:

fromlangchain_ollamaimportChatOllamafromlangchain_core.output_parsersimportStrOutputParserllm=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'

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 thealternative.
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 thatreturn Runnable instances.

Returns:

A new Runnable with the alternatives configured.

Return type:

RunnableSerializable

fromlangchain_anthropicimportChatAnthropicfromlangchain_core.runnables.utilsimportConfigurableFieldfromlangchain_openaiimportChatOpenAImodel=ChatAnthropic(model_name="claude-3-sonnet-20240229").configurable_alternatives(ConfigurableField(id="llm"),default_key="anthropic",openai=ChatOpenAI())# uses the default model ChatAnthropicprint(model.invoke("which organization created you?").content)# uses ChatOpenAIprint(model.with_config(configurable={"llm":"openai"}).invoke("which organization created you?").content)

configurable_fields(
**kwargs:ConfigurableField|ConfigurableFieldSingleOption|ConfigurableFieldMultiOption,
)→RunnableSerializable#

Configure particular Runnable fields at runtime.

Parameters:: **kwargs (ConfigurableField |ConfigurableFieldSingleOption |ConfigurableFieldMultiOption) – A dictionary of ConfigurableField instances to configure.
Returns:: A new Runnable with the fields configured.
Return type:: RunnableSerializable

fromlangchain_core.runnablesimportConfigurableFieldfromlangchain_openaiimportChatOpenAImodel=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 = 20print("max_tokens_20: ",model.invoke("tell me something about chess").content)# max_tokens = 200print("max_tokens_200: ",model.with_config(configurable={"output_token_number":200}).invoke("tell me something about chess").content)

create_llm_result( choices:Any, prompts:list[str], params:dict[str,Any], token_usage:dict[str,int], *, system_fingerprint:str|None=None, )→LLMResult #

Create the LLMResult from the choices and prompts.

Parameters:

choices (Any)
prompts (list[str])
params (dict[str,Any])
token_usage (dict[str,int])
system_fingerprint (str |None)

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], tools:Sequence|None=None, )→int#

Get the number of tokens in the messages.

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

Note: the base implementation of get_num_tokens_from_messages ignorestool schemas.

Parameters:

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

Returns:

The sum of the number of tokens across the messages.

Return type:

int

get_sub_prompts( params:dict[str,Any], prompts:list[str], stop:list[str]|None=None, )→list[list[str]]#

Get the sub prompts for llm call.

Parameters:

params (dict[str,Any])
prompts (list[str])
stop (list[str]|None)

Return type:

list[list[str]]

get_token_ids(text:str)→list[int]#

Get the token IDs using the tiktoken package.

Parameters:: text (str)
Return type:: list[int]

Transform a single input into an output.

Parameters:

input (PromptValue |str |Sequence[BaseMessage |list[str]|tuple[str,str]|str |dict[str,Any]]) – The input to the Runnable.
config (RunnableConfig |None) – A config to use when invoking the Runnable.The config supports standard keys like ‘tags’, ‘metadata’ for tracingpurposes, ‘max_concurrency’ for controlling how much work to doin parallel, and other keys. Please refer to the RunnableConfigfor more details.
stop (list[str]|None)
kwargs (Any)

Returns:

The output of the Runnable.

Return type:

str

max_tokens_for_prompt(prompt:str)→int#

Calculate the maximum number of tokens possible to generate for a prompt.

Parameters:: prompt (str) – The prompt to pass into the model.
Returns:: The maximum number of tokens to generate for a prompt.
Return type:: int

Example

max_tokens=openai.max_tokens_for_prompt("Tell me a joke.")

save(file_path:Path|str)→None#

Save the LLM.

Parameters:: file_path (Path |str) – Path to file to save the LLM to.
Raises:: ValueError – If the file path is not a string or Path object.
Return type:: None

Example

llm.save(file_path="path/llm.yaml")

Default implementation of stream, which calls invoke.

Subclasses should override this method if they support streaming output.

Parameters:

input (PromptValue |str |Sequence[BaseMessage |list[str]|tuple[str,str]|str |dict[str,Any]]) – The input to the Runnable.
config (RunnableConfig |None) – The config to use for the Runnable. Defaults to None.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.
stop (list[str]|None)

Yields:

The output of the Runnable.

Return type:

Iterator[str]

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

Bind async lifecycle listeners to a Runnable, returning a new Runnable.

on_start: Asynchronously called before the Runnable starts running.on_end: Asynchronously called after the Runnable finishes running.on_error: Asynchronously called if the Runnable throws an error.

The Run object contains information about the run, including its id,type, input, output, error, start_time, end_time, and any tags or metadataadded 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:

fromlangchain_core.runnablesimportRunnableLambda,Runnablefromdatetimeimportdatetime,timezoneimporttimeimportasynciodefformat_t(timestamp:float)->str:returndatetime.fromtimestamp(timestamp,tz=timezone.utc).isoformat()asyncdeftest_runnable(time_to_sleep:int):print(f"Runnable[{time_to_sleep}s]: starts at{format_t(time.time())}")awaitasyncio.sleep(time_to_sleep)print(f"Runnable[{time_to_sleep}s]: ends at{format_t(time.time())}")asyncdeffn_start(run_obj:Runnable):print(f"on start callback starts at{format_t(time.time())}")awaitasyncio.sleep(3)print(f"on start callback ends at{format_t(time.time())}")asyncdeffn_end(run_obj:Runnable):print(f"on end callback starts at{format_t(time.time())}")awaitasyncio.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)asyncdefconcurrent_runs():awaitasyncio.gather(runnable.ainvoke(2),runnable.ainvoke(3))asyncio.run(concurrent_runs())Result:onstartcallbackstartsat2025-03-01T07:05:22.875378+00:00onstartcallbackstartsat2025-03-01T07:05:22.875495+00:00onstartcallbackendsat2025-03-01T07:05:25.878862+00:00onstartcallbackendsat2025-03-01T07:05:25.878947+00:00Runnable[2s]:startsat2025-03-01T07:05:25.879392+00:00Runnable[3s]:startsat2025-03-01T07:05:25.879804+00:00Runnable[2s]:endsat2025-03-01T07:05:27.881998+00:00onendcallbackstartsat2025-03-01T07:05:27.882360+00:00Runnable[3s]:endsat2025-03-01T07:05:28.881737+00:00onendcallbackstartsat2025-03-01T07:05:28.882428+00:00onendcallbackendsat2025-03-01T07:05:29.883893+00:00onendcallbackendsat2025-03-01T07:05:30.884831+00:00

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

Bind config to a Runnable, returning a new Runnable.

Parameters:

config (RunnableConfig |None) – The config to bind to the Runnable.
kwargs (Any) – Additional keyword arguments to pass to the Runnable.

Returns:

A new Runnable with the config bound.

Return type:

Runnable[Input,Output]

with_fallbacks(fallbacks:Sequence[Runnable[Input,Output]],*,exceptions_to_handle:tuple[type[BaseException],...]=(<class'Exception'>,),exception_key:Optional[str]=None)→RunnableWithFallbacksT[Input,Output]#

Add fallbacks to a Runnable, returning a new Runnable.

The new Runnable will try the original Runnable, and then each fallbackin 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 passedto fallbacks as part of the input under the specified key. If None,exceptions will not be passed to fallbacks. If used, the base Runnableand its fallbacks must accept a dictionary as input. Defaults to None.

Returns:

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

Return type:

RunnableWithFallbacksT[Input, Output]

Example

fromtypingimportIteratorfromlangchain_core.runnablesimportRunnableGeneratordef_generate_immediate_error(input:Iterator)->Iterator[str]:raiseValueError()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 passedto fallbacks as part of the input under the specified key. If None,exceptions will not be passed to fallbacks. If used, the base Runnableand its fallbacks must accept a dictionary as input.

Returns:

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

Return type:

RunnableWithFallbacksT[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 metadataadded 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:

fromlangchain_core.runnablesimportRunnableLambdafromlangchain_core.tracers.schemasimportRunimporttimedeftest_runnable(time_to_sleep:int):time.sleep(time_to_sleep)deffn_start(run_obj:Run):print("start_time:",run_obj.start_time)deffn_end(run_obj:Run):print("end_time:",run_obj.end_time)chain=RunnableLambda(test_runnable).with_listeners(on_start=fn_start,on_end=fn_end)chain.invoke(2)

with_retry(*,retry_if_exception_type:tuple[type[BaseException],...]=(<class'Exception'>,),wait_exponential_jitter:bool=True,exponential_jitter_params:Optional[ExponentialJitterParams]=None,stop_after_attempt:int=3)→Runnable[Input,Output]#

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

Parameters:

retry_if_exception_type (tuple[type[BaseException],...]) – A tuple of exception types to retry on.Defaults to (Exception,).
wait_exponential_jitter (bool) – Whether to add jitter to the waittime between retries. Defaults to True.
stop_after_attempt (int) – The maximum number of attempts to make beforegiving up. Defaults to 3.
exponential_jitter_params (Optional[ExponentialJitterParams]) – Parameters fortenacity.wait_exponential_jitter. Namely:initial,max,exp_base, andjitter (all float values).

Returns:

A new Runnable that retries the original Runnable on exceptions.

Return type:

Runnable[Input, Output]

Example:

fromlangchain_core.runnablesimportRunnableLambdacount=0def_lambda(x:int)->None:globalcountcount=count+1ifx==1:raiseValueError("x is 1")else:passrunnable=RunnableLambda(_lambda)try:runnable.with_retry(stop_after_attempt=2,retry_if_exception_type=(ValueError,),).invoke(1)exceptValueError:passassert(count==2)

Not implemented on this class.

Parameters:

schema (dict |type)
kwargs (Any)

Return type:

with_types( *, input_type:type[Input]|None=None, output_type:type[Output]|None=None, )→Runnable[Input,Output]#

Bind input and output types to a Runnable, returning a new Runnable.

Parameters:

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

Returns:

A new Runnable with the types bound.

Return type:

Runnable[Input,Output]

propertymax_context_size:int#: Get max context size for this model.

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