langchain_community.output_parsers.ernie_functions
.JsonKeyOutputFunctionsParser¶
Note
JsonKeyOutputFunctionsParser 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_community.output_parsers.ernie_functions.JsonKeyOutputFunctionsParser[source]¶
Bases:
JsonOutputFunctionsParser
Parse an output as the element of the Json object.
- param args_only: bool = True¶
Whether to only return the arguments to the function call.
- param diff: bool = False¶
In streaming mode, whether to yield diffs between the previous and current parsed output, or just the current parsed output.
- param key_name: str [Required]¶
The name of the key to return.
- param strict: bool = False¶
Whether to allow non-JSON-compliant strings.
See: https://docs.python.org/3/library/json.html#encoders-and-decoders
Useful when the parsed output may include unicode characters or new lines.
- 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 ainvoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) T ¶
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 (Union[str, BaseMessage]) –
config (Optional[RunnableConfig]) –
kwargs (Optional[Any]) –
- Return type
T
- async aparse(text: str) T ¶
Async parse a single string model output into some structure.
- Parameters
text (str) – String output of a language model.
- Returns
Structured output.
- Return type
T
- async aparse_result(result: List[Generation], *, partial: bool = False) T ¶
Async parse a list of candidate model Generations into a specific format.
- The return value is parsed from only the first Generation in the result, which
is assumed to be the highest-likelihood Generation.
- Parameters
result (List[Generation]) – A list of Generations to be parsed. The Generations are assumed to be different candidate outputs for a single model input.
partial (bool) – Whether to parse the output as a partial result. This is useful for parsers that can parse partial results. Default is False.
- Returns
Structured output.
- Return type
T
- 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, andargs_schema
from a Runnable. Where possible, schemas are inferred fromrunnable.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 withargs_schema
. You can also passarg_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
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 viaargs_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 viaarg_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: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) AsyncIterator[Output] ¶
Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.
- Parameters
input (Input) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable. Defaults to None.
kwargs (Optional[Any]) – Additional keyword arguments to pass to the Runnable.
- Yields
The output of the Runnable.
- Return type
AsyncIterator[Output]
- 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 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 (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]]]
- 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 )
- get_format_instructions() str ¶
Instructions on how the LLM output should be formatted.
- Return type
str
- invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) T ¶
Transform a single input into an output. Override to implement.
- Parameters
input (Union[str, BaseMessage]) – 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.
- Returns
The output of the Runnable.
- Return type
T
- parse(text: str) Any ¶
Parse a single string model output into some structure.
- Parameters
text (str) – String output of a language model.
- Returns
Structured output.
- Return type
Any
- parse_result(result: List[Generation], *, partial: bool = False) Any [source]¶
Parse a list of candidate model Generations into a specific format.
- The return value is parsed from only the first Generation in the result, which
is assumed to be the highest-likelihood Generation.
- Parameters
result (List[Generation]) – A list of Generations to be parsed. The Generations are assumed to be different candidate outputs for a single model input.
partial (bool) – Whether to parse the output as a partial result. This is useful for parsers that can parse partial results. Default is False.
- Returns
Structured output.
- Return type
Any
- parse_with_prompt(completion: str, prompt: PromptValue) Any ¶
Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.
- Parameters
completion (str) – String output of a language model.
prompt (PromptValue) – Input PromptValue.
- Returns
Structured output.
- Return type
Any
- stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) Iterator[Output] ¶
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.
- Parameters
input (Input) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable. Defaults to None.
kwargs (Optional[Any]) – Additional keyword arguments to pass to the Runnable.
- Yields
The output of the Runnable.
- Return type
Iterator[Output]
- to_json() Union[SerializedConstructor, SerializedNotImplemented] ¶
Serialize the Runnable to JSON.
- Returns
A JSON-serializable representation of the Runnable.
- Return type