langchain_community.chat_models.pai_eas_endpoint
.PaiEasChatEndpointΒΆ
- class langchain_community.chat_models.pai_eas_endpoint.PaiEasChatEndpoint[source]ΒΆ
Bases:
BaseChatModel
Eas LLM Service chat model API.
To use, must have a deployed eas chat llm service on AliCloud. One can set the
environment variable
eas_service_url
andeas_service_token
set with your eas service url and service token.Example
from langchain_community.chat_models import PaiEasChatEndpoint eas_chat_endpoint = PaiEasChatEndpoint( eas_service_url="your_service_url", eas_service_token="your_service_token" )
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
- param cache: Optional[bool] = NoneΒΆ
Whether to cache the response.
- param callback_manager: Optional[BaseCallbackManager] = NoneΒΆ
[DEPRECATED] Callback manager to add to the run trace.
- param callbacks: Callbacks = NoneΒΆ
Callbacks to add to the run trace.
- param do_sample: Optional[bool] = FalseΒΆ
- param eas_service_token: str [Required]ΒΆ
PAI-EAS Service Infer Params
- param eas_service_url: str [Required]ΒΆ
PAI-EAS Service TOKEN
- param max_new_tokens: Optional[int] = 512ΒΆ
- param metadata: Optional[Dict[str, Any]] = NoneΒΆ
Metadata to add to the run trace.
- param model_kwargs: Optional[dict] = NoneΒΆ
- param stop_sequences: Optional[List[str]] = NoneΒΆ
Enable stream chat mode.
- param streaming: bool = FalseΒΆ
Key/value arguments to pass to the model. Reserved for future use
- param tags: Optional[List[str]] = NoneΒΆ
Tags to add to the run trace.
- param temperature: Optional[float] = 0.8ΒΆ
- param timeout: Optional[int] = 5000ΒΆ
- param top_k: Optional[int] = 10ΒΆ
- param top_p: Optional[float] = 0.1ΒΆ
- param use_cache: Optional[bool] = TrueΒΆ
- param verbose: bool [Optional]ΒΆ
Whether to print out response text.
- param version: Optional[str] = '2.0'ΒΆ
- __call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) BaseMessage ΒΆ
[Deprecated][Deprecated]
Notes
Deprecated since version 0.1.7: Use invoke instead.
- async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) List[Output] ΒΆ
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.
- async agenerate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) LLMResult ΒΆ
Asynchronously pass a sequence of prompts to a model and return generations.
This method should make use of batched calls for models that expose a batched API.
- Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
- are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
- Parameters
messages β List of list of messages.
stop β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs β Arbitrary additional keyword arguments. These are usually passed to the model provider API call.
- Returns
- An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) LLMResult ΒΆ
Asynchronously pass a sequence of prompts and return model generations.
This method should make use of batched calls for models that expose a batched API.
- Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
- are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
- Parameters
prompts β List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
stop β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs β Arbitrary additional keyword arguments. These are usually passed to the model provider API call.
- Returns
- An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- async ainvoke(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) BaseMessage ΒΆ
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.
- async apredict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str ΒΆ
[Deprecated][Deprecated]
Notes
Deprecated since version 0.1.7: Use ainvoke instead.
- async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage ΒΆ
[Deprecated][Deprecated]
Notes
Deprecated since version 0.1.7: Use ainvoke instead.
- assign(**kwargs: Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]]]]) RunnableSerializable[Any, Any] ΒΆ
Assigns new fields to the dict output of this runnable. Returns a new runnable.
- async astream(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) AsyncIterator[BaseMessageChunk] ΒΆ
Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.
- astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) AsyncIterator[StreamEvent] ΒΆ
[Beta] Generate a stream of events.
Use to create an iterator ove 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.
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.
event | name | chunk | input | output ||----------------------|ββββββ|---------------------------------|ββββββββββββββββ|-------------------------------------------------| | on_chat_model_start | [model name] | | {βmessagesβ: [[SystemMessage, HumanMessage]]} | | | on_chat_model_stream | [model name] | AIMessageChunk(content=βhelloβ) | | | | on_chat_model_end | [model name] | | {βmessagesβ: [[SystemMessage, HumanMessage]]} | {βgenerationsβ: [β¦], βllm_outputβ: None, β¦} | | on_llm_start | [model name] | | {βinputβ: βhelloβ} | | | on_llm_stream | [model name] | βHelloβ | | | | on_llm_end | [model name] | | βHello human!β | | on_chain_start | format_docs | | | | | on_chain_stream | format_docs | βhello world!, goodbye world!β | | | | on_chain_end | format_docs | | [Document(β¦)] | βhello world!, goodbye world!β | | on_tool_start | some_tool | | {βxβ: 1, βyβ: β2β} | | | on_tool_stream | some_tool | {βxβ: 1, βyβ: β2β} | | | | on_tool_end | some_tool | | | {βxβ: 1, βyβ: β2β} | | on_retriever_start | [retriever name] | | {βqueryβ: βhelloβ} | | | on_retriever_chunk | [retriever name] | {documents: [β¦]} | | | | on_retriever_end | [retriever name] | | {βqueryβ: βhelloβ} | {documents: [β¦]} | | on_prompt_start | [template_name] | | {βquestionβ: βhelloβ} | | | on_prompt_end | [template_name] | | {βquestionβ: βhelloβ} | ChatPromptValue(messages: [SystemMessage, β¦]) |
Here are declarations associated with the events shown above:
format_docs:
```python def format_docs(docs: List[Document]) -> str:
βββFormat the docs.βββ return β, β.join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs) ```
some_tool:
```python @tool def some_tool(x: int, y: str) -> dict:
βββSome_tool.βββ return {βxβ: x, βyβ: y}
prompt:
```python template = ChatPromptTemplate.from_messages(
[(βsystemβ, βYou are Cat Agent 007β), (βhumanβ, β{question}β)]
).with_config({βrun_nameβ: βmy_templateβ, βtagsβ: [βmy_templateβ]}) ```
Example:
from langchain_core.runnables import RunnableLambda async def reverse(s: str) -> str: return s[::-1] chain = RunnableLambda(func=reverse) events = [ event async for event in chain.astream_events("hello", version="v1") ] # will produce the following events (run_id has been omitted for brevity): [ { "data": {"input": "hello"}, "event": "on_chain_start", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"chunk": "olleh"}, "event": "on_chain_stream", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"output": "olleh"}, "event": "on_chain_end", "metadata": {}, "name": "reverse", "tags": [], }, ]
- Parameters
input β The input to the runnable.
config β The config to use for the runnable.
version β The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.
include_names β Only include events from runnables with matching names.
include_types β Only include events from runnables with matching types.
include_tags β Only include events from runnables with matching tags.
exclude_names β Exclude events from runnables with matching names.
exclude_types β Exclude events from runnables with matching types.
exclude_tags β Exclude events from runnables with matching tags.
kwargs β Additional keyword arguments to pass to the runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.
- Returns
An async stream of StreamEvents.[Beta] Generate a stream of events.
Use to create an iterator ove 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.
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.
event | name | chunk | input | output ||----------------------|ββββββ|---------------------------------|ββββββββββββββββ|-------------------------------------------------| | on_chat_model_start | [model name] | | {βmessagesβ: [[SystemMessage, HumanMessage]]} | | | on_chat_model_stream | [model name] | AIMessageChunk(content=βhelloβ) | | | | on_chat_model_end | [model name] | | {βmessagesβ: [[SystemMessage, HumanMessage]]} | {βgenerationsβ: [β¦], βllm_outputβ: None, β¦} | | on_llm_start | [model name] | | {βinputβ: βhelloβ} | | | on_llm_stream | [model name] | βHelloβ | | | | on_llm_end | [model name] | | βHello human!β | | on_chain_start | format_docs | | | | | on_chain_stream | format_docs | βhello world!, goodbye world!β | | | | on_chain_end | format_docs | | [Document(β¦)] | βhello world!, goodbye world!β | | on_tool_start | some_tool | | {βxβ: 1, βyβ: β2β} | | | on_tool_stream | some_tool | {βxβ: 1, βyβ: β2β} | | | | on_tool_end | some_tool | | | {βxβ: 1, βyβ: β2β} | | on_retriever_start | [retriever name] | | {βqueryβ: βhelloβ} | | | on_retriever_chunk | [retriever name] | {documents: [β¦]} | | | | on_retriever_end | [retriever name] | | {βqueryβ: βhelloβ} | {documents: [β¦]} | | on_prompt_start | [template_name] | | {βquestionβ: βhelloβ} | | | on_prompt_end | [template_name] | | {βquestionβ: βhelloβ} | ChatPromptValue(messages: [SystemMessage, β¦]) |
Here are declarations associated with the events shown above:
format_docs:
```python def format_docs(docs: List[Document]) -> str:
βββFormat the docs.βββ return β, β.join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs) ```
some_tool:
```python @tool def some_tool(x: int, y: str) -> dict:
βββSome_tool.βββ return {βxβ: x, βyβ: y}
prompt:
```python template = ChatPromptTemplate.from_messages(
[(βsystemβ, βYou are Cat Agent 007β), (βhumanβ, β{question}β)]
).with_config({βrun_nameβ: βmy_templateβ, βtagsβ: [βmy_templateβ]}) ```
Example:
from langchain_core.runnables import RunnableLambda async def reverse(s: str) -> str: return s[::-1] chain = RunnableLambda(func=reverse) events = [ event async for event in chain.astream_events("hello", version="v1") ] # will produce the following events (run_id has been omitted for brevity): [ { "data": {"input": "hello"}, "event": "on_chain_start", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"chunk": "olleh"}, "event": "on_chain_stream", "metadata": {}, "name": "reverse", "tags": [], }, { "data": {"output": "olleh"}, "event": "on_chain_end", "metadata": {}, "name": "reverse", "tags": [], }, ]
- Parameters
input β The input to the runnable.
config β The config to use for the runnable.
version β The version of the schema to use. Currently only version 1 is available. No default will be assigned until the API is stabilized.
include_names β Only include events from runnables with matching names.
include_types β Only include events from runnables with matching types.
include_tags β Only include events from runnables with matching tags.
exclude_names β Exclude events from runnables with matching names.
exclude_types β Exclude events from runnables with matching types.
exclude_tags β Exclude events from runnables with matching tags.
kwargs β Additional keyword arguments to pass to the runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.
- Returns
An async stream of StreamEvents.
Notes
- async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, with_streamed_output_list: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]] ΒΆ
Stream all output from a runnable, as reported to the callback system. This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of jsonpatch ops that describe how the state of the run has changed in each step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
- Parameters
input β The input to the runnable.
config β The config to use for the runnable.
diff β Whether to yield diffs between each step, or the current state.
with_streamed_output_list β Whether to yield the streamed_output list.
include_names β Only include logs with these names.
include_types β Only include logs with these types.
include_tags β Only include logs with these tags.
exclude_names β Exclude logs with these names.
exclude_types β Exclude logs with these types.
exclude_tags β Exclude logs with these tags.
- async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) AsyncIterator[Output] ΒΆ
Default implementation of atransform, which buffers input and calls astream. Subclasses should override this method if they can start producing output while input is still being generated.
- batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) List[Output] ΒΆ
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.
- bind(**kwargs: Any) Runnable[Input, Output] ΒΆ
Bind arguments to a Runnable, returning a new Runnable.
- call_as_llm(message: str, stop: Optional[List[str]] = None, **kwargs: Any) str ΒΆ
[Deprecated][Deprecated]
Notes
Deprecated since version 0.1.7: Use invoke instead.
- config_schema(*, include: Optional[Sequence[str]] = None) Type[BaseModel] ΒΆ
The type of config this runnable accepts specified as a pydantic model.
To mark a field as configurable, see the configurable_fields and configurable_alternatives methods.
- Parameters
include β A list of fields to include in the config schema.
- Returns
A pydantic model that can be used to validate config.
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) RunnableSerializable[Input, Output] ΒΆ
- configurable_fields(**kwargs: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) RunnableSerializable[Input, Output] ΒΆ
- classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) Model ΒΆ
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = βallowβ was set since it adds all passed values
- copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) Model ΒΆ
Duplicate a model, optionally choose which fields to include, exclude and change.
- Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep β set to True to make a deep copy of the model
- Returns
new model instance
- dict(**kwargs: Any) Dict ΒΆ
Return a dictionary of the LLM.
- format_request_payload(messages: List[BaseMessage], **model_kwargs: Any) dict [source]ΒΆ
- classmethod from_orm(obj: Any) Model ΒΆ
- generate(messages: List[List[BaseMessage]], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) LLMResult ΒΆ
Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched API.
- Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
- are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
- Parameters
messages β List of list of messages.
stop β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs β Arbitrary additional keyword arguments. These are usually passed to the model provider API call.
- Returns
- An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) LLMResult ΒΆ
Pass a sequence of prompts to the model and return model generations.
This method should make use of batched calls for models that expose a batched API.
- Use this method when you want to:
take advantage of batched calls,
need more output from the model than just the top generated value,
- are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).
- Parameters
prompts β List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
stop β Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks β Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs β Arbitrary additional keyword arguments. These are usually passed to the model provider API call.
- Returns
- An LLMResult, which contains a list of candidate Generations for each input
prompt and additional model provider-specific output.
- get_graph(config: Optional[RunnableConfig] = None) Graph ΒΆ
Return a graph representation of this runnable.
- get_input_schema(config: Optional[RunnableConfig] = None) Type[BaseModel] ΒΆ
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic input schema that depends on which configuration the runnable is invoked with.
This method allows to get an input schema for a specific configuration.
- Parameters
config β A config to use when generating the schema.
- Returns
A pydantic model that can be used to validate input.
- classmethod get_lc_namespace() List[str] ΒΆ
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the namespace is [βlangchainβ, βllmsβ, βopenaiβ]
- get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) str ΒΆ
Get the name of the runnable.
- get_num_tokens(text: str) int ΒΆ
Get the number of tokens present in the text.
Useful for checking if an input will fit in a modelβs context window.
- Parameters
text β The string input to tokenize.
- Returns
The integer number of tokens in the text.
- get_num_tokens_from_messages(messages: List[BaseMessage]) int ΒΆ
Get the number of tokens in the messages.
Useful for checking if an input will fit in a modelβs context window.
- Parameters
messages β The message inputs to tokenize.
- Returns
The sum of the number of tokens across the messages.
- get_output_schema(config: Optional[RunnableConfig] = None) Type[BaseModel] ΒΆ
Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives methods will have a dynamic output schema that depends on which configuration the runnable is invoked with.
This method allows to get an output schema for a specific configuration.
- Parameters
config β A config to use when generating the schema.
- Returns
A pydantic model that can be used to validate output.
- get_prompts(config: Optional[RunnableConfig] = None) List[BasePromptTemplate] ΒΆ
- get_token_ids(text: str) List[int] ΒΆ
Return the ordered ids of the tokens in a text.
- Parameters
text β The string input to tokenize.
- Returns
- A list of ids corresponding to the tokens in the text, in order they occur
in the text.
- invoke(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) BaseMessage ΒΆ
Transform a single input into an output. Override to implement.
- Parameters
input β The input to the runnable.
config β A config to use when invoking the runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ 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.
- classmethod is_lc_serializable() bool ΒΆ
Is this class serializable?
- json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) unicode ΒΆ
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
- classmethod lc_id() List[str] ΒΆ
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path to the object.
- map() Runnable[List[Input], List[Output]] ΒΆ
Return a new Runnable that maps a list of inputs to a list of outputs, by calling invoke() with each input.
- classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ΒΆ
- classmethod parse_obj(obj: Any) Model ΒΆ
- classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model ΒΆ
- pick(keys: Union[str, List[str]]) RunnableSerializable[Any, Any] ΒΆ
Pick keys from the dict output of this runnable. Returns a new runnable.
- pipe(*others: Union[Runnable[Any, Other], Callable[[Any], Other]], name: Optional[str] = None) RunnableSerializable[Input, Other] ΒΆ
Compose this runnable with another object to create a RunnableSequence.
- predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) str ΒΆ
[Deprecated][Deprecated]
Notes
Deprecated since version 0.1.7: Use invoke instead.
- predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) BaseMessage ΒΆ
[Deprecated][Deprecated]
Notes
Deprecated since version 0.1.7: Use invoke instead.
- classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny ΒΆ
- classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode ΒΆ
- stream(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) Iterator[BaseMessageChunk] ΒΆ
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.
- to_json() Union[SerializedConstructor, SerializedNotImplemented] ΒΆ
- to_json_not_implemented() SerializedNotImplemented ΒΆ
- transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) Iterator[Output] ΒΆ
Default implementation of transform, which buffers input and then calls stream. Subclasses should override this method if they can start producing output while input is still being generated.
- classmethod update_forward_refs(**localns: Any) None ΒΆ
Try to update ForwardRefs on fields based on this Model, globalns and localns.
- classmethod validate(value: Any) Model ΒΆ
- with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) Runnable[Input, Output] ΒΆ
Bind config to a Runnable, returning a new Runnable.
- with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) RunnableWithFallbacksT[Input, Output] ΒΆ
Add fallbacks to a runnable, returning a new Runnable.
- Parameters
fallbacks β A sequence of runnables to try if the original runnable fails.
exceptions_to_handle β A tuple of exception types to handle.
exception_key β 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.
- with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) Runnable[Input, Output] ΒΆ
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.
- with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) Runnable[Input, Output] ΒΆ
Create a new Runnable that retries the original runnable on exceptions.
- Parameters
retry_if_exception_type β A tuple of exception types to retry on
wait_exponential_jitter β Whether to add jitter to the wait time between retries
stop_after_attempt β The maximum number of attempts to make before giving up
- Returns
A new Runnable that retries the original runnable on exceptions.
- with_types(*, input_type: Optional[Type[Input]] = None, output_type: Optional[Type[Output]] = None) Runnable[Input, Output] ΒΆ
Bind input and output types to a Runnable, returning a new Runnable.
- property InputType: TypeAliasΒΆ
Get the input type for this runnable.
- property OutputType: AnyΒΆ
Get the output type for this runnable.
- property config_specs: List[ConfigurableFieldSpec]ΒΆ
List configurable fields for this runnable.
- property input_schema: Type[BaseModel]ΒΆ
The type of input this runnable accepts specified as a pydantic model.
- property lc_attributes: DictΒΆ
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
- property lc_secrets: Dict[str, str]ΒΆ
A map of constructor argument names to secret ids.
- For example,
{βopenai_api_keyβ: βOPENAI_API_KEYβ}
- name: Optional[str] = NoneΒΆ
The name of the runnable. Used for debugging and tracing.
- property output_schema: Type[BaseModel]ΒΆ
The type of output this runnable produces specified as a pydantic model.