langchain_community.chat_models.zhipuai.ChatZhipuAI

class langchain_community.chat_models.zhipuai.ChatZhipuAI

Bases: BaseChatModel

ZHIPU AI large language chat models API.

To use, you should have the zhipuai python package installed, and the environment variable ZHIPUAI_API_KEY set with your API key.

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

Example

from langchain_community.chat_models import ChatZhipuAI
zhipuai = ChatZhipuAI()

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] = None

Whether to cache the response.

param callback_manager: Optional[BaseCallbackManager] = None

Callback manager to add to the run trace.

param callbacks: Callbacks = None

Callbacks to add to the run trace.

param incremental: bool = True

When invoked by the SSE interface, it is used to control whether the content is returned incremented or full each time. If this parameter is not provided, the value is returned incremented by default.

param meta: Optional[meta] = None

Used in CharacterGLM

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

Metadata to add to the run trace.

param model: str = 'chatglm_turbo'

Model name to use. -chatglm_turbo:

According to the input of natural language instructions to complete a variety of language tasks, it is recommended to use SSE or asynchronous call request interface.

-characterglm:

It supports human-based role-playing, ultra-long multi-round memory, and thousands of character dialogues. It is widely used in anthropomorphic dialogues or game scenes such as emotional accompaniments, game intelligent NPCS, Internet celebrities/stars/movie and TV series IP clones, digital people/virtual anchors, and text adventure games.

param ref: Optional[ref] = None

This parameter is used to control the reference of external information during the request. Currently, this parameter is used to control whether to reference external information. If this field is empty or absent, the search and parameter passing format is enabled by default. {“enable”: “true”, “search_query”: “history “}

param request_id: Optional[str] = None

Parameter transmission by the client must ensure uniqueness; A unique identifier used to distinguish each request, which is generated by default by the platform when the client does not transmit it.

param return_type: str = 'json_string'

This parameter is used to control the type of content returned each time. - json_string Returns a standard JSON string. - text Returns the original text content.

param streaming: bool = False

Whether to stream the results or not.

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

Tags to add to the run trace.

param temperature: float = 0.95

What sampling temperature to use. The value ranges from 0.0 to 1.0 and cannot be equal to 0. The larger the value, the more random and creative the output; The smaller the value, the more stable or certain the output will be. You are advised to adjust top_p or temperature parameters based on application scenarios, but do not adjust the two parameters at the same time.

param top_p: float = 0.7

Another method of sampling temperature is called nuclear sampling. The value ranges from 0.0 to 1.0 and cannot be equal to 0 or 1. The model considers the results with top_p probability quality tokens. For example, 0.1 means that the model decoder only considers tokens from the top 10% probability of the candidate set. You are advised to adjust top_p or temperature parameters based on application scenarios, but do not adjust the two parameters at the same time.

param verbose: bool [Optional]

Whether to print out response text.

param zhipuai: Any = None

param zhipuai_api_key: Optional[str] = None (alias 'api_key')

Automatically inferred from env var ZHIPUAI_API_KEY if not provided.

__call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → BaseMessage

Call self as a function.

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.

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

Top Level call

async agenerate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult

Asynchronously pass a sequence of prompts and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

1. take advantage of batched calls,

2. need more output from the model than just the top generated value,

3. are building chains that are agnostic to the underlying language model

   type (e.g., pure text completion models vs chat models).

Parameters

• prompts – List 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 – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

• callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

• **kwargs – 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

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

Asynchronously pass a string to the model and return a string prediction.

Use this method when calling pure text generation models and only the top

candidate generation is needed.

Parameters

• text – String input to pass to the model.

• stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

• **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a string.

async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage

Asynchronously pass messages to the model and return a message prediction.

Use this method when calling chat models and only the top

candidate generation is needed.

Parameters

• messages – A sequence of chat messages corresponding to a single model input.

• stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

• **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a message.

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]

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]

Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

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: Optional[Any]) → Union[AsyncIterator[RunLogPatch], AsyncIterator[RunLog]]

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 – The input to the runnable.

• config – The config to use for the runnable.

• diff – Whether to yield diffs between each step, or the current state.

• with_streamed_output_list – Whether to yield the streamed_output list.

• include_names – Only include logs with these names.

• include_types – Only include logs with these types.

• include_tags – Only include logs with these tags.

• exclude_names – Exclude logs with these names.

• exclude_types – Exclude logs with these types.

• exclude_tags – Exclude logs with these tags.

async async_invoke(prompt)

async async_invoke_result(task_id)

async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]

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]

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]

Bind arguments to a Runnable, returning a new Runnable.

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

config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]

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 – 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]

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

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

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 = ‘allow’ 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

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – 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 – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

classmethod from_orm(obj: Any) → Model

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

Top Level call

generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → LLMResult

Pass a sequence of prompts to the model and return model generations.

This method should make use of batched calls for models that expose a batched API.

Use this method when you want to:

1. take advantage of batched calls,

2. need more output from the model than just the top generated value,

3. are building chains that are agnostic to the underlying language model

   type (e.g., pure text completion models vs chat models).

Parameters

• prompts – List 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 – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

• callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.

• **kwargs – 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

Return a graph representation of this runnable.

get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]

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 – 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]

Get the namespace of the langchain object.

get_name(suffix: Optional[str] = None, *, name: Optional[str] = None) → str

Get the name of the runnable.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

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

Parameters

text – The string input to tokenize.

Returns

The integer number of tokens in the text.

get_num_tokens_from_messages(messages: List[BaseMessage]) → int

Get the number of tokens in the messages.

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

Parameters

messages – 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]

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 – 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]

get_token_ids(text: str) → List[int]

Return the ordered ids of the tokens in a text.

Parameters

text – 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(prompt)

Transform a single input into an output. Override to implement.

Parameters

• input – The input to the runnable.

• config – 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.

classmethod is_lc_serializable() → bool

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

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]

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]]

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

classmethod parse_obj(obj: Any) → Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

pick(keys: Union[str, List[str]]) → RunnableSerializable[Any, Any]

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]

Compose this runnable with another object to create a RunnableSequence.

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

Pass a single string input to the model and return a string prediction.

Use this method when passing in raw text. If you want to pass in specific

types of chat messages, use predict_messages.

Parameters

• text – String input to pass to the model.

• stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

• **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a string.

predict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage

Pass a message sequence to the model and return a message prediction.

Use this method when passing in chat messages. If you want to pass in raw text,

use predict.

Parameters

• messages – A sequence of chat messages corresponding to a single model input.

• stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.

• **kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a message.

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode

sse_invoke(prompt)

stream(input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[BaseMessageChunk]

Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

to_json() → Union[SerializedConstructor, SerializedNotImplemented]

to_json_not_implemented() → SerializedNotImplemented

transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]

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

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model

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

Bind config to a Runnable, returning a new Runnable.

with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacksT[Input, Output]

Add fallbacks to a runnable, returning a new Runnable.

Parameters

• fallbacks – A sequence of runnables to try if the original runnable fails.

• exceptions_to_handle – A tuple of exception types to handle.

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]

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]

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

Parameters

• retry_if_exception_type – A tuple of exception types to retry on

• wait_exponential_jitter – Whether to add jitter to the wait time between retries

• stop_after_attempt – 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]

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

property InputType: TypeAlias

Get the input type for this runnable.

property OutputType: Any

Get the output type for this runnable.

property config_specs: List[langchain_core.runnables.utils.ConfigurableFieldSpec]

List configurable fields for this runnable.

property input_schema: Type[pydantic.main.BaseModel]

The type of input this runnable accepts specified as a pydantic model.

property lc_attributes: Dict[str, Any]

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]

A map of constructor argument names to secret ids.

For example,

{“openai_api_key”: “OPENAI_API_KEY”}

name: Optional[str] = None

The name of the runnable. Used for debugging and tracing.

property output_schema: Type[pydantic.main.BaseModel]

The type of output this runnable produces specified as a pydantic model.