id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
a6d656e65025-2 | 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¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.mlflow_ai_gateway.ChatParams.html |
1d86c670c9dc-0 | langchain.chat_models.google_palm.ChatGooglePalmError¶
class langchain.chat_models.google_palm.ChatGooglePalmError[source]¶
Error with the Google PaLM API. | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.google_palm.ChatGooglePalmError.html |
763d0ed616ee-0 | langchain.chat_models.fake.FakeListChatModel¶
class langchain.chat_models.fake.FakeListChatModel[source]¶
Bases: SimpleChatModel
Fake ChatModel for testing purposes.
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 ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-1 | 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... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-2 | async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], 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 i... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-3 | to the model provider API call.
Returns
Top model prediction as a message.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[BaseMessageChunk]¶
Default implementation of astream, which calls ainvo... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-4 | 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 w... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-5 | 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... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-6 | 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 modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-7 | 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 ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-8 | 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_... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-9 | 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¶
predict(text: str, *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → str¶
Pass a single string input to t... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-10 | stream(input: Union[PromptValue, str, List[BaseMessage]], 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() → ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-11 | 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 ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
763d0ed616ee-12 | 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¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constr... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fake.FakeListChatModel.html |
65077844837a-0 | langchain.chat_models.anthropic.ChatAnthropic¶
class langchain.chat_models.anthropic.ChatAnthropic[source]¶
Bases: BaseChatModel, _AnthropicCommon
Anthropic chat large language models.
To use, you should have the anthropic python package installed, and the
environment variable ANTHROPIC_API_KEY set with your API key, o... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-1 | param model: str = 'claude-2' (alias 'model_name')¶
Model name to use.
param model_kwargs: Dict[str, Any] [Optional]¶
param streaming: bool = False¶
Whether to stream the results.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = None¶
A non-negative float that t... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-2 | 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... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-3 | async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], 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 i... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-4 | to the model provider API call.
Returns
Top model prediction as a message.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[BaseMessageChunk]¶
Default implementation of astream, which calls ainvo... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-5 | 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 w... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-6 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
convert_prompt(prompt: PromptValue) → str[source]¶
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, Map... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-7 | 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 modeltype (e.g., pure text completion ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-8 | namespace is [“langchain”, “llms”, “openai”]
get_num_tokens(text: str) → int[source]¶
Calculate number of tokens.
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 mes... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-9 | 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[source]¶
Return whether this model can be serialized by Langchain.
json(*, include: Optional[Union... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-10 | 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 specifictypes of chat messages, use predict_messages.
Parameters
text – String input to pass to the m... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-11 | 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] = No... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-12 | 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... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
65077844837a-13 | 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”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
E... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html |
7d47e8c97445-0 | langchain.chat_models.gigachat.GigaChat¶
class langchain.chat_models.gigachat.GigaChat[source]¶
Bases: _BaseGigaChat, BaseChatModel
GigaChat large language models API.
To use, you should pass login and password to access GigaChat API or use token.
Example
from langchain.chat_models import GigaChat
giga = GigaChat(crede... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-1 | param scope: Optional[str] = None¶
Permission scope for access token
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: Optional[float] = None¶
What sampling temperature to use.
param timeout: Optional[float] = No... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-2 | 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 batche... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-3 | 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 topcandidate gen... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-4 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-5 | 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) → T... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-6 | 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 creat... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-7 | 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.
**kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-8 | 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 co... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-9 | 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_defa... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-10 | 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 specifictypes 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
fir... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-11 | 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]¶
Defau... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-12 | 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(*, ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
7d47e8c97445-13 | property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property lc_serializable: bool¶
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.gigachat.GigaChat.html |
2a495b86660f-0 | langchain.chat_models.konko.ChatKonko¶
class langchain.chat_models.konko.ChatKonko[source]¶
Bases: BaseChatModel
ChatKonko Chat large language models API.
To use, you should have the konko python package installed, and the
environment variable KONKO_API_KEY and OPENAI_API_KEY set with your API key.
Any parameters that ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-1 | Number of chat completions to generate for each prompt.
param openai_api_key: Optional[str] = None¶
param request_timeout: Optional[Union[float, Tuple[float, float]]] = None¶
Timeout for requests to Konko completion API.
param streaming: bool = False¶
Whether to stream the results or not.
param tags: Optional[List[str]... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-2 | 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 batche... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-3 | 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 topcandidate gen... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-4 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-5 | 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¶
completion_with_retry(run_manager: Optional[CallbackManagerFo... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-6 | 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... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-7 | 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 modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-8 | classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
get_num_tokens(text: str) → int¶
Get the number of tokens present in the text.
Useful for checking if an input will fit ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-9 | invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → BaseMessage¶
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 r... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-10 | 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 = No... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-11 | 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¶
stream(in... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-12 | 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_liste... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
2a495b86660f-13 | 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.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for this... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.konko.ChatKonko.html |
0e349f3a1a0d-0 | langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway¶
class langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway[source]¶
Bases: BaseChatModel
Javelin AI Gateway chat models API.
To use, you should have the javelin_sdk python package installed.
For more information, see https://docs.getjavelin.io
Exam... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-1 | param route: str [Required]¶
The route to use for the Javelin AI Gateway API.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(messages: List[BaseMessage], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[B... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-2 | 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 modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-3 | 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],... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-4 | 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... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-5 | 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', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[I... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-6 | 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, **kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-7 | 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 confi... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-8 | Returns
A pydantic model that can be used to validate output.
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 occurin the text.
invoke(input: Union[PromptVal... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-9 | 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 e... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-10 | 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.
Retur... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-11 | 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 se... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
0e349f3a1a0d-12 | 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... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.javelin_ai_gateway.ChatJavelinAIGateway.html |
063f1739bed3-0 | langchain.chat_models.yandex.ChatYandexGPT¶
class langchain.chat_models.yandex.ChatYandexGPT[source]¶
Bases: _BaseYandexGPT, BaseChatModel
Wrapper around YandexGPT large language models.
There are two authentication options for the service account
with the ai.languageModels.user role:
You can specify the token in a con... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-1 | Model name to use.
param stop: Optional[List[str]] = None¶
Sequences when completion generation will stop.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.6¶
What sampling temperature to use.
Should be a double number between 0 (inclusive) and 1 (inclusive).
param url:... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-2 | 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 batche... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-3 | 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 topcandidate gen... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-4 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-5 | 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) → T... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-6 | 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 creat... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-7 | 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.
**kwarg... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-8 | 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 ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-9 | 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_defa... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-10 | 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 specifictypes 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
fir... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-11 | 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]¶
Defau... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-12 | 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(*, ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
063f1739bed3-13 | property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model. | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.yandex.ChatYandexGPT.html |
eddf05ea8c69-0 | langchain.chat_models.fireworks.completion_with_retry¶
langchain.chat_models.fireworks.completion_with_retry(llm: ChatFireworks, use_retry: bool, *, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call. | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.fireworks.completion_with_retry.html |
0e8f429a1d47-0 | langchain.chat_models.anyscale.ChatAnyscale¶
class langchain.chat_models.anyscale.ChatAnyscale[source]¶
Bases: ChatOpenAI
Anyscale Chat large language models.
See https://www.anyscale.com/ for information about Anyscale.
To use, you should have the openai python package installed, and the
environment variable ANYSCALE_... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-1 | param default_headers: Union[Mapping[str, str], None] = None¶
param default_query: Union[Mapping[str, object], None] = None¶
param http_client: Union[Any, None] = None¶
Optional httpx.Client.
param max_retries: int = 2¶
Maximum number of retries to make when generating.
param max_tokens: Optional[int] = None¶
Maximum n... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-2 | Whether to stream the results or not.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 0.7¶
What sampling temperature to use.
param tiktoken_model_name: Optional[str] = None¶
The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-3 | 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... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-4 | async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], 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 i... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-5 | to the model provider API call.
Returns
Top model prediction as a message.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[BaseMessageChunk]¶
Default implementation of astream, which calls ainvo... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-6 | 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 w... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-7 | 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_al... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-8 | 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]] = N... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-9 | to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
static get_available_models(anyscale_api_key: Optional[str] = None, anyscale_api_base: str = 'https://api.endpoints.anyscale.com/v1') → Set[str][source... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-10 | main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
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 out... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-11 | Return whether this model can be serialized by Langchain.
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 ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-12 | 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 specifictypes 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
fir... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-13 | 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]¶
Defau... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
0e8f429a1d47-14 | 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(*, ... | lang/api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anyscale.ChatAnyscale.html |
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