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76c18831d9d4-0 | langchain.llms.gradient_ai.GradientLLM¶
class langchain.llms.gradient_ai.GradientLLM[source]¶
Bases: BaseLLM
Gradient.ai LLM Endpoints.
GradientLLM is a class to interact with LLMs on gradient.ai
To use, set the environment variable GRADIENT_ACCESS_TOKEN with your
API token and GRADIENT_WORKSPACE_ID for your gradient w... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-1 | Endpoint URL to use.
param gradient_workspace_id: Optional[str] = None¶
Underlying gradient.ai workspace_id.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_id: str [Required] (alias 'model')¶
Underlying gradient.ai model id (base or fine-tuned).
Constraints
minLength = 2
... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-2 | e.g., if the underlying runnable uses an API which supports a batch mode.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[Li... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-3 | 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 inputprompt and additional model provider-specific output.
async a... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-4 | 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.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-5 | 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[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = Non... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-6 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-7 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of pr... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-8 | 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.
For example, if the class is langcha... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-9 | 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[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
Transform a single ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-10 | 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 =... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-11 | 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.
save(file_path: Union[Path, str]) → None¶
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-12 | 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 'E... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
76c18831d9d4-13 | 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 ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.gradient_ai.GradientLLM.html |
14f2d4aa4caa-0 | langchain.llms.vllm.VLLM¶
class langchain.llms.vllm.VLLM[source]¶
Bases: BaseLLM
VLLM language model.
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 best_of: Optional[int] = None¶
Number of output seq... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-1 | generated text so far
param stop: Optional[List[str]] = None¶
List of strings that stop the generation when they are generated.
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: float = 1.0¶
Float that controls the randomness of the sampling.
param tensor_parallel_size: Optional[i... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-2 | 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(prompts: List[... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-3 | 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/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-4 | Asynchronously pass messages to the model and return a message prediction.
Use this method when calling chat models and only the topcandidate 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 a... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-5 | The jsonpatch ops can be applied in order to construct state.
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 th... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-6 | 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... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-7 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of pr... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-8 | 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.
For example, if the class is langcha... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-9 | 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[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
Transform a single ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-10 | 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 =... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-11 | 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.
save(file_path: Union[Path, str]) → None¶
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-12 | 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/llms/langchain.llms.vllm.VLLM.html |
14f2d4aa4caa-13 | 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/llms/langchain.llms.vllm.VLLM.html |
72cc19efb8e2-0 | langchain.llms.manifest.ManifestWrapper¶
class langchain.llms.manifest.ManifestWrapper[source]¶
Bases: LLM
HazyResearch’s Manifest library.
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: Option... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-1 | e.g., if the underlying runnable uses an API which supports a batch mode.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[Li... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-2 | 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 inputprompt and additional model provider-specific output.
async a... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-3 | 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.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-4 | input is still being generated.
batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs invoke in parallel using a thread pool executor.
The default i... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-5 | 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/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-6 | 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 agno... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-7 | 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 in a model’s context window.
Parameters
text – The string input to tokenize.
Returns
Th... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-8 | 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 ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-9 | 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/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-10 | .. code-block:: python
llm.save(file_path=”path/llm.yaml”)
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(input: Union[Promp... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-11 | 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/llms/langchain.llms.manifest.ManifestWrapper.html |
72cc19efb8e2-12 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: Type[str]¶
Get the input type for this runnable.
property config_specs: List[langchain.schema.runnable.utils.ConfigurableFieldSpec]¶
List configurable fields for... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.manifest.ManifestWrapper.html |
5fa2d37221af-0 | langchain.llms.koboldai.KoboldApiLLM¶
class langchain.llms.koboldai.KoboldApiLLM[source]¶
Bases: LLM
Kobold API language model.
It includes several fields that can be used to control the text generation process.
To use this class, instantiate it with the required parameters and call it with a
prompt to generate text. F... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-1 | minimum: 0
param tags: Optional[List[str]] = None¶
Tags to add to the run trace.
param temperature: Optional[float] = 0.6¶
Temperature value.
exclusiveMinimum: 0
param tfs: Optional[float] = 0.9¶
Tail free sampling value.
maximum: 1
minimum: 0
param top_a: Optional[float] = 0.9¶
Top-a sampling value.
minimum: 0
param t... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-2 | Check Cache and run the LLM on the given prompt and input.
async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using as... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-3 | 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 PromptValue is an object that can be
converted to match the format of any languag... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-4 | **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 ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-5 | 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.
async atransform(input: As... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-6 | 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[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-7 | classmethod from_orm(obj: Any) → Model¶
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metada... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-8 | 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 inputprompt and additional model provider-specific output.
get_inp... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-9 | 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/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-10 | 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/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-11 | 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/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-12 | stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[Seriali... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-13 | 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/llms/langchain.llms.koboldai.KoboldApiLLM.html |
5fa2d37221af-14 | property config_specs: List[langchain.schema.runnable.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¶
List of attribute names that should b... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.koboldai.KoboldApiLLM.html |
c00608d860e6-0 | langchain.llms.openai.AzureOpenAI¶
class langchain.llms.openai.AzureOpenAI[source]¶
Bases: BaseOpenAI
Azure-specific OpenAI large language models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-1 | param cache: Optional[bool] = None¶
param callback_manager: Optional[BaseCallbackManager] = None¶
param callbacks: Callbacks = None¶
param default_headers: Union[Mapping[str, str], None] = None¶
param default_query: Union[Mapping[str, object], None] = None¶
param deployment_name: Union[str, None] = None (alias 'azure_d... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-2 | param n: int = 1¶
How many completions to generate for each prompt.
param openai_api_base: Optional[str] = None (alias 'base_url')¶
Base URL path for API requests, leave blank if not using a proxy or service
emulator.
param openai_api_key: Union[str, None] = None (alias 'api_key')¶
Automatically inferred from env var A... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-3 | be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-4 | e.g., if the underlying runnable uses an API which supports a batch mode.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[Li... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-5 | 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 inputprompt and additional model provider-specific output.
async a... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-6 | 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.
async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-7 | input is still being generated.
batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs invoke in parallel using a thread pool executor.
The default i... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-8 | 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/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-9 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of pr... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-10 | 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.
For example, if the class is langcha... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-11 | Get the sub prompts for llm call.
get_token_ids(text: str) → List[int]¶
Get the token IDs using the tiktoken package.
invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
Transform a single input into an output. Ove... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-12 | 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.
max_tokens_for_prompt(prompt: str) → int¶
Calculate the maximum number of tokens possible to generate for a prompt.
Parameters
prompt – The prompt to pa... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-13 | 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 m... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-14 | 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... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-15 | 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_af... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
c00608d860e6-16 | For example,{“openai_api_key”: “OPENAI_API_KEY”}
property max_context_size: int¶
Get max context size for this model.
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
Examples using AzureOpenAI¶
Azure OpenAI
OpenAI | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.AzureOpenAI.html |
9eeff4678579-0 | langchain.llms.loading.load_llm¶
langchain.llms.loading.load_llm(file: Union[str, Path]) → BaseLLM[source]¶
Load LLM from file.
Examples using load_llm¶
Azure ML
Serialization | lang/api.python.langchain.com/en/latest/llms/langchain.llms.loading.load_llm.html |
6c4f34195d31-0 | langchain.llms.tongyi.stream_generate_with_retry¶
langchain.llms.tongyi.stream_generate_with_retry(llm: Tongyi, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call. | lang/api.python.langchain.com/en/latest/llms/langchain.llms.tongyi.stream_generate_with_retry.html |
9a890ba64757-0 | langchain.llms.opaqueprompts.OpaquePrompts¶
class langchain.llms.opaqueprompts.OpaquePrompts[source]¶
Bases: LLM
An LLM wrapper that uses OpaquePrompts to sanitize prompts.
Wraps another LLM and sanitizes prompts before passing it to the LLM, thende-sanitizes the response.
To use, you should have the opaqueprompts pyth... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-1 | Check Cache and run the LLM on the given prompt and input.
async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using as... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-2 | 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 PromptValue is an object that can be
converted to match the format of any languag... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-3 | **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 ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-4 | 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.
async atransform(input: As... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-5 | 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[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-6 | classmethod from_orm(obj: Any) → Model¶
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metada... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-7 | 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 inputprompt and additional model provider-specific output.
get_inp... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-8 | 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/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-9 | 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/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-10 | 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/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-11 | stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[Seriali... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-12 | 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/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
9a890ba64757-13 | property config_specs: List[langchain.schema.runnable.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¶
List of attribute names that should b... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.opaqueprompts.OpaquePrompts.html |
f5a4e85b7022-0 | langchain.llms.cerebriumai.CerebriumAI¶
class langchain.llms.cerebriumai.CerebriumAI[source]¶
Bases: LLM
CerebriumAI large language models.
To use, you should have the cerebrium python package installed, and the
environment variable CEREBRIUMAI_API_KEY set with your API key.
Any parameters that are valid to be passed t... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-1 | Check Cache and run the LLM on the given prompt and input.
async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using as... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-2 | 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 PromptValue is an object that can be
converted to match the format of any languag... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-3 | **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 ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-4 | 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.
async atransform(input: As... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-5 | 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[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-6 | classmethod from_orm(obj: Any) → Model¶
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metada... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-7 | 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 inputprompt and additional model provider-specific output.
get_inp... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-8 | 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/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-9 | 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/llms/langchain.llms.cerebriumai.CerebriumAI.html |
f5a4e85b7022-10 | 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/llms/langchain.llms.cerebriumai.CerebriumAI.html |
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