id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
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c735e3e740ec-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.baseten.Baseten.html |
1f81b0e5312a-0 | langchain.llms.anthropic.Anthropic¶
class langchain.llms.anthropic.Anthropic[source]¶
Bases: LLM, _AnthropicCommon
Anthropic 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, or pass
it as a named parameter to the... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
1f81b0e5312a-1 | param default_request_timeout: Optional[float] = None¶
Timeout for requests to Anthropic Completion API. Default is 600 seconds.
param max_tokens_to_sample: int = 256 (alias 'max_tokens')¶
Denotes the number of tokens to predict per generation.
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
1f81b0e5312a-9 | 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. Override to implement.
Parameters
input – The input to the runnable.
config – A config to use when invoking the runnable.... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
1f81b0e5312a-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/llms/langchain.llms.anthropic.Anthropic.html |
1f81b0e5312a-11 | save(file_path: Union[Path, str]) → None¶
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. 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... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
1f81b0e5312a-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.anthropic.Anthropic.html |
ee5a1d7f183f-0 | langchain.llms.openai.OpenAIChat¶
class langchain.llms.openai.OpenAIChat[source]¶
Bases: BaseLLM
OpenAI Chat 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 the openai.cre... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.OpenAIChat.html |
ee5a1d7f183f-1 | emulator.
param openai_api_key: Optional[str] = None (alias 'api_key')¶
Automatically inferred from env var OPENAI_API_KEY if not provided.
param openai_proxy: Optional[str] = None¶
param prefix_messages: List [Optional]¶
Series of messages for Chat input.
param streaming: bool = False¶
Whether to stream the results or... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.openai.OpenAIChat.html |
ee5a1d7f183f-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.openai.OpenAIChat.html |
ee5a1d7f183f-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.openai.OpenAIChat.html |
ee5a1d7f183f-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.openai.OpenAIChat.html |
ee5a1d7f183f-5 | 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.OpenAIChat.html |
ee5a1d7f183f-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/llms/langchain.llms.openai.OpenAIChat.html |
ee5a1d7f183f-7 | 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.openai.OpenAIChat.html |
ee5a1d7f183f-8 | 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.openai.OpenAIChat.html |
ee5a1d7f183f-9 | 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/llms/langchain.llms.openai.OpenAIChat.html |
ee5a1d7f183f-10 | 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.openai.OpenAIChat.html |
ee5a1d7f183f-11 | .. 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.openai.OpenAIChat.html |
ee5a1d7f183f-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/llms/langchain.llms.openai.OpenAIChat.html |
ee5a1d7f183f-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: 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.openai.OpenAIChat.html |
14d6da678166-0 | langchain.llms.predibase.Predibase¶
class langchain.llms.predibase.Predibase[source]¶
Bases: LLM
Use your Predibase models with Langchain.
To use, you should have the predibase python package installed,
and have your Predibase API key.
Create a new model by parsing and validating input data from keyword arguments.
Rais... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predibase.Predibase.html |
14d6da678166-1 | 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[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCall... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predibase.Predibase.html |
14d6da678166-2 | 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 usua... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predibase.Predibase.html |
14d6da678166-3 | 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 at the
first occurrence of any of these substrings.
**kwargs – Arbitrary add... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predibase.Predibase.html |
14d6da678166-4 | 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.predibase.Predibase.html |
14d6da678166-5 | 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.predibase.Predibase.html |
14d6da678166-6 | 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.predibase.Predibase.html |
14d6da678166-7 | 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.predibase.Predibase.html |
14d6da678166-8 | 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.predibase.Predibase.html |
14d6da678166-9 | 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.predibase.Predibase.html |
14d6da678166-10 | 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.predibase.Predibase.html |
14d6da678166-11 | 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/llms/langchain.llms.predibase.Predibase.html |
14d6da678166-12 | 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/llms/langchain.llms.predibase.Predibase.html |
6a13c62565f3-0 | langchain.llms.petals.Petals¶
class langchain.llms.petals.Petals[source]¶
Bases: LLM
Petals Bloom models.
To use, you should have the petals python package installed, and the
environment variable HUGGINGFACE_API_KEY set with your API key.
Any parameters that are valid to be passed to the call can be passed
in, even if ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.petals.Petals.html |
6a13c62565f3-1 | What sampling temperature to use
param tokenizer: Any = None¶
The tokenizer to use for the API calls.
param top_k: Optional[int] = None¶
The number of highest probability vocabulary tokens
to keep for top-k-filtering.
param top_p: float = 0.9¶
The cumulative probability for top-p sampling.
param verbose: bool [Optional... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.petals.Petals.html |
6a13c62565f3-2 | 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.petals.Petals.html |
6a13c62565f3-3 | 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.petals.Petals.html |
6a13c62565f3-4 | 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.petals.Petals.html |
6a13c62565f3-5 | 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.petals.Petals.html |
6a13c62565f3-6 | 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/llms/langchain.llms.petals.Petals.html |
6a13c62565f3-7 | 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.petals.Petals.html |
6a13c62565f3-8 | 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.petals.Petals.html |
6a13c62565f3-9 | 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.petals.Petals.html |
6a13c62565f3-10 | 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.petals.Petals.html |
6a13c62565f3-11 | .. 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.petals.Petals.html |
6a13c62565f3-12 | 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/llms/langchain.llms.petals.Petals.html |
6a13c62565f3-13 | Bind input and output types to a Runnable, returning a new Runnable.
property InputType: TypeAlias¶
Get the input type for this runnable.
property OutputType: 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.petals.Petals.html |
eca685dc96ef-0 | langchain.llms.mlflow_ai_gateway.MlflowAIGateway¶
class langchain.llms.mlflow_ai_gateway.MlflowAIGateway[source]¶
Bases: LLM
Wrapper around completions LLMs in the MLflow AI Gateway.
To use, you should have the mlflow[gateway] python package installed.
For more information, see https://mlflow.org/docs/latest/gateway/in... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
eca685dc96ef-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.mlflow_ai_gateway.MlflowAIGateway.html |
f8692fb3149d-0 | langchain_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer¶
class langchain_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer[source]¶
Bases: HuggingFacePipeline
LMFormatEnforcer wrapped LLM using HuggingFace Pipeline API.
This pipeline is experimental and not yet stable.
Create a new model by pars... | lang/api.python.langchain.com/en/latest/llms/langchain_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-6 | dict(**kwargs: Any) → Dict¶
Return a dictionary of the LLM.
classmethod from_model_id(model_id: str, task: str, device: Optional[int] = - 1, device_map: Optional[str] = None, model_kwargs: Optional[dict] = None, pipeline_kwargs: Optional[dict] = None, batch_size: int = 4, **kwargs: Any) → HuggingFacePipeline¶
Construct... | lang/api.python.langchain.com/en/latest/llms/langchain_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-7 | 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 subst... | lang/api.python.langchain.com/en/latest/llms/langchain_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-8 | 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: Op... | lang/api.python.langchain.com/en/latest/llms/langchain_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
f8692fb3149d-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_experimental.llms.lmformatenforcer_decoder.LMFormatEnforcer.html |
3ee836c585c9-0 | langchain.llms.predictionguard.PredictionGuard¶
class langchain.llms.predictionguard.PredictionGuard[source]¶
Bases: LLM
Prediction Guard large language models.
To use, you should have the predictionguard python package installed, and the
environment variable PREDICTIONGUARD_TOKEN set with your access token, or pass
it... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-1 | param token: Optional[str] = None¶
Your Prediction Guard access token.
param verbose: bool [Optional]¶
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metad... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-2 | Run the LLM on the given prompt and input.
async agenerate_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¶
Asynchronously... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-3 | 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 ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-4 | 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/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-5 | 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.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic m... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-6 | 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(**kw... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-7 | 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 subst... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-8 | 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: Op... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.predictionguard.PredictionGuard.html |
3ee836c585c9-9 | 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.predictionguard.PredictionGuard.html |
3ee836c585c9-10 | 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.predictionguard.PredictionGuard.html |
3ee836c585c9-11 | 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.predictionguard.PredictionGuard.html |
3ee836c585c9-12 | 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.predictionguard.PredictionGuard.html |
3ee836c585c9-13 | 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.predictionguard.PredictionGuard.html |
6fcbd4f912c5-0 | langchain.llms.azureml_endpoint.HFContentFormatter¶
class langchain.llms.azureml_endpoint.HFContentFormatter[source]¶
Content handler for LLMs from the HuggingFace catalog.
Attributes
accepts
The MIME type of the response data returned from the endpoint
content_type
The MIME type of the input data passed to the endpoin... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.azureml_endpoint.HFContentFormatter.html |
a7f028cf9900-0 | langchain.llms.azureml_endpoint.DollyContentFormatter¶
class langchain.llms.azureml_endpoint.DollyContentFormatter[source]¶
Content handler for the Dolly-v2-12b model
Attributes
accepts
The MIME type of the response data returned from the endpoint
content_type
The MIME type of the input data passed to the endpoint
Meth... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.azureml_endpoint.DollyContentFormatter.html |
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