`langchain.llms.openllm`.OpenLLM¶

class langchain.llms.openllm.OpenLLM(model_name: Optional[str] = None, *, model_id: Optional[str] = None, server_url: Optional[str] = None, server_type: Literal['grpc', 'http'] = 'http', embedded: bool = True, cache: Optional[bool] = None, verbose: bool = None, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, callback_manager: Optional[BaseCallbackManager] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, llm_kwargs: Dict[str, Any])[source]¶

Bases: LLM

OpenLLM, supporting both in-process model instance and remote OpenLLM servers.

To use, you should have the openllm library installed:

pip install openllm

Learn more at: https://github.com/bentoml/openllm

Example running an LLM model locally managed by OpenLLM:

from langchain.llms import OpenLLM
llm = OpenLLM(
    model_name='flan-t5',
    model_id='google/flan-t5-large',
)
llm("What is the difference between a duck and a goose?")

For all available supported models, you can run ‘openllm models’.

If you have a OpenLLM server running, you can also use it remotely:

from langchain.llms import OpenLLM
llm = OpenLLM(server_url='http://localhost:3000')
llm("What is the difference between a duck and a goose?")

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param cache: Optional[bool] = None¶

param callback_manager: Optional[BaseCallbackManager] = None¶

param callbacks: Callbacks = None¶

param embedded: bool = True¶: Initialize this LLM instance in current process by default. Should only set to False when using in conjunction with BentoML Service.

param llm_kwargs: Dict[str, Any] [Required]¶: Key word arguments to be passed to openllm.LLM

param metadata: Optional[Dict[str, Any]] = None¶: Metadata to add to the run trace.

param model_id: Optional[str] = None¶: Model Id to use. If not provided, will use the default model for the model name. See ‘openllm models’ for all available model variants.

param model_name: Optional[str] = None¶: Model name to use. See ‘openllm models’ for all available models.

param server_type: ServerType = 'http'¶: Optional server type. Either ‘http’ or ‘grpc’.

param server_url: Optional[str] = None¶: Optional server URL that currently runs a LLMServer with ‘openllm start’.

param tags: Optional[List[str]] = None¶: Tags to add to the run trace.

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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → str¶: 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, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶

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[List[str], List[List[str]]]] = None, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶: 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 pass a sequence of prompts and return model generations.

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

Use this method when you want to:

take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).

Parameters

prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

An LLMResult, which contains a list of candidate Generations for each input: prompt and additional model provider-specific output.

async ainvoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶

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

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

Use this method when calling pure text generation models and only the top: candidate generation is needed.

Parameters

text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a string.

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

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

Use this method when calling chat models and only the top: candidate generation is needed.

Parameters

messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a message.

async astream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → AsyncIterator[str]¶

batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, max_concurrency: Optional[int] = None, **kwargs: Any) → List[str]¶

dict(**kwargs: Any) → Dict¶: Return a dictionary of the LLM.

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, metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, **kwargs: Any) → LLMResult¶: 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 prompts to the model and return model generations.

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

Use this method when you want to:

take advantage of batched calls,
need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language model
type (e.g., pure text completion models vs chat models).

Parameters

prompts – List of PromptValues. A PromptValue is an object that can be converted to match the format of any language model (string for pure text generation models and BaseMessages for chat models).
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
callbacks – Callbacks to pass through. Used for executing additional functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

An LLMResult, which contains a list of candidate Generations for each input: prompt and additional model provider-specific output.

get_num_tokens(text: str) → int¶

Get the number of tokens present in the text.

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

Parameters: text – The string input to tokenize.
Returns: The integer number of tokens in the text.

get_num_tokens_from_messages(messages: List[BaseMessage]) → int¶

Get the number of tokens in the messages.

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

Parameters: messages – The message inputs to tokenize.
Returns: The sum of the number of tokens across the messages.

get_token_ids(text: str) → List[int]¶

Return the ordered ids of the tokens in a text.

Parameters

text – The string input to tokenize.

Returns

A list of ids corresponding to the tokens in the text, in order they occur: in the text.

invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶

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

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

Use this method when passing in raw text. If you want to pass in specific
types of chat messages, use predict_messages.

Parameters

text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a string.

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

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

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

Parameters

messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed to the model provider API call.

Returns

Top model prediction as a message.

validator raise_deprecation » all fields¶: Raise deprecation warning if callback_manager is used.

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”)

validator set_verbose » verbose¶

If verbose is None, set it.

This allows users to pass in None as verbose to access the global setting.

stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶

to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶

to_json_not_implemented() → SerializedNotImplemented¶

property lc_attributes: Dict¶: Return a list of attribute names that should be included in the serialized kwargs. These attributes must be accepted by the constructor.

property lc_namespace: List[str]¶: Return the namespace of the langchain object. eg. [“langchain”, “llms”, “openai”]

property lc_secrets: Dict[str, str]¶: Return a map of constructor argument names to secret ids. eg. {“openai_api_key”: “OPENAI_API_KEY”}

property lc_serializable: bool¶: Return whether or not the class is serializable.

property runner: openllm.LLMRunner¶

Get the underlying openllm.LLMRunner instance for integration with BentoML.

Example: .. code-block:: python

llm = OpenLLM(
model_name=’flan-t5’, model_id=’google/flan-t5-large’, embedded=False,

) tools = load_tools([“serpapi”, “llm-math”], llm=llm) agent = initialize_agent(

tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION

) svc = bentoml.Service(“langchain-openllm”, runners=[llm.runner])

@svc.api(input=Text(), output=Text()) def chat(input_text: str):

return agent.run(input_text)

model Config[source]¶

Bases: object

extra = 'forbid'¶

Examples using OpenLLM¶

OpenLLM

langchain.llms.openllm.OpenLLM¶

Examples using OpenLLM¶

`langchain.llms.openllm`.OpenLLM¶