LangChain中文手册模块之语言模型参考3

Example

from langchain.llms import PromptLayerOpenAI

openai = PromptLayerOpenAI(model_name="text-davinci-003")

Validators

build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str

Check Cache and run the LLM on the given prompt and input.

async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult

Run the LLM on the given prompt and input.

async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult

Take in a list of prompt values and return an LLMResult.

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

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) → langchain.schema.LLMResult

Create the LLMResult from the choices and prompts.

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult

Run the LLM on the given prompt and input.

generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Calculate num tokens with tiktoken package.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) → List[List[str]]

Get the sub prompts for llm call.

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

max_tokens_for_prompt(prompt: str) → int

Calculate the maximum number of tokens possible to generate for a prompt.

Parameters

prompt – The prompt to pass into the model.

Returns

The maximum number of tokens to generate for a prompt.

Example

max_tokens = openai.max_token_for_prompt("Tell me a joke.")

modelname_to_contextsize(modelname: str) → int

Calculate the maximum number of tokens possible to generate for a model.

Parameters

modelname – The modelname we want to know the context size for.

Returns

The maximum context size

Example

max_tokens = openai.modelname_to_contextsize("text-davinci-003")

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

prep_streaming_params(stop: Optional[List[str]] = None) → Dict[str, Any]

Prepare the params for streaming.

save(file_path: Union[pathlib.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”)

stream(prompt: str, stop: Optional[List[str]] = None) → Generator

Call OpenAI with streaming flag and return the resulting generator.

BETA: this is a beta feature while we figure out the right abstraction. Once that happens, this interface could change.

Parameters

prompt – The prompts to pass into the model.
stop – Optional list of stop words to use when generating.

Returns

A generator representing the stream of tokens from OpenAI.

Example

generator = openai.stream("Tell me a joke.")

for token in generator:

yield token

classmethod update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.PromptLayerOpenAIChat[source]

Wrapper around OpenAI large language models.

To use, you should have the openai and promptlayer python package installed, and the environment variable OPENAI_API_KEY and PROMPTLAYER_API_KEY set with your openAI API key and promptlayer key respectively.

All parameters that can be passed to the OpenAIChat LLM can also be passed here. The PromptLayerOpenAIChat adds two optional :param pl_tags: List of strings to tag the request with. :param return_pl_id: If True, the PromptLayer request ID will be

returned in the generation_info field of the Generation object.

Example

from langchain.llms import PromptLayerOpenAIChat

openaichat = PromptLayerOpenAIChat(model_name="gpt-3.5-turbo")

Validators

build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}

Set of special tokens that are allowed。

field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'

Set of special tokens that are not allowed。

field max_retries: int = 6

Maximum number of retries to make when generating.

field model_kwargs: Dict[str, Any] [Optional]

Holds any model parameters valid for create call not explicitly specified.

field model_name: str = 'gpt-3.5-turbo'

Model name to use.

field prefix_messages: List [Optional]

Series of messages for Chat input.

field streaming: bool = False

Whether to stream the results or not.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Calculate num tokens with tiktoken package.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.RWKV[source]

Wrapper around RWKV language models.

To use, you should have the rwkv python package installed, the pre-trained model file, and the model’s config information.

Example

from langchain.llms import RWKV

model = RWKV(model="./models/rwkv-3b-fp16.bin", strategy="cpu fp32")

# Simplest invocation

response = model("Once upon a time, ")

Validators

raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field CHUNK_LEN: int = 256

Batch size for prompt processing.

field max_tokens_per_generation: int = 256

Maximum number of tokens to generate.

field model: str [Required]

Path to the pre-trained RWKV model file.

field penalty_alpha_frequency: float = 0.4

Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim..

field penalty_alpha_presence: float = 0.4

Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood to talk about new topics..

field rwkv_verbose: bool = True

Print debug information.

field strategy: str = 'cpu fp32'

Token context window.

field temperature: float = 1.0

The temperature to use for sampling.

field tokens_path: str [Required]

Path to the RWKV tokens file.

field top_p: float = 0.5

The top-p value to use for sampling.

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.Replicate[source]

Wrapper around Replicate models.

To use, you should have the replicate python package installed, and the environment variable REPLICATE_API_TOKEN set with your API token. You can find your token here: https://replicate.com/account

The model param is required, but any other model parameters can also be passed in with the format input={model_param: value, …}

Example

Validators

build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.SagemakerEndpoint[source]

Wrapper around custom Sagemaker Inference Endpoints.

To use, you must supply the endpoint name from your deployed Sagemaker model & the region where it is deployed.

To authenticate, the AWS client uses the following methods to automatically load credentials: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

If a specific credential profile should be used, you must pass the name of the profile from the ~/.aws/credentials file that is to be used.

Make sure the credentials / roles used have the required policies to access the Sagemaker endpoint. See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html

Validators

raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field content_handler: langchain.llms.sagemaker_endpoint.LLMContentHandler [Required]

The content handler class that provides an input and output transform functions to handle formats between LLM and the endpoint.

field credentials_profile_name: Optional[str] = None

The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which has either access keys or role information specified. If not specified, the default credential profile or, if on an EC2 instance, credentials from IMDS will be used. See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

field endpoint_kwargs: Optional[Dict] = None

Optional attributes passed to the invoke_endpoint function. See `boto3`_. docs for more info. .. _boto3:

field endpoint_name: str = ''

The name of the endpoint from the deployed Sagemaker model. Must be unique within an AWS Region.

field model_kwargs: Optional[Dict] = None

Key word arguments to pass to the model.

field region_name: str = ''

The aws region where the Sagemaker model is deployed, eg. us-west-2.

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.SelfHostedHuggingFaceLLM[source]

Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.

Supported hardware includes auto-launched instances on AWS, GCP, Azure, and Lambda, as well as servers specified by IP address and SSH credentials (such as on-prem, or another cloud like Paperspace, Coreweave, etc.).

To use, you should have the runhouse python package installed.

Only supports text-generation, text2text-generation and summarization for now.

Example using from_model_id:

from langchain.llms import SelfHostedHuggingFaceLLM

import runhouse as rh

gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")

hf = SelfHostedHuggingFaceLLM(

model_id="google/flan-t5-large", task="text2text-generation",

hardware=gpu

)

Example passing fn that generates a pipeline (bc the pipeline is not serializable):

from langchain.llms import SelfHostedHuggingFaceLLM

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

import runhouse as rh

def get_pipeline():

model_id = "gpt2"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)

pipe = pipeline(

"text-generation", model=model, tokenizer=tokenizer

)

return pipe

hf = SelfHostedHuggingFaceLLM(

model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)

Validators

raise_deprecation » all fields
set_verbose » verbose

field device: int = 0

Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc.

field hardware: Any = None

Remote hardware to send the inference function to.

field inference_fn: Callable =

Inference function to send to the remote hardware.

field load_fn_kwargs: Optional[dict] = None

Key word arguments to pass to the model load function.

field model_id: str = 'gpt2'

Hugging Face model_id to load the model.

field model_kwargs: Optional[dict] = None

Key word arguments to pass to the model.

field model_load_fn: Callable =

Function to load the model remotely on the server.

field model_reqs: List[str] = ['./', 'transformers', 'torch']

Requirements to install on hardware to inference the model.

field task: str = 'text-generation'

Hugging Face task (“text-generation”, “text2text-generation” or “summarization”).

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → langchain.llms.base.LLM

Init the SelfHostedPipeline from a pipeline object or string.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.SelfHostedPipeline[source]

Run model inference on self-hosted remote hardware.

To use, you should have the runhouse python package installed.

Example for custom pipeline and inference functions:

from langchain.llms import SelfHostedPipeline

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

import runhouse as rh

def load_pipeline():

tokenizer = AutoTokenizer.from_pretrained("gpt2")

model = AutoModelForCausalLM.from_pretrained("gpt2")

return pipeline(

"text-generation", model=model, tokenizer=tokenizer,

max_new_tokens=10

)

def inference_fn(pipeline, prompt, stop = None):

return pipeline(prompt)[0]["generated_text"]

gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")

llm = SelfHostedPipeline(

model_load_fn=load_pipeline,

hardware=gpu,

model_reqs=model_reqs, inference_fn=inference_fn

)

Example for <2GB model (can be serialized and sent directly to the server):

from langchain.llms import SelfHostedPipeline

import runhouse as rh

gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")

my_model = ...

llm = SelfHostedPipeline.from_pipeline(

pipeline=my_model,

hardware=gpu,

model_reqs=["./", "torch", "transformers"],

)

Example passing model path for larger models:

from langchain.llms import SelfHostedPipeline

import runhouse as rh

import pickle

from transformers import pipeline

generator = pipeline(model="gpt2")

rh.blob(pickle.dumps(generator), path="models/pipeline.pkl"

).save().to(gpu, path="models")

llm = SelfHostedPipeline.from_pipeline(

pipeline="models/pipeline.pkl",

hardware=gpu,

model_reqs=["./", "torch", "transformers"],

)

Validators

raise_deprecation » all fields
set_verbose » verbose

field hardware: Any = None

Remote hardware to send the inference function to.

field inference_fn: Callable =

Inference function to send to the remote hardware.

field load_fn_kwargs: Optional[dict] = None

Key word arguments to pass to the model load function.

field model_load_fn: Callable [Required]

Function to load the model remotely on the server.

field model_reqs: List[str] = ['./', 'torch']

Requirements to install on hardware to inference the model.

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → langchain.llms.base.LLM[source]

Init the SelfHostedPipeline from a pipeline object or string.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.StochasticAI[source]

Wrapper around StochasticAI large language models.

To use, you should have the environment variable STOCHASTICAI_API_KEY set with your API key.

Example

from langchain.llms import StochasticAI

stochasticai = StochasticAI(api_url="")

Validators

build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field api_url: str = ''

Model name to use.

field model_kwargs: Dict[str, Any] [Optional]

Holds any model parameters valid for create call not explicitly specified.

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.Writer[source]

Wrapper around Writer large language models.

To use, you should have the environment variable WRITER_API_KEY and WRITER_ORG_ID set with your API key and organization ID respectively.

Example

from langchain import Writer

writer = Writer(model_id="palmyra-base")

Validators

raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field base_url: Optional[str] = None

Base url to use, if None decides based on model name.

field best_of: Optional[int] = None

Generates this many completions server-side and returns the “best”.

field logprobs: bool = False

Whether to return log probabilities.

field max_tokens: Optional[int] = None

Maximum number of tokens to generate.

field min_tokens: Optional[int] = None

Minimum number of tokens to generate.

field model_id: str = 'palmyra-instruct'

Model name to use.

field n: Optional[int] = None

How many completions to generate.

field presence_penalty: Optional[float] = None

Penalizes repeated tokens regardless of frequency.

field repetition_penalty: Optional[float] = None

Penalizes repeated tokens according to frequency.

field stop: Optional[List[str]] = None

Sequences when completion generation will stop.

field temperature: Optional[float] = None

What sampling temperature to use.

field top_p: Optional[float] = None

Total probability mass of tokens to consider at each step.

field verbose: bool [Optional]

Whether to print out response text.

field writer_api_key: Optional[str] = None

Writer API key.

field writer_org_id: Optional[str] = None

Writer organization ID.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

Wrappers on top of large language models APIs.

pydantic model langchain.llms.AI21[source]

Wrapper around AI21 large language models.

To use, you should have the environment variable AI21_API_KEY set with your API key.

Example

from langchain.llms import AI21

ai21 = AI21(model="j2-jumbo-instruct")

Validators

raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field base_url: Optional[str] = None

Base url to use, if None decides based on model name.

field countPenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)

Penalizes repeated tokens according to count.

field frequencyPenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)

Penalizes repeated tokens according to frequency.

field logitBias: Optional[Dict[str, float]] = None

Adjust the probability of specific tokens being generated.

field maxTokens: int = 256

The maximum number of tokens to generate in the completion.

field minTokens: int = 0

The minimum number of tokens to generate in the completion.

field model: str = 'j2-jumbo-instruct'

Model name to use.

field numResults: int = 1

How many completions to generate for each prompt.

field presencePenalty: langchain.llms.ai21.AI21PenaltyData = AI21PenaltyData(scale=0, applyToWhitespaces=True, applyToPunctuations=True, applyToNumbers=True, applyToStopwords=True, applyToEmojis=True)

Penalizes repeated tokens.

field temperature: float = 0.7

What sampling temperature to use.

field topP: float = 1.0

Total probability mass of tokens to consider at each step.

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.AlephAlpha[source]

Wrapper around Aleph Alpha large language models.

To use, you should have the aleph_alpha_client python package installed, and the environment variable ALEPH_ALPHA_API_KEY set with your API key, or pass it as a named parameter to the constructor.

Parameters are explained more in depth here: Aleph-Alpha/aleph-alpha-client

Example

from langchain.llms import AlephAlpha

alpeh_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key")

Validators

raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field aleph_alpha_api_key: Optional[str] = None

API key for Aleph Alpha API.

field best_of: Optional[int] = None

returns the one with the “best of” results (highest log probability per token)

field completion_bias_exclusion_first_token_only: bool = False

Only consider the first token for the completion_bias_exclusion.

field contextual_control_threshold: Optional[float] = None

If set to None, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-None value, control parameters are also applied to similar tokens.

field control_log_additive: Optional[bool] = True

True: apply control by adding the log(control_factor) to attention scores. False: (attention_scores - - attention_scores.min(-1)) * control_factor

field echo: bool = False

Echo the prompt in the completion.

field frequency_penalty: float = 0.0

Penalizes repeated tokens according to frequency.

field log_probs: Optional[int] = None

Number of top log probabilities to be returned for each generated token.

field logit_bias: Optional[Dict[int, float]] = None

The logit bias allows to influence the likelihood of generating tokens.

field maximum_tokens: int = 64

The maximum number of tokens to be generated.

field minimum_tokens: Optional[int] = 0

Generate at least this number of tokens.

field model: Optional[str] = 'luminous-base'

Model name to use.

field n: int = 1

How many completions to generate for each prompt.

field penalty_bias: Optional[str] = None

Penalty bias for the completion.

field penalty_exceptions: Optional[List[str]] = None

List of strings that may be generated without penalty, regardless of other penalty settings

field penalty_exceptions_include_stop_sequences: Optional[bool] = None

Should stop_sequences be included in penalty_exceptions.

field presence_penalty: float = 0.0

Penalizes repeated tokens.

field raw_completion: bool = False

Force the raw completion of the model to be returned.

field repetition_penalties_include_completion: bool = True

Flag deciding whether presence penalty or frequency penalty are updated from the completion.

field repetition_penalties_include_prompt: Optional[bool] = False

Flag deciding whether presence penalty or frequency penalty are updated from the prompt.

field stop_sequences: Optional[List[str]] = None

Stop sequences to use.

field temperature: float = 0.0

A non-negative float that tunes the degree of randomness in generation.

field tokens: Optional[bool] = False

return tokens of completion.

field top_k: int = 0

Number of most likely tokens to consider at each step.

field top_p: float = 0.0

Total probability mass of tokens to consider at each step.

field use_multiplicative_presence_penalty: Optional[bool] = False

Flag deciding whether presence penalty is applied multiplicatively (True) or additively (False).

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.Anthropic[source]

Wrapper around Anthropic’s 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 constructor.

Example

Validators

raise_deprecation » all fields
raise_warning » all fields
set_verbose » verbose
validate_environment » all fields

field default_request_timeout: Optional[Union[float, Tuple[float, float]]] = None

Timeout for requests to Anthropic Completion API. Default is 600 seconds.

field max_tokens_to_sample: int = 256

Denotes the number of tokens to predict per generation.

field model: str = 'claude-v1'

Model name to use.

field streaming: bool = False

Whether to stream the results.

field temperature: Optional[float] = None

A non-negative float that tunes the degree of randomness in generation.

field top_k: Optional[int] = None

Number of most likely tokens to consider at each step.

field top_p: Optional[float] = None

Total probability mass of tokens to consider at each step.

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int[source]

Calculate number of tokens.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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”)

stream(prompt: str, stop: Optional[List[str]] = None) → Generator[source]

Call Anthropic completion_stream and return the resulting generator.

BETA: this is a beta feature while we figure out the right abstraction. Once that happens, this interface could change.

Parameters

prompt – The prompt to pass into the model.
stop – Optional list of stop words to use when generating.

Returns

A generator representing the stream of tokens from Anthropic.

Example

prompt = "Write a poem about a stream."

prompt = f" Human: {prompt} Assistant:"

generator = anthropic.stream(prompt)

for token in generator:

yield token

classmethod update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.Anyscale[source]

Wrapper around Anyscale Services. To use, you should have the environment variable ANYSCALE_SERVICE_URL, ANYSCALE_SERVICE_ROUTE and ANYSCALE_SERVICE_TOKEN set with your Anyscale Service, or pass it as a named parameter to the constructor.

Example

Validators

raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field model_kwargs: Optional[dict] = None

Key word arguments to pass to the model. Reserved for future use

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Get the number of tokens present in the text.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

save(file_path: Union[pathlib.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 update_forward_refs(**localns: Any) → None

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

pydantic model langchain.llms.AzureOpenAI[source]

Wrapper around 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 the openai.create call can be passed in, even if not explicitly saved on this class.

Example

from langchain.llms import AzureOpenAI

openai = AzureOpenAI(model_name="text-davinci-003")

Validators

build_extra » all fields
raise_deprecation » all fields
set_verbose » verbose
validate_environment » all fields

field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}

Set of special tokens that are allowed。

field batch_size: int = 20

Batch size to use when passing multiple documents to generate.

field best_of: int = 1

Generates best_of completions server-side and returns the “best”.

field deployment_name: str = ''

Deployment name to use.

field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'

Set of special tokens that are not allowed。

field frequency_penalty: float = 0

Penalizes repeated tokens according to frequency.

field logit_bias: Optional[Dict[str, float]] [Optional]

Adjust the probability of specific tokens being generated.

field max_retries: int = 6

Maximum number of retries to make when generating.

field max_tokens: int = 256

The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size.

field model_kwargs: Dict[str, Any] [Optional]

Holds any model parameters valid for create call not explicitly specified.

field model_name: str = 'text-davinci-003' (alias 'model')

Model name to use.

field n: int = 1

How many completions to generate for each prompt.

field presence_penalty: float = 0

Penalizes repeated tokens.

field request_timeout: Optional[Union[float, Tuple[float, float]]] = None

Timeout for requests to OpenAI completion API. Default is 600 seconds.

field streaming: bool = False

Whether to stream the results or not.

field temperature: float = 0.7

What sampling temperature to use.

field top_p: float = 1

Total probability mass of tokens to consider at each step.

field verbose: bool [Optional]

Whether to print out response text.

Check Cache and run the LLM on the given prompt and input.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

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

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

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) → langchain.schema.LLMResult

Create the LLMResult from the choices and prompts.

dict(**kwargs: Any) → Dict

Return a dictionary of the LLM.

Run the LLM on the given prompt and input.

Take in a list of prompt values and return an LLMResult.

get_num_tokens(text: str) → int

Calculate num tokens with tiktoken package.

get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int

Get the number of tokens in the message.

get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) → List[List[str]]

Get the sub prompts for llm call.

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

max_tokens_for_prompt(prompt: str) → int

Calculate the maximum number of tokens possible to generate for a prompt.

Parameters

prompt – The prompt to pass into the model.

Returns

The maximum number of tokens to generate for a prompt.

Example

max_tokens = openai.max_token_for_prompt("Tell me a joke.")

modelname_to_contextsize(modelname: str) → int

Calculate the maximum number of tokens possible to generate for a model.

Parameters

modelname – The modelname we want to know the context size for.

Returns

The maximum context size

Example

max_tokens = openai.modelname_to_contextsize("text-davinci-003")

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

Predict text from text.

predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage

Predict message from messages.

prep_streaming_params(stop: Optional[List[str]] = None) → Dict[str, Any]

Prepare the params for streaming.

save(file_path: Union[pathlib.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”)

stream(prompt: str, stop: Optional[List[str]] = None) → Generator

Call OpenAI with streaming flag and return the resulting generator.

BETA: this is a beta feature while we figure out the right abstraction. Once that happens, this interface could change.

Parameters

prompt – The prompts to pass into the model.
stop – Optional list of stop words to use when generating.

Returns

A generator representing the stream of tokens from OpenAI.

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页面更新：2024-05-24

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