Return type
None
attribute allow_download: bool = False
If model does not exist in ~/.cache/gpt4all/, download it.
attribute context_erase: float = 0.5
Leave (n_ctx * context_erase) tokens
starting from beginning if the context has run out.
attribute echo: Optional[bool] = False
Whether to echo the prompt.
attribute embedding: bool = False
Use embedding mode only.
attribute f16_kv: bool = False
Use half-precision for key/value cache.
attribute logits_all: bool = False
Return logits for all tokens, not just the last token.
attribute model: str [Required]
Path to the pre-trained GPT4All model file.
attribute n_batch: int = 1
Batch size for prompt processing.
attribute n_ctx: int = 512
Token context window.
attribute n_parts: int = -1
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
attribute n_predict: Optional[int] = 256
The maximum number of tokens to generate.
attribute n_threads: Optional[int] = 4
Number of threads to use.
attribute repeat_last_n: Optional[int] = 64
Last n tokens to penalize
attribute repeat_penalty: Optional[float] = 1.3
The penalty to apply to repeated tokens.
attribute seed: int = 0
Seed. If -1, a random seed is used.
attribute stop: Optional[List[str]] = []
A list of strings to stop generation when encountered.
attribute streaming: bool = False
Whether to stream the results or not.
attribute tags: Optional[List[str]] = None


attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute temp: Optional[float] = 0.8
The temperature to use for sampling.
attribute top_k: Optional[int] = 40
The top-k value to use for sampling.
attribute top_p: Optional[float] = 0.95
The top-p value to use for sampling.
attribute use_mlock: bool = False
Force system to keep model in RAM.
attribute verbose: bool [Optional]
Whether to print out response text.
attribute vocab_only: bool = False
Only load the vocabulary, no weights.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –


Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]


Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)


.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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.
class langchain.llms.GooglePalm(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, google_api_key=None, model_name='models/text-bison-001', temperature=0.7, top_p=None, top_k=None, max_output_tokens=None, n=1)[source]
Bases: langchain.llms.base.BaseLLM, pydantic.main.BaseModel
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
client (Any) –
google_api_key (Optional[str]) –
model_name (str) –
temperature (float) –
top_p (Optional[float]) –


temperature (float) –
top_p (Optional[float]) –
top_k (Optional[int]) –
max_output_tokens (Optional[int]) –
n (int) –
Return type
None
attribute max_output_tokens: Optional[int] = None
Maximum number of tokens to include in a candidate. Must be greater than zero.
If unset, will default to 64.
attribute model_name: str = 'models/text-bison-001'
Model name to use.
attribute n: int = 1
Number of chat completions to generate for each prompt. Note that the API may
not return the full n completions if duplicates are generated.
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute temperature: float = 0.7
Run inference with this temperature. Must by in the closed interval
[0.0, 1.0].
attribute top_k: Optional[int] = None
Decode using top-k sampling: consider the set of top_k most probable tokens.
Must be positive.
attribute top_p: Optional[float] = None
Decode using nucleus sampling: consider the smallest set of tokens whose
probability sum is at least top_p. Must be in the closed interval [0.0, 1.0].
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str


kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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.


Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters


Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode


dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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 lc_serializable: bool
Return whether or not the class is serializable.
class langchain.llms.GooseAI(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, model_name='gpt-neo-20b', temperature=0.7, max_tokens=256, top_p=1, min_tokens=1, frequency_penalty=0, presence_penalty=0, n=1, model_kwargs=None, logit_bias=None, gooseai_api_key=None)[source]
Bases: langchain.llms.base.LLM
Wrapper around OpenAI large language models.
To use, you should have the openai python package installed, and the
environment variable GOOSEAI_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 GooseAI
gooseai = GooseAI(model_name="gpt-neo-20b")
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
client (Any) –
model_name (str) –
temperature (float) –
max_tokens (int) –
top_p (float) –
min_tokens (int) –
frequency_penalty (float) –
presence_penalty (float) –
n (int) –
model_kwargs (Dict[str, Any]) –
logit_bias (Optional[Dict[str, float]]) –
gooseai_api_key (Optional[str]) –


gooseai_api_key (Optional[str]) –
Return type
None
attribute frequency_penalty: float = 0
Penalizes repeated tokens according to frequency.
attribute logit_bias: Optional[Dict[str, float]] [Optional]
Adjust the probability of specific tokens being generated.
attribute 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.
attribute min_tokens: int = 1
The minimum number of tokens to generate in the completion.
attribute model_kwargs: Dict[str, Any] [Optional]
Holds any model parameters valid for create call not explicitly specified.
attribute model_name: str = 'gpt-neo-20b'
Model name to use
attribute n: int = 1
How many completions to generate for each prompt.
attribute presence_penalty: float = 0
Penalizes repeated tokens.
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute temperature: float = 0.7
What sampling temperature to use
attribute top_p: float = 1
Total probability mass of tokens to consider at each step.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str


property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.llms.HuggingFaceEndpoint(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, endpoint_url='', task=None, model_kwargs=None, huggingfacehub_api_token=None)[source]
Bases: langchain.llms.base.LLM
Wrapper around HuggingFaceHub Inference Endpoints.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Only supports text-generation and text2text-generation for now.
Example
from langchain.llms import HuggingFaceEndpoint
endpoint_url = (
    "https://abcdefghijklmnop.us-east-1.aws.endpoints.huggingface.cloud"
)
hf = HuggingFaceEndpoint(
    endpoint_url=endpoint_url,
    huggingfacehub_api_token="my-api-key"
)
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
endpoint_url (str) –
task (Optional[str]) –
model_kwargs (Optional[dict]) –
huggingfacehub_api_token (Optional[str]) –
Return type
None
attribute endpoint_url: str = ''
Endpoint URL to use.
attribute model_kwargs: Optional[dict] = None
Key word arguments to pass to the model.
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.


Tags to add to the run trace.
attribute task: Optional[str] = None
Task to call the model with.
Should be a task that returns generated_text or summary_text.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –


Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict


Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
Generate a JSON representation of the model, include and exclude arguments as per dict().


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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
property lc_attributes: Dict
Return a list of attribute names that should be included in the


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.
class langchain.llms.HuggingFaceHub(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, repo_id='gpt2', task=None, model_kwargs=None, huggingfacehub_api_token=None)[source]
Bases: langchain.llms.base.LLM
Wrapper around HuggingFaceHub  models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Only supports text-generation, text2text-generation and summarization for now.
Example
from langchain.llms import HuggingFaceHub
hf = HuggingFaceHub(repo_id="gpt2", huggingfacehub_api_token="my-api-key")
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
client (Any) –
repo_id (str) –
task (Optional[str]) –


repo_id (str) –
task (Optional[str]) –
model_kwargs (Optional[dict]) –
huggingfacehub_api_token (Optional[str]) –
Return type
None
attribute model_kwargs: Optional[dict] = None
Key word arguments to pass to the model.
attribute repo_id: str = 'gpt2'
Model name to use.
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute task: Optional[str] = None
Task to call the model with.
Should be a task that returns generated_text or summary_text.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters


Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include


update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters


get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python


Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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.
class langchain.llms.HuggingFacePipeline(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, pipeline=None, model_id='gpt2', model_kwargs=None, pipeline_kwargs=None)[source]
Bases: langchain.llms.base.LLM
Wrapper around HuggingFace Pipeline API.
To use, you should have the transformers python package installed.
Only supports text-generation, text2text-generation and summarization for now.
Example using from_model_id:from langchain.llms import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
    model_id="gpt2",
    task="text-generation",
    pipeline_kwargs={"max_new_tokens": 10},
)
Example passing pipeline in directly:from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline


from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
    "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
pipeline (Any) –
model_id (str) –
model_kwargs (Optional[dict]) –
pipeline_kwargs (Optional[dict]) –
Return type
None
attribute model_id: str = 'gpt2'
Model name to use.
attribute model_kwargs: Optional[dict] = None
Key word arguments passed to the model.
attribute pipeline_kwargs: Optional[dict] = None
Key word arguments passed to the pipeline.
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str


kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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.


Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
classmethod from_model_id(model_id, task, device=- 1, model_kwargs=None, pipeline_kwargs=None, **kwargs)[source]
Construct the pipeline object from model_id and task.
Parameters
model_id (str) –
task (str) –
device (int) –
model_kwargs (Optional[dict]) –
pipeline_kwargs (Optional[dict]) –
kwargs (Any) –
Return type
langchain.llms.base.LLM
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –


Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –


include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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.


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.
class langchain.llms.HuggingFaceTextGenInference(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, max_new_tokens=512, top_k=None, top_p=0.95, typical_p=0.95, temperature=0.8, repetition_penalty=None, stop_sequences=None, seed=None, inference_server_url='', timeout=120, server_kwargs=None, stream=False, client=None, async_client=None)[source]
Bases: langchain.llms.base.LLM
HuggingFace text generation inference API.
This class is a wrapper around the HuggingFace text generation inference API.
It is used to generate text from a given prompt.
Attributes:
- max_new_tokens: The maximum number of tokens to generate.
- top_k: The number of top-k tokens to consider when generating text.
- top_p: The cumulative probability threshold for generating text.
- typical_p: The typical probability threshold for generating text.
- temperature: The temperature to use when generating text.
- repetition_penalty: The repetition penalty to use when generating text.
- stop_sequences: A list of stop sequences to use when generating text.
- seed: The seed to use when generating text.
- inference_server_url: The URL of the inference server to use.
- timeout: The timeout value in seconds to use while connecting to inference server.


- timeout: The timeout value in seconds to use while connecting to inference server.
- server_kwargs: The keyword arguments to pass to the inference server.
- client: The client object used to communicate with the inference server.
- async_client: The async client object used to communicate with the server.
Methods:
- _call: Generates text based on a given prompt and stop sequences.
- _acall: Async generates text based on a given prompt and stop sequences.
- _llm_type: Returns the type of LLM.
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
max_new_tokens (int) –
top_k (Optional[int]) –
top_p (Optional[float]) –
typical_p (Optional[float]) –
temperature (float) –
repetition_penalty (Optional[float]) –
stop_sequences (List[str]) –
seed (Optional[int]) –
inference_server_url (str) –
timeout (int) –
server_kwargs (Dict[str, Any]) –
stream (bool) –
client (Any) –
async_client (Any) –
Return type
None
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –


Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)


langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult


kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –


exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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


property lc_serializable: bool
Return whether or not the class is serializable.
class langchain.llms.HumanInputLLM(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, input_func=None, prompt_func=None, separator='\n', input_kwargs={}, prompt_kwargs={})[source]
Bases: langchain.llms.base.LLM
A LLM wrapper which returns user input as the response.
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
input_func (Callable) –
prompt_func (Callable[[str], None]) –
separator (str) –
input_kwargs (Mapping[str, Any]) –
prompt_kwargs (Mapping[str, Any]) –
Return type
None
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –


Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)


Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type


kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters


Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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 lc_serializable: bool
Return whether or not the class is serializable.
class langchain.llms.LlamaCpp(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, model_path, lora_base=None, lora_path=None, n_ctx=512, n_parts=- 1, seed=- 1, f16_kv=True, logits_all=False, vocab_only=False, use_mlock=False, n_threads=None, n_batch=8, n_gpu_layers=None, suffix=None, max_tokens=256, temperature=0.8, top_p=0.95, logprobs=None, echo=False, stop=[], repeat_penalty=1.1, top_k=40, last_n_tokens_size=64, use_mmap=True, streaming=True)[source]
Bases: langchain.llms.base.LLM
Wrapper around the llama.cpp model.
To use, you should have the llama-cpp-python library installed, and provide the
path to the Llama model as a named parameter to the constructor.
Check out: https://github.com/abetlen/llama-cpp-python
Example
from langchain.llms import LlamaCppEmbeddings
llm = LlamaCppEmbeddings(model_path="/path/to/llama/model")
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
client (Any) –
model_path (str) –
lora_base (Optional[str]) –
lora_path (Optional[str]) –
n_ctx (int) –
n_parts (int) –


n_ctx (int) –
n_parts (int) –
seed (int) –
f16_kv (bool) –
logits_all (bool) –
vocab_only (bool) –
use_mlock (bool) –
n_threads (Optional[int]) –
n_batch (Optional[int]) –
n_gpu_layers (Optional[int]) –
suffix (Optional[str]) –
max_tokens (Optional[int]) –
temperature (Optional[float]) –
top_p (Optional[float]) –
logprobs (Optional[int]) –
echo (Optional[bool]) –
stop (Optional[List[str]]) –
repeat_penalty (Optional[float]) –
top_k (Optional[int]) –
last_n_tokens_size (Optional[int]) –
use_mmap (Optional[bool]) –
streaming (bool) –
Return type
None
attribute echo: Optional[bool] = False
Whether to echo the prompt.
attribute f16_kv: bool = True
Use half-precision for key/value cache.
attribute last_n_tokens_size: Optional[int] = 64
The number of tokens to look back when applying the repeat_penalty.
attribute logits_all: bool = False
Return logits for all tokens, not just the last token.
attribute logprobs: Optional[int] = None
The number of logprobs to return. If None, no logprobs are returned.
attribute lora_base: Optional[str] = None
The path to the Llama LoRA base model.
attribute lora_path: Optional[str] = None
The path to the Llama LoRA. If None, no LoRa is loaded.
attribute max_tokens: Optional[int] = 256


attribute max_tokens: Optional[int] = 256
The maximum number of tokens to generate.
attribute model_path: str [Required]
The path to the Llama model file.
attribute n_batch: Optional[int] = 8
Number of tokens to process in parallel.
Should be a number between 1 and n_ctx.
attribute n_ctx: int = 512
Token context window.
attribute n_gpu_layers: Optional[int] = None
Number of layers to be loaded into gpu memory. Default None.
attribute n_parts: int = -1
Number of parts to split the model into.
If -1, the number of parts is automatically determined.
attribute n_threads: Optional[int] = None
Number of threads to use.
If None, the number of threads is automatically determined.
attribute repeat_penalty: Optional[float] = 1.1
The penalty to apply to repeated tokens.
attribute seed: int = -1
Seed. If -1, a random seed is used.
attribute stop: Optional[List[str]] = []
A list of strings to stop generation when encountered.
attribute streaming: bool = True
Whether to stream the results, token by token.
attribute suffix: Optional[str] = None
A suffix to append to the generated text. If None, no suffix is appended.
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute temperature: Optional[float] = 0.8
The temperature to use for sampling.
attribute top_k: Optional[int] = 40
The top-k value to use for sampling.
attribute top_p: Optional[float] = 0.95
The top-p value to use for sampling.


The top-p value to use for sampling.
attribute use_mlock: bool = False
Force system to keep model in RAM.
attribute use_mmap: Optional[bool] = True
Whether to keep the model loaded in RAM
attribute verbose: bool [Optional]
Whether to print out response text.
attribute vocab_only: bool = False
Only load the vocabulary, no weights.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult


kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)


Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)[source]
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)


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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
stream(prompt, stop=None, run_manager=None)[source]
Yields results objects as they are generated in real time.
BETA: this is a beta feature while we figure out the right abstraction.
Once that happens, this interface could change.


Once that happens, this interface could change.
It also calls the callback manager’s on_llm_new_token event with
similar parameters to the OpenAI LLM class method of the same name.
Args: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 being generated.
Yields:A dictionary like objects containing a string token and metadata.
See llama-cpp-python docs and below for more.
Example:from langchain.llms import LlamaCpp
llm = LlamaCpp(
    model_path="/path/to/local/model.bin",
    temperature = 0.5
)
for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'",
        stop=["'","
“]):result = chunk[“choices”][0]
print(result[“text”], end=’’, flush=True)
Parameters
prompt (str) –
stop (Optional[List[str]]) –
run_manager (Optional[langchain.callbacks.manager.CallbackManagerForLLMRun]) –
Return type
Generator[Dict, None, None]
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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.


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.
class langchain.llms.TextGen(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, model_url, max_new_tokens=250, do_sample=True, temperature=1.3, top_p=0.1, typical_p=1, epsilon_cutoff=0, eta_cutoff=0, repetition_penalty=1.18, top_k=40, min_length=0, no_repeat_ngram_size=0, num_beams=1, penalty_alpha=0, length_penalty=1, early_stopping=False, seed=- 1, add_bos_token=True, truncation_length=2048, ban_eos_token=False, skip_special_tokens=True, stopping_strings=[], streaming=False)[source]
Bases: langchain.llms.base.LLM
Wrapper around the text-generation-webui model.
To use, you should have the text-generation-webui installed, a model loaded,
and –api added as a command-line option.
Suggested installation, use one-click installer for your OS:
https://github.com/oobabooga/text-generation-webui#one-click-installers
Paremeters below taken from text-generation-webui api example:
https://github.com/oobabooga/text-generation-webui/blob/main/api-examples/api-example.py
Example
from langchain.llms import TextGen
llm = TextGen(model_url="http://localhost:8500")
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –


callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
model_url (str) –
max_new_tokens (Optional[int]) –
do_sample (bool) –
temperature (Optional[float]) –
top_p (Optional[float]) –
typical_p (Optional[float]) –
epsilon_cutoff (Optional[float]) –
eta_cutoff (Optional[float]) –
repetition_penalty (Optional[float]) –
top_k (Optional[float]) –
min_length (Optional[int]) –
no_repeat_ngram_size (Optional[int]) –
num_beams (Optional[int]) –
penalty_alpha (Optional[float]) –
length_penalty (Optional[float]) –
early_stopping (bool) –
seed (int) –
add_bos_token (bool) –
truncation_length (Optional[int]) –
ban_eos_token (bool) –
skip_special_tokens (bool) –
stopping_strings (Optional[List[str]]) –
streaming (bool) –
Return type
None
attribute add_bos_token: bool = True
Add the bos_token to the beginning of prompts.
Disabling this can make the replies more creative.
attribute ban_eos_token: bool = False
Ban the eos_token. Forces the model to never end the generation prematurely.
attribute do_sample: bool = True
Do sample
attribute early_stopping: bool = False
Early stopping
attribute epsilon_cutoff: Optional[float] = 0
Epsilon cutoff
attribute eta_cutoff: Optional[float] = 0
ETA cutoff
attribute length_penalty: Optional[float] = 1
Length Penalty
attribute max_new_tokens: Optional[int] = 250


Length Penalty
attribute max_new_tokens: Optional[int] = 250
The maximum number of tokens to generate.
attribute min_length: Optional[int] = 0
Minimum generation length in tokens.
attribute model_url: str [Required]
The full URL to the textgen webui including http[s]://host:port
attribute no_repeat_ngram_size: Optional[int] = 0
If not set to 0, specifies the length of token sets that are completely blocked
from repeating at all. Higher values = blocks larger phrases,
lower values = blocks words or letters from repeating.
Only 0 or high values are a good idea in most cases.
attribute num_beams: Optional[int] = 1
Number of beams
attribute penalty_alpha: Optional[float] = 0
Penalty Alpha
attribute repetition_penalty: Optional[float] = 1.18
Exponential penalty factor for repeating prior tokens. 1 means no penalty,
higher value = less repetition, lower value = more repetition.
attribute seed: int = -1
Seed (-1 for random)
attribute skip_special_tokens: bool = True
Skip special tokens. Some specific models need this unset.
attribute stopping_strings: Optional[List[str]] = []
A list of strings to stop generation when encountered.
attribute streaming: bool = False
Whether to stream the results, token by token (currently unimplemented).
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute temperature: Optional[float] = 1.3
Primary factor to control randomness of outputs. 0 = deterministic
(only the most likely token is used). Higher value = more randomness.
attribute top_k: Optional[float] = 40


attribute top_k: Optional[float] = 40
Similar to top_p, but select instead only the top_k most likely tokens.
Higher value = higher range of possible random results.
attribute top_p: Optional[float] = 0.1
If not set to 1, select tokens with probabilities adding up to less than this
number. Higher value = higher range of possible random results.
attribute truncation_length: Optional[int] = 2048
Truncate the prompt up to this length. The leftmost tokens are removed if
the prompt exceeds this length. Most models require this to be at most 2048.
attribute typical_p: Optional[float] = 1
If not set to 1, select only tokens that are at least this much more likely to
appear than random tokens, given the prior text.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult


kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model


exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters


Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters


save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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.
class langchain.llms.ManifestWrapper(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, llm_kwargs=None)[source]
Bases: langchain.llms.base.LLM
Wrapper around HazyResearch’s Manifest library.
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
client (Any) –
llm_kwargs (Optional[Dict]) –
Return type
None


llm_kwargs (Optional[Dict]) –
Return type
None
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –


stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)


generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters


Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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.


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.
class langchain.llms.Modal(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, endpoint_url='', model_kwargs=None)[source]
Bases: langchain.llms.base.LLM
Wrapper around Modal large language models.
To use, you should have the modal-client python package installed.
Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.llms import Modal
modal = Modal(endpoint_url="")
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
endpoint_url (str) –
model_kwargs (Dict[str, Any]) –
Return type
None
attribute endpoint_url: str = ''
model endpoint to use
attribute model_kwargs: Dict[str, Any] [Optional]
Holds any model parameters valid for create call not
explicitly specified.
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute verbose: bool [Optional]
Whether to print out response text.


attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async agenerate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
async apredict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
async apredict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters


Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
classmethod construct(_fields_set=None, **values)
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
Parameters
_fields_set (Optional[SetStr]) –
values (Any) –
Return type
Model
copy(*, include=None, exclude=None, update=None, deep=False)
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include
update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model
self (Model) –
Returns
new model instance
Return type
Model
dict(**kwargs)
Return a dictionary of the LLM.
Parameters
kwargs (Any) –
Return type
Dict
generate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –


Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
generate_prompt(prompts, stop=None, callbacks=None, **kwargs)
Take in a list of prompt values and return an LLMResult.
Parameters
prompts (List[langchain.schema.PromptValue]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult
get_num_tokens(text)
Get the number of tokens present in the text.
Parameters
text (str) –
Return type
int
get_num_tokens_from_messages(messages)
Get the number of tokens in the message.
Parameters
messages (List[langchain.schema.BaseMessage]) –
Return type
int
get_token_ids(text)
Get the token present in the text.
Parameters
text (str) –
Return type
List[int]
json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, models_as_dict=True, **dumps_kwargs)
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().
Parameters
include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –


include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –
by_alias (bool) –
skip_defaults (Optional[bool]) –
exclude_unset (bool) –
exclude_defaults (bool) –
exclude_none (bool) –
encoder (Optional[Callable[[Any], Any]]) –
models_as_dict (bool) –
dumps_kwargs (Any) –
Return type
unicode
predict(text, *, stop=None, **kwargs)
Predict text from text.
Parameters
text (str) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
str
predict_messages(messages, *, stop=None, **kwargs)
Predict message from messages.
Parameters
messages (List[langchain.schema.BaseMessage]) –
stop (Optional[Sequence[str]]) –
kwargs (Any) –
Return type
langchain.schema.BaseMessage
save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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.


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.
class langchain.llms.MosaicML(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, endpoint_url='https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict', inject_instruction_format=False, model_kwargs=None, retry_sleep=1.0, mosaicml_api_token=None)[source]
Bases: langchain.llms.base.LLM
Wrapper around MosaicML’s LLM inference service.
To use, you should have the
environment variable MOSAICML_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor.
Example
from langchain.llms import MosaicML
endpoint_url = (
    "https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict"
)
mosaic_llm = MosaicML(
    endpoint_url=endpoint_url,
    mosaicml_api_token="my-api-key"
)
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
endpoint_url (str) –
inject_instruction_format (bool) –


endpoint_url (str) –
inject_instruction_format (bool) –
model_kwargs (Optional[dict]) –
retry_sleep (float) –
mosaicml_api_token (Optional[str]) –
Return type
None
attribute endpoint_url: str = 'https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict'
Endpoint URL to use.
attribute inject_instruction_format: bool = False
Whether to inject the instruction format into the prompt.
attribute model_kwargs: Optional[dict] = None
Key word arguments to pass to the model.
attribute retry_sleep: float = 1.0
How long to try sleeping for if a rate limit is encountered
attribute tags: Optional[List[str]] = None
Tags to add to the run trace.
attribute verbose: bool [Optional]
Whether to print out response text.
__call__(prompt, stop=None, callbacks=None, **kwargs)
Check Cache and run the LLM on the given prompt and input.
Parameters
prompt (str) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
kwargs (Any) –
Return type
str
async agenerate(prompts, stop=None, callbacks=None, *, tags=None, **kwargs)
Run the LLM on the given prompt and input.
Parameters
prompts (List[str]) –
stop (Optional[List[str]]) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
tags (Optional[List[str]]) –
kwargs (Any) –
Return type
langchain.schema.LLMResult


save(file_path)
Save the LLM.
Parameters
file_path (Union[pathlib.Path, str]) – Path to file to save the LLM to.
Return type
None
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns)
Try to update ForwardRefs on fields based on this Model, globalns and localns.
Parameters
localns (Any) –
Return type
None
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.
class langchain.llms.NLPCloud(*, cache=None, verbose=None, callbacks=None, callback_manager=None, tags=None, client=None, model_name='finetuned-gpt-neox-20b', temperature=0.7, min_length=1, max_length=256, length_no_input=True, remove_input=True, remove_end_sequence=True, bad_words=[], top_p=1, top_k=50, repetition_penalty=1.0, length_penalty=1.0, do_sample=True, num_beams=1, early_stopping=False, num_return_sequences=1, nlpcloud_api_key=None)[source]
Bases: langchain.llms.base.LLM
Wrapper around NLPCloud large language models.


Wrapper around NLPCloud large language models.
To use, you should have the nlpcloud python package installed, and the
environment variable NLPCLOUD_API_KEY set with your API key.
Example
from langchain.llms import NLPCloud
nlpcloud = NLPCloud(model="gpt-neox-20b")
Parameters
cache (Optional[bool]) –
verbose (bool) –
callbacks (Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]]) –
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) –
tags (Optional[List[str]]) –
client (Any) –
model_name (str) –
temperature (float) –
min_length (int) –
max_length (int) –
length_no_input (bool) –
remove_input (bool) –
remove_end_sequence (bool) –
bad_words (List[str]) –
top_p (int) –
top_k (int) –
repetition_penalty (float) –
length_penalty (float) –
do_sample (bool) –
num_beams (int) –
early_stopping (bool) –
num_return_sequences (int) –
nlpcloud_api_key (Optional[str]) –
Return type
None
attribute bad_words: List[str] = []
List of tokens not allowed to be generated.
attribute do_sample: bool = True
Whether to use sampling (True) or greedy decoding.
attribute early_stopping: bool = False
Whether to stop beam search at num_beams sentences.
attribute length_no_input: bool = True
Whether min_length and max_length should include the length of the input.