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0ca8e4bc65b9-3	Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-4	Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.AlephAlpha[source]# Wrapper around Aleph Alpha large language models. To use, you should have the aleph_alpha_client python package installed, and the environment variable ALEPH_ALPHA_API_KEY set with your API key, or pass it as a named parameter to the constructor. Parameters are explained more in depth here: Aleph-Alpha/aleph-alpha-client Example from langchain.llms import AlephAlpha alpeh_alpha = AlephAlpha(aleph_alpha_api_key="my-api-key") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field aleph_alpha_api_key: Optional[str] = None# API key for Aleph Alpha API. field best_of: Optional[int] = None# returns the one with the “best of” results (highest log probability per token) field completion_bias_exclusion_first_token_only: bool = False# Only consider the first token for the completion_bias_exclusion. field contextual_control_threshold: Optional[float] = None# If set to None, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-None value, control parameters are also applied to similar tokens. field control_log_additive: Optional[bool] = True# True: apply control by adding the log(control_factor) to attention scores. False: (attention_scores - - attention_scores.min(-1)) * control_factor field echo: bool = False# Echo the prompt in the completion. field frequency_penalty: float = 0.0# Penalizes repeated tokens according to frequency. field log_probs: Optional[int] = None#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-5	field log_probs: Optional[int] = None# Number of top log probabilities to be returned for each generated token. field logit_bias: Optional[Dict[int, float]] = None# The logit bias allows to influence the likelihood of generating tokens. field maximum_tokens: int = 64# The maximum number of tokens to be generated. field minimum_tokens: Optional[int] = 0# Generate at least this number of tokens. field model: Optional[str] = 'luminous-base'# Model name to use. field n: int = 1# How many completions to generate for each prompt. field penalty_bias: Optional[str] = None# Penalty bias for the completion. field penalty_exceptions: Optional[List[str]] = None# List of strings that may be generated without penalty, regardless of other penalty settings field penalty_exceptions_include_stop_sequences: Optional[bool] = None# Should stop_sequences be included in penalty_exceptions. field presence_penalty: float = 0.0# Penalizes repeated tokens. field raw_completion: bool = False# Force the raw completion of the model to be returned. field repetition_penalties_include_completion: bool = True# Flag deciding whether presence penalty or frequency penalty are updated from the completion. field repetition_penalties_include_prompt: Optional[bool] = False# Flag deciding whether presence penalty or frequency penalty are updated from the prompt. field stop_sequences: Optional[List[str]] = None# Stop sequences to use. field temperature: float = 0.0# A non-negative float that tunes the degree of randomness in generation. field tokens: Optional[bool] = False# return tokens of completion. field top_k: int = 0# Number of most likely tokens to consider at each step.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-6	Number of most likely tokens to consider at each step. field top_p: float = 0.0# Total probability mass of tokens to consider at each step. field use_multiplicative_presence_penalty: Optional[bool] = False# Flag deciding whether presence penalty is applied multiplicatively (True) or additively (False). field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-7	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-8	Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Anthropic[source]# Wrapper around Anthropic’s large language models.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-9	Wrapper around Anthropic’s large language models. To use, you should have the anthropic python package installed, and the environment variable ANTHROPIC_API_KEY set with your API key, or pass it as a named parameter to the constructor. Example Validators raise_deprecation » all fields raise_warning » all fields set_verbose » verbose validate_environment » all fields field default_request_timeout: Optional[Union[float, Tuple[float, float]]] = None# Timeout for requests to Anthropic Completion API. Default is 600 seconds. field max_tokens_to_sample: int = 256# Denotes the number of tokens to predict per generation. field model: str = 'claude-v1'# Model name to use. field streaming: bool = False# Whether to stream the results. field temperature: Optional[float] = None# A non-negative float that tunes the degree of randomness in generation. field top_k: Optional[int] = None# Number of most likely tokens to consider at each step. field top_p: Optional[float] = None# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-10	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-11	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int[source]# Calculate number of tokens. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, *dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-12	predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) stream(prompt: str, stop: Optional[List[str]] = None) → Generator[source]# Call Anthropic completion_stream and return the resulting generator. BETA: this is a beta feature while we figure out the right abstraction. Once that happens, this interface could change. Parameters prompt – The prompt to pass into the model. stop – Optional list of stop words to use when generating. Returns A generator representing the stream of tokens from Anthropic. Example prompt = "Write a poem about a stream." prompt = f"\n\nHuman: {prompt}\n\nAssistant:" generator = anthropic.stream(prompt) for token in generator: yield token classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Anyscale[source]# Wrapper around Anyscale Services. To use, you should have the environment variable ANYSCALE_SERVICE_URL, ANYSCALE_SERVICE_ROUTE and ANYSCALE_SERVICE_TOKEN set with your Anyscale Service, or pass it as a named parameter to the constructor. Example Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field model_kwargs: Optional[dict] = None#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-13	validate_environment » all fields field model_kwargs: Optional[dict] = None# Key word arguments to pass to the model. Reserved for future use field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-14	Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-15	get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.AzureOpenAI[source]# Wrapper around Azure-specific OpenAI large language models. To use, you should have the openai python package installed, and the environment variable OPENAI_API_KEY set with your API key. Any parameters that are valid to be passed to the openai.create call can be passed	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-16	Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class. Example from langchain.llms import AzureOpenAI openai = AzureOpenAI(model_name="text-davinci-003") Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}# Set of special tokens that are allowed。 field batch_size: int = 20# Batch size to use when passing multiple documents to generate. field best_of: int = 1# Generates best_of completions server-side and returns the “best”. field deployment_name: str = ''# Deployment name to use. field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'# Set of special tokens that are not allowed。 field frequency_penalty: float = 0# Penalizes repeated tokens according to frequency. field logit_bias: Optional[Dict[str, float]] [Optional]# Adjust the probability of specific tokens being generated. field max_retries: int = 6# Maximum number of retries to make when generating. field max_tokens: int = 256# The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size. field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field model_name: str = 'text-davinci-003' (alias 'model')# Model name to use. field n: int = 1# How many completions to generate for each prompt. field presence_penalty: float = 0#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-17	field presence_penalty: float = 0# Penalizes repeated tokens. field request_timeout: Optional[Union[float, Tuple[float, float]]] = None# Timeout for requests to OpenAI completion API. Default is 600 seconds. field streaming: bool = False# Whether to stream the results or not. field temperature: float = 0.7# What sampling temperature to use. field top_p: float = 1# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-18	Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) → langchain.schema.LLMResult# Create the LLMResult from the choices and prompts. dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-19	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) → List[List[str]]# Get the sub prompts for llm call. get_token_ids(text: str) → List[int]# Get the token IDs using the tiktoken package. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, *dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). max_tokens_for_prompt(prompt: str) → int# Calculate the maximum number of tokens possible to generate for a prompt. Parameters prompt – The prompt to pass into the model. Returns	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-20	Parameters prompt – The prompt to pass into the model. Returns The maximum number of tokens to generate for a prompt. Example max_tokens = openai.max_token_for_prompt("Tell me a joke.") modelname_to_contextsize(modelname: str) → int# Calculate the maximum number of tokens possible to generate for a model. Parameters modelname – The modelname we want to know the context size for. Returns The maximum context size Example max_tokens = openai.modelname_to_contextsize("text-davinci-003") predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. prep_streaming_params(stop: Optional[List[str]] = None) → Dict[str, Any]# Prepare the params for streaming. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) stream(prompt: str, stop: Optional[List[str]] = None) → Generator# Call OpenAI with streaming flag and return the resulting generator. BETA: this is a beta feature while we figure out the right abstraction. Once that happens, this interface could change. Parameters prompt – The prompts to pass into the model. stop – Optional list of stop words to use when generating. Returns A generator representing the stream of tokens from OpenAI. Example generator = openai.stream("Tell me a joke.") for token in generator: yield token	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-21	for token in generator: yield token classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Banana[source]# Wrapper around Banana large language models. To use, you should have the banana-dev python package installed, and the environment variable BANANA_API_KEY set with your API key. Any parameters that are valid to be passed to the call can be passed in, even if not explicitly saved on this class. Example Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field model_key: str = ''# model endpoint to use field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-22	Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-23	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-24	Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Beam[source]# Wrapper around Beam API for gpt2 large language model. To use, you should have the beam-sdk python package installed, and the environment variable BEAM_CLIENT_ID set with your client id and BEAM_CLIENT_SECRET set with your client secret. Information on how to get these is available here: https://docs.beam.cloud/account/api-keys. The wrapper can then be called as follows, where the name, cpu, memory, gpu, python version, and python packages can be updated accordingly. Once deployed, the instance can be called. llm = Beam(model_name=”gpt2”,name=”langchain-gpt2”, cpu=8, memory=”32Gi”, gpu=”A10G”, python_version=”python3.8”, python_packages=[ “diffusers[torch]>=0.10”, “transformers”, “torch”, “pillow”, “accelerate”, “safetensors”, “xformers”,], max_length=50) llm._deploy() call_result = llm._call(input) Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field url: str = ''# model endpoint to use	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-25	explicitly specified. field url: str = ''# model endpoint to use field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. app_creation() → None[source]# Creates a Python file which will contain your Beam app definition. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-26	Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-27	Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. run_creation() → None[source]# Creates a Python file which will be deployed on beam. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.CTransformers[source]# Wrapper around the C Transformers LLM interface. To use, you should have the ctransformers python package installed.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-28	To use, you should have the ctransformers python package installed. See marella/ctransformers Example from langchain.llms import CTransformers llm = CTransformers(model="/path/to/ggml-gpt-2.bin", model_type="gpt2") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field config: Optional[Dict[str, Any]] = None# The config parameters. See marella/ctransformers field lib: Optional[str] = None# The path to a shared library or one of avx2, avx, basic. field model: str [Required]# The path to a model file or directory or the name of a Hugging Face Hub model repo. field model_file: Optional[str] = None# The name of the model file in repo or directory. field model_type: Optional[str] = None# The model type. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-29	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-30	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-31	predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.CerebriumAI[source]# Wrapper around CerebriumAI large language models. To use, you should have the cerebrium python package installed, and the environment variable CEREBRIUMAI_API_KEY set with your API key. Any parameters that are valid to be passed to the call can be passed in, even if not explicitly saved on this class. Example Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field endpoint_url: str = ''# model endpoint to use field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-32	Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-33	Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-34	Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Cohere[source]# Wrapper around Cohere large language models. To use, you should have the cohere python package installed, and the environment variable COHERE_API_KEY set with your API key, or pass it as a named parameter to the constructor. Example from langchain.llms import Cohere	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-35	Example from langchain.llms import Cohere cohere = Cohere(model="gptd-instruct-tft", cohere_api_key="my-api-key") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field frequency_penalty: float = 0.0# Penalizes repeated tokens according to frequency. Between 0 and 1. field k: int = 0# Number of most likely tokens to consider at each step. field max_tokens: int = 256# Denotes the number of tokens to predict per generation. field model: Optional[str] = None# Model name to use. field p: int = 1# Total probability mass of tokens to consider at each step. field presence_penalty: float = 0.0# Penalizes repeated tokens. Between 0 and 1. field temperature: float = 0.75# A non-negative float that tunes the degree of randomness in generation. field truncate: Optional[str] = None# Specify how the client handles inputs longer than the maximum token length: Truncate from START, END or NONE field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-36	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-37	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-38	predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Databricks[source]# LLM wrapper around a Databricks serving endpoint or a cluster driver proxy app. It supports two endpoint types: Serving endpoint (recommended for both production and development). We assume that an LLM was registered and deployed to a serving endpoint. To wrap it as an LLM you must have “Can Query” permission to the endpoint. Set endpoint_name accordingly and do not set cluster_id and cluster_driver_port. The expected model signature is: inputs: [{"name": "prompt", "type": "string"}, {"name": "stop", "type": "list[string]"}] outputs: [{"type": "string"}] Cluster driver proxy app (recommended for interactive development). One can load an LLM on a Databricks interactive cluster and start a local HTTP server on the driver node to serve the model at / using HTTP POST method with JSON input/output. Please use a port number between [3000, 8000] and let the server listen to	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-39	the driver IP address or simply 0.0.0.0 instead of localhost only. To wrap it as an LLM you must have “Can Attach To” permission to the cluster. Set cluster_id and cluster_driver_port and do not set endpoint_name. The expected server schema (using JSON schema) is: inputs: {"type": "object", "properties": { "prompt": {"type": "string"}, "stop": {"type": "array", "items": {"type": "string"}}}, "required": ["prompt"]}` outputs: {"type": "string"} If the endpoint model signature is different or you want to set extra params, you can use transform_input_fn and transform_output_fn to apply necessary transformations before and after the query. Validators raise_deprecation » all fields set_cluster_driver_port » cluster_driver_port set_cluster_id » cluster_id set_model_kwargs » model_kwargs set_verbose » verbose field api_token: str [Optional]# Databricks personal access token. If not provided, the default value is determined by the DATABRICKS_API_TOKEN environment variable if present, or an automatically generated temporary token if running inside a Databricks notebook attached to an interactive cluster in “single user” or “no isolation shared” mode. field cluster_driver_port: Optional[str] = None# The port number used by the HTTP server running on the cluster driver node. The server should listen on the driver IP address or simply 0.0.0.0 to connect. We recommend the server using a port number between [3000, 8000]. field cluster_id: Optional[str] = None# ID of the cluster if connecting to a cluster driver proxy app. If neither endpoint_name nor cluster_id is not provided and the code runs	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-40	If neither endpoint_name nor cluster_id is not provided and the code runs inside a Databricks notebook attached to an interactive cluster in “single user” or “no isolation shared” mode, the current cluster ID is used as default. You must not set both endpoint_name and cluster_id. field endpoint_name: Optional[str] = None# Name of the model serving endpont. You must specify the endpoint name to connect to a model serving endpoint. You must not set both endpoint_name and cluster_id. field host: str [Optional]# Databricks workspace hostname. If not provided, the default value is determined by the DATABRICKS_HOST environment variable if present, or the hostname of the current Databricks workspace if running inside a Databricks notebook attached to an interactive cluster in “single user” or “no isolation shared” mode. field model_kwargs: Optional[Dict[str, Any]] = None# Extra parameters to pass to the endpoint. field transform_input_fn: Optional[Callable] = None# A function that transforms {prompt, stop, **kwargs} into a JSON-compatible request object that the endpoint accepts. For example, you can apply a prompt template to the input prompt. field transform_output_fn: Optional[Callable[[...], str]] = None# A function that transforms the output from the endpoint to the generated text. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-41	Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-42	Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-43	Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.DeepInfra[source]# Wrapper around DeepInfra deployed models. To use, you should have the requests python package installed, and the environment variable DEEPINFRA_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	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-44	Only supports text-generation and text2text-generation for now. Example from langchain.llms import DeepInfra di = DeepInfra(model_id="google/flan-t5-xl", deepinfra_api_token="my-api-key") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-45	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-46	Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.FakeListLLM[source]# Fake LLM wrapper for testing purposes. Validators	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-47	Fake LLM wrapper for testing purposes. Validators raise_deprecation » all fields set_verbose » verbose field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-48	Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-49	get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.ForefrontAI[source]# Wrapper around ForefrontAI large language models. To use, you should have the environment variable FOREFRONTAI_API_KEY set with your API key. Example from langchain.llms import ForefrontAI forefrontai = ForefrontAI(endpoint_url="") Validators	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-50	forefrontai = ForefrontAI(endpoint_url="") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field base_url: Optional[str] = None# Base url to use, if None decides based on model name. field endpoint_url: str = ''# Model name to use. field length: int = 256# The maximum number of tokens to generate in the completion. field repetition_penalty: int = 1# Penalizes repeated tokens according to frequency. field temperature: float = 0.7# What sampling temperature to use. field top_k: int = 40# The number of highest probability vocabulary tokens to keep for top-k-filtering. field top_p: float = 1.0# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-51	Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-52	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-53	Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.GPT4All[source]# Wrapper around GPT4All language models. To use, you should have the gpt4all python package installed, the pre-trained model file, and the model’s config information. Example from langchain.llms import GPT4All model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8) # Simplest invocation response = model("Once upon a time, ") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field context_erase: float = 0.5# Leave (n_ctx context_erase) tokens starting from beginning if the context has run out. field echo: Optional[bool] = False# Whether to echo the prompt. field embedding: bool = False# Use embedding mode only. field f16_kv: bool = False# Use half-precision for key/value cache. field logits_all: bool = False# Return logits for all tokens, not just the last token. field model: str [Required]# Path to the pre-trained GPT4All model file. field n_batch: int = 1# Batch size for prompt processing. field n_ctx: int = 512# Token context window. field n_parts: int = -1# Number of parts to split the model into.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-54	field n_parts: int = -1# Number of parts to split the model into. If -1, the number of parts is automatically determined. field n_predict: Optional[int] = 256# The maximum number of tokens to generate. field n_threads: Optional[int] = 4# Number of threads to use. field repeat_last_n: Optional[int] = 64# Last n tokens to penalize field repeat_penalty: Optional[float] = 1.3# The penalty to apply to repeated tokens. field seed: int = 0# Seed. If -1, a random seed is used. field stop: Optional[List[str]] = []# A list of strings to stop generation when encountered. field streaming: bool = False# Whether to stream the results or not. field temp: Optional[float] = 0.8# The temperature to use for sampling. field top_k: Optional[int] = 40# The top-k value to use for sampling. field top_p: Optional[float] = 0.95# The top-p value to use for sampling. field use_mlock: bool = False# Force system to keep model in RAM. field verbose: bool [Optional]# Whether to print out response text. field vocab_only: bool = False# Only load the vocabulary, no weights. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-55	Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-56	Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-57	Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.GooglePalm[source]# Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field 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.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-58	If unset, will default to 64. field model_name: str = 'models/text-bison-001'# Model name to use. field 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. field temperature: float = 0.7# Run inference with this temperature. Must by in the closed interval [0.0, 1.0]. field top_k: Optional[int] = None# Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive. field 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]. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-59	Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-60	Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-61	Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.GooseAI[source]# 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 Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field frequency_penalty: float = 0# Penalizes repeated tokens according to frequency. field logit_bias: Optional[Dict[str, float]] [Optional]# Adjust the probability of specific tokens being generated. field max_tokens: int = 256# The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size. field min_tokens: int = 1# The minimum number of tokens to generate in the completion. field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field model_name: str = 'gpt-neo-20b'# Model name to use field n: int = 1# How many completions to generate for each prompt. field presence_penalty: float = 0# Penalizes repeated tokens. field temperature: float = 0.7#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-62	Penalizes repeated tokens. field temperature: float = 0.7# What sampling temperature to use field top_p: float = 1# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-63	Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-64	Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.HuggingFaceEndpoint[source]# Wrapper around HuggingFaceHub Inference Endpoints. To use, you should have the huggingface_hub python package installed, and the	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-65	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" ) Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field endpoint_url: str = ''# Endpoint URL to use. field model_kwargs: Optional[dict] = None# Key word arguments to pass to the model. field task: Optional[str] = None# Task to call the model with. Should be a task that returns generated_text or summary_text. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-66	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-67	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-68	predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.HuggingFaceHub[source]# 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") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field model_kwargs: Optional[dict] = None# Key word arguments to pass to the model. field repo_id: str = 'gpt2'# Model name to use. field task: Optional[str] = None# Task to call the model with. Should be a task that returns generated_text or summary_text. field verbose: bool [Optional]# Whether to print out response text.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-69	field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-70	Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-71	get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.HuggingFacePipeline[source]# 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	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-72	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 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) Validators raise_deprecation » all fields set_verbose » verbose field model_id: str = 'gpt2'# Model name to use. field model_kwargs: Optional[dict] = None# Key word arguments passed to the model. field pipeline_kwargs: Optional[dict] = None# Key word arguments passed to the pipeline. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-73	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-74	Returns new model instance dict(kwargs: Any) → Dict# Return a dictionary of the LLM. classmethod from_model_id(model_id: str, task: str, device: int = - 1, model_kwargs: Optional[dict] = None, pipeline_kwargs: Optional[dict] = None, kwargs: Any) → langchain.llms.base.LLM[source]# Construct the pipeline object from model_id and task. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-75	Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.HuggingFaceTextGenInference[source]# 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.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-76	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. - client: The client object used to communicate with the inference server. Methods: - _call: Generates text based on a given prompt and stop sequences. - _llm_type: Returns the type of LLM. Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-77	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-78	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-79	predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.HumanInputLLM[source]# A LLM wrapper which returns user input as the response. Validators raise_deprecation » all fields set_verbose » verbose field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-80	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-81	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-82	predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.LlamaCpp[source]# 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: abetlen/llama-cpp-python Example from langchain.llms import LlamaCppEmbeddings llm = LlamaCppEmbeddings(model_path="/path/to/llama/model") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field echo: Optional[bool] = False# Whether to echo the prompt. field f16_kv: bool = True# Use half-precision for key/value cache. field last_n_tokens_size: Optional[int] = 64# The number of tokens to look back when applying the repeat_penalty. field logits_all: bool = False# Return logits for all tokens, not just the last token. field logprobs: Optional[int] = None# The number of logprobs to return. If None, no logprobs are returned.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-83	The number of logprobs to return. If None, no logprobs are returned. field lora_base: Optional[str] = None# The path to the Llama LoRA base model. field lora_path: Optional[str] = None# The path to the Llama LoRA. If None, no LoRa is loaded. field max_tokens: Optional[int] = 256# The maximum number of tokens to generate. field model_path: str [Required]# The path to the Llama model file. field n_batch: Optional[int] = 8# Number of tokens to process in parallel. Should be a number between 1 and n_ctx. field n_ctx: int = 512# Token context window. field n_gpu_layers: Optional[int] = None# Number of layers to be loaded into gpu memory. Default None. field n_parts: int = -1# Number of parts to split the model into. If -1, the number of parts is automatically determined. field n_threads: Optional[int] = None# Number of threads to use. If None, the number of threads is automatically determined. field repeat_penalty: Optional[float] = 1.1# The penalty to apply to repeated tokens. field seed: int = -1# Seed. If -1, a random seed is used. field stop: Optional[List[str]] = []# A list of strings to stop generation when encountered. field streaming: bool = True# Whether to stream the results, token by token. field suffix: Optional[str] = None# A suffix to append to the generated text. If None, no suffix is appended. field temperature: Optional[float] = 0.8# The temperature to use for sampling. field top_k: Optional[int] = 40#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-84	The temperature to use for sampling. field top_k: Optional[int] = 40# The top-k value to use for sampling. field top_p: Optional[float] = 0.95# The top-p value to use for sampling. field use_mlock: bool = False# Force system to keep model in RAM. field use_mmap: Optional[bool] = True# Whether to keep the model loaded in RAM field verbose: bool [Optional]# Whether to print out response text. field vocab_only: bool = False# Only load the vocabulary, no weights. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-85	Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-86	Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int[source]# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”)	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-87	.. code-block:: python llm.save(file_path=”path/llm.yaml”) stream(prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[langchain.callbacks.manager.CallbackManagerForLLMRun] = None) → Generator[Dict, None, 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. 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) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.Modal[source]# 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 Validators	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-88	in, even if not explicitly saved on this class. Example Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose field endpoint_url: str = ''# model endpoint to use field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-89	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-90	Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(*localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.MosaicML[source]# Wrapper around MosaicML’s LLM inference service.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-91	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" ) Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field endpoint_url: str = 'https://models.hosted-on.mosaicml.hosting/mpt-7b-instruct/v1/predict'# Endpoint URL to use. field inject_instruction_format: bool = False# Whether to inject the instruction format into the prompt. field model_kwargs: Optional[dict] = None# Key word arguments to pass to the model. field retry_sleep: float = 1.0# How long to try sleeping for if a rate limit is encountered field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-92	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-93	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-94	predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.NLPCloud[source]# 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") Validators raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field bad_words: List[str] = []# List of tokens not allowed to be generated. field do_sample: bool = True# Whether to use sampling (True) or greedy decoding. field early_stopping: bool = False# Whether to stop beam search at num_beams sentences. field length_no_input: bool = True# Whether min_length and max_length should include the length of the input. field length_penalty: float = 1.0# Exponential penalty to the length. field max_length: int = 256# The maximum number of tokens to generate in the completion.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-95	The maximum number of tokens to generate in the completion. field min_length: int = 1# The minimum number of tokens to generate in the completion. field model_name: str = 'finetuned-gpt-neox-20b'# Model name to use. field num_beams: int = 1# Number of beams for beam search. field num_return_sequences: int = 1# How many completions to generate for each prompt. field remove_end_sequence: bool = True# Whether or not to remove the end sequence token. field remove_input: bool = True# Remove input text from API response field repetition_penalty: float = 1.0# Penalizes repeated tokens. 1.0 means no penalty. field temperature: float = 0.7# What sampling temperature to use. field top_k: int = 50# The number of highest probability tokens to keep for top-k filtering. field top_p: int = 1# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-96	Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance dict(**kwargs: Any) → Dict#	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-97	Returns new model instance dict(*kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_token_ids(text: str) → List[int]# Get the token present in the text. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-98	predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. save(file_path: Union[pathlib.Path, str]) → None# Save the LLM. Parameters file_path – Path to file to save the LLM to. Example: .. code-block:: python llm.save(file_path=”path/llm.yaml”) classmethod update_forward_refs(**localns: Any) → None# Try to update ForwardRefs on fields based on this Model, globalns and localns. pydantic model langchain.llms.OpenAI[source]# Wrapper around OpenAI large language models. To use, you should have the openai python package installed, and the environment variable OPENAI_API_KEY set with your API key. Any parameters that are valid to be passed to the openai.create call can be passed in, even if not explicitly saved on this class. Example from langchain.llms import OpenAI openai = OpenAI(model_name="text-davinci-003") Validators build_extra » all fields raise_deprecation » all fields set_verbose » verbose validate_environment » all fields field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}# Set of special tokens that are allowed。 field batch_size: int = 20# Batch size to use when passing multiple documents to generate. field best_of: int = 1# Generates best_of completions server-side and returns the “best”. field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'# Set of special tokens that are not allowed。	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-99	Set of special tokens that are not allowed。 field frequency_penalty: float = 0# Penalizes repeated tokens according to frequency. field logit_bias: Optional[Dict[str, float]] [Optional]# Adjust the probability of specific tokens being generated. field max_retries: int = 6# Maximum number of retries to make when generating. field max_tokens: int = 256# The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size. field model_kwargs: Dict[str, Any] [Optional]# Holds any model parameters valid for create call not explicitly specified. field model_name: str = 'text-davinci-003' (alias 'model')# Model name to use. field n: int = 1# How many completions to generate for each prompt. field presence_penalty: float = 0# Penalizes repeated tokens. field request_timeout: Optional[Union[float, Tuple[float, float]]] = None# Timeout for requests to OpenAI completion API. Default is 600 seconds. field streaming: bool = False# Whether to stream the results or not. field temperature: float = 0.7# What sampling temperature to use. field top_p: float = 1# Total probability mass of tokens to consider at each step. field verbose: bool [Optional]# Whether to print out response text. __call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → str# Check Cache and run the LLM on the given prompt and input.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-100	Check Cache and run the LLM on the given prompt and input. async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. async apredict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. async apredict_messages(messages: List[langchain.schema.BaseMessage], , stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. classmethod construct(_fields_set: Optional[SetStr] = None, *values: Any) → Model# Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values copy(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# Duplicate a model, optionally choose which fields to include, exclude and change. Parameters include – fields to include in new model	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-101	Parameters include – fields to include in new model exclude – fields to exclude from new model, as with values this takes precedence over include update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data deep – set to True to make a deep copy of the model Returns new model instance create_llm_result(choices: Any, prompts: List[str], token_usage: Dict[str, int]) → langchain.schema.LLMResult# Create the LLMResult from the choices and prompts. dict(**kwargs: Any) → Dict# Return a dictionary of the LLM. generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Run the LLM on the given prompt and input. generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) → langchain.schema.LLMResult# Take in a list of prompt values and return an LLMResult. get_num_tokens(text: str) → int# Get the number of tokens present in the text. get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int# Get the number of tokens in the message. get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) → List[List[str]]# Get the sub prompts for llm call. get_token_ids(text: str) → List[int]# Get the token IDs using the tiktoken package.	https://python.langchain.com/en/latest/reference/modules/llms.html
0ca8e4bc65b9-102	Get the token IDs using the tiktoken package. json(, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, dumps_kwargs: Any) → unicode# Generate a JSON representation of the model, include and exclude arguments as per dict(). encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). max_tokens_for_prompt(prompt: str) → int# Calculate the maximum number of tokens possible to generate for a prompt. Parameters prompt – The prompt to pass into the model. Returns The maximum number of tokens to generate for a prompt. Example max_tokens = openai.max_token_for_prompt("Tell me a joke.") modelname_to_contextsize(modelname: str) → int# Calculate the maximum number of tokens possible to generate for a model. Parameters modelname – The modelname we want to know the context size for. Returns The maximum context size Example max_tokens = openai.modelname_to_contextsize("text-davinci-003") predict(text: str, , stop: Optional[Sequence[str]] = None) → str# Predict text from text. predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) → langchain.schema.BaseMessage# Predict message from messages. prep_streaming_params(stop: Optional[List[str]] = None) → Dict[str, Any]# Prepare the params for streaming.	https://python.langchain.com/en/latest/reference/modules/llms.html