id
stringlengths 14
16
| text
stringlengths 36
2.73k
| source
stringlengths 49
117
|
|---|---|---|
2aa15396c9dd-65 | 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 Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field allow_download: bool = False#
If model does not exist in ~/.cache/gpt4all/, download it.
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.
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# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-66 | 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-67 | Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-68 | 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-69 | Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.GooglePalm[source]#
Validators
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» 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.
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) β str# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-70 | 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-71 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-72 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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 | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-73 | in, even if not explicitly saved on this class.
Example
from langchain.llms import GooseAI
gooseai = GooseAI(model_name="gpt-neo-20b")
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» 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#
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) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-74 | 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-75 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-76 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as a named parameter to the constructor. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-77 | 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 Β» all fields
set_verbose Β» verbose
validate_environment Β» 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-78 | Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-79 | 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-80 | Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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 Β» all fields
set_verbose Β» verbose
validate_environment Β» 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.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-81 | 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-82 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-83 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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
hf = HuggingFacePipeline.from_model_id( | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-84 | 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 Β» all fields
set_verbose Β» 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) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-85 | 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-86 | Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-87 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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 |
2aa15396c9dd-88 | 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 Β» all fields
set_verbose Β» verbose
validate_environment Β» 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) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-89 | 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-90 | Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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 |
2aa15396c9dd-91 | predict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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 Β» all fields
set_verbose Β» 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) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-93 | Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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 |
2aa15396c9dd-94 | predict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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 Β» all fields
set_verbose Β» verbose
validate_environment Β» 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 |
2aa15396c9dd-95 | 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 |
2aa15396c9dd-96 | 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-97 | Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-98 | Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int[source]#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ) | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-99 | .. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[langchain.callbacks.manager.CallbackManagerForLLMRun] = None) β 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 managerβs 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=["'","
β]):result = chunk[βchoicesβ][0]
print(result[βtextβ], end=ββ, flush=True)
classmethod update_forward_refs(**localns: Any) β 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 | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-100 | in, even if not explicitly saved on this class.
Example
from langchain.llms import Modal
modal = Modal(endpoint_url="")
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-101 | Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-102 | Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-103 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.MosaicML[source]#
Wrapper around MosaicMLβs 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 Β» all fields
set_verbose Β» verbose
validate_environment Β» 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) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-104 | 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-105 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-106 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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") | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-107 | nlpcloud = NLPCloud(model="gpt-neox-20b")
Validators
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» 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.
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]# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-108 | 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-109 | Behaves as if Config.extra = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-110 | get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β 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 | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-111 | 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 Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}#
Set of special tokens that are allowedγ
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 βbestβ.
field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'#
Set of special tokens that are not allowedγ
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. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-112 | 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-113 | Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β 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]) β langchain.schema.LLMResult#
Create the LLMResult from the choices and prompts.
dict(**kwargs: Any) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-114 | 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) β List[List[str]]#
Get the sub prompts for llm call.
get_token_ids(text: str) β 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) β 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) β int#
Calculate the maximum number of tokens possible to generate for a prompt.
Parameters
prompt β The prompt to pass into the model.
Returns | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-115 | Parameters
prompt β 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) β int#
Calculate the maximum number of tokens possible to generate for a model.
Parameters
modelname β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
prep_streaming_params(stop: Optional[List[str]] = None) β Dict[str, Any]#
Prepare the params for streaming.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None) β 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 β The prompts to pass into the model.
stop β 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 |
2aa15396c9dd-116 | for token in generator:
yield token
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.OpenAIChat[source]#
Wrapper around OpenAI Chat large language models.
To use, you should have the openai python package installed, and the
environment variable OPENAI_API_KEY set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.llms import OpenAIChat
openaichat = OpenAIChat(model_name="gpt-3.5-turbo")
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}#
Set of special tokens that are allowedγ
field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'#
Set of special tokens that are not allowedγ
field max_retries: int = 6#
Maximum number of retries to make when generating.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not explicitly specified.
field model_name: str = 'gpt-3.5-turbo'#
Model name to use.
field prefix_messages: List [Optional]#
Series of messages for Chat input.
field streaming: bool = False#
Whether to stream the results or not.
field verbose: bool [Optional]#
Whether to print out response text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-117 | 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-118 | Behaves as if Config.extra = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int][source]# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-119 | get_token_ids(text: str) β List[int][source]#
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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.OpenLM[source]#
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}#
Set of special tokens that are allowedγ | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-120 | Set of special tokens that are allowedγ
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 βbestβ.
field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'#
Set of special tokens that are not allowedγ
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. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-121 | 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-122 | Behaves as if Config.extra = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β 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]) β langchain.schema.LLMResult#
Create the LLMResult from the choices and prompts.
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-123 | Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) β List[List[str]]#
Get the sub prompts for llm call.
get_token_ids(text: str) β 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) β 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) β int#
Calculate the maximum number of tokens possible to generate for a prompt.
Parameters
prompt β 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) β int#
Calculate the maximum number of tokens possible to generate for a model.
Parameters
modelname β 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") | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-124 | max_tokens = openai.modelname_to_contextsize("text-davinci-003")
predict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
prep_streaming_params(stop: Optional[List[str]] = None) β Dict[str, Any]#
Prepare the params for streaming.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None) β 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 β The prompts to pass into the model.
stop β Optional list of stop words to use when generating.
Returns
A generator representing the stream of tokens from OpenAI.
Example
generator = openai.stream("Tell me a joke.")
for token in generator:
yield token
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Petals[source]#
Wrapper around Petals Bloom models.
To use, you should have the petals python package installed, and the
environment variable HUGGINGFACE_API_KEY set with your API key.
Any parameters that are valid to be passed to the call can be passed | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-125 | Any parameters that are valid to be passed to the call can be passed
in, even if not explicitly saved on this class.
Example
from langchain.llms import petals
petals = Petals()
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field client: Any = None#
The client to use for the API calls.
field do_sample: bool = True#
Whether or not to use sampling; use greedy decoding otherwise.
field max_length: Optional[int] = None#
The maximum length of the sequence to be generated.
field max_new_tokens: int = 256#
The maximum number of new 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 = 'bigscience/bloom-petals'#
The model to use.
field temperature: float = 0.7#
What sampling temperature to use
field tokenizer: Any = None#
The tokenizer to use for the API calls.
field top_k: Optional[int] = None#
The number of highest probability vocabulary tokens
to keep for top-k-filtering.
field top_p: float = 0.9#
The cumulative probability for top-p sampling.
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) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-126 | 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-127 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-128 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PipelineAI[source]#
Wrapper around PipelineAI large language models.
To use, you should have the pipeline-ai python package installed,
and the environment variable PIPELINE_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 | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-129 | in, even if not explicitly saved on this class.
Example
from langchain import PipelineAI
pipeline = PipelineAI(pipeline_key="")
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field pipeline_key: str = ''#
The id or tag of the target pipeline
field pipeline_kwargs: Dict[str, Any] [Optional]#
Holds any pipeline 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-130 | Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-131 | Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-132 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PredictionGuard[source]#
Wrapper around Prediction Guard large language models.
To use, you should have the predictionguard python package installed, and the
environment variable PREDICTIONGUARD_TOKEN set with your access token, or pass
it as a named parameter to the constructor. To use Prediction Guardβs API along
with OpenAI models, set the environment variable OPENAI_API_KEY with your
OpenAI API key as well.
Example
pgllm = PredictionGuard(model="MPT-7B-Instruct",
token="my-access-token",
output={
"type": "boolean"
})
Validators
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field max_tokens: int = 256#
Denotes the number of tokens to predict per generation.
field model: Optional[str] = 'MPT-7B-Instruct'#
Model name to use.
field output: Optional[Dict[str, Any]] = None#
The output type or structure for controlling the LLM output.
field temperature: float = 0.75#
A non-negative float that tunes the degree of randomness in generation.
field token: Optional[str] = None#
Your Prediction Guard access token.
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) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-133 | 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-134 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-135 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PromptLayerOpenAI[source]#
Wrapper around OpenAI large language models.
To use, you should have the openai and promptlayer python
package installed, and the environment variable OPENAI_API_KEY
and PROMPTLAYER_API_KEY set with your openAI API key and
promptlayer key respectively. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-136 | promptlayer key respectively.
All parameters that can be passed to the OpenAI LLM can also
be passed here. The PromptLayerOpenAI LLM adds two optional
Parameters
pl_tags β List of strings to tag the request with.
return_pl_id β If True, the PromptLayer request ID will be
returned in the generation_info field of the
Generation object.
Example
from langchain.llms import PromptLayerOpenAI
openai = PromptLayerOpenAI(model_name="text-davinci-003")
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-137 | Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β 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]) β langchain.schema.LLMResult#
Create the LLMResult from the choices and prompts.
dict(**kwargs: Any) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-138 | 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_sub_prompts(params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None) β List[List[str]]#
Get the sub prompts for llm call.
get_token_ids(text: str) β 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) β 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) β int#
Calculate the maximum number of tokens possible to generate for a prompt.
Parameters
prompt β The prompt to pass into the model.
Returns | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-139 | Parameters
prompt β 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) β int#
Calculate the maximum number of tokens possible to generate for a model.
Parameters
modelname β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
prep_streaming_params(stop: Optional[List[str]] = None) β Dict[str, Any]#
Prepare the params for streaming.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
stream(prompt: str, stop: Optional[List[str]] = None) β 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 β The prompts to pass into the model.
stop β 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 |
2aa15396c9dd-140 | for token in generator:
yield token
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.PromptLayerOpenAIChat[source]#
Wrapper around OpenAI large language models.
To use, you should have the openai and promptlayer python
package installed, and the environment variable OPENAI_API_KEY
and PROMPTLAYER_API_KEY set with your openAI API key and
promptlayer key respectively.
All parameters that can be passed to the OpenAIChat LLM can also
be passed here. The PromptLayerOpenAIChat adds two optional
Parameters
pl_tags β List of strings to tag the request with.
return_pl_id β If True, the PromptLayer request ID will be
returned in the generation_info field of the
Generation object.
Example
from langchain.llms import PromptLayerOpenAIChat
openaichat = PromptLayerOpenAIChat(model_name="gpt-3.5-turbo")
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field allowed_special: Union[Literal['all'], AbstractSet[str]] = {}#
Set of special tokens that are allowedγ
field disallowed_special: Union[Literal['all'], Collection[str]] = 'all'#
Set of special tokens that are not allowedγ
field max_retries: int = 6#
Maximum number of retries to make when generating.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not explicitly specified.
field model_name: str = 'gpt-3.5-turbo'#
Model name to use.
field prefix_messages: List [Optional]# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-141 | Model name to use.
field prefix_messages: List [Optional]#
Series of messages for Chat input.
field streaming: bool = False#
Whether to stream the results or not.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-142 | Behaves as if Config.extra = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]# | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-143 | get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.RWKV[source]#
Wrapper around RWKV language models.
To use, you should have the rwkv python package installed, the
pre-trained model file, and the modelβs config information.
Example
from langchain.llms import RWKV | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-144 | Example
from langchain.llms import RWKV
model = RWKV(model="./models/rwkv-3b-fp16.bin", strategy="cpu fp32")
# Simplest invocation
response = model("Once upon a time, ")
Validators
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field CHUNK_LEN: int = 256#
Batch size for prompt processing.
field max_tokens_per_generation: int = 256#
Maximum number of tokens to generate.
field model: str [Required]#
Path to the pre-trained RWKV model file.
field penalty_alpha_frequency: float = 0.4#
Positive values penalize new tokens based on their existing frequency
in the text so far, decreasing the modelβs likelihood to repeat the same
line verbatim..
field penalty_alpha_presence: float = 0.4#
Positive values penalize new tokens based on whether they appear
in the text so far, increasing the modelβs likelihood to talk about
new topics..
field rwkv_verbose: bool = True#
Print debug information.
field strategy: str = 'cpu fp32'#
Token context window.
field temperature: float = 1.0#
The temperature to use for sampling.
field tokens_path: str [Required]#
Path to the RWKV tokens file.
field top_p: float = 0.5#
The top-p value to use for sampling.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-145 | 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-146 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-147 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Replicate[source]#
Wrapper around Replicate models.
To use, you should have the replicate python package installed,
and the environment variable REPLICATE_API_TOKEN set with your API token.
You can find your token here: https://replicate.com/account
The model param is required, but any other model parameters can also | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-148 | The model param is required, but any other model parameters can also
be passed in with the format input={model_param: value, β¦}
Example
from langchain.llms import Replicate
replicate = Replicate(model="stability-ai/stable-diffusion: 27b93a2413e7f36cd83da926f365628 0b2931564ff050bf9575f1fdf9bcd7478",
input={"image_dimensions": "512x512"})
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» 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) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-149 | Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-150 | 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-151 | Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SagemakerEndpoint[source]#
Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
Validators
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field content_handler: langchain.llms.sagemaker_endpoint.LLMContentHandler [Required]#
The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
field credentials_profile_name: Optional[str] = None#
The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-152 | See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
field endpoint_kwargs: Optional[Dict] = None#
Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
field endpoint_name: str = ''#
The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region.
field model_kwargs: Optional[Dict] = None#
Key word arguments to pass to the model.
field region_name: str = ''#
The aws region where the Sagemaker model is deployed, eg. us-west-2.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-153 | Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β 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) β langchain.schema.LLMResult#
Run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-154 | 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-155 | Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SelfHostedHuggingFaceLLM[source]#
Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Only supports text-generation, text2text-generation and summarization for now.
Example using from_model_id:from langchain.llms import SelfHostedHuggingFaceLLM
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-large", task="text2text-generation",
hardware=gpu
)
Example passing fn that generates a pipeline (bc the pipeline is not serializable):from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipeline():
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer
) | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-156 | "text-generation", model=model, tokenizer=tokenizer
)
return pipe
hf = SelfHostedHuggingFaceLLM(
model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
Validators
raise_deprecation Β» all fields
set_verbose Β» verbose
field device: int = 0#
Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc.
field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _generate_text>#
Inference function to send to the remote hardware.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_id: str = 'gpt2'#
Hugging Face model_id to load the model.
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
field model_load_fn: Callable = <function _load_transformer>#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'transformers', 'torch']#
Requirements to install on hardware to inference the model.
field task: str = 'text-generation'#
Hugging Face task (βtext-generationβ, βtext2text-generationβ or
βsummarizationβ).
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) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-157 | 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-158 | Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) β langchain.llms.base.LLM#
Init the SelfHostedPipeline from a pipeline object or string.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β List[int]#
Get the token present in the text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-159 | 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) β 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) β str#
Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SelfHostedPipeline[source]#
Run model inference on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.). | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-160 | cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example for custom pipeline and inference functions:from langchain.llms import SelfHostedPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def load_pipeline():
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
return pipeline(
"text-generation", model=model, tokenizer=tokenizer,
max_new_tokens=10
)
def inference_fn(pipeline, prompt, stop = None):
return pipeline(prompt)[0]["generated_text"]
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
llm = SelfHostedPipeline(
model_load_fn=load_pipeline,
hardware=gpu,
model_reqs=model_reqs, inference_fn=inference_fn
)
Example for <2GB model (can be serialized and sent directly to the server):from langchain.llms import SelfHostedPipeline
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
my_model = ...
llm = SelfHostedPipeline.from_pipeline(
pipeline=my_model,
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
)
Example passing model path for larger models:from langchain.llms import SelfHostedPipeline
import runhouse as rh
import pickle
from transformers import pipeline
generator = pipeline(model="gpt2")
rh.blob(pickle.dumps(generator), path="models/pipeline.pkl"
).save().to(gpu, path="models")
llm = SelfHostedPipeline.from_pipeline( | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-161 | llm = SelfHostedPipeline.from_pipeline(
pipeline="models/pipeline.pkl",
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
)
Validators
raise_deprecation Β» all fields
set_verbose Β» verbose
field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _generate_text>#
Inference function to send to the remote hardware.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_load_fn: Callable [Required]#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'torch']#
Requirements to install on hardware to inference the model.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β 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) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-162 | Take in a list of prompt values and return an LLMResult.
async apredict(text: str, *, stop: Optional[Sequence[str]] = None) β str#
Predict text from text.
async apredict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) β 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 = βallowβ 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) β Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include β fields to include in new model
exclude β fields to exclude from new model, as with values this takes precedence over include
update β 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 β set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) β Dict#
Return a dictionary of the LLM.
classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) β langchain.llms.base.LLM[source]#
Init the SelfHostedPipeline from a pipeline object or string. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-163 | Init the SelfHostedPipeline from a pipeline object or string.
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β 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) β langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) β int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) β int#
Get the number of tokens in the message.
get_token_ids(text: str) β 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) β 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) β str#
Predict text from text. | https://python.langchain.com/en/latest/reference/modules/llms.html |
2aa15396c9dd-164 | Predict text from text.
predict_messages(messages: List[langchain.schema.BaseMessage], *, stop: Optional[Sequence[str]] = None) β langchain.schema.BaseMessage#
Predict message from messages.
save(file_path: Union[pathlib.Path, str]) β None#
Save the LLM.
Parameters
file_path β Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=βpath/llm.yamlβ)
classmethod update_forward_refs(**localns: Any) β None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.StochasticAI[source]#
Wrapper around StochasticAI large language models.
To use, you should have the environment variable STOCHASTICAI_API_KEY
set with your API key.
Example
from langchain.llms import StochasticAI
stochasticai = StochasticAI(api_url="")
Validators
build_extra Β» all fields
raise_deprecation Β» all fields
set_verbose Β» verbose
validate_environment Β» all fields
field api_url: str = ''#
Model name to use.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not
explicitly specified.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None) β str#
Check Cache and run the LLM on the given prompt and input. | https://python.langchain.com/en/latest/reference/modules/llms.html |
Subsets and Splits