👻 Classes#

class promptmeteo.APIGenerator(language: str, model_name: str, model_provider_name: str, api_version: str, api_protocol: str, api_style_instructions: List[str] | None, **kwargs)#

API Generator Task.

This class initializes the APIGenerator Task to create APIs based on descriptions.

Parameters:

language (str) – Language for the API descriptions.
model_name (str) – Name of the model to be used for API generation.
model_provider_name (str) – Name of the model provider.
api_version (str) – Version of the API.
api_protocol (str) – Protocol of the API.
api_style_instructions (Optional[List[str]]) – Instructions for API style.
**kwargs (dict) – Additional keyword arguments.
Raises –
------ –
ValueError – If api_protocol is not in the allowed protocols or if api_version is not in the correct format.
Example –
------- –
APIGenerator (>>> from promptmeteo import) –
APIGenerator( (>>> model =) –
language="en" (...) –

:param : :param … selector_k=5: :param : :param … api_version=”3.0.3”: :param : :param … api_protocol=”REST”: :param : :param … api_style_instructions=[“Use always camel case.”: :param “Do not use acronyms.”]: :param : :param … model_provider_name=”openai”: :param : :param … model_name=”gpt-3.5-turbo-16k”: :param : :param … model_provider_token=model_token: :param : :param … ): :param >>> model.train(api_description: :param api_code): :param >>> model.predict(“API for managing account access”):

train(api_descriptions: List[str], api_codes: List[str]) → Self#

Trains the model given use cases and notes on behaviour. Check the: parameters and task behaviour in each specific model training docstring.

Train the APIGenerator from a list of APIs descriptions and contracts.

api_descriptionsList[str]
List of API descriptions.

api_codesList[str]
List of API codes - contracts.

APIGenerator
Returns the trained APIGenerator object.

class promptmeteo.APIFormatter(language: str, model_name: str, model_provider_name: str, api_version: str, api_protocol: str, api_style_instructions: List[str] = None, **kwargs)#

API Formatter Task.

This class initializes the API Formatter Task to correct and format APIs.

Parameters:

language (str) – Language for the API descriptions.
model_name (str) – Name of the model to be used for API generation.
model_provider_name (str) – Name of the model provider.
api_version (str) – Version of the API.
api_protocol (str) – Protocol of the API.
api_style_instructions (Optional[List[str]]) – Instructions for API style.
**kwargs (dict) – Additional keyword arguments.
Raises –
------ –
ValueError – If api_protocol is not in the allowed protocols or if api_version is not in the correct format.
Example –
------- –
APIFormatter (>>> from promptmeteo import) –
APIFormatter( (>>> model =) –
language="en" (...) –

:param : :param … api_version=”3.0.3”: :param : :param … api_protocol=”REST”: :param : :param … api_style_instructions=[“Use always camel case.”: :param “Do not use acronyms.”]: :param : :param … model_provider_name=”openai”: :param : :param … model_name=”gpt-3.5-turbo-16k”: :param : :param … model_provider_token=model_token: :param : :param … external_info={: :param … “servers”: :type … “servers”: [ :param … {: :param … “url”: :type … “url”: “http://localhost:8080/”, :param … “description”: :type … “description”: “Local environment”, :param … }: :param … ]: :param : :param … }: :param : :param … ): :param >>> model.train(api_code): :param >>> model.predict(api):

classmethod load_model(model_path: str) → Self#

Load a saved model from disk.

model_pathstr
Path from where the model will be loaded.

Base
Loaded model instance.

Loads a model artifact to make new predictions.

model_pathstr
Path to the model artifact.

APIFormatter
Returns the loaded APIFormatter object.

predict(api_codes: List[str], external_info: dict) → List[str]#

Predict over new text samples.

examplesList[str]
List of text samples to predict.

List[str]
List of predictions.

Receive a list of API codes and return a list with the corrected APIs.

api_codesList[str]
List of API codes.

external_infodict
External information to be added to the APIs.

List[str]
List of corrected APIs.

train(api_codes: List[str]) → Self#

Trains the model given use cases and notes on behaviour. Check the: parameters and task behaviour in each specific model training docstring.

Train the APIFormatter to extract entities and parameters.

api_codesList[str]
List of API codes.

APIFormatter
Returns the trained APIFormatter object.

class promptmeteo.CodeGenerator(**kwargs)#

Code Generator Task.

This class represents a model for generating code based on natural language descriptions.

Parameters:

**kwargs (dict) – Additional keyword arguments.
Example –
------- –
CodeGenerator (>>> from promptmeteo import) –
CodeGenerator( (>>> model =) –
language="en" (...) –

:param : :param … prompt_domain=”python”: :param : :param … model_provider_name=”openai”: :param : :param … model_name=”text-davinci-003”: :param : :param … model_provider_token=model_token: :param : :param … prompt_detail=[: :param … “add docstring in function definitions”: :param : :param … “add argument typing annotations”: :param : :param … ]: :param : :param … ): :param >>> pred = model.predict(: :param … [“A function that receives the argument foo and prints it.”]: :param … ): :param Returns: :param ——-: :param None:

class promptmeteo.DocumentClassifier(**kwargs)#

DocumentClassifier Task

This class represents a model for classifying documents into predefined categories.

Example:#

>>> from promptmeteo import DocumentClassifier
>>> clf = DocumentClassifier(
...     model_provider_name="hf_pipeline",
...     model_name="google/flan-t5-small",
...     prompt_labels=["positive", "negative", "neutral"],
... )

>>> clf.train(
...     examples=["estoy feliz", "me da igual", "no me gusta"],
...     annotations=["positive", "neutral", "negative"],
... )

>>> clf.predict(["que guay!!"])

>>> [["positive"]]

train(examples: List[str], annotations: List[str]) → Self#

Trains the model given use cases and notes on behaviour. Check the: parameters and task behaviour in each specific model training docstring.

Trains the DocumentClassifier model.

examplesList[str]
List of document examples.

annotationsList[str]
List of corresponding annotations.

Self

class promptmeteo.DocumentQA(**kwargs)#

Question Answering over Documents Task

This class represents a model for answering questions based on documents.

Example:#

>>> from promptmeteo import DocumentQA
>>> clf = DocumentQA(
...     language="en",
...     model_provider_name="hf_pipeline",
...     model_name="google/flan-t5-small",
... )

>>> clf.train(
...     examples=[
...         "The rain in spain is always in plain",
...         "The logarithm's limit is the limit's logarithm",
...         "To punish oppresors is clementy. To forgive them is cruelty",
...     ],
... )

>>> clf.predict(["How is the rain in spain?"])

>>> [["in plain"]]

class promptmeteo.Summarizer(**kwargs)#

Class for text summarization

This class represents a model for text summarization.

Example:#

>>> from promptmeteo import Summarizer
>>> model = Summarizer(
...     language="es",
...     prompt_domain="A partir del siguiente texto:",
...     model_name="anthropic.claude-v2",
...     model_provider_name="bedrock",
... )

>>> model.predict([text])

classmethod load_model(model_path: str) → Self#

Load a saved model from disk.

model_pathstr
Path from where the model will be loaded.

Base
Loaded model instance.

Loads a model artifact to make new predictions.

model_pathstr
The path to the saved model artifact.

selfSummarizer
The loaded Summarizer model.

train() → Self#

Trains the model given use cases and notes on behaviour. Check the: parameters and task behaviour in each specific model training docstring.

Train the Summarizer model.

selfSummarizer
The trained Summarizer model.