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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/images/client.py
air.images.client.ImagesClient
from air.types.constants import DEFAULT_TIMEOUT import requests from air import __version__ from air.types import ImagesResponse, SegmentationResponse class ImagesClient: """ A synchronous client for image related endpoints. This class handles sending requests to image related endpoints and converts t...
class ImagesClient: ''' A synchronous client for image related endpoints. This class handles sending requests to image related endpoints and converts the responses into Pydantic models for type safety. ''' def __init__(self, base_url: str, api_key: str, default_headers: dict[str, str] | None=N...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/client.py
air.knowledge.client.AsyncKnowledgeClient
import asyncio from functools import cached_property class AsyncKnowledgeClient: """ Asynchronous client for knowledge services, including Graph API. """ def __init__(self, base_url: str, api_key: str, default_headers: dict[str, str] | None=None): """ Initialize the async knowledge cli...
class AsyncKnowledgeClient: ''' Asynchronous client for knowledge services, including Graph API. ''' def __init__(self, base_url: str, api_key: str, default_headers: dict[str, str] | None=None): ''' Initialize the async knowledge client. Args: base_url (str): API ba...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/client.py
air.knowledge.client.KnowledgeClient
from air.knowledge.document_processing_client import DocumentProcessingClient class KnowledgeClient: """ Synchronous client for knowledge services, including document processing. """ def __init__(self, base_url: str, api_key: str, default_headers: dict[str, str] | None=None): """ Initi...
class KnowledgeClient: ''' Synchronous client for knowledge services, including document processing. ''' def __init__(self, base_url: str, api_key: str, default_headers: dict[str, str] | None=None): ''' Initialize the sync knowledge client. Args: base_url (str): API...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/document_processing_client.py
air.knowledge.document_processing_client.DocumentProcessingClient
import functools from air.api.vector_db import VectorDBRegistry from typing import Dict, List, Union import os import requests from air.types import Document, DocumentProcessingConfig import base64 from air import __base_url__, __version__, auth from air.knowledge.pipeline import ChunkingRegistry, Embedding, VectorDBUp...
class DocumentProcessingClient: ''' Interface for interacting with the AI Refinery's knowledge extraction service, allowing users to extract knowledge from input documents. ''' def __init__(self, *, base_url: str='') -> None: ''' Initialize the DocumentProcessingClient with authent...
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Accenture/airefinery-sdk
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air.knowledge.graph_visualization.graph_display.GraphDisplay
import networkx as nx from matplotlib import colormaps as cm import matplotlib.pyplot as plt from typing import List, Union import numpy as np class GraphDisplay: """ Base class that show processed graph """ @classmethod def _map_edge_color(cls, graph: nx.Graph): """ Map the graphn...
class GraphDisplay: ''' Base class that show processed graph ''' @classmethod def _map_edge_color(cls, graph: nx.Graph): ''' Map the graphnode weight to a color. Parameters: - graph (nxGraph): networkx graph Return: - List: The list of color code ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/graph_visualization/graph_processing.py
air.knowledge.graph_visualization.graph_processing.GraphProcessing
from graphrag.index.operations.cluster_graph import cluster_graph as graphrag_clustering import networkx as nx from typing import Union import pandas as pd from air.knowledge.graph_visualization.graph_display import GraphDisplay class GraphProcessing: """ Class that performs graph clustering and visualization ...
class GraphProcessing: ''' Class that performs graph clustering and visualization ''' @classmethod def cluster_graph(cls, graph: nx.Graph, max_community_size: int=1) -> pd.DataFrame: ''' Method to perform hierarchical clustering of given graph, until the resulting final comm...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/knowledge_graph/base_knowledge_graph.py
air.knowledge.knowledge_graph.base_knowledge_graph.BaseKnowledgeGraph
from air.types import KnowledgeGraphConfig from typing import Union import os from abc import ABCMeta, abstractmethod class BaseKnowledgeGraph(metaclass=KnowledgeGraphMeta): """ Base class for a knowledge graph. """ def __init__(self, graph_config: KnowledgeGraphConfig): self.base_url = os.env...
class BaseKnowledgeGraph(metaclass=KnowledgeGraphMeta): ''' Base class for a knowledge graph. ''' def __init__(self, graph_config: KnowledgeGraphConfig): pass @abstractmethod async def build(self) -> bool: ''' Add an entity to the knowledge graph. ''' ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/knowledge_graph/base_knowledge_graph.py
air.knowledge.knowledge_graph.base_knowledge_graph.KnowledgeGraphMeta
from abc import ABCMeta, abstractmethod from air.knowledge.knowledge_graph.knowledge_graph_registry import KnowledgeGraphRegistry class KnowledgeGraphMeta(ABCMeta): """ A metaclass that registers any concrete subclass of BaseKnowledgeGraph in KnowledgeGraphRegistry at creation time. Because BaseKnowle...
class KnowledgeGraphMeta(ABCMeta): ''' A metaclass that registers any concrete subclass of BaseKnowledgeGraph in KnowledgeGraphRegistry at creation time. Because BaseKnowledgeGraph already depends on ABC (which uses ABCMeta), we must inherit from ABCMeta here to avoid a metaclass conflict. ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/knowledge_graph/fast_graphrag.py
air.knowledge.knowledge_graph.fast_graphrag.FastGraphRAG
from air.types import KnowledgeGraphConfig from graphrag.config.enums import IndexingMethod from air.knowledge.knowledge_graph.graphrag import GraphRAG from air.knowledge.knowledge_graph.base_knowledge_graph import BaseKnowledgeGraph class FastGraphRAG(BaseKnowledgeGraph): """ FastGraphRAG knowledge graph clas...
class FastGraphRAG(BaseKnowledgeGraph): ''' FastGraphRAG knowledge graph class, inherits from BaseKnowledgeGraph ''' def __init__(self, config: KnowledgeGraphConfig): ''' Initialize the GraphRAG module ''' pass async def build(self) -> bool: ''' ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/knowledge_graph/graphrag.py
air.knowledge.knowledge_graph.graphrag.GraphRAG
from graphrag.cli.initialize import initialize_project_at import asyncio from pathlib import Path import networkx as nx from graphrag.logger.factory import LoggerFactory import shutil from typing import Union from graphrag.config.enums import IndexingMethod, SearchMethod from air.utils import secure_join from air.knowl...
class GraphRAG(BaseKnowledgeGraph): ''' GraphRAG knowledge graph class, inherits from BaseKnowledgeGraph ''' def __init__(self, config: KnowledgeGraphConfig): ''' Initialize the GraphRAG module ''' pass @staticmethod def add_node_edge_labels(communities_df: ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/knowledge_graph/graphrag.py
air.knowledge.knowledge_graph.graphrag.HiddenPrints
import os import sys class HiddenPrints: """ Class to suppress print statements """ def __enter__(self): self._original_stdout = sys.stdout sys.stdout = open(os.devnull, 'w', encoding='utf-8') def __exit__(self, exc_type, exc_val, exc_tb): sys.stdout.close() sys.st...
class HiddenPrints: ''' Class to suppress print statements ''' def __enter__(self): pass def __exit__(self, exc_type, exc_val, exc_tb): pass
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Accenture/airefinery-sdk
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air.knowledge.knowledge_graph.knowledge_graph_registry.KnowledgeGraphRegistry
class KnowledgeGraphRegistry: """ A global registry that keeps track of all knowledge graph classes (subclasses of BaseKnowledgeGraph). """ _registry = {} @classmethod def register(cls, subclass): """ Register a subclass in the global registry. The key is the class name, ...
class KnowledgeGraphRegistry: ''' A global registry that keeps track of all knowledge graph classes (subclasses of BaseKnowledgeGraph). ''' @classmethod def register(cls, subclass): ''' Register a subclass in the global registry. The key is the class name, but you can...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/knowledge_graph_client.py
air.knowledge.knowledge_graph_client.KnowledgeGraphClient
from air.utils import copy_files, secure_join import logging from air.types import Document, KnowledgeGraphConfig import asyncio from typing import List, Union from air.knowledge.graph_visualization import GraphProcessing import os from air.knowledge.knowledge_graph import KnowledgeGraphRegistry import aiofiles class ...
class KnowledgeGraphClient: ''' Interface for interacting with the AI Refinery's knowledge extraction service, with knowledge represented as a graph. ''' def create_project(self, graph_config: KnowledgeGraphConfig): ''' Initializes and sets up a knowledge graph project based on the...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/pipeline/chunking/base_chunking.py
air.knowledge.pipeline.chunking.base_chunking.BaseChunking
from typing import List, Tuple from abc import ABCMeta, abstractmethod from air.types import ChunkingConfig, Document class BaseChunking(metaclass=ChunkingMeta): """ Base class for chunking strategies. """ def __init__(self, chunking_config: ChunkingConfig): self.chunk_size = chunking_config.c...
class BaseChunking(metaclass=ChunkingMeta): ''' Base class for chunking strategies. ''' def __init__(self, chunking_config: ChunkingConfig): pass @abstractmethod def run(self, documents: List[Document]) -> Tuple[List[Document], bool]: ''' Chunk a list of documents a...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/pipeline/chunking/base_chunking.py
air.knowledge.pipeline.chunking.base_chunking.ChunkingMeta
from abc import ABCMeta, abstractmethod from air.knowledge.pipeline.chunking.chunking_registry import ChunkingRegistry class ChunkingMeta(ABCMeta): """ A metaclass that registers any concrete subclass of BaseChunking in ChunkingRegistry at creation time. Because BaseChunking already depends on ABC (wh...
class ChunkingMeta(ABCMeta): ''' A metaclass that registers any concrete subclass of BaseChunking in ChunkingRegistry at creation time. Because BaseChunking already depends on ABC (which uses ABCMeta), we must inherit from ABCMeta here to avoid a metaclass conflict. ''' def __init__(cl...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/pipeline/chunking/brute_force_chunking.py
air.knowledge.pipeline.chunking.brute_force_chunking.BruteForceChunking
from air.knowledge.pipeline.chunking.base_chunking import BaseChunking import uuid from typing import List, Tuple from air.types import Document, TextElement from tqdm import tqdm class BruteForceChunking(BaseChunking): """ BruteForce Chunking strategy class Split text into fixed-length chunks with option...
class BruteForceChunking(BaseChunking): ''' BruteForce Chunking strategy class Split text into fixed-length chunks with optional overlap. Instead of allowing nested document structures, each document will be split into multiple smaller documents, each containing only one text element. ''' ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/pipeline/chunking/chunking_registry.py
air.knowledge.pipeline.chunking.chunking_registry.ChunkingRegistry
class ChunkingRegistry: """ A global registry that keeps track of all chunking classes (subclasses of BaseChunking). """ _registry = {} @classmethod def register(cls, subclass): """ Register a subclass in the global registry. The key is the class name, but you can ch...
class ChunkingRegistry: ''' A global registry that keeps track of all chunking classes (subclasses of BaseChunking). ''' @classmethod def register(cls, subclass): ''' Register a subclass in the global registry. The key is the class name, but you can choose any naming ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/pipeline/chunking/semantic_chunking.py
air.knowledge.pipeline.chunking.semantic_chunking.SemanticChunking
import re from air.knowledge.pipeline.chunking.base_chunking import BaseChunking, ChunkingConfig from tqdm import tqdm import numpy as np from typing import List, Tuple from air.types import Document, TextElement class SemanticChunking(BaseChunking): """ Semantic Chunking strategy class Splits text into se...
class SemanticChunking(BaseChunking): ''' Semantic Chunking strategy class Splits text into semantically meaningful chunks based on sentence embeddings. ''' def __init__(self, chunking_config: ChunkingConfig): ''' Initializes the semantic chunker with configuration. ''' ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/pipeline/embed.py
air.knowledge.pipeline.embed.Embedding
from requests.exceptions import HTTPError from typing import List, Tuple from air.embeddings.client import EmbeddingsClient from air.types import ClientConfig, Document, EmbeddingConfig import concurrent.futures from air import __base_url__, auth from tenacity import retry, retry_if_exception, stop_after_attempt from t...
class Embedding: ''' Extends Executor to support data embedding functions. ''' def __init__(self, embedding_config: EmbeddingConfig, base_url: str): pass def refresh_client_access_token(self): ''' Refresh the access token for the OpenAI client. ''' pass ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/knowledge/pipeline/upload.py
air.knowledge.pipeline.upload.VectorDBUpload
from air.api.vector_db import VectorDBConfig, VectorDBRegistry from typing import List, Tuple from tqdm import tqdm import concurrent.futures from air.types import Document, VectorDBUploadConfig class VectorDBUpload: """ Class to upload data to vector DB """ def __init__(self, upload_config: VectorDBU...
class VectorDBUpload: ''' Class to upload data to vector DB ''' def __init__(self, upload_config: VectorDBUploadConfig, vectordb_config: VectorDBConfig): pass def run(self, document_list: List[Document]) -> Tuple[None, bool]: ''' Function to upload list of document data to...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/logconfig.py
air.logconfig.ColoredFormatter
import sys import logging import logging.config class ColoredFormatter(logging.Formatter): """Color codes for the logs""" COLOR_CODES = {'reset': '\x1b[0m', 'cyan': '\x1b[36m', 'green': '\x1b[32m', 'yellow': '\x1b[33m', 'red': '\x1b[31m'} LEVEL_COLOR = {logging.DEBUG: 'cyan', logging.INFO: 'green', logging...
class ColoredFormatter(logging.Formatter): '''Color codes for the logs''' def format(self, record): pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/models/client.py
air.models.client.AsyncModelsClient
import aiohttp from air import __version__ from air.types import AsyncPage, Model, SyncPage class AsyncModelsClient: """ An asynchronous client for listing models. This class handles sending GET requests to the models endpoint and converts the responses into Pydantic models for type safety. """ ...
class AsyncModelsClient: ''' An asynchronous client for listing models. This class handles sending GET requests to the models endpoint and converts the responses into Pydantic models for type safety. ''' def __init__(self, base_url: str, api_key: str, default_headers: dict[str, str] | None=Non...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/models/client.py
air.models.client.ModelsClient
import requests from air import __version__ from air.types import AsyncPage, Model, SyncPage class ModelsClient: """ A synchronous client for listing models. This class handles sending GET requests to the models endpoint and converts the responses into Pydantic models for type safety. """ def...
class ModelsClient: ''' A synchronous client for listing models. This class handles sending GET requests to the models endpoint and converts the responses into Pydantic models for type safety. ''' def __init__(self, base_url: str, api_key: str, default_headers: dict[str, str] | None=None): ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/audio.py
air.types.audio.APIResponse
import json from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union from air.types.base import CustomBaseModel from pydantic import TypeAdapter, ValidationError class APIResponse(Generic[T_co])...
class APIResponse(Generic[T_co]): ''' Represents a synchronous API response with optional streaming support. Wraps the parsed result and underlying HTTP response object. Provides utility methods to iterate over raw or parsed response lines. ''' def __init__(self, parsed: T_co, http_response: A...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/audio.py
air.types.audio.ASRResponse
from air.types.base import CustomBaseModel from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class ASRResponse(CustomBaseModel): """Top-level Automatic Speech Recognition response ret...
class ASRResponse(CustomBaseModel): '''Top-level Automatic Speech Recognition response returned by the API. Attributes: text (Union[str, None]): The transcription of the audio file success (bool): Whether the transcription request was successful error (Optional[str]): Optional error mes...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/audio.py
air.types.audio.AsyncAPIResponse
from air.types.base import CustomBaseModel import json from pydantic import TypeAdapter, ValidationError from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class AsyncAPIResponse(Generic[T...
class AsyncAPIResponse(Generic[T_co]): ''' Represents an asynchronous API response with optional streaming support. Wraps the parsed result and underlying async HTTP response object. Provides async utilities to iterate over response content. ''' def __init__(self, parsed: T_co, http_response: ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/audio.py
air.types.audio.AsyncResponseContextManager
from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class AsyncResponseContextManager(Generic[T_co]): """ An asynchronous context manager wrapper for API responses. Wraps an `A...
class AsyncResponseContextManager(Generic[T_co]): ''' An asynchronous context manager wrapper for API responses. Wraps an `AsyncContextManager[T]` and delegates enter/exit lifecycle. Useful for cleanly handling async resource lifetimes, like streamed HTTP responses. ''' def __init__(self, cm: ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/audio.py
air.types.audio.AsyncStream
from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class AsyncStream(Protocol[T_co]): """ Protocol for asynchronous streaming responses. Represents any object that supports as...
class AsyncStream(Protocol[T_co]): ''' Protocol for asynchronous streaming responses. Represents any object that supports async iteration over items of type `T`, typically used for consuming async streaming data. ''' def __aiter__(self) -> AsyncIterator[T_co]: pass
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Accenture/airefinery-sdk
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air.types.audio.ChunkingStrategy
from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class ChunkingStrategy(TypedDict, total=False): """ Controls how the audio is cut into chunks. Attributes: type (Lit...
class ChunkingStrategy(TypedDict, total=False): ''' Controls how the audio is cut into chunks. Attributes: type (Literal["server_vad"]): Selects server-side VAD chunking (required). prefix_padding_ms (int, optional): Lead-in context before speech, 0–5000 ms. silence_duration_ms(int...
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Accenture/airefinery-sdk
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air.types.audio.Logprob
from air.types.base import CustomBaseModel from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class Logprob(CustomBaseModel): """ Represents the log probability data for a specific...
class Logprob(CustomBaseModel): ''' Represents the log probability data for a specific token in a transcription or language model output. Attributes: token (Optional[str]): The text token for which the log probability was computed. bytes (Optional[List[int]]): The raw byte representatio...
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Accenture/airefinery-sdk
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air.types.audio.ResponseContextManager
from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class ResponseContextManager(Generic[T_co]): """ A synchronous context manager wrapper for API responses. Executes a user-pr...
class ResponseContextManager(Generic[T_co]): ''' A synchronous context manager wrapper for API responses. Executes a user-provided `enter()` function when the context is entered. Used to encapsulate setup and teardown behavior for blocking API calls. ''' def __init__(self, enter: Callable[[], ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/audio.py
air.types.audio.Stream
from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class Stream(Protocol[T_co]): """ Protocol for synchronous streaming responses. Represents any object that supports iteratio...
class Stream(Protocol[T_co]): ''' Protocol for synchronous streaming responses. Represents any object that supports iteration over items of type `T`, typically used for non-async streaming data. ''' def __iter__(self) -> Iterator[T_co]: pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/audio.py
air.types.audio.TTSResponse
from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union import aiofiles import json import os class TTSResponse: """ Response wrapper for TTS audio data. Mimics OpenAI's HttpxBinar...
class TTSResponse: ''' Response wrapper for TTS audio data. Mimics OpenAI's HttpxBinaryResponseContent for full compatibility with OpenAI's client interface. Provides both sync and async methods for reading and streaming audio content. Attributes: content: Raw audio bytes from TTS synth...
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Accenture/airefinery-sdk
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air.types.audio.TranscriptionTextDeltaEvent
from air.types.base import CustomBaseModel from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class TranscriptionTextDeltaEvent(CustomBaseModel): """ Represents an incremental tran...
class TranscriptionTextDeltaEvent(CustomBaseModel): ''' Represents an incremental transcription update event emitted during streaming transcription. This event provides a new segment of transcribed text (delta) as it becomes available, allowing for real-time transcription updates. It may optionally inc...
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Accenture/airefinery-sdk
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air.types.audio.TranscriptionTextDoneEvent
from air.types.base import CustomBaseModel from typing import Annotated, Any, AsyncContextManager, AsyncIterator, Callable, Generic, Iterator, List, Literal, Optional, Protocol, Required, TypeAlias, TypedDict, TypeVar, Union class TranscriptionTextDoneEvent(CustomBaseModel): """ Represents the final transcript...
class TranscriptionTextDoneEvent(CustomBaseModel): ''' Represents the final transcription result emitted at the end of the audio input. This event marks the completion of the transcription stream and contains the full transcribed text. It may optionally include token-leveln log probabilities if request...
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Accenture/airefinery-sdk
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air.types.base.AsyncPage
from typing import override, Generic, List, Optional, TypeVar, AsyncIterator, Iterator class AsyncPage(PageBase[T]): """An asynchronously fetched page of items. Adds asynchronous iteration support:: async for m in page: ... """ def __aiter__(self) -> AsyncIterator[T]: """...
class AsyncPage(PageBase[T]): '''An asynchronously fetched page of items. Adds asynchronous iteration support:: async for m in page: ... ''' def __aiter__(self) -> AsyncIterator[T]: '''Enables async iteration over the data in this page.''' pass async def _g...
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Accenture/airefinery-sdk
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air.types.base.CustomBaseModel
from typing import override, Generic, List, Optional, TypeVar, AsyncIterator, Iterator from pydantic import BaseModel class CustomBaseModel(BaseModel): """An internal base model with a custom string representation. Extends Pydantic's BaseModel to override the string representation so that printing an inst...
class CustomBaseModel(BaseModel): '''An internal base model with a custom string representation. Extends Pydantic's BaseModel to override the string representation so that printing an instance will display the class name and key-value pairs of the model fields. ''' @override def __str__(sel...
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Accenture/airefinery-sdk
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air.types.base.PageBase
from typing import override, Generic, List, Optional, TypeVar, AsyncIterator, Iterator class PageBase(CustomBaseModel, Generic[T]): """Common fields and iteration support for SyncPage/AsyncPage objects. Attributes: object: String label indicating the object type (e.g., "list"). data: The paylo...
class PageBase(CustomBaseModel, Generic[T]): '''Common fields and iteration support for SyncPage/AsyncPage objects. Attributes: object: String label indicating the object type (e.g., "list"). data: The payload, typically a list of items of type T. first_id: Optional identifier for the f...
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Accenture/airefinery-sdk
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air.types.base.SyncPage
class SyncPage(PageBase[T]): """A synchronously fetched page of items."""
class SyncPage(PageBase[T]): '''A synchronously fetched page of items.''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/chat.py
air.types.chat.ChatCompletion
from typing import Any, List, Optional from air.types.base import CustomBaseModel class ChatCompletion(CustomBaseModel): """Top-level ChatCompletion response returned by the API. Attributes: id: Unique identifier for this ChatCompletion. object: The object type, typically "chat.completion". ...
class ChatCompletion(CustomBaseModel): '''Top-level ChatCompletion response returned by the API. Attributes: id: Unique identifier for this ChatCompletion. object: The object type, typically "chat.completion". created: A UNIX timestamp indicating creation time. model: The langua...
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Accenture/airefinery-sdk
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air.types.chat.ChatCompletionMessage
from typing import Any, List, Optional from air.types.base import CustomBaseModel class ChatCompletionMessage(CustomBaseModel): """Represents one message within a conversation, possibly including tool calls. Attributes: role: The role of the sender (e.g., 'assistant', 'user', or 'system'). con...
class ChatCompletionMessage(CustomBaseModel): '''Represents one message within a conversation, possibly including tool calls. Attributes: role: The role of the sender (e.g., 'assistant', 'user', or 'system'). content: The main text of the message, if any. refusal: A refusal statement, i...
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Accenture/airefinery-sdk
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air.types.chat.ChatCompletionMessageToolCall
from air.types.base import CustomBaseModel from typing import Any, List, Optional class ChatCompletionMessageToolCall(CustomBaseModel): """A single tool-call object within a message. Attributes: id: The unique identifier of this tool call. function: The function details, if any. type: ...
class ChatCompletionMessageToolCall(CustomBaseModel): '''A single tool-call object within a message. Attributes: id: The unique identifier of this tool call. function: The function details, if any. type: The type of call (e.g., 'function'). ''' pass
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Accenture/airefinery-sdk
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air.types.chat.Choice
from air.types.base import CustomBaseModel from typing import Any, List, Optional class Choice(CustomBaseModel): """Represents a single choice from a ChatCompletion response. Attributes: index: The index of this choice within the list of choices. finish_reason: The reason this choice completed...
class Choice(CustomBaseModel): '''Represents a single choice from a ChatCompletion response. Attributes: index: The index of this choice within the list of choices. finish_reason: The reason this choice completed (e.g., 'stop', 'tool_calls'). message: The message returned with this choi...
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Accenture/airefinery-sdk
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air.types.chat.CompletionUsage
from typing import Any, List, Optional from air.types.base import CustomBaseModel class CompletionUsage(CustomBaseModel): """Tracks usage details for a ChatCompletion response. Attributes: prompt_tokens: Number of tokens used in the prompt. completion_tokens: Number of tokens produced in the c...
class CompletionUsage(CustomBaseModel): '''Tracks usage details for a ChatCompletion response. Attributes: prompt_tokens: Number of tokens used in the prompt. completion_tokens: Number of tokens produced in the completion. total_tokens: Total tokens used (prompt + completion). c...
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Accenture/airefinery-sdk
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air.types.chat.Function
from air.types.base import CustomBaseModel class Function(CustomBaseModel): """Represents the function details used in a tool call. Attributes: name: The name of the function being invoked. arguments: A serialized set of arguments for the function call. """ name: str arguments: str
class Function(CustomBaseModel): '''Represents the function details used in a tool call. Attributes: name: The name of the function being invoked. arguments: A serialized set of arguments for the function call. ''' pass
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Accenture/airefinery-sdk
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air.types.distiller.executor.a2a_config.A2AClientAgentConfig
from pydantic import Field, BaseModel, model_validator class A2AClientAgentConfig(BaseModel): """ A2A Client Agent Config - base_url: Required, must be non-empty - agent_card: Required, dictionary or instance of AgentCardConfig. - response_prefs: Optional, dictionary or instance of ResponsePrefsCon...
class A2AClientAgentConfig(BaseModel): ''' A2A Client Agent Config - base_url: Required, must be non-empty - agent_card: Required, dictionary or instance of AgentCardConfig. - response_prefs: Optional, dictionary or instance of ResponsePrefsConfig. - wait_time: Optional, integer. - contexts...
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Accenture/airefinery-sdk
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air.types.distiller.executor.a2a_config.AgentCardConfig
from pydantic import Field, BaseModel, model_validator class AgentCardConfig(BaseModel): """ Dictionary containing public or private agent card information. Two fields, public required. """ public: PublicAgentCardConfig = Field(default=PublicAgentCardConfig(public_agent_card_path='dummy_path'), des...
class AgentCardConfig(BaseModel): ''' Dictionary containing public or private agent card information. Two fields, public required. ''' @model_validator(mode='after') def check_public_agent_card_non_empty(self): pass
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Accenture/airefinery-sdk
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air.types.distiller.executor.a2a_config.PrivateAgentCardConfig
from pydantic import Field, BaseModel, model_validator class PrivateAgentCardConfig(BaseModel): """ Private agent card details. Two fields, both optional. """ extended_agent_card_path: str = Field(default='/agent/authenticatedExtendedCard', description='Path to the extended agent card.') authen...
class PrivateAgentCardConfig(BaseModel): ''' Private agent card details. Two fields, both optional. ''' pass
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Accenture/airefinery-sdk
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air.types.distiller.executor.a2a_config.PublicAgentCardConfig
from pydantic import Field, BaseModel, model_validator class PublicAgentCardConfig(BaseModel): """ Public agent card details. Two fields, at least one required, not allowed concurrently empty. """ public_agent_card_path: str = Field(default='', description='Path to the public agent card.') rpc_...
class PublicAgentCardConfig(BaseModel): ''' Public agent card details. Two fields, at least one required, not allowed concurrently empty. ''' @model_validator(mode='after') def check_public_agent_card_fields(self): pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/a2a_config.py
air.types.distiller.executor.a2a_config.ResponsePrefsConfig
from pydantic import Field, BaseModel, model_validator import warnings class ResponsePrefsConfig(BaseModel): """ Dictionary containing the preferences for how to handle the agent's response. Two fields, both optional, not allowed concurrently True - second ignored if first True. """ tracing: bool |...
class ResponsePrefsConfig(BaseModel): ''' Dictionary containing the preferences for how to handle the agent's response. Two fields, both optional, not allowed concurrently True - second ignored if first True. ''' @model_validator(mode='after') def check_conflicting_repsonse_prefs(self): ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/amazon_bedrock_config.py
air.types.distiller.executor.amazon_bedrock_config.AmazonBedrockAgentConfig
from pydantic import Field, BaseModel, model_validator class AmazonBedrockAgentConfig(BaseModel): """ AmazonBedrock Agent Config """ client_key: str = Field(default='', description='The environment variable containing the AmazonBedrock Client key.') client_secret: str = Field(default='', descriptio...
class AmazonBedrockAgentConfig(BaseModel): ''' AmazonBedrock Agent Config ''' @model_validator(mode='after') def check_connection_params_non_empty(self): ''' Checking if required connection parameters are populated in the config ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/azure_config.py
air.types.distiller.executor.azure_config.AzureAgentConfig
from pydantic import Field, BaseModel, model_validator class AzureAgentConfig(BaseModel): """ Azure Agent Config """ connection_string: str = Field(default='', description='The environment variable containing the Azure connection string.') agent_id: str = Field(default='', description='The environm...
class AzureAgentConfig(BaseModel): ''' Azure Agent Config ''' @model_validator(mode='after') def check_connection_params_non_empty(self): ''' Checking if required connection parameters are populated in the config ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/databricks_config.py
air.types.distiller.executor.databricks_config.DatabricksAgentConfig
from pydantic import Field, BaseModel, model_validator class DatabricksAgentConfig(BaseModel): """ Databricks Agent Config """ client_id: str = Field(default='', description='The environment variable containing the Databricks Client ID.') client_secret: str = Field(default='', description='The envi...
class DatabricksAgentConfig(BaseModel): ''' Databricks Agent Config ''' @model_validator(mode='after') def check_connection_params_non_empty(self): ''' Checking if required connection parameters are populated in the config ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/google_config.py
air.types.distiller.executor.google_config.GoogleAgentConfig
from pydantic import Field, BaseModel, model_validator class GoogleAgentConfig(BaseModel): """ Google Vertex Agent Config """ resource_name: str = Field(default='', description='The resource name to identify the Google Vertex agent.') @model_validator(mode='after') def check_connection_params_...
class GoogleAgentConfig(BaseModel): ''' Google Vertex Agent Config ''' @model_validator(mode='after') def check_connection_params_non_empty(self): ''' Checking if required connection parameters are populated in the config ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/human_config.py
air.types.distiller.executor.human_config.FeedbackFieldConfig
from pydantic import BaseModel, Field class FeedbackFieldConfig(BaseModel): """Schema field configuration for expected user feedback. Attributes: type (str): The type of the field (e.g., "str", "int"). description (str): Description of what the field represents. required (bool): Whethe...
class FeedbackFieldConfig(BaseModel): '''Schema field configuration for expected user feedback. Attributes: type (str): The type of the field (e.g., "str", "int"). description (str): Description of what the field represents. required (bool): Whether the field is mandatory in the feedbac...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/human_config.py
air.types.distiller.executor.human_config.HumanAgentConfig
from pydantic import BaseModel, Field from typing import Dict class HumanAgentConfig(BaseModel): """ Configuration settings for the Human Agent. """ wait_time: int = Field(default=300, description='Maximum wait time (in seconds) for human input before timing out.') user_input_method: str = Field(de...
class HumanAgentConfig(BaseModel): ''' Configuration settings for the Human Agent. ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/mcp_config.py
air.types.distiller.executor.mcp_config.MCPClientAgentConfig
from pydantic import Field, BaseModel, model_validator class MCPClientAgentConfig(BaseModel): """ MCPClient Agent Config """ mcp_sse_url: str = Field(default='', description='The URL where the MCP server is hosted.') enable_interpreter: bool = Field(default=False, description='The setting controlli...
class MCPClientAgentConfig(BaseModel): ''' MCPClient Agent Config ''' @model_validator(mode='after') def check_connection_params_non_empty(self): ''' Checking if required connection parameters are populated in the config ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/salesforce_config.py
air.types.distiller.executor.salesforce_config.SalesforceAgentConfig
from pydantic import Field, BaseModel, model_validator class SalesforceAgentConfig(BaseModel): """ Salesforce Agent Config """ client_key: str = Field(default='', description='The environment variable containing the Salesforce Client key.') client_secret: str = Field(default='', description='The en...
class SalesforceAgentConfig(BaseModel): ''' Salesforce Agent Config ''' @model_validator(mode='after') def check_connection_params_non_empty(self): ''' Checking if required connection parameters are populated in the config ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/sap_config.py
air.types.distiller.executor.sap_config.SAPAgentConfig
from pydantic import BaseModel, Field, model_validator class SAPAgentConfig(BaseModel): """ SAP Agent Config - url: Required, must be non-empty - contexts: Optional, list """ url: str = Field(default='', description='The URL where the agent is located.') contexts: list = Field(default_facto...
class SAPAgentConfig(BaseModel): ''' SAP Agent Config - url: Required, must be non-empty - contexts: Optional, list ''' @model_validator(mode='after') def check_empty_url(self): pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/distiller/executor/writer_config.py
air.types.distiller.executor.writer_config.WriterAIAgentConfig
from pydantic import Field, BaseModel, model_validator class WriterAIAgentConfig(BaseModel): """ WriterAI Agent Config """ api_key_env_var: str = Field(default='', description='The environment variable containing the WriterAI API key.') application_id: str = Field(default='', description='The appli...
class WriterAIAgentConfig(BaseModel): ''' WriterAI Agent Config ''' @model_validator(mode='after') def check_connection_params_non_empty(self): ''' Checking if required connection parameters are populated in the config ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/embeddings.py
air.types.embeddings.CreateEmbeddingResponse
from typing import List from air.types.base import CustomBaseModel class CreateEmbeddingResponse(CustomBaseModel): """Represents the full response returned by the Embeddings *create* endpoint. Attributes: data: A list of embedding objects generated by the model. model: The model identifier (e....
class CreateEmbeddingResponse(CustomBaseModel): '''Represents the full response returned by the Embeddings *create* endpoint. Attributes: data: A list of embedding objects generated by the model. model: The model identifier (e.g., "text-embedding-3-small"). object: Type label of the ret...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/embeddings.py
air.types.embeddings.Embedding
from typing import List from air.types.base import CustomBaseModel class Embedding(CustomBaseModel): """Represents a single embedding vector and its metadata. Attributes: object: Identifies the resource type (e.g., "embedding"). embedding: The embedding vector as a list of floats. inde...
class Embedding(CustomBaseModel): '''Represents a single embedding vector and its metadata. Attributes: object: Identifies the resource type (e.g., "embedding"). embedding: The embedding vector as a list of floats. index: Position of this item in the response list. ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/embeddings.py
air.types.embeddings.Usage
from air.types.base import CustomBaseModel class Usage(CustomBaseModel): """Represents token-usage statistics for an embedding request. Attributes: prompt_tokens: Number of tokens in the prompt. total_tokens: The total tokens consumed (identical to prompt_tokens for the embeddings ...
class Usage(CustomBaseModel): '''Represents token-usage statistics for an embedding request. Attributes: prompt_tokens: Number of tokens in the prompt. total_tokens: The total tokens consumed (identical to prompt_tokens for the embeddings endpoint). ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/images.py
air.types.images.Image
from air.types.base import CustomBaseModel from typing import Dict, List, Optional class Image(CustomBaseModel): """Represents one generated image and its metadata. Attributes: b64_json: Base64-encoded image data (only present when `response_format="b64_json"`) revised_prompt: The final prompt...
class Image(CustomBaseModel): '''Represents one generated image and its metadata. Attributes: b64_json: Base64-encoded image data (only present when `response_format="b64_json"`) revised_prompt: The final prompt string the model actually used for image generation, which ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/images.py
air.types.images.ImagesResponse
from air.types.base import CustomBaseModel from typing import Dict, List, Optional class ImagesResponse(CustomBaseModel): """Represents the full response returned by the Images *generate* endpoint. Attributes: created: The Unix timestamp (in seconds) of when the images were created data: A lis...
class ImagesResponse(CustomBaseModel): '''Represents the full response returned by the Images *generate* endpoint. Attributes: created: The Unix timestamp (in seconds) of when the images were created data: A list of generated images usage: Aggregate token-usage information for the reque...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/images.py
air.types.images.Mask
from air.types.base import CustomBaseModel from typing import Dict, List, Optional class Mask(CustomBaseModel): """Represents one segmentation mask and its metadata. Attributes: b64_json: Base64-encoded categorical mask image label: Optional semantic class label, if provided score: Opt...
class Mask(CustomBaseModel): '''Represents one segmentation mask and its metadata. Attributes: b64_json: Base64-encoded categorical mask image label: Optional semantic class label, if provided score: Optional confidence score from the model, if provided ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/images.py
air.types.images.SegmentationResponse
from air.types.base import CustomBaseModel from typing import Dict, List, Optional class SegmentationResponse(CustomBaseModel): """Represents the full response returned by the point-prompt segmentation endpoint Attributes: created: The Unix timestamp (in seconds) when the masks were created ...
class SegmentationResponse(CustomBaseModel): '''Represents the full response returned by the point-prompt segmentation endpoint Attributes: created: The Unix timestamp (in seconds) when the masks were created data: A list of generated masks usage: Aggregate token-usage information f...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/images.py
air.types.images.Usage
from typing import Dict, List, Optional from air.types.base import CustomBaseModel class Usage(CustomBaseModel): """Represents token-usage statistics for image related requests. Attributes: input_tokens: The number of tokens (images and text) in the input prompt input_tokens_details: The input...
class Usage(CustomBaseModel): '''Represents token-usage statistics for image related requests. Attributes: input_tokens: The number of tokens (images and text) in the input prompt input_tokens_details: The input tokens detailed information for the image generation output_tokens: The num...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/knowledge.py
air.types.knowledge.ChunkingConfig
from pydantic import BaseModel, Field class ChunkingConfig(BaseModel): """ Chunking configuration class """ algorithm: str = Field(default='BruteForceChunking', description='Type of Chunking Algorithm') chunk_size: int = Field(..., description='Max length per chunk') overlap_size: int = Field(d...
class ChunkingConfig(BaseModel): ''' Chunking configuration class ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/knowledge.py
air.types.knowledge.ClientConfig
from pydantic import BaseModel, Field from air import __base_url__ class ClientConfig(BaseModel): """ Configuration for the OpenAI client. """ base_url: str = Field(default=__base_url__, description='Base URL for the OpenAI API') api_key: str = Field(..., description='API key for authentication') ...
class ClientConfig(BaseModel): ''' Configuration for the OpenAI client. ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/knowledge.py
air.types.knowledge.Document
from pydantic import BaseModel, Field from typing import List, Literal, Optional class Document(BaseModel): """ Document Object data class. Attributes: filename (str): Name of the file file_type (str): File type/extension elements (list): List of file elements metadata (dic...
class Document(BaseModel): ''' Document Object data class. Attributes: filename (str): Name of the file file_type (str): File type/extension elements (list): List of file elements metadata (dict): Metadata related to the document ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/knowledge.py
air.types.knowledge.DocumentProcessingConfig
from pydantic import BaseModel, Field from air.api.vector_db.base_vectordb import VectorDBConfig class DocumentProcessingConfig(BaseModel): """ Configuration for document processing """ upload_config: VectorDBUploadConfig = Field(default=VectorDBUploadConfig(), description='Vector DB upload configurati...
class DocumentProcessingConfig(BaseModel): ''' Configuration for document processing ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/knowledge.py
air.types.knowledge.EmbeddingConfig
from pydantic import BaseModel, Field class EmbeddingConfig(BaseModel): """ Embedding configuration class """ model: str = Field(..., description='Name of the model to use for embedding') batch_size: int = Field(default=50, description='Number of rows in a batch per embedding request') max_work...
class EmbeddingConfig(BaseModel): ''' Embedding configuration class ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/knowledge.py
air.types.knowledge.KnowledgeGraphConfig
from typing import List, Literal, Optional from pydantic import BaseModel, Field class KnowledgeGraphConfig(BaseModel): """ KnowledgeGraph configuration class """ type: str = Field(default='GraphRAG', description='Type of the Knowledge Graph') work_dir: str = Field(default='graph_dir', description=...
class KnowledgeGraphConfig(BaseModel): ''' KnowledgeGraph configuration class ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/knowledge.py
air.types.knowledge.TextElement
from pydantic import BaseModel, Field from typing import List, Literal, Optional class TextElement(BaseModel): """ Document element data config Attributes: id (str): Unique identifier for the element text (str): Text of the element page_number (int): Document page number from which...
class TextElement(BaseModel): ''' Document element data config Attributes: id (str): Unique identifier for the element text (str): Text of the element page_number (int): Document page number from which element was extracted element_type (str): Type of element, one of (text, ...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/knowledge.py
air.types.knowledge.VectorDBUploadConfig
from pydantic import BaseModel, Field class VectorDBUploadConfig(BaseModel): """ VectorDB upload configuration class """ batch_size: int = Field(default=50, description='Number of rows in a batch per upload request') max_workers: int = Field(default=8, description='Number of parallel threads to spa...
class VectorDBUploadConfig(BaseModel): ''' VectorDB upload configuration class ''' pass
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/models.py
air.types.models.Model
from air.types.base import CustomBaseModel from typing import List, Optional class Model(CustomBaseModel): """Represents metadata describing a single model. Attributes: id: The model identifier (e.g., "gpt-4o-mini"). created: The Unix timestamp when the model was created. object: Alway...
class Model(CustomBaseModel): '''Represents metadata describing a single model. Attributes: id: The model identifier (e.g., "gpt-4o-mini"). created: The Unix timestamp when the model was created. object: Always "model". owned_by: The organization that owns this model. ro...
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Accenture/airefinery-sdk
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/Accenture_airefinery-sdk/air/types/models.py
air.types.models.ModelPermission
from air.types.base import CustomBaseModel from typing import List, Optional class ModelPermission(CustomBaseModel): """Represents a fine-grained permission set attached to a model. Attributes: id: Unique identifier of this permission object. object: Always "model_permission". created:...
class ModelPermission(CustomBaseModel): '''Represents a fine-grained permission set attached to a model. Attributes: id: Unique identifier of this permission object. object: Always "model_permission". created: Unix timestamp indicating when this permission was created. allow_cre...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/ai2d/grader.py
benchmarks.ai2d.grader.AI2DGrader
import re from typing import Any from ...datasets.interface import Row from ...graders.interface import IGrader class AI2DGrader(IGrader): def __init__(self, grader_id: str): super().__init__(grader_id) def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str,...
class AI2DGrader(IGrader): def __init__(self, grader_id: str): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def topline_metric(self) -> str: pass def grade_row(self, row: Row) -> dict[str, Any]: pa...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/bfcl/grader.py
benchmarks.bfcl.grader.BfclGrader
from ...graders.interface import IGrader, Row import traceback from ...evaluator.interface import EvalResult import re import json from ...utils.utils import map_with_progress from typing import Any class BfclGrader(IGrader): def __init__(self, grader_id: str, is_grading_api: bool=False): super().__init__...
class BfclGrader(IGrader): def __init__(self, grader_id: str, is_grading_api: bool=False): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def calculate_overall_metrics(self, subset_metrics: dict[str, dict[str, Any]]) -> ...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/bfcl/preprocessor.py
benchmarks.bfcl.preprocessor.BfclPreprocessor
import json from ...datasets.interface import Row from ...preprocessor.interface import IPreprocessor class BfclPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): super().__init__(preprocessor_id) def preprocess_row(self, row: Row) -> Row: return {'id': row['id'], 'test_ca...
class BfclPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): pass def preprocess_row(self, row: Row) -> Row: pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/common/graders/chrf.py
benchmarks.common.graders.chrf.ChrfGrader
from llama_verifications.benchmarks.datasets.interface import Row from llama_verifications.benchmarks.graders.interface import IGrader from typing import Any class ChrfGrader(IGrader): def __init__(self, grader_id: str): super().__init__(grader_id) def calculate_aggregate_metrics(self, results: list[...
class ChrfGrader(IGrader): def __init__(self, grader_id: str): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def topline_metric(self) -> str: pass def grade_row(self, row: Row) -> dict[str, Any]: pa...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/common/graders/regex_parser_multiple_choice_grader.py
benchmarks.common.graders.regex_parser_multiple_choice_grader.RegexParserMultipleChoiceGrader
import re from typing import Any from ....graders.interface import IGrader from ....datasets.interface import Row class RegexParserMultipleChoiceGrader(IGrader): def __init__(self, grader_id: str): super().__init__(grader_id) def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: ...
class RegexParserMultipleChoiceGrader(IGrader): def __init__(self, grader_id: str): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def topline_metric(self) -> str: pass def grade_row(self, row: Row) -> dict[...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/common/preprocessors.py
benchmarks.common.preprocessors.ChatCompletionInputPreprocessor
import json from ...datasets.interface import Row from ...preprocessor.interface import IPreprocessor class ChatCompletionInputPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): super().__init__(preprocessor_id) def preprocess_row(self, row: Row) -> Row: return {'expected_...
class ChatCompletionInputPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): pass def preprocess_row(self, row: Row) -> Row: pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/docvqa/grader.py
benchmarks.docvqa.grader.DocVQAGrader
from ...datasets.interface import Row from typing import Any from ...graders.interface import IGrader import json class DocVQAGrader(IGrader): def __init__(self, grader_id: str): super().__init__(grader_id) def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[...
class DocVQAGrader(IGrader): def __init__(self, grader_id: str): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def topline_metric(self) -> str: pass def grade_row(self, row: Row) -> dict[str, Any]: ...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/docvqa/preprocessor.py
benchmarks.docvqa.preprocessor.DocVQAPreprocessor
import json from ...datasets.interface import Row from ...preprocessor.interface import IPreprocessor class DocVQAPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): super().__init__(preprocessor_id) def preprocess_row(self, row: Row) -> Row: encoded_image = pillow_image_to...
class DocVQAPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): pass def preprocess_row(self, row: Row) -> Row: pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/ifeval/grader.py
benchmarks.ifeval.grader.IfEvalGrader
from ...graders.interface import IGrader from typing import Any from ...datasets.interface import Row class IfEvalGrader(IGrader): def __init__(self, grader_id: str): super().__init__(grader_id) def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: ...
class IfEvalGrader(IGrader): def __init__(self, grader_id: str): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def topline_metric(self) -> str: pass def grade_row(self, row: Row) -> dict[str, Any]: ...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/ifeval/preprocessor.py
benchmarks.ifeval.preprocessor.IfEvalPreprocessor
import json from ...preprocessor.interface import IPreprocessor from ...datasets.interface import Row class IfEvalPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): super().__init__(preprocessor_id) def preprocess_row(self, row: Row) -> Row: return {'prompt': row['prompt']...
class IfEvalPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): pass def preprocess_row(self, row: Row) -> Row: pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/math500/grader.py
benchmarks.math500.grader.Math500Grader
from ...graders.interface import IGrader from typing import Any from ...datasets.interface import Row class Math500Grader(IGrader): def __init__(self, grader_id: str): super().__init__(grader_id) def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: ...
class Math500Grader(IGrader): def __init__(self, grader_id: str): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def topline_metric(self) -> str: pass def grade_row(self, row: Row) -> dict[str, Any]: ...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/math500/grader.py
benchmarks.math500.grader.TimeoutError
class TimeoutError(Exception): pass
class TimeoutError(Exception): pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/mmmu/grader.py
benchmarks.mmmu.grader.MMMUGrader
from ...graders.interface import IGrader from .mmmu_utils import eval_multi_choice, eval_open, parse_multi_choice_response, parse_open_response from typing import Any from ...datasets.interface import Row class MMMUGrader(IGrader): def __init__(self, grader_id: str): super().__init__(grader_id) def c...
class MMMUGrader(IGrader): def __init__(self, grader_id: str): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def topline_metric(self) -> str: pass def grade_row(self, row: Row) -> dict[str, Any]: pa...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/mmmu/preprocessor.py
benchmarks.mmmu.preprocessor.MMMUPreprocessor
from ...datasets.interface import Row from ...preprocessor.interface import IPreprocessor class MMMUPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): super().__init__(preprocessor_id) def preprocess_row(self, row: Row) -> Row: return preprocess(row)
class MMMUPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): pass def preprocess_row(self, row: Row) -> Row: pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/mmmu/preprocessor.py
benchmarks.mmmu.preprocessor.MMMUProPreprocessor
from ...preprocessor.interface import IPreprocessor from ...datasets.interface import Row class MMMUProPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str, version: str): super().__init__(preprocessor_id) self.version = version def preprocess_row(self, row: Row) -> Row: ...
class MMMUProPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str, version: str): pass def preprocess_row(self, row: Row) -> Row: pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/registry.py
benchmarks.registry.Benchmark
from typing import Literal from pydantic import BaseModel class Benchmark(BaseModel): dataset_id: str grader_ids: list[str] development_status: Literal['verified'] | Literal['under_development'] | Literal['unverified'] preprocessor_id: str | None = None print_subsets: bool = False is_multi_turn...
class Benchmark(BaseModel): pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/registry.py
benchmarks.registry.BenchmarkRegistry
class BenchmarkRegistry: _benchmark_registry: dict[str, Benchmark] = {} @staticmethod def register_benchmark(benchmark_id: str, benchmark: Benchmark): BenchmarkRegistry._benchmark_registry[benchmark_id] = benchmark @staticmethod def get_benchmark(benchmark_id: str) -> Benchmark: re...
class BenchmarkRegistry: @staticmethod def register_benchmark(benchmark_id: str, benchmark: Benchmark): pass @staticmethod def get_benchmark(benchmark_id: str) -> Benchmark: pass @staticmethod def get_benchmark_ids() -> list[str]: pass @staticmethod def get_verif...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/simpleqa/grader.py
benchmarks.simpleqa.grader.SimpleQAGrader
from typing import Any from ...graders.interface import IGrader from ...models.interface import IModel from ...datasets.interface import Row import re class SimpleQAGrader(IGrader): def __init__(self, grader_id: str, grader_model: IModel): super().__init__(grader_id) self.grader_model = grader_mod...
class SimpleQAGrader(IGrader): def __init__(self, grader_id: str, grader_model: IModel): pass def calculate_aggregate_metrics(self, results: list[dict[str, Any]], rows: list[Row]) -> dict[str, Any]: pass def topline_metric(self) -> str: pass def grade_row(self, row: Row) -> ...
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/benchmarks/simpleqa/preprocessor.py
benchmarks.simpleqa.preprocessor.SimpleQAPreprocessor
from ...datasets.interface import Row from ...preprocessor.interface import IPreprocessor class SimpleQAPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): super().__init__(preprocessor_id) def preprocess_row(self, row: Row) -> Row: return {'input_query': row['input_query']...
class SimpleQAPreprocessor(IPreprocessor): def __init__(self, preprocessor_id: str): pass def preprocess_row(self, row: Row) -> Row: pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/datasets/huggingface.py
datasets.huggingface.HuggingFaceDataset
from .interface import HuggingfaceDataSource, IDataset, Row from collections.abc import Iterator class HuggingFaceDataset(IDataset): def __init__(self, dataset_id: str, config: HuggingfaceDataSource): super().__init__(dataset_id) self.config = config def load(self) -> None: from datas...
class HuggingFaceDataset(IDataset): def __init__(self, dataset_id: str, config: HuggingfaceDataSource): pass def load(self) -> None: pass def __iter__(self) -> Iterator[Row]: pass def __len__(self) -> int: pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/datasets/interface.py
datasets.interface.DatasetSource
from pydantic import BaseModel class DatasetSource(BaseModel): type: str
class DatasetSource(BaseModel): pass
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meta-llama/llama-verifications
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/meta-llama_llama-verifications/llama_verifications/benchmarks/datasets/interface.py
datasets.interface.HuggingfaceDataSource
from typing import Any, Literal class HuggingfaceDataSource(DatasetSource): type: Literal['huggingface'] = 'huggingface' args: dict[str, Any] subsets: list[str] | None = None
class HuggingfaceDataSource(DatasetSource): pass
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