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src.models.config.ModelContextProtocolServer
class ModelContextProtocolServer(ConfigurationBase): """model context protocol server configuration.""" name: str provider_id: str = 'model-context-protocol' url: str
class ModelContextProtocolServer(ConfigurationBase): '''model context protocol server configuration.''' pass
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src.models.config.PostgreSQLDatabaseConfiguration
from typing_extensions import Self, Literal from pydantic import BaseModel, ConfigDict, Field, model_validator, FilePath, AnyHttpUrl, PositiveInt, SecretStr from typing import Optional, Any, Pattern import constants class PostgreSQLDatabaseConfiguration(ConfigurationBase): """PostgreSQL database configuration.""" ...
class PostgreSQLDatabaseConfiguration(ConfigurationBase): '''PostgreSQL database configuration.''' @model_validator(mode='after') def check_postgres_configuration(self) -> Self: '''Check PostgreSQL configuration.''' pass
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src.models.config.SQLiteDatabaseConfiguration
class SQLiteDatabaseConfiguration(ConfigurationBase): """SQLite database configuration.""" db_path: str
class SQLiteDatabaseConfiguration(ConfigurationBase): '''SQLite database configuration.''' pass
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src.models.config.ServiceConfiguration
from typing_extensions import Self, Literal from pydantic import BaseModel, ConfigDict, Field, model_validator, FilePath, AnyHttpUrl, PositiveInt, SecretStr class ServiceConfiguration(ConfigurationBase): """Service configuration.""" host: str = 'localhost' port: PositiveInt = 8080 auth_enabled: bool = ...
class ServiceConfiguration(ConfigurationBase): '''Service configuration.''' @model_validator(mode='after') def check_service_configuration(self) -> Self: '''Check service configuration.''' pass
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lightspeed-core/lightspeed-stack
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src.models.config.TLSConfiguration
from typing import Optional, Any, Pattern from typing_extensions import Self, Literal from pydantic import BaseModel, ConfigDict, Field, model_validator, FilePath, AnyHttpUrl, PositiveInt, SecretStr class TLSConfiguration(ConfigurationBase): """TLS configuration.""" tls_certificate_path: Optional[FilePath] = N...
class TLSConfiguration(ConfigurationBase): '''TLS configuration.''' @model_validator(mode='after') def check_tls_configuration(self) -> Self: '''Check TLS configuration.''' pass
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lightspeed-core/lightspeed-stack
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src.models.config.UserDataCollection
from pydantic import BaseModel, ConfigDict, Field, model_validator, FilePath, AnyHttpUrl, PositiveInt, SecretStr from typing_extensions import Self, Literal from typing import Optional, Any, Pattern class UserDataCollection(ConfigurationBase): """User data collection configuration.""" feedback_enabled: bool = ...
class UserDataCollection(ConfigurationBase): '''User data collection configuration.''' @model_validator(mode='after') def check_storage_location_is_set_when_needed(self) -> Self: '''Check that storage_location is set when enabled.''' pass
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/models/database/base.py
src.models.database.base.Base
from sqlalchemy.orm import DeclarativeBase class Base(DeclarativeBase): """Base class for all SQLAlchemy ORM models."""
class Base(DeclarativeBase): '''Base class for all SQLAlchemy ORM models.''' pass
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lightspeed-core/lightspeed-stack
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src.models.database.conversations.UserConversation
from models.database.base import Base from datetime import datetime from sqlalchemy.orm import Mapped, mapped_column from sqlalchemy import DateTime, func class UserConversation(Base): """Model for storing user conversation metadata.""" __tablename__ = 'user_conversation' id: Mapped[str] = mapped_column(pr...
class UserConversation(Base): '''Model for storing user conversation metadata.''' pass
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src.models.requests.Attachment
from pydantic import BaseModel, model_validator, field_validator, Field class Attachment(BaseModel): """Model representing an attachment that can be send from the UI as part of query. A list of attachments can be an optional part of 'query' request. Attributes: attachment_type: The attachment typ...
class Attachment(BaseModel): '''Model representing an attachment that can be send from the UI as part of query. A list of attachments can be an optional part of 'query' request. Attributes: attachment_type: The attachment type, like "log", "configuration" etc. content_type: The content type...
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src.models.requests.FeedbackCategory
from enum import Enum class FeedbackCategory(str, Enum): """Enum representing predefined feedback categories for AI responses. These categories help provide structured feedback about AI inference quality when users provide negative feedback (thumbs down). Multiple categories can be selected to provide...
class FeedbackCategory(str, Enum): '''Enum representing predefined feedback categories for AI responses. These categories help provide structured feedback about AI inference quality when users provide negative feedback (thumbs down). Multiple categories can be selected to provide comprehensive feedback...
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src.models.requests.FeedbackRequest
from pydantic import BaseModel, model_validator, field_validator, Field from utils import suid from typing import Optional, Self class FeedbackRequest(BaseModel): """Model representing a feedback request. Attributes: conversation_id: The required conversation ID (UUID). user_question: The requ...
class FeedbackRequest(BaseModel): '''Model representing a feedback request. Attributes: conversation_id: The required conversation ID (UUID). user_question: The required user question. llm_response: The required LLM response. sentiment: The optional sentiment. user_feedb...
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lightspeed-core/lightspeed-stack
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src.models.requests.FeedbackStatusUpdateRequest
from pydantic import BaseModel, model_validator, field_validator, Field class FeedbackStatusUpdateRequest(BaseModel): """Model representing a feedback status update request. Attributes: status: Value of the desired feedback enabled state. Example: ```python feedback_request = Feed...
class FeedbackStatusUpdateRequest(BaseModel): '''Model representing a feedback status update request. Attributes: status: Value of the desired feedback enabled state. Example: ```python feedback_request = FeedbackRequest( status=false ) ``` ''' d...
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src.models.requests.QueryRequest
from pydantic import BaseModel, model_validator, field_validator, Field from utils import suid from typing import Optional, Self from llama_stack_client.types.agents.turn_create_params import Document class QueryRequest(BaseModel): """Model representing a request for the LLM (Language Model). Attributes: ...
class QueryRequest(BaseModel): '''Model representing a request for the LLM (Language Model). Attributes: query: The query string. conversation_id: The optional conversation ID (UUID). provider: The optional provider. model: The optional model. system_prompt: The optional...
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src.models.responses.AuthorizedResponse
from pydantic import BaseModel, Field class AuthorizedResponse(BaseModel): """Model representing a response to an authorization request. Attributes: user_id: The ID of the logged in user. username: The name of the logged in user. """ user_id: str = Field(..., description='User ID, for ...
class AuthorizedResponse(BaseModel): '''Model representing a response to an authorization request. Attributes: user_id: The ID of the logged in user. username: The name of the logged in user. ''' pass
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src.models.responses.ConversationDeleteResponse
from pydantic import BaseModel, Field class ConversationDeleteResponse(BaseModel): """Model representing a response for deleting a conversation. Attributes: conversation_id: The conversation ID (UUID) that was deleted. success: Whether the deletion was successful. response: A message a...
class ConversationDeleteResponse(BaseModel): '''Model representing a response for deleting a conversation. Attributes: conversation_id: The conversation ID (UUID) that was deleted. success: Whether the deletion was successful. response: A message about the deletion result. Example: ...
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src.models.responses.ConversationDetails
from pydantic import BaseModel, Field from typing import Any, Optional class ConversationDetails(BaseModel): """Model representing the details of a user conversation. Attributes: conversation_id: The conversation ID (UUID). created_at: When the conversation was created. last_message_at...
class ConversationDetails(BaseModel): '''Model representing the details of a user conversation. Attributes: conversation_id: The conversation ID (UUID). created_at: When the conversation was created. last_message_at: When the last message was sent. message_count: Number of user ...
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lightspeed-core/lightspeed-stack
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src.models.responses.ConversationResponse
from typing import Any, Optional from pydantic import BaseModel, Field class ConversationResponse(BaseModel): """Model representing a response for retrieving a conversation. Attributes: conversation_id: The conversation ID (UUID). chat_history: The simplified chat history as a list of conversa...
class ConversationResponse(BaseModel): '''Model representing a response for retrieving a conversation. Attributes: conversation_id: The conversation ID (UUID). chat_history: The simplified chat history as a list of conversation turns. Example: ```python conversation_response...
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src.models.responses.ConversationsListResponse
from pydantic import BaseModel, Field class ConversationsListResponse(BaseModel): """Model representing a response for listing conversations of a user. Attributes: conversations: List of conversation details associated with the user. Example: ```python conversations_list = Convers...
class ConversationsListResponse(BaseModel): '''Model representing a response for listing conversations of a user. Attributes: conversations: List of conversation details associated with the user. Example: ```python conversations_list = ConversationsListResponse( conversa...
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lightspeed-core/lightspeed-stack
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src.models.responses.ErrorResponse
from pydantic import BaseModel, Field class ErrorResponse(BaseModel): """Model representing error response for query endpoint.""" detail: dict[str, str] = Field(description='Error details', examples=[{'response': 'Error while validation question', 'cause': 'Failed to handle request to https://bam-api.res.ibm.c...
class ErrorResponse(BaseModel): '''Model representing error response for query endpoint.''' pass
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src.models.responses.FeedbackResponse
from pydantic import BaseModel, Field class FeedbackResponse(BaseModel): """Model representing a response to a feedback request. Attributes: response: The response of the feedback request. Example: ```python feedback_response = FeedbackResponse(response="feedback received") ...
class FeedbackResponse(BaseModel): '''Model representing a response to a feedback request. Attributes: response: The response of the feedback request. Example: ```python feedback_response = FeedbackResponse(response="feedback received") ``` ''' pass
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lightspeed-core/lightspeed-stack
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src.models.responses.FeedbackStatusUpdateResponse
from pydantic import BaseModel, Field class FeedbackStatusUpdateResponse(BaseModel): """ Model representing a response to a feedback status update request. Attributes: status: The previous and current status of the service and who updated it. Example: ```python status_response...
class FeedbackStatusUpdateResponse(BaseModel): ''' Model representing a response to a feedback status update request. Attributes: status: The previous and current status of the service and who updated it. Example: ```python status_response = StatusResponse( status={ ...
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lightspeed-core/lightspeed-stack
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src.models.responses.ForbiddenResponse
class ForbiddenResponse(UnauthorizedResponse): """Model representing response for forbidden access.""" model_config = {'json_schema_extra': {'examples': [{'detail': 'Forbidden: User is not authorized to access this resource'}]}}
class ForbiddenResponse(UnauthorizedResponse): '''Model representing response for forbidden access.''' pass
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src.models.responses.InfoResponse
from pydantic import BaseModel, Field class InfoResponse(BaseModel): """Model representing a response to an info request. Attributes: name: Service name. service_version: Service version. llama_stack_version: Llama Stack version. Example: ```python info_response = ...
class InfoResponse(BaseModel): '''Model representing a response to an info request. Attributes: name: Service name. service_version: Service version. llama_stack_version: Llama Stack version. Example: ```python info_response = InfoResponse( name="Lightspe...
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src.models.responses.LivenessResponse
from pydantic import BaseModel, Field class LivenessResponse(BaseModel): """Model representing a response to a liveness request. Attributes: alive: If app is alive. Example: ```python liveness_response = LivenessResponse(alive=True) ``` """ alive: bool model_co...
class LivenessResponse(BaseModel): '''Model representing a response to a liveness request. Attributes: alive: If app is alive. Example: ```python liveness_response = LivenessResponse(alive=True) ``` ''' pass
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lightspeed-core/lightspeed-stack
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src.models.responses.ModelsResponse
from typing import Any, Optional from pydantic import BaseModel, Field class ModelsResponse(BaseModel): """Model representing a response to models request.""" models: list[dict[str, Any]] = Field(..., description='List of models available', examples=[{'identifier': 'openai/gpt-4-turbo', 'metadata': {}, 'api_mo...
class ModelsResponse(BaseModel): '''Model representing a response to models request.''' pass
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lightspeed-core/lightspeed-stack
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src.models.responses.NotAvailableResponse
from pydantic import BaseModel, Field class NotAvailableResponse(BaseModel): """Model representing error response for readiness endpoint.""" detail: dict[str, str] model_config = {'json_schema_extra': {'examples': [{'detail': {'response': 'Service is not ready', 'cause': 'Index is not ready'}}, {'detail': ...
class NotAvailableResponse(BaseModel): '''Model representing error response for readiness endpoint.''' pass
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/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/models/responses.py
src.models.responses.ProviderHealthStatus
from typing import Any, Optional from pydantic import BaseModel, Field class ProviderHealthStatus(BaseModel): """Model representing the health status of a provider. Attributes: provider_id: The ID of the provider. status: The health status ('ok', 'unhealthy', 'not_implemented'). messag...
class ProviderHealthStatus(BaseModel): '''Model representing the health status of a provider. Attributes: provider_id: The ID of the provider. status: The health status ('ok', 'unhealthy', 'not_implemented'). message: Optional message about the health status. ''' pass
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/models/responses.py
src.models.responses.QueryResponse
from pydantic import BaseModel, Field from typing import Any, Optional class QueryResponse(BaseModel): """Model representing LLM response to a query. Attributes: conversation_id: The optional conversation ID (UUID). response: The response. """ conversation_id: Optional[str] = Field(Non...
class QueryResponse(BaseModel): '''Model representing LLM response to a query. Attributes: conversation_id: The optional conversation ID (UUID). response: The response. ''' pass
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/models/responses.py
src.models.responses.ReadinessResponse
from pydantic import BaseModel, Field class ReadinessResponse(BaseModel): """Model representing response to a readiness request. Attributes: ready: If service is ready. reason: The reason for the readiness. providers: List of unhealthy providers in case of readiness failure. Examp...
class ReadinessResponse(BaseModel): '''Model representing response to a readiness request. Attributes: ready: If service is ready. reason: The reason for the readiness. providers: List of unhealthy providers in case of readiness failure. Example: ```python readiness_...
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/models/responses.py
src.models.responses.StatusResponse
from pydantic import BaseModel, Field class StatusResponse(BaseModel): """Model representing a response to a status request. Attributes: functionality: The functionality of the service. status: The status of the service. Example: ```python status_response = StatusResponse(...
class StatusResponse(BaseModel): '''Model representing a response to a status request. Attributes: functionality: The functionality of the service. status: The status of the service. Example: ```python status_response = StatusResponse( functionality="feedback", ...
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/models/responses.py
src.models.responses.UnauthorizedResponse
from pydantic import BaseModel, Field class UnauthorizedResponse(BaseModel): """Model representing response for missing or invalid credentials.""" detail: str model_config = {'json_schema_extra': {'examples': [{'detail': 'Unauthorized: No auth header found'}]}}
class UnauthorizedResponse(BaseModel): '''Model representing response for missing or invalid credentials.''' pass
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/utils/checks.py
src.utils.checks.InvalidConfigurationError
class InvalidConfigurationError(Exception): """Lightspeed configuration is invalid."""
class InvalidConfigurationError(Exception): '''Lightspeed configuration is invalid.''' pass
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/utils/llama_stack_version.py
src.utils.llama_stack_version.InvalidLlamaStackVersionException
class InvalidLlamaStackVersionException(Exception): """Llama Stack version is not valid."""
class InvalidLlamaStackVersionException(Exception): '''Llama Stack version is not valid.''' pass
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/utils/types.py
src.utils.types.GraniteToolParser
from llama_stack_client.types.shared.completion_message import CompletionMessage from typing import Any, Optional from llama_stack_client.lib.agents.tool_parser import ToolParser from llama_stack_client.types.shared.tool_call import ToolCall class GraniteToolParser(ToolParser): """Workaround for 'tool_calls' with ...
class GraniteToolParser(ToolParser): '''Workaround for 'tool_calls' with granite models.''' def get_tool_calls(self, output_message: CompletionMessage) -> list[ToolCall]: '''Use 'tool_calls' associated with the CompletionMessage, if available.''' pass @staticmethod def get_parser(model...
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/utils/types.py
src.utils.types.Singleton
class Singleton(type): """Metaclass for Singleton support.""" _instances = {} def __call__(cls, *args, **kwargs): """Ensure a single instance is created.""" if cls not in cls._instances: cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) return cls._in...
class Singleton(type): '''Metaclass for Singleton support.''' def __call__(cls, *args, **kwargs): '''Ensure a single instance is created.''' pass
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/utils/types.py
src.utils.types.ToolCallSummary
from typing import Any, Optional from pydantic.main import BaseModel class ToolCallSummary(BaseModel): """Represents a tool call for data collection. Use our own tool call model to keep things consistent across llama upgrades or if we used something besides llama in the future. """ id: str nam...
class ToolCallSummary(BaseModel): '''Represents a tool call for data collection. Use our own tool call model to keep things consistent across llama upgrades or if we used something besides llama in the future. ''' pass
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lightspeed-core/lightspeed-stack
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/lightspeed-core_lightspeed-stack/src/utils/types.py
src.utils.types.TurnSummary
from llama_stack_client.lib.agents.event_logger import interleaved_content_as_str from llama_stack_client.types.tool_execution_step import ToolExecutionStep from pydantic.main import BaseModel class TurnSummary(BaseModel): """Summary of a turn in llama stack.""" llm_response: str tool_calls: list[ToolCallS...
class TurnSummary(BaseModel): '''Summary of a turn in llama stack.''' def append_tool_calls_from_llama(self, tec: ToolExecutionStep) -> None: '''Append the tool calls from a llama tool execution step.''' pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/chat_models/oci_data_science.py
langchain_oci.chat_models.oci_data_science.ChatOCIModelDeployment
from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Literal, Optional, Sequence, Type, Union from pydantic import BaseModel, Field, model_validator from operator import itemgetter import importlib from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.lang...
class ChatOCIModelDeployment(BaseChatModel, BaseOCIModelDeployment): '''OCI Data Science Model Deployment chat model integration. Prerequisite The OCI Model Deployment plugins are installable only on python version 3.9 and above. If you're working inside the notebook, try installing the...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/chat_models/oci_data_science.py
langchain_oci.chat_models.oci_data_science.ChatOCIModelDeploymentTGI
from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Literal, Optional, Sequence, Type, Union class ChatOCIModelDeploymentTGI(ChatOCIModelDeployment): """OCI large language chat models deployed with Text Generation Inference. To use, you must provide the model HTTP endpoint from your deploye...
class ChatOCIModelDeploymentTGI(ChatOCIModelDeployment): '''OCI large language chat models deployed with Text Generation Inference. To use, you must provide the model HTTP endpoint from your deployed model, e.g. https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<ocid>/predict. To authenticate, ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/chat_models/oci_data_science.py
langchain_oci.chat_models.oci_data_science.ChatOCIModelDeploymentVLLM
from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Literal, Optional, Sequence, Type, Union class ChatOCIModelDeploymentVLLM(ChatOCIModelDeployment): """OCI large language chat models deployed with vLLM. To use, you must provide the model HTTP endpoint from your deployed model, e.g. ht...
class ChatOCIModelDeploymentVLLM(ChatOCIModelDeployment): '''OCI large language chat models deployed with vLLM. To use, you must provide the model HTTP endpoint from your deployed model, e.g. https://modeldeployment.us-ashburn-1.oci.customer-oci.com/<ocid>/predict. To authenticate, `oracle-ads` has bee...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/chat_models/oci_generative_ai.py
langchain_oci.chat_models.oci_generative_ai.ChatOCIGenAI
from typing import Any, Callable, Dict, Iterator, List, Literal, Mapping, Optional, Sequence, Set, Type, Union from langchain_core.language_models.chat_models import BaseChatModel, generate_from_stream from langchain_core.tools import BaseTool from langchain_core.output_parsers.openai_tools import JsonOutputKeyToolsPar...
class ChatOCIGenAI(BaseChatModel, OCIGenAIBase): '''ChatOCIGenAI chat model integration. Setup: Install ``langchain-oci`` and the ``oci`` sdk. .. code-block:: bash pip install -U langchain-oci oci Key init args — completion params: model_id: str Id of the OCIGenAI ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/chat_models/oci_generative_ai.py
langchain_oci.chat_models.oci_generative_ai.CohereProvider
import uuid from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_function from langchain_core.messages.tool import ToolCallChunk, tool_call_chunk from pydantic import BaseModel, ConfigDict from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage...
class CohereProvider(Provider): '''Provider implementation for Cohere.''' def __init__(self) -> None: pass def chat_response_to_text(self, response: Any) -> str: '''Extract text from a Cohere chat response.''' pass def chat_stream_to_text(self, event_data: Dict) -> str: ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/chat_models/oci_generative_ai.py
langchain_oci.chat_models.oci_generative_ai.GenericProvider
from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_function from langchain_core.messages.tool import ToolCallChunk, tool_call_chunk from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, ChatMessage, HumanMessage, SystemMessage, ToolCall, T...
class GenericProvider(Provider): '''Provider for models using generic API spec.''' def __init__(self) -> None: pass def chat_response_to_text(self, response: Any) -> str: '''Extract text from Meta chat response.''' pass def chat_stream_to_text(self, event_data: Dict) -> str: ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/chat_models/oci_generative_ai.py
langchain_oci.chat_models.oci_generative_ai.OCIUtils
import uuid import re from typing import Any, Callable, Dict, Iterator, List, Literal, Mapping, Optional, Sequence, Set, Type, Union import json from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, ChatMessage, HumanMessage, SystemMessage, ToolCall, ToolMessage from pydantic import BaseModel, Con...
class OCIUtils: '''Utility functions for OCI Generative AI integration.''' @staticmethod def is_pydantic_class(obj: Any) -> bool: '''Check if an object is a Pydantic BaseModel subclass.''' pass @staticmethod def remove_signature_from_tool_description(name: str, description: str) -> ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/chat_models/oci_generative_ai.py
langchain_oci.chat_models.oci_generative_ai.Provider
from typing import Any, Callable, Dict, Iterator, List, Literal, Mapping, Optional, Sequence, Set, Type, Union from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, ChatMessage, HumanMessage, SystemMessage, ToolCall, ToolMessage from pydantic import BaseModel, ConfigDict from langchain_core.messag...
class Provider(ABC): '''Abstract base class for OCI Generative AI providers.''' @property @abstractmethod def stop_sequence_key(self) -> str: '''Return the stop sequence key for the provider.''' pass @abstractmethod def chat_response_to_text(self, response: Any) -> str: ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/embeddings/oci_data_science_model_deployment_endpoint.py
langchain_oci.embeddings.oci_data_science_model_deployment_endpoint.OCIModelDeploymentEndpointEmbeddings
from typing import Any, Callable, Dict, List, Mapping, Optional from pydantic import BaseModel, Field, model_validator import requests from langchain_core.embeddings import Embeddings from langchain_core.utils import get_from_dict_or_env class OCIModelDeploymentEndpointEmbeddings(BaseModel, Embeddings): """Embeddi...
class OCIModelDeploymentEndpointEmbeddings(BaseModel, Embeddings): '''Embedding model deployed on OCI Data Science Model Deployment. Example: .. code-block:: python from langchain_oci.embeddings import OCIModelDeploymentEndpointEmbeddings embeddings = OCIModelDeploymentEndpointE...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/embeddings/oci_data_science_model_deployment_endpoint.py
langchain_oci.embeddings.oci_data_science_model_deployment_endpoint.TokenExpiredError
class TokenExpiredError(Exception): pass
class TokenExpiredError(Exception): pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/embeddings/oci_generative_ai.py
langchain_oci.embeddings.oci_generative_ai.OCIGenAIEmbeddings
from pydantic import BaseModel, ConfigDict from langchain_core.embeddings import Embeddings from langchain_core.utils import pre_init from typing import Any, Dict, Iterator, List, Mapping, Optional class OCIGenAIEmbeddings(BaseModel, Embeddings): """OCI embedding models. To authenticate, the OCI client uses t...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_data_science_model_deployment_endpoint.py
langchain_oci.llms.oci_data_science_model_deployment_endpoint.BaseOCIModelDeployment
from contextlib import asynccontextmanager from langchain_core.callbacks import AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun from langchain_core.load.serializable import Serializable import traceback from langchain_core.utils import get_from_dict_or_env import aiohttp from pydantic import Field, model_valida...
class BaseOCIModelDeployment(Serializable): '''Base class for LLM deployed on OCI Data Science Model Deployment.''' @model_validator(mode='before') @classmethod def validate_environment(cls, values: Dict) -> Dict: '''Checks if oracle-ads is installed and get credentials/endpoint from en...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_data_science_model_deployment_endpoint.py
langchain_oci.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentLLM
from pydantic import Field, model_validator import json from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator from langchain_core.callbacks import AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun from typing import Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List,...
class OCIModelDeploymentLLM(BaseLLM, BaseOCIModelDeployment): '''LLM deployed on OCI Data Science Model Deployment. To use, you must provide the model HTTP endpoint from your deployed model, e.g. https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict. To authenticate, `oracle-ads` has ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_data_science_model_deployment_endpoint.py
langchain_oci.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentTGI
from langchain_core.outputs import Generation, GenerationChunk, LLMResult from typing import Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, Literal, Optional, Union class OCIModelDeploymentTGI(OCIModelDeploymentLLM): """OCI Data Science Model Deployment TGI Endpoint. To use, you must prov...
class OCIModelDeploymentTGI(OCIModelDeploymentLLM): '''OCI Data Science Model Deployment TGI Endpoint. To use, you must provide the model HTTP endpoint from your deployed model, e.g. https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict. To authenticate, `oracle-ads` has been used to ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_data_science_model_deployment_endpoint.py
langchain_oci.llms.oci_data_science_model_deployment_endpoint.OCIModelDeploymentVLLM
from typing import Any, AsyncGenerator, AsyncIterator, Callable, Dict, Iterator, List, Literal, Optional, Union class OCIModelDeploymentVLLM(OCIModelDeploymentLLM): """VLLM deployed on OCI Data Science Model Deployment To use, you must provide the model HTTP endpoint from your deployed model, e.g. https:/...
class OCIModelDeploymentVLLM(OCIModelDeploymentLLM): '''VLLM deployed on OCI Data Science Model Deployment To use, you must provide the model HTTP endpoint from your deployed model, e.g. https://modeldeployment.<region>.oci.customer-oci.com/<md_ocid>/predict. To authenticate, `oracle-ads` has been used...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_data_science_model_deployment_endpoint.py
langchain_oci.llms.oci_data_science_model_deployment_endpoint.ServerError
class ServerError(Exception): """Raises when encounter server error when making inference."""
class ServerError(Exception): '''Raises when encounter server error when making inference.''' pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_data_science_model_deployment_endpoint.py
langchain_oci.llms.oci_data_science_model_deployment_endpoint.TokenExpiredError
class TokenExpiredError(Exception): """Raises when token expired."""
class TokenExpiredError(Exception): '''Raises when token expired.''' pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_generative_ai.py
langchain_oci.llms.oci_generative_ai.CohereProvider
from typing import Any, Dict, Iterator, List, Mapping, Optional class CohereProvider(Provider): stop_sequence_key: str = 'stop_sequences' def __init__(self) -> None: from oci.generative_ai_inference import models self.llm_inference_request = models.CohereLlmInferenceRequest def completion...
class CohereProvider(Provider): def __init__(self) -> None: pass def completion_response_to_text(self, response: Any) -> str: pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_generative_ai.py
langchain_oci.llms.oci_generative_ai.GenericProvider
from typing import Any, Dict, Iterator, List, Mapping, Optional class GenericProvider(Provider): """Provider for models using generic API spec.""" stop_sequence_key: str = 'stop' def __init__(self) -> None: from oci.generative_ai_inference import models self.llm_inference_request = models....
class GenericProvider(Provider): '''Provider for models using generic API spec.''' def __init__(self) -> None: pass def completion_response_to_text(self, response: Any) -> str: pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_generative_ai.py
langchain_oci.llms.oci_generative_ai.MetaProvider
class MetaProvider(GenericProvider): """Provider for Meta models. This provider is for backward compatibility.""" pass
class MetaProvider(GenericProvider): '''Provider for Meta models. This provider is for backward compatibility.''' pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_generative_ai.py
langchain_oci.llms.oci_generative_ai.OCIAuthType
from enum import Enum class OCIAuthType(Enum): """OCI authentication types as enumerator.""" API_KEY = 1 SECURITY_TOKEN = 2 INSTANCE_PRINCIPAL = 3 RESOURCE_PRINCIPAL = 4
class OCIAuthType(Enum): '''OCI authentication types as enumerator.''' pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_generative_ai.py
langchain_oci.llms.oci_generative_ai.OCIGenAI
import json from langchain_oci.llms.utils import enforce_stop_tokens from pydantic import BaseModel, ConfigDict, Field from typing import Any, Dict, Iterator, List, Mapping, Optional from langchain_core.outputs import GenerationChunk from langchain_core.language_models.llms import LLM from langchain_core.callbacks impo...
class OCIGenAI(LLM, OCIGenAIBase): '''OCI large language models. To authenticate, the OCI client uses the methods described in https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm The authentifcation method is passed through auth_type and should be one of: API_KEY ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_generative_ai.py
langchain_oci.llms.oci_generative_ai.OCIGenAIBase
from pydantic import BaseModel, ConfigDict, Field from abc import ABC, abstractmethod from typing import Any, Dict, Iterator, List, Mapping, Optional from langchain_core.utils import pre_init class OCIGenAIBase(BaseModel, ABC): """Base class for OCI GenAI models""" client: Any = Field(default=None, exclude=Tru...
class OCIGenAIBase(BaseModel, ABC): '''Base class for OCI GenAI models''' @pre_init def validate_environment(cls, values: Dict) -> Dict: '''Validate that OCI config and python package exists in environment.''' pass def make_security_token_signer(oci_config): ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oci/langchain_oci/llms/oci_generative_ai.py
langchain_oci.llms.oci_generative_ai.Provider
from typing import Any, Dict, Iterator, List, Mapping, Optional from abc import ABC, abstractmethod class Provider(ABC): @property @abstractmethod def stop_sequence_key(self) -> str: ... @abstractmethod def completion_response_to_text(self, response: Any) -> str: ...
class Provider(ABC): @property @abstractmethod def stop_sequence_key(self) -> str: pass @abstractmethod def completion_response_to_text(self, response: Any) -> str: pass
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oracledb/langchain_oracledb/document_loaders/oracleadb_loader.py
langchain_oracledb.document_loaders.oracleadb_loader.OracleAutonomousDatabaseLoader
import oracledb from typing import Any, Dict, List, Optional, Union from langchain_community.document_loaders.base import BaseLoader from langchain_core.documents import Document class OracleAutonomousDatabaseLoader(BaseLoader): """ Load from Oracle ADB Autonomous Database connection can be made by connec...
class OracleAutonomousDatabaseLoader(BaseLoader): ''' Load from Oracle ADB Autonomous Database connection can be made by connection_string or tns alias. wallet_location and wallet_password are required for TLS connection. Each document will represent one row of the query result. Columns are...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oracledb/langchain_oracledb/document_loaders/oracleai.py
langchain_oracledb.document_loaders.oracleai.OracleDocLoader
from langchain_core.documents import Document from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import os import oracledb import traceback from langchain_core.document_loaders import BaseLoader import json class OracleDocLoader(BaseLoader): """Read documents using OracleDocLoader Args: ...
class OracleDocLoader(BaseLoader): '''Read documents using OracleDocLoader Args: conn: Oracle Connection, params: Loader parameters. ''' def __init__(self, conn: Connection, params: Dict[str, Any], **kwargs: Any): pass def load(self) -> List[Document]: '''Load data...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oracledb/langchain_oracledb/document_loaders/oracleai.py
langchain_oracledb.document_loaders.oracleai.OracleDocReader
import random import oracledb from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union from langchain_core.documents import Document import struct import hashlib import json import time class OracleDocReader: """Read a file""" @staticmethod def generate_object_id(input_string: Union[str, ...
class OracleDocReader: '''Read a file''' @staticmethod def generate_object_id(input_string: Union[str, None]=None) -> str: pass @staticmethod def read_file(conn: Connection, file_path: str, params: dict) -> Union[Document, None]: '''Read a file using OracleReader Args: ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oracledb/langchain_oracledb/document_loaders/oracleai.py
langchain_oracledb.document_loaders.oracleai.OracleTextSplitter
from langchain_text_splitters import TextSplitter from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union import oracledb import traceback import json class OracleTextSplitter(TextSplitter): """Splitting text using Oracle chunker.""" def __init__(self, conn: Connection, params: Dict[str, Any...
class OracleTextSplitter(TextSplitter): '''Splitting text using Oracle chunker.''' def __init__(self, conn: Connection, params: Dict[str, Any], **kwargs: Any) -> None: '''Initialize.''' pass def split_text(self, text: str) -> List[str]: '''Split incoming text and return chunks.'''...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oracledb/langchain_oracledb/document_loaders/oracleai.py
langchain_oracledb.document_loaders.oracleai.ParseOracleDocMetadata
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union from html.parser import HTMLParser class ParseOracleDocMetadata(HTMLParser): """Parse Oracle doc metadata...""" def __init__(self) -> None: super().__init__() self.reset() self.match = False self.metadata...
class ParseOracleDocMetadata(HTMLParser): '''Parse Oracle doc metadata...''' def __init__(self) -> None: pass def handle_starttag(self, tag: str, attrs: List[Tuple[str, Optional[str]]]) -> None: pass def handle_data(self, data: str) -> None: pass def get_metadata(self) -...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oracledb/langchain_oracledb/embeddings/oracleai.py
langchain_oracledb.embeddings.oracleai.OracleEmbeddings
from pydantic import BaseModel, ConfigDict import json from typing import TYPE_CHECKING, Any, Dict, List, Optional import traceback import oracledb from langchain_core.embeddings import Embeddings class OracleEmbeddings(BaseModel, Embeddings): """Get Embeddings""" conn: Any = None params: Dict[str, Any] ...
class OracleEmbeddings(BaseModel, Embeddings): '''Get Embeddings''' def __init__(self, **kwargs: Any): pass @staticmethod def load_onnx_model(conn: Connection, dir: str, onnx_file: str, model_name: str) -> None: '''Load an ONNX model to Oracle Database. Args: conn: ...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oracledb/langchain_oracledb/utilities/oracleai.py
langchain_oracledb.utilities.oracleai.OracleSummary
from typing import TYPE_CHECKING, Any, Dict, List, Optional import oracledb import json from langchain_core.documents import Document import traceback class OracleSummary: """Get Summary Args: conn: Oracle Connection, params: Summary parameters, proxy: Proxy """ def __init__(se...
class OracleSummary: '''Get Summary Args: conn: Oracle Connection, params: Summary parameters, proxy: Proxy ''' def __init__(self, conn: Connection, params: Dict[str, Any], proxy: Optional[str]=None): pass def get_summary(self, docs: Any) -> List[str]: '''G...
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oracle/langchain-oracle
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/oracle_langchain-oracle/libs/oracledb/langchain_oracledb/vectorstores/oraclevs.py
langchain_oracledb.vectorstores.oraclevs.OracleVS
from langchain_core.documents import Document import inspect from langchain_core.embeddings import Embeddings import numpy as np from langchain_core.vectorstores import VectorStore from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, TypeVar, Union, cast import oracledb from ..e...
class OracleVS(VectorStore): '''`OracleVS` vector store. To use, you should have both: - the ``oracledb`` python package installed - a connection string associated with a OracleDBCluster having deployed an Search index Example: .. code-block:: python from langchain_oracle...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/adapters/client/vscode.py
apm_cli.adapters.client.vscode.VSCodeClientAdapter
import json from ...registry.integration import RegistryIntegration from ...registry.client import SimpleRegistryClient import os from pathlib import Path from .base import MCPClientAdapter class VSCodeClientAdapter(MCPClientAdapter): """VSCode implementation of MCP client adapter. This adapter handles VSCode...
class VSCodeClientAdapter(MCPClientAdapter): '''VSCode implementation of MCP client adapter. This adapter handles VSCode-specific configuration for MCP servers using a repository-level .vscode/mcp.json file, following the format specified in the VSCode documentation. ''' def __init__(self, reg...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/adapters/package_manager/default_manager.py
apm_cli.adapters.package_manager.default_manager.DefaultMCPPackageManager
from ...registry.integration import RegistryIntegration from .base import MCPPackageManagerAdapter from ...config import get_default_client class DefaultMCPPackageManager(MCPPackageManagerAdapter): """Implementation of the default MCP package manager.""" def install(self, package_name, version=None): ...
class DefaultMCPPackageManager(MCPPackageManagerAdapter): '''Implementation of the default MCP package manager.''' def install(self, package_name, version=None): '''Install an MCP package. Args: package_name (str): Name of the package to install. version (str, optional)...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/compilation/agents_compiler.py
apm_cli.compilation.agents_compiler.AgentsCompiler
from ..version import get_version from ..primitives.models import PrimitiveCollection import datetime from .link_resolver import resolve_markdown_links, validate_link_targets from ..primitives.discovery import discover_primitives from typing import List, Optional, Dict, Any from pathlib import Path from .template_build...
class AgentsCompiler: '''Main compiler for generating AGENTS.md files.''' def __init__(self, base_dir: str='.'): '''Initialize the compiler. Args: base_dir (str): Base directory for compilation. Defaults to current directory. ''' pass def compile(self, config: ...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/compilation/agents_compiler.py
apm_cli.compilation.agents_compiler.CompilationConfig
from pathlib import Path from typing import List, Optional, Dict, Any from dataclasses import dataclass @dataclass class CompilationConfig: """Configuration for AGENTS.md compilation.""" output_path: str = 'AGENTS.md' chatmode: Optional[str] = None resolve_links: bool = True dry_run: bool = False ...
@dataclass class CompilationConfig: '''Configuration for AGENTS.md compilation.''' @classmethod def from_apm_yml(cls, **overrides) -> 'CompilationConfig': '''Create configuration from apm.yml with command-line overrides. Args: **overrides: Command-line arguments that override con...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/compilation/agents_compiler.py
apm_cli.compilation.agents_compiler.CompilationResult
from dataclasses import dataclass from typing import List, Optional, Dict, Any @dataclass class CompilationResult: """Result of AGENTS.md compilation.""" success: bool output_path: str content: str warnings: List[str] errors: List[str] stats: Dict[str, Any]
@dataclass class CompilationResult: '''Result of AGENTS.md compilation.''' pass
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/core/script_runner.py
apm_cli.core.script_runner.ScriptRunner
import re import yaml from typing import Dict, Optional from pathlib import Path import subprocess class ScriptRunner: """Executes APM scripts with auto-compilation of .prompt.md files.""" def __init__(self, compiler=None): """Initialize script runner with optional compiler.""" self.compiler =...
class ScriptRunner: '''Executes APM scripts with auto-compilation of .prompt.md files.''' def __init__(self, compiler=None): '''Initialize script runner with optional compiler.''' pass def run_script(self, script_name: str, params: Dict[str, str]) -> bool: '''Run a script from apm...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/primitives/models.py
apm_cli.primitives.models.Context
from typing import Optional, List, Union from pathlib import Path from dataclasses import dataclass @dataclass class Context: """Represents a context primitive.""" name: str file_path: Path content: str description: Optional[str] = None author: Optional[str] = None version: Optional[str] = ...
@dataclass class Context: '''Represents a context primitive.''' def validate(self) -> List[str]: '''Validate context structure. Returns: List[str]: List of validation errors. ''' pass
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/primitives/models.py
apm_cli.primitives.models.Instruction
from typing import Optional, List, Union from pathlib import Path from dataclasses import dataclass @dataclass class Instruction: """Represents an instruction primitive.""" name: str file_path: Path description: str apply_to: str content: str author: Optional[str] = None version: Option...
@dataclass class Instruction: '''Represents an instruction primitive.''' def validate(self) -> List[str]: '''Validate instruction structure. Returns: List[str]: List of validation errors. ''' pass
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/primitives/models.py
apm_cli.primitives.models.PrimitiveCollection
from dataclasses import dataclass from typing import Optional, List, Union @dataclass class PrimitiveCollection: """Collection of discovered primitives.""" chatmodes: List[Chatmode] instructions: List[Instruction] contexts: List[Context] def __init__(self): self.chatmodes = [] self...
@dataclass class PrimitiveCollection: '''Collection of discovered primitives.''' def __init__(self): pass def add_primitive(self, primitive: Primitive) -> None: '''Add a primitive to the appropriate collection.''' pass def all_primitives(self) -> List[Primitive]: '''Ge...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/registry/client.py
apm_cli.registry.client.SimpleRegistryClient
import requests import os from typing import Dict, List, Optional, Any, Tuple class SimpleRegistryClient: """Simple client for querying MCP registries for server discovery.""" def __init__(self, registry_url: Optional[str]=None): """Initialize the registry client. Args: registry_u...
class SimpleRegistryClient: '''Simple client for querying MCP registries for server discovery.''' def __init__(self, registry_url: Optional[str]=None): '''Initialize the registry client. Args: registry_url (str, optional): URL of the MCP registry. If not provided, u...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/registry/integration.py
apm_cli.registry.integration.RegistryIntegration
from .client import SimpleRegistryClient from typing import Dict, List, Any, Optional import requests class RegistryIntegration: """Integration class for connecting registry discovery to package manager.""" def __init__(self, registry_url: Optional[str]=None): """Initialize the registry integration. ...
class RegistryIntegration: '''Integration class for connecting registry discovery to package manager.''' def __init__(self, registry_url: Optional[str]=None): '''Initialize the registry integration. Args: registry_url (str, optional): URL of the MCP registry. If not...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/runtime/codex_runtime.py
apm_cli.runtime.codex_runtime.CodexRuntime
import subprocess import shutil from typing import Dict, Any, Optional from .base import RuntimeAdapter class CodexRuntime(RuntimeAdapter): """APM adapter for the Codex CLI.""" def __init__(self, model_name: Optional[str]=None): """Initialize Codex runtime. Args: model_name: Model...
class CodexRuntime(RuntimeAdapter): '''APM adapter for the Codex CLI.''' def __init__(self, model_name: Optional[str]=None): '''Initialize Codex runtime. Args: model_name: Model name (not used for Codex, included for compatibility) ''' pass def execute_prompt(s...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/runtime/factory.py
apm_cli.runtime.factory.RuntimeFactory
from typing import List, Dict, Any, Optional, Type from .llm_runtime import LLMRuntime from .base import RuntimeAdapter from .codex_runtime import CodexRuntime class RuntimeFactory: """Factory for creating runtime adapters with auto-detection.""" _RUNTIME_ADAPTERS: List[Type[RuntimeAdapter]] = [CodexRuntime, L...
class RuntimeFactory: '''Factory for creating runtime adapters with auto-detection.''' @classmethod def get_available_runtimes(cls) -> List[Dict[str, Any]]: '''Get list of available runtimes on the system. Returns: List[Dict[str, Any]]: List of available runtime information ...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/runtime/llm_runtime.py
apm_cli.runtime.llm_runtime.LLMRuntime
from typing import Dict, Any, Optional from .base import RuntimeAdapter import subprocess class LLMRuntime(RuntimeAdapter): """APM adapter for the llm CLI.""" def __init__(self, model_name: Optional[str]=None): """Initialize LLM runtime with specified model. Args: model_name: Name...
class LLMRuntime(RuntimeAdapter): '''APM adapter for the llm CLI.''' def __init__(self, model_name: Optional[str]=None): '''Initialize LLM runtime with specified model. Args: model_name: Name of the LLM model to use (optional) ''' pass def execute_prompt(self, ...
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danielmeppiel/apm-cli
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/danielmeppiel_apm-cli/src/apm_cli/runtime/manager.py
apm_cli.runtime.manager.RuntimeManager
import sys import shutil import subprocess import tempfile from typing import Dict, List, Optional import click from pathlib import Path from colorama import Fore, Style class RuntimeManager: """Manages AI runtime installation and configuration via embedded scripts.""" def __init__(self): self.runtime...
class RuntimeManager: '''Manages AI runtime installation and configuration via embedded scripts.''' def __init__(self): pass def get_embedded_script(self, script_name: str) -> str: '''Get embedded setup script content.''' pass def get_common_script(self) -> str: '''Ge...
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pangeo-data/ncar-hackathon-xarray-on-gpus
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pangeo-data_ncar-hackathon-xarray-on-gpus/zarr_ML_optimization/era5_dataloader.py
era5_dataloader.ERA5Dataset
import os import xarray as xr import numpy as np class ERA5Dataset: """ Load multiple years of ERA5 and forcing datasets from Zarr. Each __getitem__(index) returns (input, target) as NumPy arrays. """ def __init__(self, data_path, start_year, end_year, input_vars, target_vars=None, forecast_step=...
class ERA5Dataset: ''' Load multiple years of ERA5 and forcing datasets from Zarr. Each __getitem__(index) returns (input, target) as NumPy arrays. ''' def __init__(self, data_path, start_year, end_year, input_vars, target_vars=None, forecast_step=1, use_synthetic=False): ''' Init...
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pangeo-data/ncar-hackathon-xarray-on-gpus
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pangeo-data_ncar-hackathon-xarray-on-gpus/zarr_ML_optimization/era5_dataloader.py
era5_dataloader.PyTorchERA5Dataset
import torch from torch.utils.data import Dataset, DataLoader class PyTorchERA5Dataset(Dataset): """ Wraps the ERA5TimeSeriesDataset so it can be used in PyTorch DataLoader. """ def __init__(self, era5_dataset, forecast_step=1): """ era5_dataset (ERA5Dataset): An instance of the custom...
class PyTorchERA5Dataset(Dataset): ''' Wraps the ERA5TimeSeriesDataset so it can be used in PyTorch DataLoader. ''' def __init__(self, era5_dataset, forecast_step=1): ''' era5_dataset (ERA5Dataset): An instance of the custom ERA5 dataset. forecast_step (int): The forecast step ...
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pangeo-data/ncar-hackathon-xarray-on-gpus
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pangeo-data_ncar-hackathon-xarray-on-gpus/zarr_ML_optimization/era5_dataloader.py
era5_dataloader.SeqZarrSource
import numpy as np import zarr import cupy as cp from contextlib import nullcontext class SeqZarrSource: """ DALI Source for loading a zarr array. The arrays will be indexed along the first dimension (usually time). https://github.com/NVIDIA/modulus/blob/e6d7b02fb19ab9cdb3138de228ca3d6f0c99e7d1/exampl...
class SeqZarrSource: ''' DALI Source for loading a zarr array. The arrays will be indexed along the first dimension (usually time). https://github.com/NVIDIA/modulus/blob/e6d7b02fb19ab9cdb3138de228ca3d6f0c99e7d1/examples/weather/unified_recipe/seq_zarr_datapipe.py#L186 ''' def __init__(self, f...
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pangeo-data/ncar-hackathon-xarray-on-gpus
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/pangeo-data_ncar-hackathon-xarray-on-gpus/zarr_dali_example/zarr_dali_minimal.py
zarr_dali_minimal.ExternalInputIterator
import zarr from random import shuffle import zarr.storage class ExternalInputIterator: def __init__(self, batch_size: int): self.root = 'data/example.zarr/' self.variable = 'images' self.batch_size = batch_size self.indices = list(range(zarr.open_array(self.root, path=self.variabl...
class ExternalInputIterator: def __init__(self, batch_size: int): pass def __iter__(self): pass def __next__(self): pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.AlreadyExistsError
class AlreadyExistsError(ArtifactsError): """Base class for resource already exists errors."""
class AlreadyExistsError(ArtifactsError): '''Base class for resource already exists errors.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.ArtifactsError
class ArtifactsError(Exception): """Base exception for the Artifacts system."""
class ArtifactsError(Exception): '''Base exception for the Artifacts system.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.ConcurrencyError
class ConcurrencyError(ArtifactsError): """ Raised on lock acquisition timeouts or other concurrency control failures. This typically indicates high contention and the operation should be retried. """
class ConcurrencyError(ArtifactsError): ''' Raised on lock acquisition timeouts or other concurrency control failures. This typically indicates high contention and the operation should be retried. ''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.ConfigurationError
class ConfigurationError(ArtifactsError): """Raised for invalid configuration or dependency setup errors."""
class ConfigurationError(ArtifactsError): '''Raised for invalid configuration or dependency setup errors.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.DocumentAlreadyExists
class DocumentAlreadyExists(AlreadyExistsError): """Raised when trying to create a document that already exists."""
class DocumentAlreadyExists(AlreadyExistsError): '''Raised when trying to create a document that already exists.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.DocumentNotFound
class DocumentNotFound(NotFoundError): """Raised when a requested document does not exist."""
class DocumentNotFound(NotFoundError): '''Raised when a requested document does not exist.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.NotFoundError
class NotFoundError(ArtifactsError): """Base class for resource not found errors."""
class NotFoundError(ArtifactsError): '''Base class for resource not found errors.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.SessionAlreadyExists
class SessionAlreadyExists(AlreadyExistsError): """Raised when trying to create a session that already exists."""
class SessionAlreadyExists(AlreadyExistsError): '''Raised when trying to create a session that already exists.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.SessionNotFound
class SessionNotFound(NotFoundError): """Raised when a requested session does not exist."""
class SessionNotFound(NotFoundError): '''Raised when a requested session does not exist.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.StorageError
class StorageError(ArtifactsError): """ Wraps lower-level I/O errors (e.g., PermissionError, disk full). This exception indicates problems with the underlying storage system that may require operational intervention. """
class StorageError(ArtifactsError): ''' Wraps lower-level I/O errors (e.g., PermissionError, disk full). This exception indicates problems with the underlying storage system that may require operational intervention. ''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.TemplateError
class TemplateError(ArtifactsError): """Raised for template parsing, loading, or rendering errors."""
class TemplateError(ArtifactsError): '''Raised for template parsing, loading, or rendering errors.''' pass
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khive-ai/khive.d
/Users/umroot/Documents/PhD_works/PhD-Core-Contents/Class-level-dataset-curation/unseen_data/git_repos_for_analysis/khive-ai_khive.d/src/khive/services/artifacts/exceptions.py
artifacts.exceptions.TemplateNotFound
class TemplateNotFound(NotFoundError): """Raised when a requested template does not exist."""
class TemplateNotFound(NotFoundError): '''Raised when a requested template does not exist.''' pass
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