mirror of
https://github.com/lfnovo/open-notebook.git
synced 2026-05-02 13:20:30 +00:00
d7b0fff954
Creates the API layer for Open Notebook Creates a services API gateway for the Streamlit front-end Migrates the SurrealDB SDK to the official one Change all database calls to async New podcast framework supporting multiple speaker configurations Implement the surreal-commands library for async processing Improve docker image and docker-compose configurations
264 lines
No EOL
9.4 KiB
Python
264 lines
No EOL
9.4 KiB
Python
from typing import Any, Dict, List, Literal, Optional
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from pydantic import BaseModel, Field, ConfigDict
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# Notebook models
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class NotebookCreate(BaseModel):
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name: str = Field(..., description="Name of the notebook")
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description: str = Field(default="", description="Description of the notebook")
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class NotebookUpdate(BaseModel):
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name: Optional[str] = Field(None, description="Name of the notebook")
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description: Optional[str] = Field(None, description="Description of the notebook")
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archived: Optional[bool] = Field(None, description="Whether the notebook is archived")
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class NotebookResponse(BaseModel):
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id: str
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name: str
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description: str
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archived: bool
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created: str
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updated: str
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# Search models
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class SearchRequest(BaseModel):
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query: str = Field(..., description="Search query")
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type: Literal["text", "vector"] = Field("text", description="Search type")
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limit: int = Field(100, description="Maximum number of results", le=1000)
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search_sources: bool = Field(True, description="Include sources in search")
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search_notes: bool = Field(True, description="Include notes in search")
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minimum_score: float = Field(0.2, description="Minimum score for vector search", ge=0, le=1)
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class SearchResponse(BaseModel):
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results: List[Dict[str, Any]] = Field(..., description="Search results")
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total_count: int = Field(..., description="Total number of results")
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search_type: str = Field(..., description="Type of search performed")
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class AskRequest(BaseModel):
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question: str = Field(..., description="Question to ask the knowledge base")
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strategy_model: str = Field(..., description="Model ID for query strategy")
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answer_model: str = Field(..., description="Model ID for individual answers")
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final_answer_model: str = Field(..., description="Model ID for final answer")
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class AskResponse(BaseModel):
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answer: str = Field(..., description="Final answer from the knowledge base")
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question: str = Field(..., description="Original question")
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# Models API models
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class ModelCreate(BaseModel):
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name: str = Field(..., description="Model name (e.g., gpt-4o-mini, claude, gemini)")
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provider: str = Field(..., description="Provider name (e.g., openai, anthropic, gemini)")
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type: str = Field(..., description="Model type (language, embedding, text_to_speech, speech_to_text)")
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class ModelResponse(BaseModel):
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id: str
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name: str
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provider: str
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type: str
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created: str
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updated: str
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class DefaultModelsResponse(BaseModel):
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default_chat_model: Optional[str] = None
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default_transformation_model: Optional[str] = None
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large_context_model: Optional[str] = None
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default_text_to_speech_model: Optional[str] = None
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default_speech_to_text_model: Optional[str] = None
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default_embedding_model: Optional[str] = None
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default_tools_model: Optional[str] = None
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# Transformations API models
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class TransformationCreate(BaseModel):
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name: str = Field(..., description="Transformation name")
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title: str = Field(..., description="Display title for the transformation")
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description: str = Field(..., description="Description of what this transformation does")
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prompt: str = Field(..., description="The transformation prompt")
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apply_default: bool = Field(False, description="Whether to apply this transformation by default")
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class TransformationUpdate(BaseModel):
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name: Optional[str] = Field(None, description="Transformation name")
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title: Optional[str] = Field(None, description="Display title for the transformation")
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description: Optional[str] = Field(None, description="Description of what this transformation does")
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prompt: Optional[str] = Field(None, description="The transformation prompt")
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apply_default: Optional[bool] = Field(None, description="Whether to apply this transformation by default")
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class TransformationResponse(BaseModel):
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id: str
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name: str
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title: str
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description: str
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prompt: str
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apply_default: bool
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created: str
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updated: str
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class TransformationExecuteRequest(BaseModel):
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model_config = ConfigDict(protected_namespaces=())
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transformation_id: str = Field(..., description="ID of the transformation to execute")
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input_text: str = Field(..., description="Text to transform")
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model_id: str = Field(..., description="Model ID to use for the transformation")
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class TransformationExecuteResponse(BaseModel):
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model_config = ConfigDict(protected_namespaces=())
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output: str = Field(..., description="Transformed text")
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transformation_id: str = Field(..., description="ID of the transformation used")
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model_id: str = Field(..., description="Model ID used")
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# Notes API models
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class NoteCreate(BaseModel):
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title: Optional[str] = Field(None, description="Note title")
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content: str = Field(..., description="Note content")
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note_type: Optional[str] = Field("human", description="Type of note (human, ai)")
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notebook_id: Optional[str] = Field(None, description="Notebook ID to add the note to")
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class NoteUpdate(BaseModel):
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title: Optional[str] = Field(None, description="Note title")
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content: Optional[str] = Field(None, description="Note content")
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note_type: Optional[str] = Field(None, description="Type of note (human, ai)")
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class NoteResponse(BaseModel):
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id: str
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title: Optional[str]
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content: Optional[str]
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note_type: Optional[str]
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created: str
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updated: str
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# Embedding API models
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class EmbedRequest(BaseModel):
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item_id: str = Field(..., description="ID of the item to embed")
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item_type: str = Field(..., description="Type of item (source, note)")
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class EmbedResponse(BaseModel):
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success: bool = Field(..., description="Whether embedding was successful")
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message: str = Field(..., description="Result message")
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item_id: str = Field(..., description="ID of the item that was embedded")
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item_type: str = Field(..., description="Type of item that was embedded")
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# Settings API models
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class SettingsResponse(BaseModel):
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default_content_processing_engine_doc: Optional[str] = None
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default_content_processing_engine_url: Optional[str] = None
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default_embedding_option: Optional[str] = None
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auto_delete_files: Optional[str] = None
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youtube_preferred_languages: Optional[List[str]] = None
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class SettingsUpdate(BaseModel):
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default_content_processing_engine_doc: Optional[str] = None
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default_content_processing_engine_url: Optional[str] = None
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default_embedding_option: Optional[str] = None
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auto_delete_files: Optional[str] = None
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youtube_preferred_languages: Optional[List[str]] = None
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# Sources API models
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class AssetModel(BaseModel):
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file_path: Optional[str] = None
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url: Optional[str] = None
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class SourceCreate(BaseModel):
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notebook_id: str = Field(..., description="Notebook ID to add the source to")
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type: str = Field(..., description="Source type: link, upload, or text")
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url: Optional[str] = Field(None, description="URL for link type")
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file_path: Optional[str] = Field(None, description="File path for upload type")
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content: Optional[str] = Field(None, description="Text content for text type")
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title: Optional[str] = Field(None, description="Source title")
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transformations: Optional[List[str]] = Field(default_factory=list, description="Transformation IDs to apply")
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embed: bool = Field(False, description="Whether to embed content for vector search")
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delete_source: bool = Field(False, description="Whether to delete uploaded file after processing")
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class SourceUpdate(BaseModel):
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title: Optional[str] = Field(None, description="Source title")
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topics: Optional[List[str]] = Field(None, description="Source topics")
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class SourceResponse(BaseModel):
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id: str
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title: Optional[str]
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topics: Optional[List[str]]
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asset: Optional[AssetModel]
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full_text: Optional[str]
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embedded_chunks: int
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created: str
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updated: str
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class SourceListResponse(BaseModel):
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id: str
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title: Optional[str]
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topics: Optional[List[str]]
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asset: Optional[AssetModel]
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embedded_chunks: int
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insights_count: int
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created: str
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updated: str
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# Context API models
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class ContextConfig(BaseModel):
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sources: Dict[str, str] = Field(default_factory=dict, description="Source inclusion config {source_id: level}")
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notes: Dict[str, str] = Field(default_factory=dict, description="Note inclusion config {note_id: level}")
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class ContextRequest(BaseModel):
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notebook_id: str = Field(..., description="Notebook ID to get context for")
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context_config: Optional[ContextConfig] = Field(None, description="Context configuration")
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class ContextResponse(BaseModel):
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notebook_id: str
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sources: List[Dict[str, Any]] = Field(..., description="Source context data")
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notes: List[Dict[str, Any]] = Field(..., description="Note context data")
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total_tokens: Optional[int] = Field(None, description="Estimated token count")
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# Insights API models
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class SourceInsightResponse(BaseModel):
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id: str
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source_id: str
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insight_type: str
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content: str
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created: str
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updated: str
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class SaveAsNoteRequest(BaseModel):
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notebook_id: Optional[str] = Field(None, description="Notebook ID to add note to")
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class CreateSourceInsightRequest(BaseModel):
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model_config = ConfigDict(protected_namespaces=())
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transformation_id: str = Field(..., description="ID of transformation to apply")
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model_id: Optional[str] = Field(None, description="Model ID (uses default if not provided)")
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# Error response
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class ErrorResponse(BaseModel):
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error: str
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message: str |