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  • DataBridge Docs
  • Getting Started
    • Installation
    • Quick Start
  • API Reference
    • Overview
    • Endpoints
      • Ingest
      • Search
      • Query
      • Cache
      • Response Models
  • User Guides
    • Shell
    • Document Ingestion
    • Processing Rules
    • Semantic Search
    • Completions
    • Monitoring & Observability
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  • Document Models
  • Document
  • DocumentContent
  • DocumentResult
  • Search Models
  • ChunkResult
  • Query Models
  • CompletionResponse
  • TokenUsage
  1. API Reference
  2. Endpoints

Response Models

This page documents all the data models used in DataBridge's API responses.

Document Models

Document

The core document model representing an ingested document.

class Document:
    external_id: str
    owner: Dict[str, str]
    content_type: str
    filename: Optional[str]
    metadata: Dict[str, Any]  # user-defined metadata
    storage_info: Dict[str, str]  # storage backend info
    system_metadata: Dict[str, Any]  # creation date, version, etc.
    additional_metadata: Dict[str, Any]  # e.g., frame descriptions and transcripts for videos
    access_control: Dict[str, List[str]]  # readers, writers, admins
    chunk_ids: List[str]

DocumentContent

Represents the content of a document, either as a direct string or URL.

class DocumentContent:
    type: Literal["url", "string"]  # Content type
    value: str  # URL or actual content
    filename: Optional[str]  # Required for URL type, None for string type

DocumentResult

A document search result with relevance score.

class DocumentResult:
    score: float  # Highest chunk score
    document_id: str  # external_id
    metadata: Dict[str, Any]
    content: DocumentContent  # type and value fields
    additional_metadata: Dict[str, Any]  # e.g., frame descriptions and transcripts

Search Models

ChunkResult

Represents a matching chunk from a semantic search.

class ChunkResult:
    content: str
    score: float
    document_id: str  # external_id
    chunk_number: int
    metadata: Dict[str, Any]
    content_type: str
    filename: Optional[str]
    download_url: Optional[str]

    def augmented_content(self, doc: DocumentResult) -> str:
        """Get augmented content for video chunks with frame/transcript info"""

Query Models

CompletionResponse

The response from an AI completion request.

class CompletionResponse:
    completion: str
    usage: TokenUsage  # completion_tokens, prompt_tokens, total_tokens

TokenUsage

Details about token usage in completion requests.

class TokenUsage:
    completion_tokens: int
    prompt_tokens: int
    total_tokens: int
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Last updated 3 months ago