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594bcc09e0
* parallelize hatchet (no-mistakes) * dry (no-mistakes) (minimal) * comments * self-review * self-review * self-review * self-review * pr comments * pr comments --------- Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
172 lines
3.8 KiB
Python
172 lines
3.8 KiB
Python
"""
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Pydantic models for Hatchet workflow task return types.
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Provides static typing for all task outputs, enabling type checking
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and better IDE support.
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"""
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from pydantic import BaseModel
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from reflector.processors.types import TitleSummary, Word
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from reflector.utils.string import NonEmptyString
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class ParticipantInfo(BaseModel):
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"""Participant info with speaker index for workflow result."""
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participant_id: NonEmptyString
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user_name: NonEmptyString
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speaker: int
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class PadTrackResult(BaseModel):
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"""Result from pad_track task."""
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padded_key: NonEmptyString # S3 key (not presigned URL) - presign on demand to avoid stale URLs on replay
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bucket_name: (
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NonEmptyString | None
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) # None means use default transcript storage bucket
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size: int
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track_index: int
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class TranscribeTrackResult(BaseModel):
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"""Result from transcribe_track task."""
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words: list[Word]
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track_index: int
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class RecordingResult(BaseModel):
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"""Result from get_recording task."""
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id: NonEmptyString | None
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mtg_session_id: NonEmptyString | None
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duration: float
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class ParticipantsResult(BaseModel):
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"""Result from get_participants task."""
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participants: list[ParticipantInfo]
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num_tracks: int
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source_language: NonEmptyString
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target_language: NonEmptyString
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class PaddedTrackInfo(BaseModel):
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"""Info for a padded track - S3 key + bucket for on-demand presigning."""
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key: NonEmptyString
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bucket_name: NonEmptyString | None # None = use default storage bucket
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class ProcessTracksResult(BaseModel):
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"""Result from process_tracks task."""
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all_words: list[Word]
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padded_tracks: list[PaddedTrackInfo] # S3 keys, not presigned URLs
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word_count: int
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num_tracks: int
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target_language: NonEmptyString
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created_padded_files: list[NonEmptyString]
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class MixdownResult(BaseModel):
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"""Result from mixdown_tracks task."""
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audio_key: NonEmptyString
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duration: float
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tracks_mixed: int
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class WaveformResult(BaseModel):
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"""Result from generate_waveform task."""
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waveform_generated: bool
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class TopicChunkResult(BaseModel):
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"""Result from topic chunk child workflow."""
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chunk_index: int
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title: str
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summary: str
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timestamp: float
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duration: float
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words: list[Word]
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class TopicsResult(BaseModel):
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"""Result from detect_topics task."""
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topics: list[TitleSummary]
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class TitleResult(BaseModel):
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"""Result from generate_title task."""
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title: str | None
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class SubjectsResult(BaseModel):
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"""Result from extract_subjects task."""
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subjects: list[str]
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transcript_text: str # Formatted transcript for LLM consumption
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participant_names: list[str]
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participant_name_to_id: dict[str, str]
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class SubjectSummaryResult(BaseModel):
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"""Result from subject summary child workflow."""
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subject: str
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subject_index: int
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detailed_summary: str
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paragraph_summary: str
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class ProcessSubjectsResult(BaseModel):
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"""Result from process_subjects fan-out task."""
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subject_summaries: list[SubjectSummaryResult]
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class RecapResult(BaseModel):
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"""Result from generate_recap task."""
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short_summary: str # Recap paragraph
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long_summary: str # Full markdown summary
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class ActionItemsResult(BaseModel):
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"""Result from identify_action_items task."""
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action_items: dict # ActionItemsResponse as dict (may have empty lists)
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class FinalizeResult(BaseModel):
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"""Result from finalize task."""
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status: NonEmptyString
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class ConsentResult(BaseModel):
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"""Result from cleanup_consent task."""
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class ZulipResult(BaseModel):
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"""Result from post_zulip task."""
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zulip_message_id: int | None = None
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skipped: bool = False
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class WebhookResult(BaseModel):
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"""Result from send_webhook task."""
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webhook_sent: bool
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skipped: bool = False
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response_code: int | None = None
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