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Igor Monadical
4acde4b7fd
Topic detection was timing out on longer transcripts when LLM responses are slow. This affects detect_chunk_topic and other LLM-calling tasks that use TIMEOUT_MEDIUM. Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
"""
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Hatchet workflow constants.
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"""
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from enum import StrEnum
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class TaskName(StrEnum):
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GET_RECORDING = "get_recording"
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GET_PARTICIPANTS = "get_participants"
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PROCESS_TRACKS = "process_tracks"
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MIXDOWN_TRACKS = "mixdown_tracks"
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GENERATE_WAVEFORM = "generate_waveform"
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DETECT_TOPICS = "detect_topics"
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GENERATE_TITLE = "generate_title"
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EXTRACT_SUBJECTS = "extract_subjects"
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PROCESS_SUBJECTS = "process_subjects"
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GENERATE_RECAP = "generate_recap"
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IDENTIFY_ACTION_ITEMS = "identify_action_items"
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FINALIZE = "finalize"
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CLEANUP_CONSENT = "cleanup_consent"
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POST_ZULIP = "post_zulip"
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SEND_WEBHOOK = "send_webhook"
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PAD_TRACK = "pad_track"
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TRANSCRIBE_TRACK = "transcribe_track"
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DETECT_CHUNK_TOPIC = "detect_chunk_topic"
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GENERATE_DETAILED_SUMMARY = "generate_detailed_summary"
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# Rate limit key for LLM API calls (shared across all LLM-calling tasks)
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LLM_RATE_LIMIT_KEY = "llm"
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# Max LLM calls per second across all tasks
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LLM_RATE_LIMIT_PER_SECOND = 10
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# Task execution timeouts (seconds)
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TIMEOUT_SHORT = 60 # Quick operations: API calls, DB updates
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TIMEOUT_MEDIUM = (
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300 # Single LLM calls, waveform generation (5m for slow LLM responses)
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)
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TIMEOUT_LONG = 180 # Action items (larger context LLM)
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TIMEOUT_AUDIO = 720 # Audio processing: padding, mixdown
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TIMEOUT_HEAVY = 600 # Transcription, fan-out LLM tasks
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