mirror of
https://github.com/Monadical-SAS/reflector.git
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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>
108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
"""
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Hatchet child workflow: SubjectProcessing
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Handles individual subject/topic summary generation.
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Spawned dynamically by the main diarization pipeline for each extracted subject
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via aio_run_many() for parallel processing.
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"""
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from datetime import timedelta
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from hatchet_sdk import Context
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from hatchet_sdk.rate_limit import RateLimit
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from pydantic import BaseModel
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from reflector.hatchet.client import HatchetClientManager
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from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, TIMEOUT_MEDIUM
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from reflector.hatchet.workflows.models import SubjectSummaryResult
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from reflector.logger import logger
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from reflector.processors.summary.prompts import (
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DETAILED_SUBJECT_PROMPT_TEMPLATE,
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PARAGRAPH_SUMMARY_PROMPT,
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build_participant_instructions,
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)
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class SubjectInput(BaseModel):
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"""Input for individual subject processing."""
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subject: str
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subject_index: int
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transcript_text: str
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participant_names: list[str]
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participant_name_to_id: dict[str, str]
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hatchet = HatchetClientManager.get_client()
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subject_workflow = hatchet.workflow(
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name="SubjectProcessing", input_validator=SubjectInput
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)
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@subject_workflow.task(
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execution_timeout=timedelta(seconds=TIMEOUT_MEDIUM),
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retries=3,
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rate_limits=[RateLimit(static_key=LLM_RATE_LIMIT_KEY, units=2)],
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)
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async def generate_detailed_summary(
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input: SubjectInput, ctx: Context
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) -> SubjectSummaryResult:
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"""Generate detailed analysis for a single subject, then condense to paragraph."""
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ctx.log(
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f"generate_detailed_summary: subject '{input.subject}' (index {input.subject_index})"
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)
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logger.info(
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"[Hatchet] generate_detailed_summary",
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subject=input.subject,
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subject_index=input.subject_index,
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)
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# Deferred imports: Hatchet workers fork processes, fresh imports ensure
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# LLM HTTP connection pools aren't shared across forks
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from reflector.llm import LLM # noqa: PLC0415
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from reflector.settings import settings # noqa: PLC0415
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llm = LLM(settings=settings)
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participant_instructions = build_participant_instructions(input.participant_names)
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detailed_prompt = DETAILED_SUBJECT_PROMPT_TEMPLATE.format(subject=input.subject)
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if participant_instructions:
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detailed_prompt = f"{detailed_prompt}\n\n{participant_instructions}"
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ctx.log("generate_detailed_summary: calling LLM for detailed analysis")
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detailed_response = await llm.get_response(
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detailed_prompt,
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[input.transcript_text],
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tone_name="Topic assistant",
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)
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detailed_summary = str(detailed_response)
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paragraph_prompt = PARAGRAPH_SUMMARY_PROMPT
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if participant_instructions:
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paragraph_prompt = f"{paragraph_prompt}\n\n{participant_instructions}"
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ctx.log("generate_detailed_summary: calling LLM for paragraph summary")
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paragraph_response = await llm.get_response(
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paragraph_prompt,
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[detailed_summary],
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tone_name="Topic summarizer",
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)
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paragraph_summary = str(paragraph_response)
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ctx.log(f"generate_detailed_summary complete: subject '{input.subject}'")
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logger.info(
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"[Hatchet] generate_detailed_summary complete",
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subject=input.subject,
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subject_index=input.subject_index,
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detailed_len=len(detailed_summary),
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paragraph_len=len(paragraph_summary),
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)
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return SubjectSummaryResult(
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subject=input.subject,
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subject_index=input.subject_index,
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detailed_summary=detailed_summary,
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paragraph_summary=paragraph_summary,
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)
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