mirror of
https://github.com/Monadical-SAS/reflector.git
synced 2026-03-27 09:26:46 +00:00
c155f66982
* Increase max connections * Classify hard and transient hatchet errors * Fan out partial success * Force reprocessing of error transcripts * Stop retrying on 402 payment required * Avoid httpx/hatchet timeout race * Add retry wrapper to get_response for for transient errors * Add retry backoff * Return falsy results so get_response won't retry on empty string * Skip error status in on_workflow_failure when transcript already ended * Fix precommit issues * Fail step on first fan-out failure instead of skipping
110 lines
3.5 KiB
Python
110 lines
3.5 KiB
Python
"""
|
|
Hatchet child workflow: SubjectProcessing
|
|
|
|
Handles individual subject/topic summary generation.
|
|
Spawned dynamically by the main diarization pipeline for each extracted subject
|
|
via aio_run_many() for parallel processing.
|
|
"""
|
|
|
|
from datetime import timedelta
|
|
|
|
from hatchet_sdk import Context
|
|
from hatchet_sdk.rate_limit import RateLimit
|
|
from pydantic import BaseModel
|
|
|
|
from reflector.hatchet.client import HatchetClientManager
|
|
from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, TIMEOUT_HEAVY
|
|
from reflector.hatchet.workflows.models import SubjectSummaryResult
|
|
from reflector.logger import logger
|
|
from reflector.processors.summary.prompts import (
|
|
DETAILED_SUBJECT_PROMPT_TEMPLATE,
|
|
PARAGRAPH_SUMMARY_PROMPT,
|
|
build_participant_instructions,
|
|
)
|
|
|
|
|
|
class SubjectInput(BaseModel):
|
|
"""Input for individual subject processing."""
|
|
|
|
subject: str
|
|
subject_index: int
|
|
transcript_text: str
|
|
participant_names: list[str]
|
|
participant_name_to_id: dict[str, str]
|
|
|
|
|
|
hatchet = HatchetClientManager.get_client()
|
|
|
|
subject_workflow = hatchet.workflow(
|
|
name="SubjectProcessing", input_validator=SubjectInput
|
|
)
|
|
|
|
|
|
@subject_workflow.task(
|
|
execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
|
|
retries=5,
|
|
backoff_factor=2.0,
|
|
backoff_max_seconds=60,
|
|
rate_limits=[RateLimit(static_key=LLM_RATE_LIMIT_KEY, units=2)],
|
|
)
|
|
async def generate_detailed_summary(
|
|
input: SubjectInput, ctx: Context
|
|
) -> SubjectSummaryResult:
|
|
"""Generate detailed analysis for a single subject, then condense to paragraph."""
|
|
ctx.log(
|
|
f"generate_detailed_summary: subject '{input.subject}' (index {input.subject_index})"
|
|
)
|
|
logger.info(
|
|
"[Hatchet] generate_detailed_summary",
|
|
subject=input.subject,
|
|
subject_index=input.subject_index,
|
|
)
|
|
|
|
# Deferred imports: Hatchet workers fork processes, fresh imports ensure
|
|
# LLM HTTP connection pools aren't shared across forks
|
|
from reflector.llm import LLM # noqa: PLC0415
|
|
from reflector.settings import settings # noqa: PLC0415
|
|
|
|
llm = LLM(settings=settings)
|
|
|
|
participant_instructions = build_participant_instructions(input.participant_names)
|
|
detailed_prompt = DETAILED_SUBJECT_PROMPT_TEMPLATE.format(subject=input.subject)
|
|
if participant_instructions:
|
|
detailed_prompt = f"{detailed_prompt}\n\n{participant_instructions}"
|
|
|
|
ctx.log("generate_detailed_summary: calling LLM for detailed analysis")
|
|
detailed_response = await llm.get_response(
|
|
detailed_prompt,
|
|
[input.transcript_text],
|
|
tone_name="Topic assistant",
|
|
)
|
|
detailed_summary = str(detailed_response)
|
|
|
|
paragraph_prompt = PARAGRAPH_SUMMARY_PROMPT
|
|
if participant_instructions:
|
|
paragraph_prompt = f"{paragraph_prompt}\n\n{participant_instructions}"
|
|
|
|
ctx.log("generate_detailed_summary: calling LLM for paragraph summary")
|
|
paragraph_response = await llm.get_response(
|
|
paragraph_prompt,
|
|
[detailed_summary],
|
|
tone_name="Topic summarizer",
|
|
)
|
|
paragraph_summary = str(paragraph_response)
|
|
|
|
ctx.log(f"generate_detailed_summary complete: subject '{input.subject}'")
|
|
logger.info(
|
|
"[Hatchet] generate_detailed_summary complete",
|
|
subject=input.subject,
|
|
subject_index=input.subject_index,
|
|
detailed_len=len(detailed_summary),
|
|
paragraph_len=len(paragraph_summary),
|
|
)
|
|
|
|
return SubjectSummaryResult(
|
|
subject=input.subject,
|
|
subject_index=input.subject_index,
|
|
detailed_summary=detailed_summary,
|
|
paragraph_summary=paragraph_summary,
|
|
)
|