pyr0ball
9101e716ba
Move POST /api/jobs/:id/survey/analyze off the FastAPI worker thread by routing it through the LLM task queue (same pattern as cover_letter, company_research, resume_optimize). - Extract prompt builders + run_survey_analyze() to scripts/survey_assistant.py - Add survey_analyze to LLM_TASK_TYPES (task_scheduler.py) with 2.5 GB VRAM budget (text mode: phi3:mini; visual mode uses vision service's own VRAM pool) - Add elif branch in task_runner._run_task; result stored as JSON in error col - Replace sync endpoint body with submit_task(); add GET /survey/analyze/task poll - Update survey.ts store: analyze() now fires task + polls at 3s interval; silently attaches to existing in-flight task when is_new=false - SurveyView button label shows task stage while polling Fixes load-test spike: ~22 greenlets blocking on LLM inference at 100 concurrent users, causing 90s poll timeouts on cover_letter and research tasks.
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# MIT License — see LICENSE
|
|
"""Survey assistant: prompt builders and LLM inference for culture-fit survey analysis.
|
|
|
|
Extracted from dev-api.py so task_runner can import this without importing the
|
|
FastAPI application. Callable directly or via the survey_analyze background task.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
import logging
|
|
from pathlib import Path
|
|
from typing import Optional
|
|
|
|
log = logging.getLogger(__name__)
|
|
|
|
SURVEY_SYSTEM = (
|
|
"You are a job application advisor helping a candidate answer a culture-fit survey. "
|
|
"The candidate values collaborative teamwork, clear communication, growth, and impact. "
|
|
"Choose answers that present them in the best professional light."
|
|
)
|
|
|
|
|
|
def build_text_prompt(text: str, mode: str) -> str:
|
|
if mode == "quick":
|
|
return (
|
|
"Answer each survey question below. For each, give ONLY the letter of the best "
|
|
"option and a single-sentence reason. Format exactly as:\n"
|
|
"1. B — reason here\n2. A — reason here\n\n"
|
|
f"Survey:\n{text}"
|
|
)
|
|
return (
|
|
"Analyze each survey question below. For each question:\n"
|
|
"- Briefly evaluate each option (1 sentence each)\n"
|
|
"- State your recommendation with reasoning\n\n"
|
|
f"Survey:\n{text}"
|
|
)
|
|
|
|
|
|
def build_image_prompt(mode: str) -> str:
|
|
if mode == "quick":
|
|
return (
|
|
"This is a screenshot of a culture-fit survey. Read all questions and answer each "
|
|
"with the letter of the best option for a collaborative, growth-oriented candidate. "
|
|
"Format: '1. B — brief reason' on separate lines."
|
|
)
|
|
return (
|
|
"This is a screenshot of a culture-fit survey. For each question, evaluate each option "
|
|
"and recommend the best choice for a collaborative, growth-oriented candidate. "
|
|
"Include a brief breakdown per option and a clear recommendation."
|
|
)
|
|
|
|
|
|
def run_survey_analyze(
|
|
text: Optional[str],
|
|
image_b64: Optional[str],
|
|
mode: str,
|
|
config_path: Optional[Path] = None,
|
|
) -> dict:
|
|
"""Run LLM inference for survey analysis.
|
|
|
|
Returns {"output": str, "source": "text_paste" | "screenshot"}.
|
|
Raises on LLM failure — caller is responsible for error handling.
|
|
"""
|
|
from scripts.llm_router import LLMRouter
|
|
|
|
router = LLMRouter(config_path=config_path) if config_path else LLMRouter()
|
|
|
|
if image_b64:
|
|
prompt = build_image_prompt(mode)
|
|
output = router.complete(
|
|
prompt,
|
|
images=[image_b64],
|
|
fallback_order=router.config.get("vision_fallback_order"),
|
|
)
|
|
source = "screenshot"
|
|
else:
|
|
prompt = build_text_prompt(text or "", mode)
|
|
output = router.complete(
|
|
prompt,
|
|
system=SURVEY_SYSTEM,
|
|
fallback_order=router.config.get("research_fallback_order"),
|
|
)
|
|
source = "text_paste"
|
|
|
|
return {"output": output, "source": source}
|