aa858bd2c1
- scripts/screenshots.py + scripts/Dockerfile.shots: playwright-in-compose runner, 7 demo shots against the seeded stack (DinD-safe, output volume) - docker-compose.yml: api + web services (nginx SPA + /api proxy-less via build-arg VITE_API_BASE), shots service with volume output - apps/api: add CORSMiddleware (SPA origin web:80 could not read api:8000), batch scoring now refuses to rewrite applications beyond discovered/scored (kanban page load previously reset sent/interviewing to scored), prompt-aware cv_tailor mock so demo passes hallucination guard, hallucination-guard tests repointed to monkeypatched run_task - apps/web: Applications kanban batches only unscored applications, red flag display falls back to stored rationale - 159 api tests + 14 web tests green after fixes
222 lines
No EOL
8.1 KiB
Python
222 lines
No EOL
8.1 KiB
Python
"""LLM gateway integration.
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Per the task spec: LLM usage via packages.llm_gateway. Since T2 hasn't built
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the gateway yet, this module provides a minimal mock-mode interface that the
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API endpoints call. When packages.llm_gateway is available (importable), it
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delegates to the real gateway. Otherwise, it returns deterministic canned
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outputs per task name.
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Task names used by the API:
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- cv_assist: returns suggestions for a CV section bullet
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- cl_critique: returns critique comments for a cover letter
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"""
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from __future__ import annotations
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import hashlib
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import json
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import os
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import time
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from typing import Any
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# Try to import the real llm_gateway package
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try:
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from packages.llm_gateway.client import run_task as _real_run_task # type: ignore
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HAS_REAL_GATEWAY = True
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except Exception:
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HAS_REAL_GATEWAY = False
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def _has_api_key() -> bool:
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return bool(
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os.environ.get("LLM_PRIMARY_KEY")
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or os.environ.get("OLLAMA_API_KEY")
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)
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# Deterministic mock outputs per task name
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MOCK_OUTPUTS: dict[str, dict[str, Any]] = {
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"cv_assist": {
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"suggestions": [
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"Improved bullet: Led cross-functional team of 8 to deliver feature X 2 weeks ahead of schedule.",
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"Alternative: Streamlined process reducing cycle time by 30% via automation.",
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]
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},
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"cl_critique": {
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"comments": [
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{
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"quote": "I am writing to apply",
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"suggestion": "Consider a stronger opening that references the specific role.",
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"severity": "medium",
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},
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{
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"quote": "I have experience",
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"suggestion": "Quantify with a specific achievement rather than a generic claim.",
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"severity": "low",
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},
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]
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},
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"score": {
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"score": 72,
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"rationale": {
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"match": 0.72,
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"factors": {"skills": 0.8, "location": 0.6, "experience": 0.75},
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},
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"red_flags": [],
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},
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"cv_extract": {
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"drafts": [
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{
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"kind": "experience",
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"title": "Software Engineer",
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"org": "Extracted Company",
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"location": "Malmo",
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"start_date": "2020-01",
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"end_date": None,
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"bullets": ["Developed web applications", "Led team of 3"],
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"tags": ["python", "javascript"],
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},
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{
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"kind": "education",
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"title": "MSc Computer Science",
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"org": "Lund University",
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"location": "Lund",
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"start_date": "2016-09",
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"end_date": "2018-06",
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"bullets": ["Specialized in distributed systems"],
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"tags": ["algorithms", "distributed systems"],
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},
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]
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},
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"interview_prep": {
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"content": (
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"# Interview Prep\n\n"
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"## Q1: Tell us about yourself\n"
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"**Suggested angle:** Highlight your experience with Python and FastAPI, "
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"and your ability to deliver features on time.\n\n"
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"## Q2: Why are you interested in this role?\n"
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"**Suggested angle:** Reference the specific technologies mentioned in the "
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"posting and your experience with similar stacks.\n\n"
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"## Q3: Describe a challenging project\n"
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"**Suggested angle:** Use the STAR method. Reference your experience building "
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"distributed systems at Lund University.\n\n"
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"## Q4: How do you handle tight deadlines?\n"
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"**Suggested angle:** Mention your track record of delivering 2 weeks ahead "
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"of schedule and your automation-first approach.\n\n"
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"## Q5: What are your salary expectations?\n"
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"**Suggested angle:** Research market rates for the Skane region. "
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"Be prepared to give a range.\n\n"
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"## Q6: Tell us about a time you failed\n"
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"**Suggested angle:** Pick something real but not catastrophic. Show what "
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"you learned and how you changed your approach.\n\n"
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"## Q7: How do you stay current with technology?\n"
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"**Suggested angle:** Mention your tags: python, javascript, distributed "
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"systems. Talk about hands-on side projects.\n\n"
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"## Q8: Describe your ideal work environment\n"
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"**Suggested angle:** Be honest but flexible. Mention collaboration and "
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"autonomy.\n\n"
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"## Q9: What questions do you have for us?\n"
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"**Suggested angle:** Ask about team structure, current projects, and "
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"growth opportunities.\n\n"
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"## Q10: Why should we hire you?\n"
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"**Suggested angle:** Summarize your top 3 qualifications matching the "
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"posting requirements. Be specific."
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),
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},
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"email_classify": {
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"classification": "interview_invite",
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"state_proposal": "interviewing",
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"reason": "The email mentions an interview invitation.",
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},
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"cv_tailor": {
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"tailored_cv": {
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"summary": "Senior Python Developer with 6+ years building scalable backend systems.",
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"skills": [
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"Python",
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"Fast API",
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"PostgreSQL",
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"Docker",
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"Kubernetes",
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"AWS",
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],
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"experience": [
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{
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"company": "TechCorp",
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"role": "Senior Backend Engineer",
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"bullets": [
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"Led migration of monolith to microservices using Fast API",
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"Reduced API latency by 40% through query optimization and caching",
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],
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},
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],
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},
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"change_log": [
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{"action": "reordered", "detail": "Moved Python and Fast API to top of skills"},
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{"action": "rephrased", "detail": "Rewrote first experience bullet to emphasize Fast API"},
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],
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},
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"deadline_extract": {
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"apply_by": None,
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},
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}
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def _mock_cv_tailor(prompt: str) -> dict[str, Any]:
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"""Prompt-aware mock tailor: extracts source bullets from the prompt and
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rephrases them deterministically, so the result always passes the
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hallucination guard (which requires traceable source overlap)."""
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import re
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bullets = re.findall(r'"([A-ZÅÄÖ][^"]{20,300})"', prompt)
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bullets = [b for b in bullets if "{" not in b and ":" not in b][:4]
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if not bullets:
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bullets = ["Experienced backend developer focused on reliability"]
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tailored = []
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change_log = []
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for b in bullets[:2]:
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tailored.append(f"{b} (tailored for this posting)")
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change_log.append({"action": "rephrased", "detail": f"Emphasized relevance of: {b[:60]}..."})
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for b in bullets[2:]:
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tailored.append(b)
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change_log.append({"action": "kept", "detail": f"Retained as-is: {b[:60]}..."})
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return {
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"tailored_cv": {"summary": bullets[0][:160], "bullets": tailored},
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"change_log": change_log,
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}
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def run_task(
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task: str,
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prompt: str,
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schema: dict[str, Any] | None = None,
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telemetry_sink: callable | None = None,
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) -> dict[str, Any]:
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"""Run an LLM task. Uses mock mode when no API key is present.
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Returns a dict with the task result. If a telemetry_sink callable is
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provided, it is called with a dict of token/cost info.
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"""
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if HAS_REAL_GATEWAY and _has_api_key():
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return _real_run_task(task, prompt, schema)
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# Mock mode
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time.sleep(0.01) # simulate latency
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result = MOCK_OUTPUTS.get(task, {"result": "mock"})
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if task == "cv_tailor":
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result = _mock_cv_tailor(prompt)
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# Validate against schema if provided (basic check)
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# In real gateway this would be jsonschema validation
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if telemetry_sink is not None:
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telemetry_sink({
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"task": task,
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"model": "mock",
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"provider": "mock",
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"input_tokens": len(prompt) // 4, # rough estimate
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"output_tokens": len(json.dumps(result)) // 4,
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"cost_usd": 0.0,
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"duration_ms": 10,
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})
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return result |