8d8a863300
packages/artifacts: - render_cv_pdf(profile, sections) -> bytes: data-driven CV PDF generation using fpdf2 with bundled DejaVuSans TTF for unicode (Swedish chars tested). Jinja2 template for layout data prep, adapted from build_cv.py approach. - render_cover_letter(text, profile) -> bytes: simple cover letter PDF. - hash_bytes(b) -> str: sha256 hex digest. - next_version(existing) -> int: version numbering helper. - 16 tests, all passing: PDF validity, Swedish characters, hash stability, cover letter rendering, hash correctness, version logic. packages/llm-gateway: - Async-first Gateway class with provider config from env. - Mock mode default when no API key env present (deterministic canned outputs per task name, defined in mock.py). - Telemetry sink injectable (async or sync callable, receives TelemetryRow). - Budget guard raises BudgetExceeded BEFORE any provider call is made. - Retry policy: max 2 retries on 429/5xx, then fallback provider for STRONG tasks only. CHEAP tasks never use fallback (paid provider protection). - Schema validation via jsonschema; SchemaValidationError on mismatch. - Task class routing: CHEAP (score, extract, cv_assist) vs STRONG (critique, cl_critique, research). Model routing per task class. - Paid provider detection heuristic; warns on paid fallback config. - 31 tests, all passing: mock determinism, schema pass/fail, budget guard (mock + real mode), telemetry sink (async/sync/none), config from env, provider calls with mocked HTTP (retry, fallback, no-fallback-for-cheap). docker-compose.yml: - postgres:16 service, user/pass/db = jobhunt, host port 5433->5432, named volume jobhunt_pgdata, healthcheck. .env.example: - DATABASE_URL, LLM provider config (primary + fallback), task budgets, API and web settings.
96 lines
No EOL
2.7 KiB
Python
96 lines
No EOL
2.7 KiB
Python
"""Mock mode for the LLM gateway.
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When no API key is configured, the gateway returns deterministic canned
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outputs per task name. This allows the API and tests to run offline.
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"""
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from __future__ import annotations
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import json
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import time
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import uuid
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# Deterministic canned outputs per task name.
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# Each entry is a dict that will be returned as the task result.
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MOCK_OUTPUTS: dict[str, dict] = {
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"score": {
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"score": 75,
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"rationale": {
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"match": "good",
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"reasons": ["skills align", "location matches"],
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},
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},
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"extract": {
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"company": "Example Corp",
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"title": "Software Engineer",
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"location": "Stockholm",
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"requirements": ["Python", "PostgreSQL", "Docker"],
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},
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"cv_assist": {
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"suggestions": [
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"Led a team of 5 developers to deliver a critical integration",
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"Reduced API latency by 40% through caching and query optimization",
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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 a hard worker",
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"suggestion": "Replace generic claim with a specific achievement metric",
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"severity": "medium",
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},
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{
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"quote": "Dear Sir/Madam",
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"suggestion": "Address the hiring manager by name if known",
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"severity": "low",
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},
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],
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},
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"critique": {
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"comments": [
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{
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"quote": "sample text",
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"suggestion": "improve clarity",
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"severity": "low",
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},
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],
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},
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"research": {
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"summary": "The company is a mid-size tech firm focused on cloud infrastructure.",
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"key_points": ["Founded in 2015", "Series B funding", "Remote-first culture"],
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},
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}
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# Default mock output for unknown task names.
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DEFAULT_MOCK_OUTPUT: dict = {
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"result": "mock output",
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"task": "unknown",
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}
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def get_mock_output(task: str) -> dict:
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"""Return a deterministic mock output for *task*.
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For unknown tasks, returns DEFAULT_MOCK_OUTPUT with the task name filled in.
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"""
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if task in MOCK_OUTPUTS:
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# Return a copy so callers cannot mutate the canned data.
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return json.loads(json.dumps(MOCK_OUTPUTS[task]))
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result = json.loads(json.dumps(DEFAULT_MOCK_OUTPUT))
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result["task"] = task
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return result
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def mock_telemetry_row(task: str, model: str) -> dict:
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"""Build a mock telemetry row dict for offline mode."""
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return {
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"id": str(uuid.uuid4()),
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"task": task,
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"model": model,
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"provider": "mock",
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"input_tokens": 0,
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"output_tokens": 0,
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"cost_usd": 0.0,
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"duration_ms": 0,
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"mock": True,
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} |