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import type {
AnalyzeJobRequest,
JobAnalysisPayload,
JobAnalysisResponse,
} from "../models/job";
import { isJobAnalysisPayload } from "../models/job";
import type { Tier } from "../models/user";
/** Model output violated the schema even after the repair retry → HTTP 502. */
export class JobSchemaError extends Error {}
// Authoritative structured-output schema — specs/008-job-posting-analyzer/contracts/analyze-job.md
const JOB_ANALYSIS_SCHEMA = {
type: "object",
additionalProperties: false,
required: [
"isJobPosting",
"title",
"company",
"location",
"arrangement",
"arrangementConfidence",
"arrangementEvidence",
"daysInOffice",
"daysRemote",
"remoteRestrictions",
"salary",
"seniority",
"techStack",
"fit",
],
properties: {
isJobPosting: { type: "boolean" },
title: { type: ["string", "null"] },
company: { type: ["string", "null"] },
location: { type: ["string", "null"] },
arrangement: { enum: ["remote", "hybrid", "onsite", "unspecified"] },
arrangementConfidence: { enum: ["explicit", "inferred", "none"] },
arrangementEvidence: {
type: ["string", "null"],
description:
"Verbatim quote from the posting; required when arrangement != unspecified",
},
daysInOffice: { type: ["integer", "null"], minimum: 0, maximum: 7 },
daysRemote: { type: ["integer", "null"], minimum: 0, maximum: 7 },
remoteRestrictions: { type: ["string", "null"] },
salary: {
type: ["object", "null"],
additionalProperties: false,
required: ["min", "max", "currency", "period"],
properties: {
min: { type: ["number", "null"] },
max: { type: ["number", "null"] },
currency: { type: ["string", "null"] },
period: { enum: ["year", "month", "day", "hour", null] },
},
},
seniority: {
enum: [
"junior",
"mid",
"senior",
"staff",
"principal",
"manager",
"director",
"executive",
"unspecified",
],
},
techStack: { type: "array", items: { type: "string" }, maxItems: 25 },
fit: {
type: ["object", "null"],
additionalProperties: false,
required: [
"score",
"rationale",
"matching",
"missing",
"desired",
"strengths",
"weaknesses",
],
properties: {
score: { type: "integer", minimum: 0, maximum: 100 },
rationale: { type: "string", maxLength: 600 },
matching: { type: "array", items: { type: "string" }, maxItems: 10 },
missing: { type: "array", items: { type: "string" }, maxItems: 10 },
desired: { type: "array", items: { type: "string" }, maxItems: 10 },
strengths: { type: "array", items: { type: "string" }, maxItems: 5 },
weaknesses: { type: "array", items: { type: "string" }, maxItems: 5 },
},
},
},
} as const;
function buildSystemPrompt(hasProfile: boolean, assumeJobPosting: boolean): string {
const lines = [
"You are a precise job-posting analyzer. Extract structured facts from the page content.",
"Read the ENTIRE page content end to end — every section (overview, responsibilities, requirements, qualifications, nice-to-have, benefits) — before answering any field. Do not base an answer on only the first paragraph or the first section; a detail mentioned once, late in the posting, is as valid as one mentioned in the opening line.",
"Rules:",
"- JSON-LD and microdata JobPosting values are trusted ground truth for title, company, location, and salary unless the body text contradicts them; when the body contradicts structured data, follow the body. Where JSON-LD and microdata disagree with each other, prefer JSON-LD.",
"- NEVER invent a work arrangement or day counts. If the posting does not state or clearly imply the arrangement, answer arrangement=\"unspecified\" with arrangementConfidence=\"none\" and arrangementEvidence=null.",
"- arrangementEvidence MUST be a verbatim substring of the provided content — copy it exactly, never paraphrase.",
"- arrangementConfidence is \"explicit\" when the arrangement is stated outright, \"inferred\" when derived from indirect wording (inference still requires a verbatim evidence quote).",
"- JSON-LD jobLocationType \"TELECOMMUTE\" means arrangement=\"remote\" with confidence \"explicit\".",
"- daysInOffice/daysRemote: only for hybrid roles and only when stated or directly inferable, with the evidence quote covering them; otherwise null.",
"- remoteRestrictions: geographic or timezone limits on remote work (e.g. \"US only\"), else null.",
"- techStack: named programming languages, frameworks, libraries, databases, cloud platforms, and developer tools/infrastructure ONLY — e.g. \"Python\", \"Kubernetes\", \"PostgreSQL\", \"AWS\", \"React\". EXCLUDE generic business/process terms, methodologies, and soft skills even if the posting calls them out prominently (e.g. \"on-call\", \"incident response\", \"vendor management\", \"unit economics\", \"QA\", \"Agile\", \"stakeholder management\") — these are not technologies. Scan the entire posting, including team/stack overviews and nice-to-have lines, not just the main requirements section — a technology named once late in the posting counts. Each entry appears exactly once (no duplicates, no near-duplicate variants of the same name). Max 25 entries, most prominent/required first. If the posting names no concrete technologies, return an empty array — do not pad it with process or domain terms.",
"- isJobPosting: false when the content is not a job posting (news article, list page, etc.); still fill any fields you can.",
];
if (assumeJobPosting) {
lines.push(
"- The user has confirmed this page should be treated as a job posting: set isJobPosting=true and extract on a best-effort basis."
);
}
if (hasProfile) {
lines.push(
"Fit scoring (a candidate profile is provided):",
"- fit.score is 0-100 for how well the posting matches the profile; fit.rationale is two to three sentences summarizing the match, drawing on the posting as a whole (not just its opening paragraph).",
"- fit.matching: the required responsibilities and skills from the posting that the candidate's profile clearly covers (short phrases, most important first).",
"- fit.missing: required responsibilities and skills from the posting that the profile shows no evidence of.",
"- fit.desired: nice-to-have / preferred qualifications from the posting that the profile does not cover.",
"- fit.strengths: what makes this specific role a strong choice for this candidate (e.g. seniority alignment, domain overlap, growth direction).",
"- fit.weaknesses: risks or downsides of this role for this candidate (e.g. skill gaps to close, seniority mismatch, unstated arrangement, below-preference salary).",
"- Base every item on the posting and profile text only; never invent skills or requirements.",
"- If the posting violates any stated dealbreaker in the profile, cap fit.score at 20 or below and name the dealbreaker in the rationale."
);
} else {
lines.push("No candidate profile was provided: set fit to null.");
}
return lines.join("\n");
}
function buildUserMessage(req: AnalyzeJobRequest): string {
const parts: string[] = [];
if (req.profile) {
parts.push(`Candidate profile:\n${req.profile}`);
}
if (req.extract.jsonLd.length > 0) {
parts.push(`JSON-LD JobPosting data:\n${JSON.stringify(req.extract.jsonLd)}`);
}
for (const source of req.extract.structuredSources ?? []) {
parts.push(`Structured data (${source.kind}):\n${JSON.stringify(source.data)}`);
}
parts.push(`Page title: ${req.extract.title}`);
parts.push(`Page content:\n${req.extract.mainText}`);
return parts.join("\n\n");
}
function normalizeWhitespace(text: string): string {
return text.replace(/\s+/g, " ").trim();
}
/** Anti-hallucination backstop: evidence must be a substring of the model input. */
function validateEvidence(
payload: JobAnalysisPayload,
req: AnalyzeJobRequest,
warn: (message: string) => void
): JobAnalysisPayload {
if (payload.arrangement === "unspecified") return payload;
const haystack = normalizeWhitespace(
`${JSON.stringify(req.extract.jsonLd)} ${JSON.stringify(req.extract.structuredSources ?? [])} ${req.extract.title} ${req.extract.mainText}`
);
const evidence = payload.arrangementEvidence
? normalizeWhitespace(payload.arrangementEvidence)
: "";
if (evidence.length === 0 || !haystack.includes(evidence)) {
warn(
`arrangementEvidence is not a substring of the input; downgrading ${payload.arrangement} -> unspecified`
);
return {
...payload,
arrangement: "unspecified",
arrangementConfidence: "none",
arrangementEvidence: null,
daysInOffice: null,
daysRemote: null,
};
}
return payload;
}
function enforceConsistency(
payload: JobAnalysisPayload,
req: AnalyzeJobRequest
): JobAnalysisPayload {
const result = { ...payload };
if (result.arrangement !== "hybrid") {
result.daysInOffice = null;
result.daysRemote = null;
}
if (!req.profile) {
result.fit = null;
}
return result;
}
export async function orchestrateJobAnalysis(
req: AnalyzeJobRequest,
tier: Tier = "free",
warn: (message: string) => void = () => {}
): Promise<JobAnalysisResponse> {
const endpoint = process.env.AZURE_OPENAI_ENDPOINT ?? "";
const apiKey = process.env.AZURE_OPENAI_API_KEY ?? "";
const freeDeployment =
process.env.AZURE_OPENAI_JOB_DEPLOYMENT ??
process.env.AZURE_OPENAI_DEPLOYMENT ??
"gpt-4o-mini";
// Premium falls back to the free deployment when unset (including an
// empty-string placeholder, e.g. local.settings.json before the premium
// deployment is provisioned) — a safe default in every environment
// (data-model.md, research.md R6).
const deployment =
tier === "premium" && process.env.AZURE_OPENAI_JOB_DEPLOYMENT_PREMIUM
? process.env.AZURE_OPENAI_JOB_DEPLOYMENT_PREMIUM
: freeDeployment;
const client = new AzureOpenAI({ endpoint, apiKey, deployment, apiVersion: "2024-08-01-preview" });
const messages = [
{
role: "system" as const,
content: buildSystemPrompt(Boolean(req.profile), Boolean(req.assumeJobPosting)),
},
{ role: "user" as const, content: buildUserMessage(req) },
];
const complete = async (
extraInstruction?: string
): Promise<JobAnalysisPayload | null> => {
const completion = await client.chat.completions.create({
model: deployment,
messages: extraInstruction
? [...messages, { role: "system" as const, content: extraInstruction }]
: messages,
response_format: {
type: "json_schema",
json_schema: {
name: "job_analysis",
strict: true,
schema: JOB_ANALYSIS_SCHEMA as unknown as Record<string, unknown>,
},
},
temperature: 0,
// Sized for the full fit breakdown (matching/missing/desired lists) on
// top of the extraction fields. max_completion_tokens (not max_tokens)
// — required by the gpt-5.x deployment generation, and accepted by
// gpt-4o-mini too.
max_completion_tokens: 3000,
});
const content = completion.choices[0]?.message?.content;
if (!content) return null;
let parsed: unknown;
try {
parsed = JSON.parse(content);
} catch {
return null;
}
return isJobAnalysisPayload(parsed) ? parsed : null;
};
let payload = await complete();
if (!payload) {
warn("job analysis output failed to parse; retrying with repair instruction");
payload = await complete(
"Your previous reply was not valid JSON for the required schema. Respond again with ONLY a valid JSON object matching the schema exactly."
);
}
if (!payload) {
throw new JobSchemaError("Model output did not match the job analysis schema.");
}
payload = validateEvidence(payload, req, warn);
payload = enforceConsistency(payload, req);
return {
...payload,
model: deployment,
analyzedAt: new Date().toISOString(),
};
}
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