// PoC: NL → ODMDB query (seekers)
// Usage:
//   1) export OPENAI_API_KEY=sk-...
//   2) node poc.js

import fs from "node:fs";
import OpenAI from "openai";
import { z } from "zod";

// ---- Config ----
const MODEL = process.env.OPENAI_MODEL || "gpt-5";
const MAIN_SCHEMA_PATH = "./main.json"; // optional context; safe if missing
const LG_SCHEMA_PATH = "./lg.json"; // optional context; safe if missing

// Hardcoded NL query for the PoC (no multi-turn)
const NL_QUERY =
  "give me new seekers since last week with email and experience";

// ---- Load schemas if present (not required for output) ----
function loadJsonSafe(path) {
  try {
    if (fs.existsSync(path)) {
      return JSON.parse(fs.readFileSync(path, "utf-8"));
    }
  } catch {}
  return null;
}
const SCHEMAS = {
  main: loadJsonSafe(MAIN_SCHEMA_PATH),
  lg: loadJsonSafe(LG_SCHEMA_PATH),
};

// ---- Seekers mapping (from our agreement) ----
const seekersMapping = {
  object: "seekers",
  readableFieldsForRecruiters: [
    "alias",
    "email",
    "seekstatus",
    "seekworkingyear",
    "seekjobtitleexperience",
  ],
};

// ---- Output contract (strict) ----
const OdmdbQueryZ = z.object({
  object: z.literal("seekers"),
  condition: z.array(z.string()),
  fields: z.array(z.string()), // always an array
});

// JSON Schema for Structured Output
const RESPONSE_JSON_SCHEMA = {
  type: "object",
  additionalProperties: false,
  properties: {
    object: { type: "string", enum: ["seekers"] },
    condition: { type: "array", items: { type: "string" } },
    fields: { type: "array", items: { type: "string" }, minItems: 1 },
  },
  required: ["object", "condition", "fields"],
};

// ---- Prompt builders ----
function systemPrompt() {
  return [
    "You convert a natural language request into an ODMDB search payload.",
    "Return ONLY a compact JSON object that matches the provided JSON Schema. The 'fields' property MUST be an array of strings.",
    "",
    "ODMDB DSL:",
    "- join(remoteObject:localKey:remoteProp:operator:value)",
    "- idx.<indexName>(value)",
    "- prop.<field>(operator:value) with dates or scalars.",
    "",
    "Rules:",
    "- Object must be 'seekers'.",
    "- For 'new'/'recent' recency, map to prop.dt_create with a resolved absolute date.",
    "- For 'experience', map to seekworkingyear.",
    "- Prefer recruiter-readable fields if a small set is requested. If the request is generic, return this default shortlist:",
    seekersMapping.readableFieldsForRecruiters.join(", "),
    "",
    "Timezone is Europe/Paris. Today is 2025-10-13.",
    "Interpret 'last week' as now minus 7 days → 2025-10-06.",
    "",
    "Schemas (context only, may be null):",
    JSON.stringify(SCHEMAS, null, 2),
  ].join("\n");
}
function userPrompt(nl) {
  return `Natural language request: "${nl}"\nReturn ONLY the JSON object.`;
}

// ---- OpenAI call using Responses API (text.format) ----
const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function inferQuery(nlText) {
  const resp = await client.responses.create({
    model: MODEL,
    input: [
      { role: "system", content: systemPrompt() },
      { role: "user", content: userPrompt(nlText) },
    ],
    text: {
      // <= new location for structured output format
      format: {
        name: "OdmdbQuery",
        type: "json_schema",
        schema: RESPONSE_JSON_SCHEMA,
        strict: true,
      },
    },
  });

  const jsonText =
    resp.output_text ||
    resp.output?.[0]?.content?.[0]?.text ||
    (() => {
      throw new Error("Empty model output");
    })();

  const parsed = JSON.parse(jsonText);
  const validated = OdmdbQueryZ.parse(parsed);

  // Light safety check on DSL tokens
  const allowed = ["join(", "idx.", "prop."];
  for (const c of validated.condition) {
    const ok = allowed.some((t) => c.includes(t));
    const ascii = /^[\x09\x0A\x0D\x20-\x7E()_:\[\].,=><!'"-]+$/.test(c);
    if (!ok || !ascii) throw new Error(`Malformed condition: ${c}`);
  }
  return validated;
}

// ---- Run PoC (print only the created query; do not execute) ----
(async () => {
  try {
    if (!process.env.OPENAI_API_KEY) {
      throw new Error("Missing OPENAI_API_KEY env var.");
    }

    const out = await inferQuery(NL_QUERY);

    // Just output the created query (no execution)
    console.log(
      JSON.stringify(
        {
          object: out.object,
          condition: out.condition,
          fields: out.fields,
        },
        null,
        2
      )
    );
  } catch (e) {
    console.error("PoC failed:", e.message || e);
    process.exit(1);
  }
})();