feat: implement article optimization workflow

src/lib/llm/client.ts

@@ -0,0 +1,112 @@
+import OpenAI from "openai";
+
+export interface GenerateInput {
+  system?: string;
+  prompt: string;
+  model?: string;
+  temperature?: number;
+}
+
+export interface LlmProviderStatus {
+  provider: "deepseek" | "openai";
+  configured: boolean;
+  model: string;
+  baseURL?: string;
+  reason?: string;
+}
+
+function getProvider() {
+  return (process.env.LLM_PROVIDER || "deepseek").toLowerCase();
+}
+
+export function getLlmProviderStatus(): LlmProviderStatus {
+  const provider = getProvider();
+
+  if (provider === "openai") {
+    return {
+      provider: "openai",
+      configured: Boolean(process.env.OPENAI_API_KEY),
+      model: process.env.OPENAI_MODEL || "gpt-4.1-mini",
+      reason: process.env.OPENAI_API_KEY ? undefined : "OPENAI_API_KEY is missing",
+    };
+  }
+
+  return {
+    provider: "deepseek",
+    configured: Boolean(process.env.DEEPSEEK_API_KEY),
+    model: process.env.DEEPSEEK_MODEL || "deepseek-v4-pro",
+    baseURL: process.env.DEEPSEEK_BASE_URL || "https://api.deepseek.com",
+    reason: process.env.DEEPSEEK_API_KEY
+      ? undefined
+      : "DEEPSEEK_API_KEY is missing",
+  };
+}
+
+export function isLlmConfigured() {
+  return getLlmProviderStatus().configured;
+}
+
+function createClient() {
+  const status = getLlmProviderStatus();
+
+  if (!status.configured) {
+    throw new Error(status.reason ?? "LLM provider is not configured");
+  }
+
+  if (status.provider === "openai") {
+    return {
+      client: new OpenAI({ apiKey: process.env.OPENAI_API_KEY }),
+      model: status.model,
+    };
+  }
+
+  return {
+    client: new OpenAI({
+      apiKey: process.env.DEEPSEEK_API_KEY,
+      baseURL: status.baseURL,
+    }),
+    model: status.model,
+  };
+}
+
+export async function generateText(input: GenerateInput) {
+  try {
+    const { client, model } = createClient();
+    const response = await client.chat.completions.create({
+      model: input.model ?? model,
+      temperature: input.temperature ?? 0.2,
+      messages: [
+        ...(input.system ? [{ role: "system" as const, content: input.system }] : []),
+        { role: "user" as const, content: input.prompt },
+      ],
+    });
+
+    return response.choices[0]?.message.content ?? "";
+  } catch (error) {
+    throw normalizeLlmError(error);
+  }
+}
+
+export async function generateJson<T>(input: GenerateInput): Promise<T> {
+  try {
+    const { client, model } = createClient();
+    const response = await client.chat.completions.create({
+      model: input.model ?? model,
+      temperature: input.temperature ?? 0.1,
+      response_format: { type: "json_object" },
+      messages: [
+        ...(input.system ? [{ role: "system" as const, content: input.system }] : []),
+        { role: "user" as const, content: input.prompt },
+      ],
+    });
+    const content = response.choices[0]?.message.content ?? "{}";
+    return JSON.parse(content) as T;
+  } catch (error) {
+    throw normalizeLlmError(error);
+  }
+}
+
+function normalizeLlmError(error: unknown) {
+  const message = error instanceof Error ? error.message : "Unknown LLM error";
+  return new Error(`LLM provider error: ${message}`);
+}

src/lib/llm/prompts.ts

@@ -0,0 +1,14 @@
+export const JSON_ONLY_PROMPT =
+  "Return valid JSON only. Do not include markdown fences or commentary.";
+
+export const ARTICLE_OPTIMIZER_SYSTEM_PROMPT = [
+  "You optimize GEO-related articles under a confirmed fact card.",
+  "Never invent numbers, cases, qualifications, company names, products, or years.",
+  JSON_ONLY_PROMPT,
+].join(" ");
+
+export const QUALITY_INSPECTOR_SYSTEM_PROMPT = [
+  "Evaluate article quality against the confirmed fact card and target platform.",
+  "Return one structured check per required quality gate.",
+  JSON_ONLY_PROMPT,
+].join(" ");