AI Product Agent Skill

import { z } from "zod";
import OpenAI from "openai";

// 1. Define your schema
const ProductReviewSchema = z.object({
  sentiment: z.enum(["positive", "negative", "neutral"]),
  score: z.number().min(0).max(10),
  summary: z.string().max(200),
  keyTopics: z.array(z.string()).max(5),
});

type ProductReview = z.infer<typeof ProductReviewSchema>;

// 2. Call with structured output
const openai = new OpenAI();

async function analyzeReview(reviewText: string): Promise<ProductReview> {
  const response = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    response_format: { type: "json_object" },
    messages: [
      {
        role: "system",
        content: `Analyze the product review. Return JSON matching this schema:
          { sentiment: "positive"|"negative"|"neutral", score: 0-10, summary: string, keyTopics: string[] }`,
      },
      { role: "user", content: reviewText },
    ],
  });

  const raw = JSON.parse(response.choices[0].message.content!);

  // 3. Always validate — the model can return anything
  const result = ProductReviewSchema.parse(raw);
  return result;
}

async function streamResponse(prompt: string, onChunk: (text: string) => void) {
  const stream = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: prompt }],
    stream: true,
  });

  let fullText = "";
  for await (const chunk of stream) {
    const delta = chunk.choices[0]?.delta?.content || "";
    fullText += delta;
    onChunk(delta); // Update UI incrementally
  }

  return fullText;
}

// React example: streaming into state
function useStreamingAI() {
  const [text, setText] = useState("");
  const [isStreaming, setIsStreaming] = useState(false);

  const generate = async (prompt: string) => {
    setIsStreaming(true);
    setText("");
    await streamResponse(prompt, (chunk) => {
      setText((prev) => prev + chunk);
    });
    setIsStreaming(false);
  };

  return { text, isStreaming, generate };
}

// prompts/v3-review-analyzer.ts
export const REVIEW_ANALYZER_PROMPT = {
  version: "3.0",
  system: `You are a product review analyst. Extract sentiment, score, summary, and topics.
    Always return valid JSON. Never hallucinate topics not mentioned in the review.`,
  temperature: 0.1, // Low temp for consistent structured output
  maxTokens: 500,
};

// tests/prompts/review-analyzer.test.ts
describe("Review Analyzer Prompt v3", () => {
  const testCases = [
    {
      input: "This product is amazing! Great battery life and beautiful screen.",
      expected: { sentiment: "positive", minScore: 7 },
    },
    {
      input: "Terrible. Broke after 2 days. Worst purchase ever.",
      expected: { sentiment: "negative", maxScore: 3 },
    },
    {
      input: "It's okay. Does what it says but nothing special.",
      expected: { sentiment: "neutral", minScore: 4, maxScore: 6 },
    },
  ];

  test.each(testCases)("correctly analyzes: $input", async ({ input, expected }) => {
    const result = await analyzeReview(input);
    expect(result.sentiment).toBe(expected.sentiment);
    if (expected.minScore) expect(result.score).toBeGreaterThanOrEqual(expected.minScore);
    if (expected.maxScore) expect(result.score).toBeLessThanOrEqual(expected.maxScore);
  });
});

User Query
    ↓
Query Processing (rewrite, expand, decompose)
    ↓
Embedding (text → vector)
    ↓
Vector Search (find relevant chunks)
    ↓
Re-ranking (order by relevance)
    ↓
Context Assembly (fit within token budget)
    ↓
LLM Generation (with retrieved context)
    ↓
Citation Extraction + Validation
    ↓
Response with Sources

import { OpenAI } from "openai";

const openai = new OpenAI();

// 1. Chunk documents at ingest time
function chunkDocument(text: string, maxChunkSize = 500, overlap = 50): string[] {
  const sentences = text.split(/(?<=[.!?])\s+/);
  const chunks: string[] = [];
  let current = "";

  for (const sentence of sentences) {
    if ((current + sentence).length > maxChunkSize && current) {
      chunks.push(current.trim());
      // Keep overlap for context continuity
      const words = current.split(" ");
      current = words.slice(-overlap).join(" ") + " " + sentence;
    } else {
      current += " " + sentence;
    }
  }
  if (current.trim()) chunks.push(current.trim());
  return chunks;
}

// 2. Embed and store
async function embedChunks(chunks: string[]) {
  const response = await openai.embeddings.create({
    model: "text-embedding-3-small",
    input: chunks,
  });
  return response.data.map((d, i) => ({
    text: chunks[i],
    embedding: d.embedding,
  }));
}

// 3. Retrieve relevant context
async function retrieve(query: string, topK = 5) {
  const queryEmbedding = await openai.embeddings.create({
    model: "text-embedding-3-small",
    input: query,
  });

  // Search your vector DB (Pinecone, Weaviate, pgvector, etc.)
  const results = await vectorDB.search({
    vector: queryEmbedding.data[0].embedding,
    topK,
  });

  return results.map((r) => r.metadata.text);
}

// 4. Generate with context
async function ragGenerate(query: string) {
  const context = await retrieve(query);

  const response = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [
      {
        role: "system",
        content: `Answer based ONLY on the provided context. If the context doesn't contain the answer, say "I don't have enough information to answer that."
        
Context:
${context.map((c, i) => `[${i + 1}] ${c}`).join("\n\n")}`,
      },
      { role: "user", content: query },
    ],
  });

  return response.choices[0].message.content;
}

// Bad: Spinner for 10 seconds with no context
<Spinner />

// Good: Progressive disclosure
function AILoadingState({ stage }: { stage: string }) {
  return (
    <div className="ai-loading">
      <div className="pulse-dot" />
      <span className="text-sm text-muted">{stage}</span>
    </div>
  );
}

// Usage with stages
const stages = [
  "Understanding your question...",
  "Searching relevant documents...",
  "Generating response...",
];

// Always have AI-specific error handling
function AIErrorBoundary({ error, onRetry }: { error: Error; onRetry: () => void }) {
  const messages: Record<string, string> = {
    RATE_LIMITED: "Too many requests. Please wait a moment.",
    CONTEXT_TOO_LONG: "Your input is too long. Try shortening it.",
    SAFETY_FILTERED: "This request couldn't be processed. Try rephrasing.",
    API_ERROR: "AI service is temporarily unavailable.",
  };

  return (
    <div className="ai-error">
      <p>{messages[error.message] || "Something went wrong."}</p>
      <button onClick={onRetry}>Try Again</button>
    </div>
  );
}

function sanitizeInput(userInput: string): string {
  // 1. Length limit
  if (userInput.length > 10_000) {
    throw new Error("CONTEXT_TOO_LONG");
  }

  // 2. Strip known injection patterns
  const cleaned = userInput
    .replace(/ignore (all |previous |above )?instructions/gi, "[filtered]")
    .replace(/you are now/gi, "[filtered]")
    .replace(/system:\s/gi, "[filtered]");

  return cleaned;
}

async function safeGenerate(prompt: string) {
  const response = await generate(prompt);

  // 1. Check for refusal (model declined to answer)
  if (response.includes("I cannot") || response.includes("I'm not able to")) {
    return { type: "refused", content: response };
  }

  // 2. Check for hallucinated URLs/emails
  const urls = response.match(/https?:\/\/[^\s]+/g) || [];
  const validatedUrls = await Promise.all(urls.map(validateUrl));
  if (validatedUrls.some((v) => !v)) {
    return { type: "contains_hallucinated_links", content: stripInvalidUrls(response) };
  }

  // 3. Content policy check (use a classifier or moderation endpoint)
  const moderation = await openai.moderations.create({ input: response });
  if (moderation.results[0].flagged) {
    return { type: "flagged", content: "Response filtered for safety." };
  }

  return { type: "ok", content: response };
}

function estimateTokens(text: string): number {
  // Rough estimate: 1 token ≈ 4 characters for English
  return Math.ceil(text.length / 4);
}

function fitWithinBudget(
  systemPrompt: string,
  context: string[],
  userQuery: string,
  maxTokens: number = 120_000 // model context window
): string[] {
  const reservedForOutput = 4_000;
  const systemTokens = estimateTokens(systemPrompt);
  const queryTokens = estimateTokens(userQuery);
  let budget = maxTokens - reservedForOutput - systemTokens - queryTokens;

  const fitted: string[] = [];
  for (const chunk of context) {
    const chunkTokens = estimateTokens(chunk);
    if (chunkTokens <= budget) {
      fitted.push(chunk);
      budget -= chunkTokens;
    } else {
      break;
    }
  }
  return fitted;
}

async function trackAICost(feature: string, userId: string, apiCall: () => Promise<any>) {
  const start = Date.now();
  const result = await apiCall();
  const duration = Date.now() - start;

  await analytics.track("ai_usage", {
    feature,         // "review-analyzer", "chat", "summarizer"
    userId,
    model: result.model,
    inputTokens: result.usage.prompt_tokens,
    outputTokens: result.usage.completion_tokens,
    cost: calculateCost(result.model, result.usage),
    latency: duration,
    cached: result.usage.prompt_tokens_details?.cached_tokens > 0,
  });

  return result;
}

async function resilientGenerate(prompt: string) {
  const providers = [
    { name: "openai", fn: () => callOpenAI(prompt) },
    { name: "anthropic", fn: () => callAnthropic(prompt) },
    { name: "cached", fn: () => getCachedFallback(prompt) },
  ];

  for (const provider of providers) {
    try {
      return await withRetry(provider.fn, { maxRetries: 2, backoff: "exponential" });
    } catch (error) {
      console.error(`${provider.name} failed:`, error);
      continue;
    }
  }

  throw new Error("API_ERROR");
}

async function withRetry<T>(
  fn: () => Promise<T>,
  opts: { maxRetries: number; backoff: "exponential" }
): Promise<T> {
  let lastError: Error;
  for (let i = 0; i <= opts.maxRetries; i++) {
    try {
      return await fn();
    } catch (error) {
      lastError = error as Error;
      if (i < opts.maxRetries) {
        await sleep(Math.pow(2, i) * 1000); // 1s, 2s, 4s
      }
    }
  }
  throw lastError!;
}

// Don't block request handlers with LLM calls

// API: enqueue the job
app.post("/api/analyze", async (req, res) => {
  const jobId = await queue.add("analyze", {
    userId: req.user.id,
    input: req.body.input,
  });
  res.json({ jobId, status: "processing" });
});

// Worker: process async
queue.process("analyze", async (job) => {
  const result = await analyzeWithAI(job.data.input);
  await db.results.create({ jobId: job.id, ...result });
  await notify(job.data.userId, { jobId: job.id, status: "complete" });
});

// Client: poll or use websocket
app.get("/api/analyze/:jobId", async (req, res) => {
  const result = await db.results.findUnique({ where: { jobId: req.params.jobId } });
  res.json(result || { status: "processing" });
});