import { OpenAIEmbeddings } from "@langchain/openai";
import { PGVectorStore } from "@langchain/community/vectorstores/pgvector";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";

// 1. Chunking - Split documents into retrieval-friendly segments
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
  separators: ["\n\n", "\n", ". ", " "],
});

const chunks = await splitter.splitDocuments(documents);

// 2. Embedding - Convert chunks to vectors
const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
  dimensions: 1536,
});

// 3. Storage - Index in vector database
const vectorStore = await PGVectorStore.fromDocuments(chunks, embeddings, {
  postgresConnectionOptions: {
    connectionString: process.env.DATABASE_URL,
  },
  tableName: "documents",
  columns: {
    idColumnName: "id",
    vectorColumnName: "embedding",
    contentColumnName: "content",
    metadataColumnName: "metadata",
  },
});

// 4. Retrieval - Find relevant context for a query
async function retrieve(query: string, k: number = 5) {
  const results = await vectorStore.similaritySearchWithScore(query, k);

  // Filter by relevance threshold
  return results
    .filter(([_, score]) => score > 0.7)
    .map(([doc]) => doc);
}

// 5. Generation - Augment prompt with retrieved context
async function generateAnswer(query: string): Promise<string> {
  const context = await retrieve(query);

  const contextText = context
    .map((doc) => doc.pageContent)
    .join("\n---\n");

  const response = await openai.chat.completions.create({
    model: "gpt-4o",
    messages: [
      {
        role: "system",
        content: `Answer questions using ONLY the provided context. If the context doesn't contain the answer, say "I don't have information about that."

Context:
${contextText}`,
      },
      { role: "user", content: query },
    ],
    temperature: 0.1,
  });

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

import { z } from "zod";
import OpenAI from "openai";
import { zodResponseFormat } from "openai/helpers/zod";

// Define the output schema
const ProductReviewAnalysis = z.object({
  sentiment: z.enum(["positive", "negative", "neutral", "mixed"]),
  confidence: z.number().min(0).max(1),
  themes: z.array(z.object({
    name: z.string(),
    sentiment: z.enum(["positive", "negative", "neutral"]),
    mentions: z.number(),
  })),
  summary: z.string().max(200),
  actionItems: z.array(z.string()),
});

type ProductReviewAnalysis = z.infer<typeof ProductReviewAnalysis>;

const openai = new OpenAI();

async function analyzeReviews(
  reviews: string[]
): Promise<ProductReviewAnalysis> {
  const response = await openai.beta.chat.completions.parse({
    model: "gpt-4o-2024-08-06",
    messages: [
      {
        role: "system",
        content: "Analyze product reviews and extract structured insights.",
      },
      {
        role: "user",
        content: `Analyze these reviews:\n${reviews.join("\n---\n")}`,
      },
    ],
    response_format: zodResponseFormat(ProductReviewAnalysis, "review_analysis"),
  });

  const parsed = response.choices[0].message.parsed;
  if (!parsed) {
    throw new Error("Failed to parse structured output");
  }
  return parsed;
}

import OpenAI from "openai";

const tools: OpenAI.Chat.Completions.ChatCompletionTool[] = [
  {
    type: "function",
    function: {
      name: "search_knowledge_base",
      description: "Search the internal knowledge base for relevant articles",
      parameters: {
        type: "object",
        properties: {
          query: { type: "string", description: "Search query" },
          category: {
            type: "string",
            enum: ["billing", "technical", "account"],
            description: "Category to filter by",
          },
        },
        required: ["query"],
      },
    },
  },
  {
    type: "function",
    function: {
      name: "create_support_ticket",
      description: "Create a support ticket for unresolved issues",
      parameters: {
        type: "object",
        properties: {
          title: { type: "string" },
          description: { type: "string" },
          priority: { type: "string", enum: ["low", "medium", "high", "urgent"] },
        },
        required: ["title", "description", "priority"],
      },
    },
  },
];

// Tool implementations
const toolHandlers: Record<string, (args: unknown) => Promise<string>> = {
  search_knowledge_base: async (args) => {
    const { query, category } = args as { query: string; category?: string };
    const results = await knowledgeBase.search(query, { category });
    return JSON.stringify(results);
  },
  create_support_ticket: async (args) => {
    const ticket = args as { title: string; description: string; priority: string };
    const created = await ticketSystem.create(ticket);
    return JSON.stringify({ ticketId: created.id, status: "created" });
  },
};

// Agentic loop - let the model decide which tools to call
async function agentLoop(
  messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[]
): Promise<string> {
  const MAX_ITERATIONS = 10;

  for (let i = 0; i < MAX_ITERATIONS; i++) {
    const response = await openai.chat.completions.create({
      model: "gpt-4o",
      messages,
      tools,
      tool_choice: "auto",
    });

    const message = response.choices[0].message;
    messages.push(message);

    // If no tool calls, we have the final answer
    if (!message.tool_calls || message.tool_calls.length === 0) {
      return message.content ?? "";
    }

    // Execute each tool call
    for (const toolCall of message.tool_calls) {
      const handler = toolHandlers[toolCall.function.name];
      if (!handler) {
        throw new Error(`Unknown tool: ${toolCall.function.name}`);
      }

      const args = JSON.parse(toolCall.function.arguments);
      const result = await handler(args);

      messages.push({
        role: "tool",
        tool_call_id: toolCall.id,
        content: result,
      });
    }
  }

  throw new Error("Agent exceeded maximum iterations");
}

import { Redis } from "ioredis";
import { createHash } from "crypto";

interface CacheEntry {
  response: string;
  embedding: number[];
  createdAt: number;
}

class SemanticCache {
  private redis: Redis;
  private ttlSeconds: number;
  private similarityThreshold: number;

  constructor(options: {
    redisUrl: string;
    ttlSeconds?: number;
    similarityThreshold?: number;
  }) {
    this.redis = new Redis(options.redisUrl);
    this.ttlSeconds = options.ttlSeconds ?? 3600;
    this.similarityThreshold = options.similarityThreshold ?? 0.95;
  }

  // Exact match cache (fast, cheap)
  private exactKey(prompt: string): string {
    const hash = createHash("sha256").update(prompt).digest("hex");
    return `llm:exact:${hash}`;
  }

  // Check exact cache first, then semantic similarity
  async get(prompt: string): Promise<string | null> {
    // 1. Try exact match
    const exact = await this.redis.get(this.exactKey(prompt));
    if (exact) return exact;

    // 2. Try semantic match (more expensive, but catches paraphrases)
    const embedding = await getEmbedding(prompt);
    const candidates = await this.redis.smembers("llm:semantic:keys");

    for (const candidateKey of candidates) {
      const raw = await this.redis.get(`llm:semantic:${candidateKey}`);
      if (!raw) continue;

      const entry: CacheEntry = JSON.parse(raw);
      const similarity = cosineSimilarity(embedding, entry.embedding);

      if (similarity >= this.similarityThreshold) {
        return entry.response;
      }
    }

    return null;
  }

  async set(prompt: string, response: string): Promise<void> {
    const embedding = await getEmbedding(prompt);
    const key = createHash("sha256").update(prompt).digest("hex");

    // Store exact match
    await this.redis.setex(this.exactKey(prompt), this.ttlSeconds, response);

    // Store semantic entry
    const entry: CacheEntry = {
      response,
      embedding,
      createdAt: Date.now(),
    };
    await this.redis.setex(
      `llm:semantic:${key}`,
      this.ttlSeconds,
      JSON.stringify(entry)
    );
    await this.redis.sadd("llm:semantic:keys", key);
  }
}

function cosineSimilarity(a: number[], b: number[]): number {
  let dot = 0, magA = 0, magB = 0;
  for (let i = 0; i < a.length; i++) {
    dot += a[i] * b[i];
    magA += a[i] * a[i];
    magB += b[i] * b[i];
  }
  return dot / (Math.sqrt(magA) * Math.sqrt(magB));
}

// Server - Next.js Route Handler with streaming
import { OpenAI } from "openai";
import { NextRequest } from "next/server";

export async function POST(req: NextRequest) {
  const { messages } = await req.json();
  const openai = new OpenAI();

  const stream = await openai.chat.completions.create({
    model: "gpt-4o",
    messages,
    stream: true,
  });

  // Convert OpenAI stream to ReadableStream
  const encoder = new TextEncoder();
  const readable = new ReadableStream({
    async start(controller) {
      for await (const chunk of stream) {
        const text = chunk.choices[0]?.delta?.content ?? "";
        if (text) {
          controller.enqueue(encoder.encode(`data: ${JSON.stringify({ text })}\n\n`));
        }
      }
      controller.enqueue(encoder.encode("data: [DONE]\n\n"));
      controller.close();
    },
  });

  return new Response(readable, {
    headers: {
      "Content-Type": "text/event-stream",
      "Cache-Control": "no-cache",
      Connection: "keep-alive",
    },
  });
}

// Client - React hook for consuming streams
function useStreamingChat() {
  const [messages, setMessages] = useState<Message[]>([]);
  const [isStreaming, setIsStreaming] = useState(false);

  const sendMessage = async (content: string) => {
    const userMessage = { role: "user" as const, content };
    const updatedMessages = [...messages, userMessage];
    setMessages(updatedMessages);
    setIsStreaming(true);

    const response = await fetch("/api/chat", {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify({ messages: updatedMessages }),
    });

    const reader = response.body?.getReader();
    const decoder = new TextDecoder();
    let assistantContent = "";

    setMessages((prev) => [...prev, { role: "assistant", content: "" }]);

    while (reader) {
      const { done, value } = await reader.read();
      if (done) break;

      const text = decoder.decode(value);
      const lines = text.split("\n").filter((line) => line.startsWith("data: "));

      for (const line of lines) {
        const data = line.slice(6);
        if (data === "[DONE]") break;

        const parsed = JSON.parse(data);
        assistantContent += parsed.text;

        setMessages((prev) => {
          const updated = [...prev];
          updated[updated.length - 1] = {
            role: "assistant",
            content: assistantContent,
          };
          return updated;
        });
      }
    }

    setIsStreaming(false);
  };

  return { messages, sendMessage, isStreaming };
}

import { evaluate } from "braintrust";

// Define evaluation dataset
const dataset = [
  {
    input: "What is our refund policy?",
    expected: "30-day money-back guarantee",
    tags: ["policy", "refund"],
  },
  {
    input: "How do I reset my password?",
    expected: "Go to Settings > Security > Reset Password",
    tags: ["account", "password"],
  },
];

// Custom scoring functions
function factualAccuracy(output: string, expected: string): number {
  const expectedFacts = expected.toLowerCase().split(/[,.]/).map((s) => s.trim());
  const matchedFacts = expectedFacts.filter((fact) =>
    output.toLowerCase().includes(fact)
  );
  return matchedFacts.length / expectedFacts.length;
}

function answerRelevance(output: string, input: string): number {
  // Use an LLM as a judge
  // Returns 0-1 score for how relevant the answer is to the question
  return llmJudge(input, output, "relevance");
}

// Run evaluation
const results = await evaluate("support-bot-v2", {
  data: dataset,
  task: async (input) => {
    return await generateAnswer(input.input);
  },
  scores: [
    {
      name: "factual_accuracy",
      scorer: (args) => factualAccuracy(args.output, args.expected),
    },
    {
      name: "answer_relevance",
      scorer: (args) => answerRelevance(args.output, args.input),
    },
    {
      name: "no_hallucination",
      scorer: (args) => {
        const hallucinationCheck = detectHallucination(args.output, args.context);
        return hallucinationCheck ? 0 : 1;
      },
    },
  ],
});

console.log(`Average factual accuracy: ${results.scores.factual_accuracy}`);
console.log(`Average relevance: ${results.scores.answer_relevance}`);