import { GoogleGenerativeAIEmbeddings } from "@langchain/google-genai";
import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import * as dotenv from "dotenv";
import path from "path";
import { HttpsProxyAgent } from "https-proxy-agent";
import nodeFetch from "node-fetch";

dotenv.config({ path: ".env.local" });
dotenv.config();

// Proxy setup
if (process.env.HTTPS_PROXY) {
  const agent = new HttpsProxyAgent(process.env.HTTPS_PROXY);
  (global as any).fetch = (url: any, init: any) => {
    return nodeFetch(url, { ...init, agent }) as any;
  };
}

const VECTOR_STORE_PATH = path.join(process.cwd(), "vector_store");

const run = async () => {
    const query = process.argv[2];
    if (!query) {
        console.log("Usage: npm run query 'your search term'");
        return;
    }

    if (!process.env.GOOGLE_GENERATIVE_AI_API_KEY) {
        throw new Error("GOOGLE_GENERATIVE_AI_API_KEY is missing");
    }

    console.log(`Loading vector store from ${VECTOR_STORE_PATH}...`);
    
    const embeddings = new GoogleGenerativeAIEmbeddings({
        modelName: "text-embedding-004",
        apiKey: process.env.GOOGLE_GENERATIVE_AI_API_KEY,
    });

    try {
        const vectorStore = await HNSWLib.load(VECTOR_STORE_PATH, embeddings);

        console.log(`Searching for: "${query}"...`);
        const results = await vectorStore.similaritySearch(query, 3);

        if (results.length === 0) {
            console.log("No results found.");
            return;
        }

        console.log(`\nFound ${results.length} relevant documents:\n`);
        results.forEach((doc, i) => {
            console.log(`[Result ${i + 1}] Score: (Implicit via retrieval)`);
            console.log(`Title: ${doc.metadata.title || 'Untitled'}`);
            console.log(`Source: ${doc.metadata.source || 'Unknown'}`);
            console.log(`Preview: ${doc.pageContent.substring(0, 150).replace(/\n/g, ' ')}...`);
            console.log("-".repeat(50));
        });
    } catch (error) {
        console.error("Error loading vector store. Have you run the ingestion script?");
        console.error(`Path checked: ${VECTOR_STORE_PATH}`);
        console.error(error);
    }
};

run().catch(console.error);