import { S3Client, GetObjectCommand } from "@aws-sdk/client-s3";
import { BedrockRuntimeClient, InvokeModelCommand } from "@aws-sdk/client-bedrock-runtime";
import { Pinecone } from "@pinecone-database/pinecone";

const s3 = new S3Client({});
const bedrock = new BedrockRuntimeClient({});
const pc = new Pinecone({ apiKey: process.env.PINECONE_API_KEY });

export const handler = async (event) => {
    const bucket = event.Records[0].s3.bucket.name;
    const key = decodeURIComponent(event.Records[0].s3.object.key.replace(/\+/g, " "));

    const s3Response = await s3.send(new GetObjectCommand({ Bucket: bucket, Key: key }));
    const rawText = await s3Response.Body.transformToString();

    const chunks = rawText.match(/[\s\S]{1,500}/g) || [];

    const index = pc.index(process.env.PINECONE_INDEX_NAME);

    for (let i = 0; i < chunks.length; i++) {
        const chunk = chunks[i];

        const bedrockResponse = await bedrock.send(new InvokeModelCommand({
            modelId: "amazon.titan-embed-text-v1",
            contentType: "application/json",
            accept: "application/json",
            body: JSON.stringify({ inputText: chunk })
        }));

        const { embedding } = JSON.parse(new TextDecoder().decode(bedrockResponse.body));

        // 4. Upsert into Vector Database
        await index.upsert([{
            id: `${key}_chunk_${i}`,
            values: embedding,
            metadata: { text: chunk, source: key }
        }]);
    }

    return { status: "Success", processedChunks: chunks.length };
};