rag

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.promptPreprocessor = promptPreprocessor;
const config_1 = require("./config");
const embedder_1 = require("./embedder");
const vectorStore_1 = require("./vectorStore");
const path_1 = require("path");
async function promptPreprocessor(ctl, userMessage) {
    const history = await ctl.pullHistory();
    if (history.length !== 0)
        return userMessage;
    const cfg = ctl.getPluginConfig(config_1.pluginConfigSchematics);
    if (!cfg.get("autoInject"))
        return userMessage;
    const query = userMessage.getText().trim();
    if (!query)
        return userMessage;
    try {
        const dp = cfg.get("dataPath").trim() || (0, path_1.join)(process.env.HOME ?? "~", "rag-data");
        const embId = cfg.get("embeddingModelIdentifier").trim();
        const topK = cfg.get("topK");
        const embed = await (0, embedder_1.getEmbedFn)(ctl.client, embId);
        const [queryVec] = await embed([query]);
        const idx = (0, vectorStore_1.getIndex)(dp, "default");
        const results = await (0, vectorStore_1.queryChunks)(idx, queryVec, topK);
        if (results.length === 0)
            return userMessage;
        const contextBlock = results
            .map((r, i) => `[${i + 1}] (${r.metadata.fileName})\n${r.metadata.text}`)
            .join("\n\n---\n\n");
        const injection = `[Retrieved context from your document index:\n\n${contextBlock}\n]`;
        return `${injection}\n\n${query}`;
    }
    catch {
        // No embedding model loaded or index empty — pass through silently
        return userMessage;
    }
}