testLangChaingoExtras.js

import BosBase from "bosbase";

const baseUrl = process.env.BOSBASE_BASE_URL ?? "http://127.0.0.1:8090";
const authEmail =
  process.env.BOSBASE_EMAIL ??
  process.env.BOSBASE_SUPERUSER_EMAIL ??
  "try@bosbase.com";
const authPassword =
  process.env.BOSBASE_PASSWORD ??
  process.env.BOSBASE_SUPERUSER_PASSWORD ??
  "bosbasepass";

const runLangChaingo = process.env.BOSBASE_RUN_LANGCHAINGO === "1";
const modelProvider = process.env.BOSBASE_LANGCHAINGO_PROVIDER;
const modelName = process.env.BOSBASE_LANGCHAINGO_MODEL;

async function main() {
  if (!runLangChaingo) {
    console.log(
      "[SKIP] LangChaingo extras skipped (set BOSBASE_RUN_LANGCHAINGO=1 to enable live calls).",
    );
    return;
  }

  let pb;
  let collectionName;
  try {
    console.log("[INFO] LANGCHAINGO extras doc test starting...");
    pb = new BosBase(baseUrl);
    await pb.collection("_superusers").authWithPassword(authEmail, authPassword);
    console.log("[SUCCESS] Authenticated as superuser");

    if (!pb.langchaingo) {
      throw new Error("LangChaingo service is missing from the client");
    }

    collectionName = `langchaingo_rag_${Date.now()}`;
    await pb.llmDocuments.createCollection(collectionName, { domain: "test" });
    await pb.llmDocuments.insert(
      {
        content: "The sky is blue because of Rayleigh scattering.",
        metadata: { topic: "physics" },
      },
      { collection: collectionName },
    );
    await pb.llmDocuments.insert(
      {
        content: "Photosynthesis lets plants convert sunlight into energy.",
        metadata: { topic: "biology" },
      },
      { collection: collectionName },
    );
    console.log(`[SUCCESS] LLM docs ready in collection "${collectionName}"`);

    const modelConfig =
      modelProvider || modelName
        ? { provider: modelProvider, model: modelName }
        : undefined;

    const rag = await pb.langchaingo.rag({
      collection: collectionName,
      question: "Why is the sky blue?",
      topK: 2,
      returnSources: true,
      model: modelConfig,
    });
    console.log("[SUCCESS] RAG answer:", rag?.answer || "(no answer)");
    console.log("[INFO] RAG sources:", (rag?.sources || []).length);

    const docQuery = await pb.langchaingo.queryDocuments({
      collection: collectionName,
      query: "Give one fact about plant energy.",
      topK: 2,
      returnSources: true,
      model: modelConfig,
    });
    console.log("[SUCCESS] Document query answer:", docQuery?.answer || "(no answer)");
    console.log("[INFO] Query sources:", (docQuery?.sources || []).length);

    const sql = await pb.langchaingo.sql({
      query: "Count the number of superusers and describe the result.",
      tables: ["_superusers"],
      topK: 1,
      model: modelConfig,
    });
    console.log("[SUCCESS] SQL generated:", sql?.sql || "(no sql)");
    console.log("[INFO] SQL answer summary:", sql?.answer || "(no answer)");

    console.log("\n========== LANGCHAINGO extras doc test completed ==========");
  } catch (error) {
    console.error("[ERROR] LangChaingo extras doc test failed:");
    if (error?.response) {
      console.error("Status:", error.response.status);
      console.error("Data:", JSON.stringify(error.response.data, null, 2));
      if (error.response.data?.message) {
        console.error("Message:", error.response.data.message);
      }
    } else {
      console.error(error);
    }
    process.exit(1);
  } finally {
    if (pb && collectionName) {
      try {
        await pb.llmDocuments.deleteCollection(collectionName);
        console.log(`[CLEANUP] Deleted test LLM collection "${collectionName}"`);
      } catch (cleanupError) {
        console.warn(
          "[WARN] Failed to delete LLM test collection:",
          cleanupError?.message || cleanupError,
        );
      }
    }
  }
}

main();