FlaskAI/examples/langchain/langchainjs-localai-example/src/index.mts

import { OpenAIChat } from "langchain/llms/openai";
import { loadQAStuffChain } from "langchain/chains";
import { Document } from "langchain/document";
import { initializeAgentExecutorWithOptions } from "langchain/agents";
import {Calculator} from "langchain/tools/calculator";

const pathToLocalAi = process.env['OPENAI_API_BASE'] || 'http://api:8080/v1';
const fakeApiKey = process.env['OPENAI_API_KEY'] || '-';
const modelName = process.env['MODEL_NAME'] || 'gpt-3.5-turbo';

function getModel(): OpenAIChat {
  return new OpenAIChat({
    prefixMessages: [
      {
        role: "system",
        content: "You are a helpful assistant that answers in pirate language",
      },
    ],
    modelName: modelName,
    maxTokens: 50,
    openAIApiKey: fakeApiKey,
    maxRetries: 2
  }, {
    basePath: pathToLocalAi,
    apiKey: fakeApiKey,
  });
}

// Minimal example.
export const run = async () => {
  const model = getModel();
  console.log(`about to model.call at ${new Date().toUTCString()}`);
  const res = await model.call(
    "What would be a good company name a company that makes colorful socks?"
  );
  console.log(`${new Date().toUTCString()}`);
  console.log({ res });
};

await run();

// This example uses the `StuffDocumentsChain`
export const run2 = async () => {
  const model = getModel();
  const chainA = loadQAStuffChain(model);
  const docs = [
    new Document({ pageContent: "Harrison went to Harvard." }),
    new Document({ pageContent: "Ankush went to Princeton." }),
  ];
  const resA = await chainA.call({
    input_documents: docs,
    question: "Where did Harrison go to college?",
  });
  console.log({ resA });
};

await run2();

// Quickly thrown together example of using tools + agents.
// This seems like it should work, but it doesn't yet.
export const temporarilyBrokenToolTest = async () => {
  const model = getModel();

  const executor = await initializeAgentExecutorWithOptions([new Calculator(true)], model, {
    agentType: "zero-shot-react-description",
  });

  console.log("Loaded agent.");

  const input = `What is the value of (500 *2) + 350 - 13?`;

  console.log(`Executing with input "${input}"...`);

  const result = await executor.call({ input });

  console.log(`Got output ${result.output}`);
}

await temporarilyBrokenToolTest();