Add LangchainJS Examples (#146)

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Dave 2023-05-01 17:18:14 -04:00 committed by GitHub
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node_modules/
dist/

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{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"type": "node",
"request": "launch",
"name": "Launch Program",
// "skipFiles": [
// "<node_internals>/**"
// ],
"program": "${workspaceFolder}\\dist\\index.mjs",
"outFiles": [
"${workspaceFolder}/**/*.js"
]
}
]
}

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{
"name": "langchainjs-localai-example",
"version": "0.1.0",
"description": "Trivial Example of using langchain + the OpenAI API + LocalAI together",
"main": "index.mjs",
"scripts": {
"build": "tsc --build",
"clean": "tsc --build --clean",
"start": "node --trace-warnings dist/index.mjs"
},
"author": "dave@gray101.com",
"license": "MIT",
"devDependencies": {
"@types/node": "^18.16.3",
"typescript": "^5.0.4"
},
"dependencies": {
"langchain": "^0.0.67",
"typeorm": "^0.3.15"
}
}

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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_HOST'] || '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();

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{
"compilerOptions": {
"target": "es2022",
"lib": ["ES2022", "DOM"],
"module": "ES2022",
"moduleResolution": "node",
"strict": true,
"esModuleInterop": true,
"allowSyntheticDefaultImports": true,
"isolatedModules": true,
"outDir": "./dist"
},
"include": ["src", "test"],
"exclude": ["node_modules", "dist"]
}