7 ابزار منبع باز شما برای تسلط بر توسعه AI به عنوان یک مبتدی نیاز دارید 🧙‍♂

npm install composio-core openai

import { Composio } from "composio-core";

const client = new Composio({ apiKey: "" });

const entity = await client.getEntity("Jessica");
const connection = await entity.initiateConnection({appName: 'github'});

console.log(`Open this URL to authenticate: ${connection.redirectUrl}`);
Initialize Composio and OpenAI

import { OpenAI } from "openai";
import { OpenAIToolSet } from "composio-core";

const openai_client = new OpenAI();
const composio_toolset = new OpenAIToolSet();
Fetch GitHub actions and pass them to the LLM

const tools = await composio_toolset.getTools({
actions: ["github_star_a_repository_for_the_authenticated_user"]
});

const instruction = "Star the repo composiohq/composio on GitHub";

const response = await openai_client.chat.completions.create({
model: "gpt-4o",
messages: [{ role: "user", content: instruction }],
tools: tools,
tool_choice: "auto",
});
Execute the tool calls.

const result = await composio_toolset.handleToolCall(response);
console.log(result);

npm i @e2b/code-interpreter @anthropic-ai/sdk dotenv

import { Sandbox } from '@e2b/sdk'

async function runCode() {
  // Create a new sandbox
  const sandbox = await Sandbox.create()

  // Execute code in the sandbox
  const result = await sandbox.run(`
    const data = require('fs').readFileSync('data.csv', 'utf8')
    console.log(data)
  `)

  // Close the sandbox when done
  await sandbox.close()
}

runCode()

npm install ai

import { useChat } from 'ai/react'

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat()

  return (
    <div>
      <ul>
        {messages.map((m, i) => (
          <li key={i}>{m.content}li>
        ))}
      ul>
      <form onSubmit={handleSubmit}>
        <input
          value={input}
          onChange={handleInputChange}
          placeholder="Say something..."
        />
      form>
    div>
  )
}

npm install langgraph langsmith

import { LangGraph, StateManager } from 'langgraph'
import { LangSmith } from 'langsmith'

const workflow = new LangGraph({
  nodes: {
    start: async (state) => {
      const response = await llm.chat("Process this request: " + state.input)
      return { output: response }
    },
    analyze: async (state) => {
      return { analysis: await processResponse(state.output) }
    }
  },
  edges: {
    start: ['analyze'],
    analyze: ['end']
  }
})

const result = await workflow.invoke({ input: "User query" })

npm install chromadb

import { ChromaClient } from 'chromadb'

async function searchDocuments() {
  const client = new ChromaClient()

  *// Create a collection*
  const collection = await client.createCollection('documents')

  *// Add documents with embeddings*
  await collection.add({
    ids: ["id1", "id2"],
    embeddings: [[1.1, 2.3, 3.2], [4.5, 6.9, 7.2]],
    documents: ["first document", "second document"]
  })

  *// Query similar documents*
  const results = await collection.query({
    queryEmbeddings: [1.1, 2.3, 3.2],
    nResults: 2
  })
}

npm install litellm

import { LiteLLM } from 'litellm'

async function callMultipleModels() {
  const llm = new LiteLLM({
    api_key: process.env.OPENAI_API_KEY
  })

  *// Call different models with the same interface*
  const gpt4Response = await llm.complete({
    model: "gpt-4",
    messages: [{ role: "user", content: "Hello!" }]
  })

  const claudeResponse = await llm.complete({
    model: "claude-2",
    messages: [{ role: "user", content: "Hello!" }]
  })
}

npm install llamaindex

import { Document, VectorStoreIndex } from 'llamaindex'

async function queryDocuments() {
  *// Create documents*
  const documents = [
    new Document({ text: "Sample document content" }),
    new Document({ text: "Another document here" })
  ]

  *// Create and query index*
  const index = await VectorStoreIndex.fromDocuments(documents)
  const queryEngine = index.asQueryEngine()

  const response = await queryEngine.query(
    "What are the main topics in the documents?"
  )

  console.log(response.toString())
}