agents 0.0.4
Install from the command line:
Learn more about npm packages
$ npm install @huggingface/agents@0.0.4
Install via package.json:
"@huggingface/agents": "0.0.4"
About this version
A way to call Hugging Face models and inference APIs from natural language, using an LLM.
pnpm add @huggingface/agents
npm add @huggingface/agents
yarn add @huggingface/agents// esm.sh
import { HfAgent } from "https://esm.sh/@huggingface/agent"
// or npm:
import { HfAgent } from "npm:@huggingface/agent"Check out the full documentation.
Agents.js leverages LLMs hosted as Inference APIs on HF, so you need to create an account and generate an access token.
import { HfAgent } from "@huggingface/agents";
const agent = new HfAgent("hf_...");
const code = await agent.generateCode("Draw a picture of a cat, wearing a top hat.")
console.log(code) // always good to check the generated code before running it
const outputs = await agent.evaluateCode(code);
console.log(outputs) You can also use your own LLM, by calling one of the LLMFrom* functions.
You can specify any valid model on the hub as long as they have an API.
import { HfAgent, LLMFromHub } from "@huggingface/agents";
const agent = new HfAgent(
"hf_...",
LLMFromHub("hf_...", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5")
);You can also specify your own endpoint, as long as it implements the same API, for exemple using text generation inference and Inference Endpoints.
import { HfAgent, LLMFromEndpoint } from "@huggingface/agents";
const agent = new HfAgent(
"hf_...",
LLMFromEndpoint("hf_...", "http://...")
);A LLM in this context is defined as any async function that takes a string input and returns a string. For example if you wanted to use the OpenAI API you could do so like this:
import { HfAgent } from "@huggingface/agents";
import { Configuration, OpenAIApi } from "openai";
const api = new OpenAIApi(new Configuration({ apiKey: "sk-..." }));
const llmOpenAI = async (prompt: string): Promise<string> => {
return (
(
await api.createCompletion({
model: "text-davinci-003",
prompt: prompt,
max_tokens: 1000,
})
).data.choices[0].text ?? ""
);
};
const agent = new HfAgent(
"hf_...",
llmOpenAI
);
// do anything you want with the agent hereBy default, agents ship with 4 tools. (textToImage, textToSpeech, imageToText, speechToText)
But you can expand the list of tools easily by creating new tools and passing them at initialization.
import { HfAgent, defaultTools, LLMFromHub } from "@huggingface/agents";
import type { Tool } from "@huggingface/agents/src/types";
// define the tool
const uppercaseTool: Tool = {
name: "uppercase",
description: "uppercase the input string and returns it ",
examples: [
{
prompt: "uppercase the string: hello world",
code: `const output = uppercase("hello world")`,
tools: ["uppercase"],
},
],
call: async (input) => {
const data = await input;
if (typeof data !== "string") {
throw new Error("Input must be a string");
}
return data.toUpperCase();
},
};
// pass it in the agent
const agent = new HfAgent(process.env.HF_ACCESS_TOKEN,
LLMFromHub("hf_...", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"),
[uppercaseTool, ...defaultTools]);-
@huggingface/inference: Required to call the inference endpoints themselves.
