JavaScript & TypeScript#

npm i shad0wllm

import { decision } from "shad0wllm";

const intent = await decision("intent", {
  options: { refund: "wants money back", lost_card: "card lost or stolen", balance: "asks about balance", other: "anything else" },
  llm: "openai/gpt-6-luna",
  apiKey: process.env.OPENAI_API_KEY,
  bundle: "shad0w/intent/bundle",          // a trained table: a folder in Node, a URL in browsers and workers
  log: "shad0w/intent/log.jsonl",          // where your LLM's answers go (the training data)
});

const d = await intent.decide("my card was stolen");
// { answer: "lost_card", source: "table", confidence: 0.99, certified: true, flag: null,
//   why: "certified: answered by the table", threshold: 0.91, question: "intent", latencyUs: 9.8 }

import { configure, decision } from "shad0wllm";

configure({ llm: "openai/gpt-6-luna", apiKeyEnv: "SUPPORT_BOT_OPENAI_KEY" });   // once, for the whole process
const intent = await decision("intent", { options: ["refund", "lost_card", "other"] });
console.log(String(intent));   // Shadow("intent", options=3, logging (no table yet), llm=openai/gpt-6-luna, key: set via SUPPORT_BOT_OPENAI_KEY (sk-…3f9a))
configure();                   // no argument: clear the defaults

pip install "shad0wllm[compile]"
shad0w train --log shad0w/intent/log.jsonl --out shad0w/intent/bundle

import { decision } from "shad0wllm";
let intent;
export default {
  async fetch(req, env) {
    intent ??= await decision("intent", {
      llm: "openai/gpt-6-luna", apiKey: env.OPENAI_API_KEY,
      bundle: env.BUNDLE_URL,                          // e.g. a static-assets or R2 URL
      log: (row) => env.LOG_QUEUE.send(row),           // collect LLM answers for the next training run
    });
    const { text } = await req.json();
    return Response.json(await intent.decide(text));
  },
};

import { Bundle } from "https://cdn.jsdelivr.net/npm/shad0wllm@0.3.2/index.mjs";
const bundle = await Bundle.load("/models/intent");
const a = bundle.decide(input.value).answers.intent;   // { choice, confidence, certified, flag, probabilities }
if (a.certified) show(a.choice);                       // otherwise ask your server

const results = await intent.decideMany(texts, { concurrency: 8 });   // decisions, in the same order as texts

import { wrapLanguageModel, experimental_decide } from "ai";
import { openai } from "@ai-sdk/openai";
import { decision, shad0wMiddleware, decisionModel } from "shad0wllm";

const intent = await decision("intent", {
  options: ["refund", "lost_card", "other"], bundle: "shad0w/intent/bundle", log: "shad0w/intent/log.jsonl",
});

// generateText / generateObject: certified answers skip the model; the model's answers are logged for training.
const model = wrapLanguageModel({ model: openai("gpt-6-luna"), middleware: shad0wMiddleware(intent, { specificationVersion: "v3" }) });

// experimental_decide: the table answers the questions it certifies, the fallback decision model answers the rest.
const result = await experimental_decide({
  model: decisionModel(intent, { fallback: openai.decisionModel("gpt-6-luna") }),
  state: "my card was stolen",
  questions: { intent: { type: "choice", instructions: "What does the customer want?", criteria: { refund: null, lost_card: null, other: null } } },
});

const intent = await decision("intent", {
  options: { lost_card: "card lost or stolen", refund: "wants money back", other: "anything else" },
  llm: "systemone/kev", baseURL: "http://127.0.0.1:8081",   // any /v1/systemone server: Jev, Kev, Laya, llama.cpp
  // llm: "openai-decisions/gpt-6-luna",                     // the OpenAI Decisions API (reads OPENAI_API_KEY)
});