wehub-resource-sync
f73e710e38
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415 lines
21 KiB
TypeScript
415 lines
21 KiB
TypeScript
/* eslint-disable no-useless-escape */
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import {
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Role,
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MessagePlaceholders,
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ConvTemplateConfig,
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ChatConfig,
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} from "../src/config";
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import {
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getConversation,
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getConversationFromChatCompletionRequest,
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getFunctionCallUsage,
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} from "../src/conversation";
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import { ChatCompletionRequest } from "../src/openai_api_protocols/chat_completion";
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import { describe, expect, test } from "@jest/globals";
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import { llama3_1ChatConfig } from "./constants";
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describe("Test gorilla conversation template", () => {
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const gorillaConv: ConvTemplateConfig = {
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system_template: `${MessagePlaceholders.system}\n`,
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system_message:
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"A chat between a curious user and an artificial intelligence assistant. " +
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"The assistant gives helpful, detailed, and polite answers to the user's questions.",
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roles: {
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[Role.user]: "USER",
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[Role.assistant]: "ASSISTANT",
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[Role.tool]: "TOOL",
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},
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role_templates: {
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[Role.user]: `<<question>> ${MessagePlaceholders.user} <<function>> ${MessagePlaceholders.function}`,
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},
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seps: ["\n", "<|EOT|>"],
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stop_str: ["<|EOT|>"],
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system_prefix_token_ids: [1],
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stop_token_ids: [2],
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};
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test("Test getPromptArrayInternal", () => {
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const conv = getConversation(gorillaConv);
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conv.appendMessage(
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Role.user,
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'Call me an Uber ride type "Plus" in Berkeley at zipcode 94704 in 10 minutes',
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"Tom",
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);
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const prompt_array = conv.getPromptArray();
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expect(prompt_array).toEqual([
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"A chat between a curious user and an artificial intelligence assistant. " +
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"The assistant gives helpful, detailed, and polite answers to the user's questions.\n",
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'Tom: <<question>> Call me an Uber ride type "Plus" in Berkeley at zipcode 94704 in 10 minutes <<function>> \n',
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]);
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});
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test("Test getPromptArrayInternal function call", () => {
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const conv = getConversation(gorillaConv);
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conv.appendMessage(
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Role.user,
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'Call me an Uber ride type "Plus" in Berkeley at zipcode 94704 in 10 minutes',
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);
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conv.use_function_calling = true;
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conv.function_string = JSON.stringify([
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{
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name: "Uber Carpool",
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api_name: "uber.ride",
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description:
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"Find suitable ride for customers given the location, type of ride, and the amount of time the customer is willing to wait as parameters",
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parameters: [
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{
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name: "loc",
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description: "Location of the starting place of the Uber ride",
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},
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{
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name: "type",
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enum: ["plus", "comfort", "black"],
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description: "Types of Uber ride user is ordering",
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},
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{
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name: "time",
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description:
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"The amount of time in minutes the customer is willing to wait",
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},
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],
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},
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]);
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const prompt_array = conv.getPromptArray();
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expect(prompt_array).toEqual([
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"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n",
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'USER: <<question>> Call me an Uber ride type "Plus" in Berkeley at zipcode 94704 in 10 minutes <<function>> [{"name":"Uber Carpool","api_name":"uber.ride","description":"Find suitable ride for customers given the location, type of ride, and the amount of time the customer is willing to wait as parameters","parameters":[{"name":"loc","description":"Location of the starting place of the Uber ride"},{"name":"type","enum":["plus","comfort","black"],"description":"Types of Uber ride user is ordering"},{"name":"time","description":"The amount of time in minutes the customer is willing to wait"}]}]\n',
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]);
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});
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});
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describe("Test gorilla MLCEngine", () => {
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test("Test getFunctionCallUsage none", () => {
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const request: ChatCompletionRequest = {
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model: "gorilla-openfunctions-v1-q4f16_1_MLC",
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messages: [
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{ role: "system", content: "You are a helpful assistant." },
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{ role: "user", content: "Hello!" },
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],
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tool_choice: "none",
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tools: [
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{
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type: "function",
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function: {
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description: "A",
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name: "fn_A",
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parameters: { foo: "bar" },
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},
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},
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{
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type: "function",
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function: {
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description: "B",
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name: "fn_B",
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parameters: { foo: "bar" },
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},
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},
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{
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type: "function",
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function: {
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description: "C",
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name: "fn_C",
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parameters: { foo: "bar" },
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},
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},
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],
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};
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expect(getFunctionCallUsage(request)).toEqual("");
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});
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test("Test getFunctionCallUsage auto", () => {
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const request: ChatCompletionRequest = {
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model: "gorilla-openfunctions-v1-q4f16_1_MLC",
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messages: [
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{ role: "system", content: "You are a helpful assistant." },
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{ role: "user", content: "Hello!" },
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],
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tool_choice: "auto",
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tools: [
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{
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type: "function",
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function: {
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description: "A",
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name: "fn_A",
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parameters: { foo: "bar" },
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},
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},
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{
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type: "function",
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function: {
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description: "B",
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name: "fn_B",
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parameters: { foo: "bar" },
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},
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},
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{
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type: "function",
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function: {
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description: "C",
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name: "fn_C",
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parameters: { foo: "bar" },
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},
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},
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],
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};
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expect(getFunctionCallUsage(request)).toEqual(
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'[{"description":"A","name":"fn_A","parameters":{"foo":"bar"}},{"description":"B","name":"fn_B","parameters":{"foo":"bar"}},{"description":"C","name":"fn_C","parameters":{"foo":"bar"}}]',
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);
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});
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test("Test getFunctionCallUsage function", () => {
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const request: ChatCompletionRequest = {
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model: "gorilla-openfunctions-v1-q4f16_1_MLC",
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messages: [
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{ role: "system", content: "You are a helpful assistant." },
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{ role: "user", content: "Hello!" },
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],
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tool_choice: {
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type: "function",
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function: {
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name: "fn_B",
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},
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},
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tools: [
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{
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type: "function",
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function: {
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description: "A",
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name: "fn_A",
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parameters: { foo: "bar" },
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},
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},
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{
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type: "function",
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function: {
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description: "B",
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name: "fn_B",
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parameters: { foo: "bar" },
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},
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},
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{
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type: "function",
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function: {
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description: "C",
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name: "fn_C",
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parameters: { foo: "bar" },
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},
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},
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],
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};
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expect(getFunctionCallUsage(request)).toEqual(
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'[{"description":"B","name":"fn_B","parameters":{"foo":"bar"}}]',
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);
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});
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});
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describe("Test Hermes2 formatting", () => {
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const hermes2LlamaChatConfig: ChatConfig = {
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vocab_size: 128288,
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context_window_size: 8192,
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sliding_window_size: -1,
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attention_sink_size: -1,
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temperature: 1.0,
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presence_penalty: 0.0,
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frequency_penalty: 0.0,
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repetition_penalty: 1.0,
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top_p: 1.0,
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tokenizer_files: ["tokenizer.json", "tokenizer_config.json"],
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tokenizer_info: {
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token_postproc_method: "byte_level",
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prepend_space_in_encode: false,
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strip_space_in_decode: false,
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},
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conv_template: {
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system_template: "<|im_start|>system\n{system_message}<|im_end|>\n",
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system_message:
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'You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.',
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add_role_after_system_message: true,
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roles: {
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user: "<|im_start|>user",
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assistant: "<|im_start|>assistant",
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tool: "<|im_start|>tool",
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},
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role_templates: {
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user: "{user_message}",
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assistant: "{assistant_message}",
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tool: "{tool_message}",
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},
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seps: ["<|im_end|>\n"],
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role_content_sep: "\n",
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role_empty_sep: "\n",
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stop_str: ["<|im_end|>"],
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stop_token_ids: [128001, 128009, 128003],
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},
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bos_token_id: 128000,
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};
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// Follows https://github.com/NousResearch/Hermes-Function-Calling/blob/96ebfd7c903216b05e1eb7b155f7d5842b0fbce8/README.md#prompt-format
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test("Test formatting", () => {
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const system_prompt = `You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:\n<tool_call>\n{"arguments": <args-dict>, "name": <function-name>}\n</tool_call>`;
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const request: ChatCompletionRequest = {
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messages: [
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{ role: "system", content: system_prompt },
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{
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role: "user",
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content: "Fetch the stock fundamentals data for Tesla (TSLA)",
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},
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{
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role: "assistant",
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content: `<tool_call>\n{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}\n</tool_call>`,
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},
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{
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role: "tool",
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tool_call_id: "0",
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content: `<tool_response>\n{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}\n</tool_response>\n`,
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},
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{ role: "assistant", content: "Some replies here" },
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{ role: "user", content: "Thank you." },
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],
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};
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// Since we treat last input as PrefillStep input, last message is not included in `conv`
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const conv = getConversationFromChatCompletionRequest(
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request,
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hermes2LlamaChatConfig,
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);
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const promptArray = conv.getPromptArray();
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let finalMessage = "";
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for (const msg of promptArray) {
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finalMessage += msg;
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}
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const expected =
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`<|im_start|>system\n` +
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system_prompt +
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`<|im_end|>\n` +
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`<|im_start|>user\nFetch the stock fundamentals data for Tesla (TSLA)<|im_end|>\n` +
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`<|im_start|>assistant\n<tool_call>\n{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}\n</tool_call><|im_end|>\n` +
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`<|im_start|>tool\n<tool_response>\n{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}\n</tool_response>\n<|im_end|>\n` +
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`<|im_start|>assistant\nSome replies here<|im_end|>\n`;
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expect(finalMessage).toEqual(expected);
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});
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});
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describe("Test Llama3.1 formatting", () => {
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// Follows https://github.com/NousResearch/Hermes-Function-Calling/blob/96ebfd7c903216b05e1eb7b155f7d5842b0fbce8/README.md#prompt-format
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test("Test formatting", () => {
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const system_prompt = `Cutting Knowledge Date: December 2023
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Today Date: 23 Jul 2024
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# Tool Instructions
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- When looking for real time information use relevant functions if available
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You have access to the following functions:
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{
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"type": "function",
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"function": {
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"name": "get_current_temperature",
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"description": "Get the current temperature at a location.",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The location to get the temperature for, in the format \"City, Country\""
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}
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},
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"required": [
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"location"
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]
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},
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"return": {
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"type": "number",
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"description": "The current temperature at the specified location in the specified units, as a float."
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}
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}
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}
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{
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"type": "function",
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"function": {
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"name": "send_message",
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"description": "Send a message to a recipient.",
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"parameters": {
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"type": "object",
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"properties": {
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"recipient": {
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"type": "string",
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"description": "Name of the recipient of the message"
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}
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"content": {
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"type": "string",
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"description": "Content of the message"
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}
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},
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"required": [
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"recipient",
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"content"
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]
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},
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"return": {
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"type": "None"
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}
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}
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}
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If a you choose to call a function ONLY reply in the following format:
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<function>{"name": function name, "parameters": dictionary of argument name and its value}</function>
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Here is an example,
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<function>{"name": "example_function_name", "parameters": {"example_name": "example_value"}}</function>
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Reminder:
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- Function calls MUST follow the specified format and use BOTH <function> and </function>
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- Required parameters MUST be specified
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- Only call one function at a time
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- When calling a function, do NOT add any other words, ONLY the function calling
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- Put the entire function call reply on one line
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- Always add your sources when using search results to answer the user query
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You are a helpful Assistant.`;
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const user1 = "Hey, what's the temperature in Paris right now?";
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const assistant1 = `<function>{"name": "get_current_temperature", "parameters": {"location": "Paris, France"}}</function>`;
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const tool1 = `{"output": 22.5}`;
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const assistant2 = `The current temperature in Paris is 22.5°C.`;
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const user2 = "Send a message to Tom to tell him this information.";
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const assistant3 = `<function>{"name": "send_message", "parameters": {"recipient": "Tom", "content": "The current temperature in Paris is 22.5°C."}}</function>`;
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const tool2 = `{"output": None}`;
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const assistant4 = `The message has been sent to Tom.`;
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const request: ChatCompletionRequest = {
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messages: [
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{ role: "system", content: system_prompt },
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{ role: "user", content: user1 },
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{ role: "assistant", content: assistant1 },
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{ role: "tool", tool_call_id: "0", content: tool1 },
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{ role: "assistant", content: assistant2 },
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{ role: "user", content: user2 },
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{ role: "assistant", content: assistant3 },
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{ role: "tool", tool_call_id: "1", content: tool2 },
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{ role: "assistant", content: assistant4 },
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{ role: "user", content: "Thank you." },
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],
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};
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// Since we treat last input as PrefillStep input, last message is not included in `conv`
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const conv = getConversationFromChatCompletionRequest(
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request,
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llama3_1ChatConfig,
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);
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const promptArray = conv.getPromptArray();
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let finalMessage = "";
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for (const msg of promptArray) {
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finalMessage += msg;
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}
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// Expected is generated with transformers in Python `tokenizer.apply_chat_template()`
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const expected = `<|start_header_id|>system<|end_header_id|>\n\nCutting Knowledge Date: December 2023\n Today Date: 23 Jul 2024\n # Tool Instructions\n - When looking for real time information use relevant functions if available\n You have access to the following functions:\n \n {\n "type": "function",\n "function": {\n "name": "get_current_temperature",\n "description": "Get the current temperature at a location.",\n "parameters": {\n "type": "object",\n "properties": {\n "location": {\n "type": "string",\n "description": "The location to get the temperature for, in the format "City, Country""\n }\n },\n "required": [\n "location"\n ]\n },\n "return": {\n "type": "number",\n "description": "The current temperature at the specified location in the specified units, as a float."\n }\n }\n }\n {\n "type": "function",\n "function": {\n "name": "send_message",\n "description": "Send a message to a recipient.",\n "parameters": {\n "type": "object",\n "properties": {\n "recipient": {\n "type": "string",\n "description": "Name of the recipient of the message"\n }\n "content": {\n "type": "string",\n "description": "Content of the message"\n }\n },\n "required": [\n "recipient",\n "content"\n ]\n },\n "return": {\n "type": "None"\n }\n }\n }\n If a you choose to call a function ONLY reply in the following format:\n <function>{"name": function name, "parameters": dictionary of argument name and its value}</function>\n Here is an example,\n <function>{"name": "example_function_name", "parameters": {"example_name": "example_value"}}</function>\n Reminder:\n - Function calls MUST follow the specified format and use BOTH <function> and </function>\n - Required parameters MUST be specified\n - Only call one function at a time\n - When calling a function, do NOT add any other words, ONLY the function calling\n - Put the entire function call reply on one line\n - Always add your sources when using search results to answer the user query\n You are a helpful Assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHey, what\'s the temperature in Paris right now?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n<function>{"name": "get_current_temperature", "parameters": {"location": "Paris, France"}}</function><|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\n{"output": 22.5}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nThe current temperature in Paris is 22.5°C.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSend a message to Tom to tell him this information.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n<function>{"name": "send_message", "parameters": {"recipient": "Tom", "content": "The current temperature in Paris is 22.5°C."}}</function><|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\n{"output": None}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nThe message has been sent to Tom.<|eot_id|>`;
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expect(finalMessage).toEqual(expected);
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});
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});
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