mirror of
https://github.com/getcompanion-ai/co-mono.git
synced 2026-04-16 13:04:08 +00:00
efaa5cdb39
- Updated generate-models.ts to fetch these providers directly from models.dev API - OpenRouter now only used for xAI and other third-party providers - Fixed test model IDs to match new model names from models.dev - Removed unused import from google.ts
618 lines
No EOL
21 KiB
TypeScript
618 lines
No EOL
21 KiB
TypeScript
import { describe, it, beforeAll, afterAll, expect } from "vitest";
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import { GoogleLLM } from "../src/providers/google.js";
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import { OpenAICompletionsLLM } from "../src/providers/openai-completions.js";
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import { OpenAIResponsesLLM } from "../src/providers/openai-responses.js";
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import { AnthropicLLM } from "../src/providers/anthropic.js";
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import type { LLM, LLMOptions, Context, Tool, AssistantMessage, Model, ImageContent } from "../src/types.js";
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import { spawn, ChildProcess, execSync } from "child_process";
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import { createLLM, getModel } from "../src/models.js";
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import { readFileSync } from "fs";
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import { join, dirname } from "path";
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import { fileURLToPath } from "url";
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = dirname(__filename);
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// Calculator tool definition (same as examples)
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const calculatorTool: Tool = {
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name: "calculator",
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description: "Perform basic arithmetic operations",
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parameters: {
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type: "object",
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properties: {
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a: { type: "number", description: "First number" },
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b: { type: "number", description: "Second number" },
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operation: {
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type: "string",
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enum: ["add", "subtract", "multiply", "divide"],
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description: "The operation to perform"
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}
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},
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required: ["a", "b", "operation"]
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}
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};
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async function basicTextGeneration<T extends LLMOptions>(llm: LLM<T>) {
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const context: Context = {
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systemPrompt: "You are a helpful assistant. Be concise.",
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messages: [
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{ role: "user", content: "Reply with exactly: 'Hello test successful'" }
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]
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};
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const response = await llm.generate(context);
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expect(response.role).toBe("assistant");
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expect(response.content).toBeTruthy();
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expect(response.usage.input + response.usage.cacheRead).toBeGreaterThan(0);
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expect(response.usage.output).toBeGreaterThan(0);
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expect(response.error).toBeFalsy();
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expect(response.content.map(b => b.type == "text" ? b.text : "").join("")).toContain("Hello test successful");
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context.messages.push(response);
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context.messages.push({ role: "user", content: "Now say 'Goodbye test successful'" });
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const secondResponse = await llm.generate(context);
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expect(secondResponse.role).toBe("assistant");
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expect(secondResponse.content).toBeTruthy();
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expect(secondResponse.usage.input + secondResponse.usage.cacheRead).toBeGreaterThan(0);
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expect(secondResponse.usage.output).toBeGreaterThan(0);
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expect(secondResponse.error).toBeFalsy();
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expect(secondResponse.content.map(b => b.type == "text" ? b.text : "").join("")).toContain("Goodbye test successful");
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}
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async function handleToolCall<T extends LLMOptions>(llm: LLM<T>) {
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const context: Context = {
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systemPrompt: "You are a helpful assistant that uses tools when asked.",
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messages: [{
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role: "user",
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content: "Calculate 15 + 27 using the calculator tool."
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}],
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tools: [calculatorTool]
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};
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const response = await llm.generate(context);
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expect(response.stopReason).toBe("toolUse");
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expect(response.content.some(b => b.type == "toolCall")).toBeTruthy();
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const toolCall = response.content.find(b => b.type == "toolCall")!;
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expect(toolCall.name).toBe("calculator");
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expect(toolCall.id).toBeTruthy();
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}
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async function handleStreaming<T extends LLMOptions>(llm: LLM<T>) {
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let textStarted = false;
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let textChunks = "";
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let textCompleted = false;
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const context: Context = {
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messages: [{ role: "user", content: "Count from 1 to 3" }]
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};
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const response = await llm.generate(context, {
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onEvent: (event) => {
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if (event.type === "text_start") {
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textStarted = true;
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} else if (event.type === "text_delta") {
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textChunks += event.delta;
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} else if (event.type === "text_end") {
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textCompleted = true;
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}
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}
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} as T);
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expect(textStarted).toBe(true);
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expect(textChunks.length).toBeGreaterThan(0);
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expect(textCompleted).toBe(true);
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expect(response.content.some(b => b.type == "text")).toBeTruthy();
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}
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async function handleThinking<T extends LLMOptions>(llm: LLM<T>, options: T) {
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let thinkingStarted = false;
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let thinkingChunks = "";
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let thinkingCompleted = false;
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const context: Context = {
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messages: [{ role: "user", content: `Think about ${(Math.random() * 255) | 0} + 27. Think step by step. Then output the result.` }]
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};
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const response = await llm.generate(context, {
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onEvent: (event) => {
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if (event.type === "thinking_start") {
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thinkingStarted = true;
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} else if (event.type === "thinking_delta") {
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expect(event.content.endsWith(event.delta)).toBe(true);
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thinkingChunks += event.delta;
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} else if (event.type === "thinking_end") {
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thinkingCompleted = true;
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}
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},
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...options
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});
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expect(response.stopReason, `Error: ${(response as any).error}`).toBe("stop");
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expect(thinkingStarted).toBe(true);
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expect(thinkingChunks.length).toBeGreaterThan(0);
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expect(thinkingCompleted).toBe(true);
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expect(response.content.some(b => b.type == "thinking")).toBeTruthy();
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}
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async function handleImage<T extends LLMOptions>(llm: LLM<T>) {
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// Check if the model supports images
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const model = llm.getModel();
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if (!model.input.includes("image")) {
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console.log(`Skipping image test - model ${model.id} doesn't support images`);
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return;
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}
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// Read the test image
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const imagePath = join(__dirname, "data", "red-circle.png");
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const imageBuffer = readFileSync(imagePath);
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const base64Image = imageBuffer.toString("base64");
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const imageContent: ImageContent = {
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type: "image",
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data: base64Image,
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mimeType: "image/png",
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};
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const context: Context = {
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messages: [
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{
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role: "user",
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content: [
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{ type: "text", text: "What do you see in this image? Please describe the shape (circle, rectangle, square, triangle, ...) and color (red, blue, green, ...)." },
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imageContent,
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],
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},
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],
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};
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const response = await llm.generate(context);
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// Check the response mentions red and circle
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expect(response.content.length > 0).toBeTruthy();
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const lowerContent = response.content.find(b => b.type == "text")?.text || "";
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expect(lowerContent).toContain("red");
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expect(lowerContent).toContain("circle");
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}
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async function multiTurn<T extends LLMOptions>(llm: LLM<T>, thinkingOptions: T) {
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const context: Context = {
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systemPrompt: "You are a helpful assistant that can use tools to answer questions.",
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messages: [
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{
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role: "user",
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content: "Think about this briefly, then calculate 42 * 17 and 453 + 434 using the calculator tool."
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}
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],
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tools: [calculatorTool]
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};
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// Collect all text content from all assistant responses
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let allTextContent = "";
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let hasSeenThinking = false;
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let hasSeenToolCalls = false;
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const maxTurns = 5; // Prevent infinite loops
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for (let turn = 0; turn < maxTurns; turn++) {
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const response = await llm.generate(context, thinkingOptions);
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// Add the assistant response to context
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context.messages.push(response);
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// Process content blocks
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for (const block of response.content) {
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if (block.type === "text") {
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allTextContent += block.text;
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} else if (block.type === "thinking") {
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hasSeenThinking = true;
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} else if (block.type === "toolCall") {
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hasSeenToolCalls = true;
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// Process the tool call
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expect(block.name).toBe("calculator");
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expect(block.id).toBeTruthy();
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expect(block.arguments).toBeTruthy();
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const { a, b, operation } = block.arguments;
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let result: number;
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switch (operation) {
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case "add": result = a + b; break;
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case "multiply": result = a * b; break;
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default: result = 0;
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}
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// Add tool result to context
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context.messages.push({
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role: "toolResult",
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toolCallId: block.id,
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toolName: block.name,
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content: `${result}`,
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isError: false
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});
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}
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}
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// If we got a stop response with text content, we're likely done
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expect(response.stopReason).not.toBe("error");
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if (response.stopReason === "stop") {
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break;
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}
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}
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// Verify we got either thinking content or tool calls (or both)
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expect(hasSeenThinking || hasSeenToolCalls).toBe(true);
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// The accumulated text should reference both calculations
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expect(allTextContent).toBeTruthy();
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expect(allTextContent.includes("714")).toBe(true);
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expect(allTextContent.includes("887")).toBe(true);
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}
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describe("AI Providers E2E Tests", () => {
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describe.skipIf(!process.env.GEMINI_API_KEY)("Gemini Provider (gemini-2.5-flash)", () => {
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let llm: GoogleLLM;
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beforeAll(() => {
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llm = new GoogleLLM(getModel("google", "gemini-2.5-flash")!, process.env.GEMINI_API_KEY!);
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});
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it("should complete basic text generation", async () => {
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await basicTextGeneration(llm);
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});
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it("should handle tool calling", async () => {
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await handleToolCall(llm);
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});
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it("should handle streaming", async () => {
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await handleStreaming(llm);
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});
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it("should handle thinking mode", async () => {
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await handleThinking(llm, {thinking: { enabled: true, budgetTokens: 1024 }});
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});
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it("should handle multi-turn with thinking and tools", async () => {
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await multiTurn(llm, {thinking: { enabled: true, budgetTokens: 2048 }});
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});
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it("should handle image input", async () => {
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await handleImage(llm);
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});
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});
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describe.skipIf(!process.env.OPENAI_API_KEY)("OpenAI Completions Provider (gpt-4o-mini)", () => {
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let llm: OpenAICompletionsLLM;
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beforeAll(() => {
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llm = new OpenAICompletionsLLM(getModel("openai", "gpt-4o-mini")!, process.env.OPENAI_API_KEY!);
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});
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it("should complete basic text generation", async () => {
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await basicTextGeneration(llm);
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});
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it("should handle tool calling", async () => {
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await handleToolCall(llm);
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});
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it("should handle streaming", async () => {
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await handleStreaming(llm);
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});
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it("should handle image input", async () => {
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await handleImage(llm);
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});
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});
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describe.skipIf(!process.env.OPENAI_API_KEY)("OpenAI Responses Provider (gpt-5-mini)", () => {
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let llm: OpenAIResponsesLLM;
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beforeAll(() => {
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llm = new OpenAIResponsesLLM(getModel("openai", "gpt-5-mini")!, process.env.OPENAI_API_KEY!);
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});
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it("should complete basic text generation", async () => {
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await basicTextGeneration(llm);
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});
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it("should handle tool calling", async () => {
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await handleToolCall(llm);
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});
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it("should handle streaming", async () => {
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await handleStreaming(llm);
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});
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it("should handle thinking mode", {retry: 2}, async () => {
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await handleThinking(llm, {reasoningEffort: "high"});
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});
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it("should handle multi-turn with thinking and tools", async () => {
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await multiTurn(llm, {reasoningEffort: "high"});
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});
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it("should handle image input", async () => {
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await handleImage(llm);
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});
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});
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describe.skipIf(!process.env.ANTHROPIC_OAUTH_TOKEN)("Anthropic Provider (claude-sonnet-4-20250514)", () => {
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let llm: AnthropicLLM;
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beforeAll(() => {
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llm = new AnthropicLLM(getModel("anthropic", "claude-sonnet-4-20250514")!, process.env.ANTHROPIC_OAUTH_TOKEN!);
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});
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it("should complete basic text generation", async () => {
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await basicTextGeneration(llm);
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});
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it("should handle tool calling", async () => {
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await handleToolCall(llm);
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});
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it("should handle streaming", async () => {
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await handleStreaming(llm);
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});
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it("should handle thinking mode", async () => {
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await handleThinking(llm, {thinking: { enabled: true } });
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});
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it("should handle multi-turn with thinking and tools", async () => {
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await multiTurn(llm, {thinking: { enabled: true, budgetTokens: 2048 }});
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});
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it("should handle image input", async () => {
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await handleImage(llm);
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});
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});
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describe.skipIf(!process.env.XAI_API_KEY)("xAI Provider (grok-code-fast-1 via OpenAI Completions)", () => {
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let llm: OpenAICompletionsLLM;
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beforeAll(() => {
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llm = new OpenAICompletionsLLM(getModel("xai", "grok-code-fast-1")!, process.env.XAI_API_KEY!);
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});
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it("should complete basic text generation", async () => {
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await basicTextGeneration(llm);
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});
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it("should handle tool calling", async () => {
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await handleToolCall(llm);
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});
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it("should handle streaming", async () => {
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await handleStreaming(llm);
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});
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it("should handle thinking mode", async () => {
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await handleThinking(llm, {reasoningEffort: "medium"});
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});
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it("should handle multi-turn with thinking and tools", async () => {
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await multiTurn(llm, {reasoningEffort: "medium"});
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});
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});
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describe.skipIf(!process.env.GROQ_API_KEY)("Groq Provider (gpt-oss-20b via OpenAI Completions)", () => {
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let llm: OpenAICompletionsLLM;
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beforeAll(() => {
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llm = new OpenAICompletionsLLM(getModel("groq", "openai/gpt-oss-20b")!, process.env.GROQ_API_KEY!);
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});
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it("should complete basic text generation", async () => {
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await basicTextGeneration(llm);
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});
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it("should handle tool calling", async () => {
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await handleToolCall(llm);
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});
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it("should handle streaming", async () => {
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await handleStreaming(llm);
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});
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it("should handle thinking mode", async () => {
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await handleThinking(llm, {reasoningEffort: "medium"});
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});
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it("should handle multi-turn with thinking and tools", async () => {
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await multiTurn(llm, {reasoningEffort: "medium"});
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});
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});
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describe.skipIf(!process.env.CEREBRAS_API_KEY)("Cerebras Provider (gpt-oss-120b via OpenAI Completions)", () => {
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let llm: OpenAICompletionsLLM;
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beforeAll(() => {
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llm = new OpenAICompletionsLLM(getModel("cerebras", "gpt-oss-120b")!, process.env.CEREBRAS_API_KEY!);
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});
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it("should complete basic text generation", async () => {
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await basicTextGeneration(llm);
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});
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it("should handle tool calling", async () => {
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await handleToolCall(llm);
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});
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it("should handle streaming", async () => {
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await handleStreaming(llm);
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});
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it("should handle thinking mode", async () => {
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await handleThinking(llm, {reasoningEffort: "medium"});
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});
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it("should handle multi-turn with thinking and tools", async () => {
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await multiTurn(llm, {reasoningEffort: "medium"});
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});
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});
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describe.skipIf(!process.env.OPENROUTER_API_KEY)("OpenRouter Provider (glm-4.5v via OpenAI Completions)", () => {
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let llm: OpenAICompletionsLLM;
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beforeAll(() => {
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llm = new OpenAICompletionsLLM(getModel("openrouter", "z-ai/glm-4.5v")!, process.env.OPENROUTER_API_KEY!);;
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});
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it("should complete basic text generation", async () => {
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await basicTextGeneration(llm);
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});
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it("should handle tool calling", async () => {
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await handleToolCall(llm);
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});
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it("should handle streaming", async () => {
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await handleStreaming(llm);
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});
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it("should handle thinking mode", async () => {
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await handleThinking(llm, {reasoningEffort: "medium"});
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});
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it("should handle multi-turn with thinking and tools", { retry: 2 }, async () => {
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await multiTurn(llm, {reasoningEffort: "medium"});
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});
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it("should handle image input", async () => {
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await handleImage(llm);
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});
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});
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// Check if ollama is installed
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let ollamaInstalled = false;
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try {
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execSync("which ollama", { stdio: "ignore" });
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ollamaInstalled = true;
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} catch {
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ollamaInstalled = false;
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}
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describe.skipIf(!ollamaInstalled)("Ollama Provider (gpt-oss-20b via OpenAI Completions)", () => {
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let llm: OpenAICompletionsLLM;
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let ollamaProcess: ChildProcess | null = null;
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beforeAll(async () => {
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// Check if model is available, if not pull it
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try {
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||
execSync("ollama list | grep -q 'gpt-oss:20b'", { stdio: "ignore" });
|
||
} catch {
|
||
console.log("Pulling gpt-oss:20b model for Ollama tests...");
|
||
try {
|
||
execSync("ollama pull gpt-oss:20b", { stdio: "inherit" });
|
||
} catch (e) {
|
||
console.warn("Failed to pull gpt-oss:20b model, tests will be skipped");
|
||
return;
|
||
}
|
||
}
|
||
|
||
// Start ollama server
|
||
ollamaProcess = spawn("ollama", ["serve"], {
|
||
detached: false,
|
||
stdio: "ignore"
|
||
});
|
||
|
||
// Wait for server to be ready
|
||
await new Promise<void>((resolve) => {
|
||
const checkServer = async () => {
|
||
try {
|
||
const response = await fetch("http://localhost:11434/api/tags");
|
||
if (response.ok) {
|
||
resolve();
|
||
} else {
|
||
setTimeout(checkServer, 500);
|
||
}
|
||
} catch {
|
||
setTimeout(checkServer, 500);
|
||
}
|
||
};
|
||
setTimeout(checkServer, 1000); // Initial delay
|
||
});
|
||
|
||
const model: Model = {
|
||
id: "gpt-oss:20b",
|
||
provider: "ollama",
|
||
baseUrl: "http://localhost:11434/v1",
|
||
reasoning: true,
|
||
input: ["text"],
|
||
contextWindow: 128000,
|
||
maxTokens: 16000,
|
||
cost: {
|
||
input: 0,
|
||
output: 0,
|
||
cacheRead: 0,
|
||
cacheWrite: 0,
|
||
},
|
||
name: "Ollama GPT-OSS 20B"
|
||
}
|
||
llm = new OpenAICompletionsLLM(model, "dummy");
|
||
}, 30000); // 30 second timeout for setup
|
||
|
||
afterAll(() => {
|
||
// Kill ollama server
|
||
if (ollamaProcess) {
|
||
ollamaProcess.kill("SIGTERM");
|
||
ollamaProcess = null;
|
||
}
|
||
});
|
||
|
||
it("should complete basic text generation", async () => {
|
||
await basicTextGeneration(llm);
|
||
});
|
||
|
||
it("should handle tool calling", async () => {
|
||
await handleToolCall(llm);
|
||
});
|
||
|
||
it("should handle streaming", async () => {
|
||
await handleStreaming(llm);
|
||
});
|
||
|
||
it("should handle thinking mode", async () => {
|
||
await handleThinking(llm, {reasoningEffort: "medium"});
|
||
});
|
||
|
||
it("should handle multi-turn with thinking and tools", async () => {
|
||
await multiTurn(llm, {reasoningEffort: "medium"});
|
||
});
|
||
});
|
||
|
||
/*
|
||
describe.skipIf(!process.env.ANTHROPIC_API_KEY)("Anthropic Provider (Haiku 3.5)", () => {
|
||
let llm: AnthropicLLM;
|
||
|
||
beforeAll(() => {
|
||
llm = createLLM("anthropic", "claude-3-5-haiku-latest");
|
||
});
|
||
|
||
it("should complete basic text generation", async () => {
|
||
await basicTextGeneration(llm);
|
||
});
|
||
|
||
it("should handle tool calling", async () => {
|
||
await handleToolCall(llm);
|
||
});
|
||
|
||
it("should handle streaming", async () => {
|
||
await handleStreaming(llm);
|
||
});
|
||
|
||
it("should handle multi-turn with thinking and tools", async () => {
|
||
await multiTurn(llm, {thinking: {enabled: true}});
|
||
});
|
||
|
||
it("should handle image input", async () => {
|
||
await handleImage(llm);
|
||
});
|
||
});
|
||
*/
|
||
}); |