oh-my-pi/packages/coding-agent/test/model-resolver.test.ts

import { describe, expect, test } from "bun:test";
import type { Model } from "@oh-my-pi/pi-ai";
import { parseModelPattern, resolveCliModel } from "@oh-my-pi/pi-coding-agent/config/model-resolver";

// Mock models for testing
const mockModels: Model<"anthropic-messages">[] = [
	{
		id: "claude-sonnet-4-5",
		name: "Claude Sonnet 4.5",
		api: "anthropic-messages",
		provider: "anthropic",
		baseUrl: "https://api.anthropic.com",
		reasoning: true,
		input: ["text", "image"],
		cost: { input: 3, output: 15, cacheRead: 0.3, cacheWrite: 3.75 },
		contextWindow: 200000,
		maxTokens: 8192,
	},
	{
		id: "gpt-4o",
		name: "GPT-4o",
		api: "anthropic-messages", // Using same type for simplicity
		provider: "openai",
		baseUrl: "https://api.openai.com",
		reasoning: false,
		input: ["text", "image"],
		cost: { input: 5, output: 15, cacheRead: 0.5, cacheWrite: 5 },
		contextWindow: 128000,
		maxTokens: 4096,
	},
];

// Mock OpenRouter models with colons in IDs
const mockOpenRouterModels: Model<"anthropic-messages">[] = [
	{
		id: "qwen/qwen3-coder:exacto",
		name: "Qwen3 Coder Exacto",
		api: "anthropic-messages",
		provider: "openrouter",
		baseUrl: "https://openrouter.ai/api/v1",
		reasoning: true,
		input: ["text"],
		cost: { input: 1, output: 2, cacheRead: 0.1, cacheWrite: 1 },
		contextWindow: 128000,
		maxTokens: 8192,
	},
	{
		id: "openai/gpt-4o:extended",
		name: "GPT-4o Extended",
		api: "anthropic-messages",
		provider: "openrouter",
		baseUrl: "https://openrouter.ai/api/v1",
		reasoning: false,
		input: ["text", "image"],
		cost: { input: 5, output: 15, cacheRead: 0.5, cacheWrite: 5 },
		contextWindow: 128000,
		maxTokens: 4096,
	},
];

const mockProviderOverlapModels: Model<"anthropic-messages">[] = [
	{
		id: "kimi-k2.5",
		name: "Kimi K2.5",
		api: "anthropic-messages",
		provider: "kimi-code",
		baseUrl: "https://api.kimi.ai",
		reasoning: false,
		input: ["text"],
		cost: { input: 2, output: 6, cacheRead: 0.2, cacheWrite: 2 },
		contextWindow: 128000,
		maxTokens: 8192,
	},
	{
		id: "moonshotai/kimi-k2.5",
		name: "Kimi K2.5 (OpenRouter)",
		api: "anthropic-messages",
		provider: "openrouter",
		baseUrl: "https://openrouter.ai/api/v1",
		reasoning: false,
		input: ["text"],
		cost: { input: 2.2, output: 6.2, cacheRead: 0.22, cacheWrite: 2.2 },
		contextWindow: 128000,
		maxTokens: 8192,
	},
];

const allModels = [...mockModels, ...mockOpenRouterModels, ...mockProviderOverlapModels];

describe("parseModelPattern", () => {
	describe("simple patterns without colons", () => {
		test("exact match returns model with undefined thinking level", () => {
			const result = parseModelPattern("claude-sonnet-4-5", allModels);
			expect(result.model?.id).toBe("claude-sonnet-4-5");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toBeUndefined();
		});

		test("partial match returns best model with undefined thinking level", () => {
			const result = parseModelPattern("sonnet", allModels);
			expect(result.model?.id).toBe("claude-sonnet-4-5");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toBeUndefined();
		});

		test("no match returns undefined model and thinking level", () => {
			const result = parseModelPattern("nonexistent", allModels);
			expect(result.model).toBeUndefined();
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toBeUndefined();
		});
	});

	describe("patterns with valid thinking levels", () => {
		test("sonnet:high returns sonnet with high thinking level", () => {
			const result = parseModelPattern("sonnet:high", allModels);
			expect(result.model?.id).toBe("claude-sonnet-4-5");
			expect(result.thinkingLevel).toBe("high");
			expect(result.warning).toBeUndefined();
		});

		test("gpt-4o:medium returns gpt-4o with medium thinking level", () => {
			const result = parseModelPattern("gpt-4o:medium", allModels);
			expect(result.model?.id).toBe("gpt-4o");
			expect(result.thinkingLevel).toBe("medium");
			expect(result.warning).toBeUndefined();
		});

		test("all valid thinking levels work", () => {
			const levels = ["off", "minimal", "low", "medium", "high", "xhigh"] as const;
			for (const level of levels) {
				const result = parseModelPattern(`sonnet:${level}`, allModels);
				expect(result.model?.id).toBe("claude-sonnet-4-5");
				expect(result.thinkingLevel).toBe(level);
				expect(result.warning).toBeUndefined();
			}
		});
	});

	describe("patterns with invalid thinking levels", () => {
		test("sonnet:random returns sonnet with undefined thinking level and warning", () => {
			const result = parseModelPattern("sonnet:random", allModels);
			expect(result.model?.id).toBe("claude-sonnet-4-5");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toContain("Invalid thinking level");
			expect(result.warning).toContain("random");
		});

		test("gpt-4o:invalid returns gpt-4o with undefined thinking level and warning", () => {
			const result = parseModelPattern("gpt-4o:invalid", allModels);
			expect(result.model?.id).toBe("gpt-4o");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toContain("Invalid thinking level");
		});
	});

	describe("OpenRouter models with colons in IDs", () => {
		test("qwen3-coder:exacto matches the model with undefined thinking level", () => {
			const result = parseModelPattern("qwen/qwen3-coder:exacto", allModels);
			expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toBeUndefined();
		});

		test("openrouter/qwen/qwen3-coder:exacto matches with provider prefix", () => {
			const result = parseModelPattern("openrouter/qwen/qwen3-coder:exacto", allModels);
			expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
			expect(result.model?.provider).toBe("openrouter");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toBeUndefined();
		});

		test("qwen3-coder:exacto:high matches model with high thinking level", () => {
			const result = parseModelPattern("qwen/qwen3-coder:exacto:high", allModels);
			expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
			expect(result.thinkingLevel).toBe("high");
			expect(result.explicitThinkingLevel).toBe(true);
			expect(result.warning).toBeUndefined();
		});

		test("openrouter/qwen/qwen3-coder:exacto:high matches with provider and thinking level", () => {
			const result = parseModelPattern("openrouter/qwen/qwen3-coder:exacto:high", allModels);
			expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
			expect(result.model?.provider).toBe("openrouter");
			expect(result.thinkingLevel).toBe("high");
			expect(result.explicitThinkingLevel).toBe(true);
			expect(result.warning).toBeUndefined();
		});

		test("gpt-4o:extended matches the extended model with undefined thinking level", () => {
			const result = parseModelPattern("openai/gpt-4o:extended", allModels);
			expect(result.model?.id).toBe("openai/gpt-4o:extended");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toBeUndefined();
		});
	});

	describe("invalid thinking levels with OpenRouter models", () => {
		test("qwen3-coder:exacto:random returns model with undefined thinking level and warning", () => {
			const result = parseModelPattern("qwen/qwen3-coder:exacto:random", allModels);
			expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toContain("Invalid thinking level");
			expect(result.warning).toContain("random");
		});

		test("qwen3-coder:exacto:high:random returns model with undefined thinking level and warning", () => {
			const result = parseModelPattern("qwen/qwen3-coder:exacto:high:random", allModels);
			expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
			expect(result.warning).toContain("Invalid thinking level");
			expect(result.warning).toContain("random");
		});
	});

	describe("edge cases", () => {
		test("empty pattern matches via partial matching", () => {
			// Empty string is included in all model IDs, so partial matching finds a match
			const result = parseModelPattern("", allModels);
			expect(result.model).not.toBeNull();
			expect(result.thinkingLevel).toBeUndefined();
			expect(result.explicitThinkingLevel).toBe(false);
		});

		test("pattern ending with colon treats empty suffix as invalid", () => {
			const result = parseModelPattern("sonnet:", allModels);
			// Empty string after colon is not a valid thinking level
			// So it tries to match "sonnet:" which won't match, then tries "sonnet"
			expect(result.model?.id).toBe("claude-sonnet-4-5");
			expect(result.warning).toContain("Invalid thinking level");
		});
	});

	describe("preference logic", () => {
		test("prefers most recently used model when multiple providers match", () => {
			const result = parseModelPattern("k2.5", allModels, {
				usageOrder: ["kimi-code/kimi-k2.5"],
			});
			expect(result.model?.provider).toBe("kimi-code");
		});

		test("falls back to deprioritizing openrouter when no usage data", () => {
			const result = parseModelPattern("k2.5", allModels, { usageOrder: [] });
			expect(result.model?.provider).toBe("kimi-code");
		});

		test("respects most recently used provider even if openrouter", () => {
			const result = parseModelPattern("k2.5", allModels, {
				usageOrder: ["openrouter/moonshotai/kimi-k2.5"],
			});
			expect(result.model?.provider).toBe("openrouter");
			expect(result.model?.id).toBe("moonshotai/kimi-k2.5");
		});
	});
});

describe("resolveCliModel", () => {
	test("resolves --model provider/id without --provider", () => {
		const registry = {
			getAll: () => allModels,
		} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];

		const result = resolveCliModel({
			cliModel: "openai/gpt-4o",
			modelRegistry: registry,
		});

		expect(result.error).toBeUndefined();
		expect(result.model?.provider).toBe("openai");
		expect(result.model?.id).toBe("gpt-4o");
	});

	test("resolves fuzzy patterns within an explicit provider", () => {
		const registry = {
			getAll: () => allModels,
		} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];

		const result = resolveCliModel({
			cliProvider: "openai",
			cliModel: "4o",
			modelRegistry: registry,
		});

		expect(result.error).toBeUndefined();
		expect(result.model?.provider).toBe("openai");
		expect(result.model?.id).toBe("gpt-4o");
	});

	test("supports --model <pattern>:<thinking> (without explicit --thinking)", () => {
		const registry = {
			getAll: () => allModels,
		} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];

		const result = resolveCliModel({
			cliModel: "sonnet:high",
			modelRegistry: registry,
		});

		expect(result.error).toBeUndefined();
		expect(result.model?.id).toBe("claude-sonnet-4-5");
		expect(result.thinkingLevel).toBe("high");
	});

	test("prefers exact model id match over provider inference (OpenRouter-style ids)", () => {
		const registry = {
			getAll: () => allModels,
		} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];

		const result = resolveCliModel({
			cliModel: "openai/gpt-4o:extended",
			modelRegistry: registry,
		});

		expect(result.error).toBeUndefined();
		expect(result.model?.provider).toBe("openrouter");
		expect(result.model?.id).toBe("openai/gpt-4o:extended");
	});

	test("does not strip invalid :suffix as thinking level in --model (fail fast)", () => {
		const registry = {
			getAll: () => allModels,
		} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];

		const result = resolveCliModel({
			cliProvider: "openai",
			cliModel: "gpt-4o:extended",
			modelRegistry: registry,
		});

		expect(result.model).toBeUndefined();
		expect(result.error).toContain("not found");
	});

	test("returns a clear error when there are no models", () => {
		const registry = {
			getAll: () => [],
		} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];

		const result = resolveCliModel({
			cliProvider: "openai",
			cliModel: "gpt-4o",
			modelRegistry: registry,
		});

		expect(result.model).toBeUndefined();
		expect(result.error).toContain("No models available");
	});

	test("resolves provider-prefixed fuzzy patterns (openrouter/qwen -> openrouter model)", () => {
		const registry = {
			getAll: () => allModels,
		} as unknown as Parameters<typeof resolveCliModel>[0]["modelRegistry"];

		const result = resolveCliModel({
			cliModel: "openrouter/qwen",
			modelRegistry: registry,
		});

		expect(result.error).toBeUndefined();
		expect(result.model?.provider).toBe("openrouter");
		expect(result.model?.id).toBe("qwen/qwen3-coder:exacto");
	});
});