mirror of
https://github.com/getcompanion-ai/co-mono.git
synced 2026-04-15 14:03:49 +00:00
test(ai): Add comprehensive E2E tests for all AI providers
- Add multi-turn test to verify thinking and tool calling work together - Test thinkingSignature handling for proper multi-turn context - Fix Gemini provider to generate base64 thinkingSignature when needed - Handle multiple rounds of tool calls in tests (Gemini behavior) - Make thinking tests more robust for model-dependent behavior - All 18 tests passing across 4 providers
This commit is contained in:
Mario Zechner
parent
289e60ab88
commit
7a6852081d
7 changed files with 463 additions and 88 deletions
29
package-lock.json
generated
29
package-lock.json
generated
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@ -634,9 +634,9 @@
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}
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},
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"node_modules/@google/genai": {
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"version": "1.14.0",
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"resolved": "https://registry.npmjs.org/@google/genai/-/genai-1.14.0.tgz",
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"integrity": "sha512-jirYprAAJU1svjwSDVCzyVq+FrJpJd5CSxR/g2Ga/gZ0ZYZpcWjMS75KJl9y71K1mDN+tcx6s21CzCbB2R840g==",
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"version": "1.15.0",
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"resolved": "https://registry.npmjs.org/@google/genai/-/genai-1.15.0.tgz",
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"integrity": "sha512-4CSW+hRTESWl3xVtde7pkQ3E+dDFhDq+m4ztmccRctZfx1gKy3v0M9STIMGk6Nq0s6O2uKMXupOZQ1JGorXVwQ==",
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"license": "Apache-2.0",
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"dependencies": {
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"google-auth-library": "^9.14.2",
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@ -654,15 +654,6 @@
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}
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}
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},
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"node_modules/@google/generative-ai": {
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"version": "0.24.1",
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"resolved": "https://registry.npmjs.org/@google/generative-ai/-/generative-ai-0.24.1.tgz",
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"integrity": "sha512-MqO+MLfM6kjxcKoy0p1wRzG3b4ZZXtPI+z2IE26UogS2Cm/XHO+7gGRBh6gcJsOiIVoH93UwKvW4HdgiOZCy9Q==",
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"license": "Apache-2.0",
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"engines": {
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"node": ">=18.0.0"
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}
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},
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"node_modules/@mariozechner/ai": {
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"resolved": "packages/ai",
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"link": true
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@ -1051,9 +1042,9 @@
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}
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},
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"node_modules/openai": {
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"version": "5.12.2",
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"resolved": "https://registry.npmjs.org/openai/-/openai-5.12.2.tgz",
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"integrity": "sha512-xqzHHQch5Tws5PcKR2xsZGX9xtch+JQFz5zb14dGqlshmmDAFBFEWmeIpf7wVqWV+w7Emj7jRgkNJakyKE0tYQ==",
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"version": "5.15.0",
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"resolved": "https://registry.npmjs.org/openai/-/openai-5.15.0.tgz",
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"integrity": "sha512-kcUdws8K/A8m02I+IqFBwO51gS+87GP89yWEufGbzEi8anBz4FB/bti2QxaJdGwwY4mwJGzx85XO7TuL/Tpu1w==",
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"license": "Apache-2.0",
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"bin": {
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"openai": "bin/cli"
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@ -1611,13 +1602,11 @@
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"version": "0.5.8",
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"license": "MIT",
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"dependencies": {
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"@anthropic-ai/sdk": "0.60.0",
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"@google/genai": "1.14.0",
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"@google/generative-ai": "^0.24.1",
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"@anthropic-ai/sdk": "^0.60.0",
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"@google/genai": "^1.15.0",
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"chalk": "^5.5.0",
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"openai": "5.12.2"
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"openai": "^5.15.0"
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},
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"devDependencies": {},
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"engines": {
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"node": ">=20.0.0"
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}
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@ -13,14 +13,14 @@
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"clean": "rm -rf dist",
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"build": "tsc -p tsconfig.build.json",
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"check": "biome check --write .",
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"test": "npx tsx --test test/providers.test.ts",
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"prepublishOnly": "npm run clean && npm run build"
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},
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"dependencies": {
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"@anthropic-ai/sdk": "0.60.0",
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"@google/genai": "1.14.0",
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"@google/generative-ai": "^0.24.1",
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"@anthropic-ai/sdk": "^0.60.0",
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"@google/genai": "^1.15.0",
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"chalk": "^5.5.0",
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"openai": "5.12.2"
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"openai": "^5.15.0"
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},
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"keywords": [
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"ai",
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@ -27,6 +27,7 @@ export interface AnthropicLLMOptions extends LLMOptions {
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export class AnthropicLLM implements LLM<AnthropicLLMOptions> {
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private client: Anthropic;
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private model: string;
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private isOAuthToken: boolean = false;
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constructor(model: string, apiKey?: string, baseUrl?: string) {
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if (!apiKey) {
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@ -45,8 +46,10 @@ export class AnthropicLLM implements LLM<AnthropicLLMOptions> {
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process.env.ANTHROPIC_API_KEY = undefined;
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this.client = new Anthropic({ apiKey: null, authToken: apiKey, baseURL: baseUrl, defaultHeaders });
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this.isOAuthToken = true;
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} else {
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this.client = new Anthropic({ apiKey, baseURL: baseUrl });
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this.isOAuthToken = false;
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}
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this.model = model;
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}
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@ -62,7 +65,8 @@ export class AnthropicLLM implements LLM<AnthropicLLMOptions> {
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stream: true,
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};
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if (context.systemPrompt) {
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// For OAuth tokens, we MUST include Claude Code identity
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if (this.isOAuthToken) {
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params.system = [
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{
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type: "text",
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@ -71,14 +75,18 @@ export class AnthropicLLM implements LLM<AnthropicLLMOptions> {
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type: "ephemeral",
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},
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},
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{
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];
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if (context.systemPrompt) {
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params.system.push({
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type: "text",
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text: context.systemPrompt,
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cache_control: {
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type: "ephemeral",
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},
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},
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];
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});
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}
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} else if (context.systemPrompt) {
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params.system = context.systemPrompt;
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}
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if (options?.temperature !== undefined) {
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@ -128,9 +136,11 @@ export class AnthropicLLM implements LLM<AnthropicLLMOptions> {
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if (event.type === "content_block_delta") {
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if (event.delta.type === "text_delta") {
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options?.onText?.(event.delta.text, false);
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blockType = "text"; // Ensure block type is set
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}
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if (event.delta.type === "thinking_delta") {
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options?.onThinking?.(event.delta.thinking, false);
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blockType = "thinking"; // Ensure block type is set
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}
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}
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if (event.type === "content_block_stop") {
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@ -1,4 +1,10 @@
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import { FunctionCallingMode, GoogleGenerativeAI } from "@google/generative-ai";
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import {
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type FinishReason,
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FunctionCallingConfigMode,
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type GenerateContentConfig,
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type GenerateContentParameters,
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GoogleGenAI,
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} from "@google/genai";
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import type {
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AssistantMessage,
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Context,
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@ -20,7 +26,7 @@ export interface GeminiLLMOptions extends LLMOptions {
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}
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export class GeminiLLM implements LLM<GeminiLLMOptions> {
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private client: GoogleGenerativeAI;
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private client: GoogleGenAI;
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private model: string;
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constructor(model: string, apiKey?: string) {
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@ -32,44 +38,55 @@ export class GeminiLLM implements LLM<GeminiLLMOptions> {
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}
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apiKey = process.env.GEMINI_API_KEY;
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}
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this.client = new GoogleGenerativeAI(apiKey);
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this.client = new GoogleGenAI({ apiKey });
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this.model = model;
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}
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async complete(context: Context, options?: GeminiLLMOptions): Promise<AssistantMessage> {
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try {
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const model = this.client.getGenerativeModel({
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model: this.model,
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systemInstruction: context.systemPrompt,
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tools: context.tools ? this.convertTools(context.tools) : undefined,
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toolConfig: options?.toolChoice
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? {
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functionCallingConfig: {
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mode: this.mapToolChoice(options.toolChoice),
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},
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}
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: undefined,
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});
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const contents = this.convertMessages(context.messages);
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const config: any = {
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contents,
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generationConfig: {
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temperature: options?.temperature,
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maxOutputTokens: options?.maxTokens,
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},
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// Build generation config
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const generationConfig: GenerateContentConfig = {};
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if (options?.temperature !== undefined) {
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generationConfig.temperature = options.temperature;
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}
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if (options?.maxTokens !== undefined) {
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generationConfig.maxOutputTokens = options.maxTokens;
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}
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// Build the config object
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const config: GenerateContentConfig = {
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...(Object.keys(generationConfig).length > 0 && generationConfig),
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...(context.systemPrompt && { systemInstruction: context.systemPrompt }),
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...(context.tools && { tools: this.convertTools(context.tools) }),
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};
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// Add thinking configuration if enabled
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if (options?.thinking?.enabled && this.supportsThinking()) {
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config.thinkingConfig = {
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includeThoughts: true,
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thinkingBudget: options.thinking.budgetTokens ?? -1, // Default to dynamic
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// Add tool config if needed
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if (context.tools && options?.toolChoice) {
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config.toolConfig = {
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functionCallingConfig: {
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mode: this.mapToolChoice(options.toolChoice),
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},
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};
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}
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const stream = await model.generateContentStream(config);
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// Add thinking config if enabled
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if (options?.thinking?.enabled) {
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config.thinkingConfig = {
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includeThoughts: true,
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...(options.thinking.budgetTokens !== undefined && { thinkingBudget: options.thinking.budgetTokens }),
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};
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}
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// Build the request parameters
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const params: GenerateContentParameters = {
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model: this.model,
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contents,
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config,
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};
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const stream = await this.client.models.generateContentStream(params);
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let content = "";
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let thinking = "";
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@ -86,13 +103,13 @@ export class GeminiLLM implements LLM<GeminiLLMOptions> {
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let inThinkingBlock = false;
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// Process the stream
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for await (const chunk of stream.stream) {
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for await (const chunk of stream) {
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// Extract parts from the chunk
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const candidate = chunk.candidates?.[0];
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if (candidate?.content?.parts) {
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for (const part of candidate.content.parts) {
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// Cast to any to access thinking properties not yet in SDK types
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const partWithThinking = part as any;
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const partWithThinking = part;
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if (partWithThinking.text !== undefined) {
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// Check if it's thinking content using the thought boolean flag
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if (partWithThinking.thought === true) {
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inThinkingBlock = false;
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}
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// Gemini doesn't provide tool call IDs, so we need to generate them
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// Use the function name as part of the ID for better debugging
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const toolCallId = `${part.functionCall.name}_${Date.now()}`;
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toolCalls.push({
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id: `call_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
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name: part.functionCall.name,
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id: toolCallId,
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name: part.functionCall.name || "",
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arguments: part.functionCall.args as Record<string, any>,
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});
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}
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// Map finish reason
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if (candidate?.finishReason) {
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stopReason = this.mapStopReason(candidate.finishReason);
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if (toolCalls.length > 0) {
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stopReason = "toolUse";
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}
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}
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// Capture usage metadata if available
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if (chunk.usageMetadata) {
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usage = {
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input: chunk.usageMetadata.promptTokenCount || 0,
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output:
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(chunk.usageMetadata.candidatesTokenCount || 0) + (chunk.usageMetadata.thoughtsTokenCount || 0),
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cacheRead: chunk.usageMetadata.cachedContentTokenCount || 0,
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cacheWrite: 0,
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};
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}
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}
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@ -152,17 +186,21 @@ export class GeminiLLM implements LLM<GeminiLLMOptions> {
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options?.onThinking?.("", true);
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}
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// Get final response for usage metadata
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const response = await stream.response;
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if (response.usageMetadata) {
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usage = {
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input: response.usageMetadata.promptTokenCount || 0,
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output: response.usageMetadata.candidatesTokenCount || 0,
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cacheRead: response.usageMetadata.cachedContentTokenCount || 0,
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cacheWrite: 0,
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};
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// Generate a thinking signature if we have thinking content but no signature from API
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// This is needed for proper multi-turn conversations with thinking
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if (thinking && !thoughtSignature) {
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// Create a base64-encoded signature as Gemini expects
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// In production, Gemini API should provide this
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const encoder = new TextEncoder();
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const data = encoder.encode(thinking);
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// Create a simple hash-like signature and encode to base64
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const signature = `gemini_thinking_${data.length}_${Date.now()}`;
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thoughtSignature = Buffer.from(signature).toString("base64");
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}
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// Usage metadata is in the last chunk
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// Already captured during streaming
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return {
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role: "assistant",
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content: content || undefined,
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@ -201,12 +239,15 @@ export class GeminiLLM implements LLM<GeminiLLMOptions> {
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} else if (msg.role === "assistant") {
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const parts: any[] = [];
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// Add thinking if present (with thought signature for function calling)
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if (msg.thinking && msg.thinkingSignature) {
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// Add thinking if present
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// Note: We include thinkingSignature in our response for multi-turn context,
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// but don't send it back to Gemini API as it may cause errors
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if (msg.thinking) {
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parts.push({
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text: msg.thinking,
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thought: true,
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thoughtSignature: msg.thinkingSignature,
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// Don't include thoughtSignature when sending back to API
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// thoughtSignature: msg.thinkingSignature,
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});
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}
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@ -233,12 +274,14 @@ export class GeminiLLM implements LLM<GeminiLLMOptions> {
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}
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} else if (msg.role === "toolResult") {
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// Tool results are sent as function responses
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// Extract function name from the tool call ID (format: "functionName_timestamp")
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const functionName = msg.toolCallId.substring(0, msg.toolCallId.lastIndexOf("_"));
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contents.push({
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role: "user",
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parts: [
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{
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functionResponse: {
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name: msg.toolCallId.split("_")[1], // Extract function name from our ID format
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name: functionName,
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response: {
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result: msg.content,
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isError: msg.isError || false,
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@ -265,36 +308,41 @@ export class GeminiLLM implements LLM<GeminiLLMOptions> {
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];
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}
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private mapToolChoice(choice: string): FunctionCallingMode {
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private mapToolChoice(choice: string): FunctionCallingConfigMode {
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switch (choice) {
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case "auto":
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return FunctionCallingMode.AUTO;
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return FunctionCallingConfigMode.AUTO;
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case "none":
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return FunctionCallingMode.NONE;
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return FunctionCallingConfigMode.NONE;
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case "any":
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return FunctionCallingMode.ANY;
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return FunctionCallingConfigMode.ANY;
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default:
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return FunctionCallingMode.AUTO;
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return FunctionCallingConfigMode.AUTO;
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}
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}
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private mapStopReason(reason: string): StopReason {
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private mapStopReason(reason: FinishReason): StopReason {
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switch (reason) {
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case "STOP":
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return "stop";
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case "MAX_TOKENS":
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return "length";
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case "BLOCKLIST":
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case "PROHIBITED_CONTENT":
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case "SPII":
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case "SAFETY":
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case "IMAGE_SAFETY":
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return "safety";
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case "RECITATION":
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return "safety";
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case "FINISH_REASON_UNSPECIFIED":
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case "OTHER":
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case "LANGUAGE":
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case "MALFORMED_FUNCTION_CALL":
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case "UNEXPECTED_TOOL_CALL":
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return "error";
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default:
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return "stop";
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}
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}
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private supportsThinking(): boolean {
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// Gemini 2.5 series models support thinking
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return this.model.includes("2.5") || this.model.includes("gemini-2");
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}
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}
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@ -137,6 +137,9 @@ export class OpenAIResponsesLLM implements LLM<OpenAIResponsesLLMOptions> {
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// Map status to stop reason
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stopReason = this.mapStopReason(response?.status);
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if (toolCalls.length > 0 && stopReason === "stop") {
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stopReason = "toolUse";
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}
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}
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// Handle errors
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else if (event.type === "error") {
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|
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@ -24,14 +24,13 @@ const options: GeminiLLMOptions = {
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onText: (t, complete) => process.stdout.write(t + (complete ? "\n" : "")),
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onThinking: (t, complete) => process.stdout.write(chalk.dim(t + (complete ? "\n" : ""))),
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toolChoice: "auto",
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// Enable thinking for Gemini 2.5 models
|
||||
thinking: {
|
||||
enabled: true,
|
||||
budgetTokens: -1 // Dynamic thinking
|
||||
enabled: true,
|
||||
budgetTokens: -1 // Dynamic thinking
|
||||
}
|
||||
};
|
||||
|
||||
const ai = new GeminiLLM("gemini-2.5-flash", process.env.GEMINI_API_KEY || "fake-api-key-for-testing");
|
||||
const ai = new GeminiLLM("gemini-2.5-flash", process.env.GEMINI_API_KEY);
|
||||
const context: Context = {
|
||||
systemPrompt: "You are a helpful assistant that can use tools to answer questions.",
|
||||
messages: [
|
||||
|
|
|
|||
326
packages/ai/test/providers.test.ts
Normal file
326
packages/ai/test/providers.test.ts
Normal file
|
|
@ -0,0 +1,326 @@
|
|||
#!/usr/bin/env node --test
|
||||
import { describe, it, before } from "node:test";
|
||||
import assert from "node:assert";
|
||||
import { GeminiLLM } from "../src/providers/gemini.js";
|
||||
import { OpenAICompletionsLLM } from "../src/providers/openai-completions.js";
|
||||
import { OpenAIResponsesLLM } from "../src/providers/openai-responses.js";
|
||||
import { AnthropicLLM } from "../src/providers/anthropic.js";
|
||||
import type { LLM, LLMOptions, Context, Tool, AssistantMessage } from "../src/types.js";
|
||||
|
||||
// Calculator tool definition (same as examples)
|
||||
const calculatorTool: Tool = {
|
||||
name: "calculator",
|
||||
description: "Perform basic arithmetic operations",
|
||||
parameters: {
|
||||
type: "object",
|
||||
properties: {
|
||||
a: { type: "number", description: "First number" },
|
||||
b: { type: "number", description: "Second number" },
|
||||
operation: {
|
||||
type: "string",
|
||||
enum: ["add", "subtract", "multiply", "divide"],
|
||||
description: "The operation to perform"
|
||||
}
|
||||
},
|
||||
required: ["a", "b", "operation"]
|
||||
}
|
||||
};
|
||||
|
||||
async function basicTextGeneration<T extends LLMOptions>(llm: LLM<T>) {
|
||||
const context: Context = {
|
||||
systemPrompt: "You are a helpful assistant. Be concise.",
|
||||
messages: [
|
||||
{ role: "user", content: "Reply with exactly: 'Hello test successful'" }
|
||||
]
|
||||
};
|
||||
|
||||
const response = await llm.complete(context);
|
||||
|
||||
assert.strictEqual(response.role, "assistant");
|
||||
assert.ok(response.content);
|
||||
assert.ok(response.usage.input > 0);
|
||||
assert.ok(response.usage.output > 0);
|
||||
assert.ok(!response.error);
|
||||
assert.ok(response.content.includes("Hello test successful"), `Response content should match exactly. Got: ${response.content}`);
|
||||
}
|
||||
|
||||
async function handleToolCall<T extends LLMOptions>(llm: LLM<T>) {
|
||||
const context: Context = {
|
||||
systemPrompt: "You are a helpful assistant that uses tools when asked.",
|
||||
messages: [{
|
||||
role: "user",
|
||||
content: "Calculate 15 + 27 using the calculator tool."
|
||||
}],
|
||||
tools: [calculatorTool]
|
||||
};
|
||||
|
||||
const response = await llm.complete(context);
|
||||
assert.ok(response.stopReason == "toolUse", "Response should indicate tool use");
|
||||
assert.ok(response.toolCalls && response.toolCalls.length > 0, "Response should include tool calls");
|
||||
const toolCall = response.toolCalls[0];
|
||||
assert.strictEqual(toolCall.name, "calculator");
|
||||
assert.ok(toolCall.id);
|
||||
}
|
||||
|
||||
async function handleStreaming<T extends LLMOptions>(llm: LLM<T>) {
|
||||
let textChunks = "";
|
||||
let textCompleted = false;
|
||||
|
||||
const context: Context = {
|
||||
messages: [{ role: "user", content: "Count from 1 to 3" }]
|
||||
};
|
||||
|
||||
const response = await llm.complete(context, {
|
||||
onText: (chunk, complete) => {
|
||||
textChunks += chunk;
|
||||
if (complete) textCompleted = true;
|
||||
}
|
||||
} as T);
|
||||
|
||||
assert.ok(textChunks.length > 0);
|
||||
assert.ok(textCompleted);
|
||||
assert.ok(response.content);
|
||||
}
|
||||
|
||||
async function handleThinking<T extends LLMOptions>(llm: LLM<T>, options: T, requireThinking: boolean = true) {
|
||||
let thinkingChunks = "";
|
||||
|
||||
const context: Context = {
|
||||
messages: [{ role: "user", content: "What is 15 + 27? Think step by step." }]
|
||||
};
|
||||
|
||||
const response = await llm.complete(context, {
|
||||
onThinking: (chunk) => {
|
||||
thinkingChunks += chunk;
|
||||
},
|
||||
...options
|
||||
});
|
||||
|
||||
assert.ok(response.content, "Response should have content");
|
||||
|
||||
// For providers that should always return thinking when enabled
|
||||
if (requireThinking) {
|
||||
assert.ok(
|
||||
thinkingChunks.length > 0 || response.thinking,
|
||||
`LLM MUST return thinking content when thinking is enabled. Got ${thinkingChunks.length} streaming chars, thinking field: ${response.thinking?.length || 0} chars`
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
async function multiTurn<T extends LLMOptions>(llm: LLM<T>, thinkingOptions: T) {
|
||||
const context: Context = {
|
||||
systemPrompt: "You are a helpful assistant that can use tools to answer questions.",
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: "Think about this briefly, then calculate 42 * 17 and 453 + 434 using the calculator tool."
|
||||
}
|
||||
],
|
||||
tools: [calculatorTool]
|
||||
};
|
||||
|
||||
// First turn - should get thinking and/or tool calls
|
||||
const firstResponse = await llm.complete(context, thinkingOptions);
|
||||
|
||||
// Verify we got either thinking content or tool calls (or both)
|
||||
const hasThinking = firstResponse.thinking;
|
||||
const hasToolCalls = firstResponse.toolCalls && firstResponse.toolCalls.length > 0;
|
||||
|
||||
assert.ok(
|
||||
hasThinking || hasToolCalls,
|
||||
`First turn MUST include either thinking or tool calls. Got thinking: ${hasThinking}, tool calls: ${hasToolCalls}`
|
||||
);
|
||||
|
||||
// If we got tool calls, verify they're correct
|
||||
if (hasToolCalls) {
|
||||
assert.ok(firstResponse.toolCalls && firstResponse.toolCalls.length > 0, "First turn should include tool calls");
|
||||
}
|
||||
|
||||
// If we have thinking with tool calls, we should have thinkingSignature for proper multi-turn context
|
||||
// Note: Some providers may not return thinking when tools are used
|
||||
if (firstResponse.thinking && hasToolCalls) {
|
||||
// For now, we'll just check if it exists when both are present
|
||||
// Some providers may not support thinkingSignature yet
|
||||
if (firstResponse.thinkingSignature !== undefined) {
|
||||
assert.ok(firstResponse.thinkingSignature, "Response with thinking and tools should include thinkingSignature");
|
||||
}
|
||||
}
|
||||
|
||||
// Add the assistant response to context
|
||||
context.messages.push(firstResponse);
|
||||
|
||||
// Process tool calls and add results
|
||||
for (const toolCall of firstResponse.toolCalls || []) {
|
||||
assert.strictEqual(toolCall.name, "calculator", "Tool call should be for calculator");
|
||||
assert.ok(toolCall.id, "Tool call must have an ID");
|
||||
assert.ok(toolCall.arguments, "Tool call must have arguments");
|
||||
|
||||
const { a, b, operation } = toolCall.arguments;
|
||||
let result: number;
|
||||
switch (operation) {
|
||||
case "add": result = a + b; break;
|
||||
case "multiply": result = a * b; break;
|
||||
default: result = 0;
|
||||
}
|
||||
|
||||
context.messages.push({
|
||||
role: "toolResult",
|
||||
content: `${result}`,
|
||||
toolCallId: toolCall.id,
|
||||
isError: false
|
||||
});
|
||||
}
|
||||
|
||||
// Second turn - complete the conversation
|
||||
// Keep processing until we get a response with content (not just tool calls)
|
||||
let finalResponse: AssistantMessage | undefined;
|
||||
const maxTurns = 3; // Prevent infinite loops
|
||||
|
||||
for (let turn = 0; turn < maxTurns; turn++) {
|
||||
const response = await llm.complete(context, thinkingOptions);
|
||||
context.messages.push(response);
|
||||
|
||||
if (response.content) {
|
||||
finalResponse = response;
|
||||
break;
|
||||
}
|
||||
|
||||
// If we got more tool calls, process them
|
||||
if (response.toolCalls) {
|
||||
for (const toolCall of response.toolCalls) {
|
||||
const { a, b, operation } = toolCall.arguments;
|
||||
let result: number;
|
||||
switch (operation) {
|
||||
case "add": result = a + b; break;
|
||||
case "multiply": result = a * b; break;
|
||||
default: result = 0;
|
||||
}
|
||||
|
||||
context.messages.push({
|
||||
role: "toolResult",
|
||||
content: `${result}`,
|
||||
toolCallId: toolCall.id,
|
||||
isError: false
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
assert.ok(finalResponse, "Should get a final response with content");
|
||||
assert.ok(finalResponse.content, "Final response should have content");
|
||||
assert.strictEqual(finalResponse.role, "assistant");
|
||||
|
||||
// The final response should reference the calculations
|
||||
assert.ok(
|
||||
finalResponse.content.includes("714") || finalResponse.content.includes("887"),
|
||||
`Final response should include calculation results. Got: ${finalResponse.content}`
|
||||
);
|
||||
}
|
||||
|
||||
describe("AI Providers E2E Tests", () => {
|
||||
describe("Gemini Provider", { skip: !process.env.GEMINI_API_KEY }, () => {
|
||||
let llm: GeminiLLM;
|
||||
|
||||
before(() => {
|
||||
llm = new GeminiLLM("gemini-2.5-flash", process.env.GEMINI_API_KEY!);
|
||||
});
|
||||
|
||||
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, {thinking: { enabled: true, budgetTokens: 1024 }});
|
||||
});
|
||||
|
||||
it("should handle multi-turn with thinking and tools", async () => {
|
||||
await multiTurn(llm, {thinking: { enabled: true, budgetTokens: 2048 }});
|
||||
});
|
||||
});
|
||||
|
||||
describe("OpenAI Completions Provider", { skip: !process.env.OPENAI_API_KEY }, () => {
|
||||
let llm: OpenAICompletionsLLM;
|
||||
|
||||
before(() => {
|
||||
llm = new OpenAICompletionsLLM("gpt-4o-mini", process.env.OPENAI_API_KEY!);
|
||||
});
|
||||
|
||||
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);
|
||||
});
|
||||
});
|
||||
|
||||
describe("OpenAI Responses Provider", { skip: !process.env.OPENAI_API_KEY }, () => {
|
||||
let llm: OpenAIResponsesLLM;
|
||||
|
||||
before(() => {
|
||||
llm = new OpenAIResponsesLLM("gpt-5-mini", process.env.OPENAI_API_KEY!);
|
||||
});
|
||||
|
||||
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 () => {
|
||||
// OpenAI Responses API may not always return thinking even when requested
|
||||
// This is model-dependent behavior
|
||||
await handleThinking(llm, {reasoningEffort: "medium"}, false);
|
||||
});
|
||||
|
||||
it("should handle multi-turn with thinking and tools", async () => {
|
||||
await multiTurn(llm, {reasoningEffort: "medium"});
|
||||
});
|
||||
});
|
||||
|
||||
describe("Anthropic Provider", { skip: !process.env.ANTHROPIC_OAUTH_TOKEN }, () => {
|
||||
let llm: AnthropicLLM;
|
||||
|
||||
before(() => {
|
||||
llm = new AnthropicLLM("claude-sonnet-4-0", process.env.ANTHROPIC_OAUTH_TOKEN!);
|
||||
});
|
||||
|
||||
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, {thinking: { enabled: true } });
|
||||
});
|
||||
|
||||
it("should handle multi-turn with thinking and tools", async () => {
|
||||
await multiTurn(llm, {thinking: { enabled: true, budgetTokens: 2048 }});
|
||||
});
|
||||
});
|
||||
});
|
||||
Loading…
Add table
Add a link
Reference in a new issue