mirror of
https://github.com/getcompanion-ai/co-mono.git
synced 2026-04-15 19:05:11 +00:00
030788140a
- Remove setApiKey, resolveApiKey, and global apiKeys Map from stream.ts - Rename getApiKey to getApiKeyFromEnv (only checks env vars) - Remove OAuth storage layer (storage.ts deleted) - OAuth login/refresh functions now return credentials instead of saving - getOAuthApiKey/refreshOAuthToken now take credentials as params - Add test/oauth.ts helper for ai package tests - Simplify root npm run check (single biome + tsgo pass) - Remove redundant check scripts from most packages - Add web-ui and coding-agent examples to biome/tsgo includes coding-agent still has compile errors - needs refactoring for new API
586 lines
23 KiB
TypeScript
586 lines
23 KiB
TypeScript
/**
|
|
* Test context overflow error handling across providers.
|
|
*
|
|
* Context overflow occurs when the input (prompt + history) exceeds
|
|
* the model's context window. This is different from output token limits.
|
|
*
|
|
* Expected behavior: All providers should return stopReason: "error"
|
|
* with an errorMessage that indicates the context was too large,
|
|
* OR (for z.ai) return successfully with usage.input > contextWindow.
|
|
*
|
|
* The isContextOverflow() function must return true for all providers.
|
|
*/
|
|
|
|
import type { ChildProcess } from "child_process";
|
|
import { execSync, spawn } from "child_process";
|
|
import { afterAll, beforeAll, describe, expect, it } from "vitest";
|
|
import { getModel } from "../src/models.js";
|
|
import { complete } from "../src/stream.js";
|
|
import type { AssistantMessage, Context, Model, Usage } from "../src/types.js";
|
|
import { isContextOverflow } from "../src/utils/overflow.js";
|
|
import { resolveApiKey } from "./oauth.js";
|
|
|
|
// Resolve OAuth tokens at module level (async, runs before tests)
|
|
const oauthTokens = await Promise.all([
|
|
resolveApiKey("github-copilot"),
|
|
resolveApiKey("google-gemini-cli"),
|
|
resolveApiKey("google-antigravity"),
|
|
]);
|
|
const [githubCopilotToken, geminiCliToken, antigravityToken] = oauthTokens;
|
|
|
|
// Lorem ipsum paragraph for realistic token estimation
|
|
const LOREM_IPSUM = `Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. `;
|
|
|
|
// Generate a string that will exceed the context window
|
|
// Using chars/4 as token estimate (works better with varied text than repeated chars)
|
|
function generateOverflowContent(contextWindow: number): string {
|
|
const targetTokens = contextWindow + 10000; // Exceed by 10k tokens
|
|
const targetChars = targetTokens * 4 * 1.5;
|
|
const repetitions = Math.ceil(targetChars / LOREM_IPSUM.length);
|
|
return LOREM_IPSUM.repeat(repetitions);
|
|
}
|
|
|
|
interface OverflowResult {
|
|
provider: string;
|
|
model: string;
|
|
contextWindow: number;
|
|
stopReason: string;
|
|
errorMessage: string | undefined;
|
|
usage: Usage;
|
|
hasUsageData: boolean;
|
|
response: AssistantMessage;
|
|
}
|
|
|
|
async function testContextOverflow(model: Model<any>, apiKey: string): Promise<OverflowResult> {
|
|
const overflowContent = generateOverflowContent(model.contextWindow);
|
|
|
|
const context: Context = {
|
|
systemPrompt: "You are a helpful assistant.",
|
|
messages: [
|
|
{
|
|
role: "user",
|
|
content: overflowContent,
|
|
timestamp: Date.now(),
|
|
},
|
|
],
|
|
};
|
|
|
|
const response = await complete(model, context, { apiKey });
|
|
|
|
const hasUsageData = response.usage.input > 0 || response.usage.cacheRead > 0;
|
|
|
|
return {
|
|
provider: model.provider,
|
|
model: model.id,
|
|
contextWindow: model.contextWindow,
|
|
stopReason: response.stopReason,
|
|
errorMessage: response.errorMessage,
|
|
usage: response.usage,
|
|
hasUsageData,
|
|
response,
|
|
};
|
|
}
|
|
|
|
function logResult(result: OverflowResult) {
|
|
console.log(`\n${result.provider} / ${result.model}:`);
|
|
console.log(` contextWindow: ${result.contextWindow}`);
|
|
console.log(` stopReason: ${result.stopReason}`);
|
|
console.log(` errorMessage: ${result.errorMessage}`);
|
|
console.log(` usage: ${JSON.stringify(result.usage)}`);
|
|
console.log(` hasUsageData: ${result.hasUsageData}`);
|
|
}
|
|
|
|
// =============================================================================
|
|
// Anthropic
|
|
// Expected pattern: "prompt is too long: X tokens > Y maximum"
|
|
// =============================================================================
|
|
|
|
describe("Context overflow error handling", () => {
|
|
describe.skipIf(!process.env.ANTHROPIC_API_KEY)("Anthropic (API Key)", () => {
|
|
it("claude-3-5-haiku - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("anthropic", "claude-3-5-haiku-20241022");
|
|
const result = await testContextOverflow(model, process.env.ANTHROPIC_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/prompt is too long/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
describe.skipIf(!process.env.ANTHROPIC_OAUTH_TOKEN)("Anthropic (OAuth)", () => {
|
|
it("claude-sonnet-4 - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("anthropic", "claude-sonnet-4-20250514");
|
|
const result = await testContextOverflow(model, process.env.ANTHROPIC_OAUTH_TOKEN!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/prompt is too long/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// GitHub Copilot (OAuth)
|
|
// Tests both OpenAI and Anthropic models via Copilot
|
|
// =============================================================================
|
|
|
|
describe("GitHub Copilot (OAuth)", () => {
|
|
// OpenAI model via Copilot
|
|
it.skipIf(!githubCopilotToken)(
|
|
"gpt-4o - should detect overflow via isContextOverflow",
|
|
async () => {
|
|
const model = getModel("github-copilot", "gpt-4o");
|
|
const result = await testContextOverflow(model, githubCopilotToken!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/exceeds the limit of \d+/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
},
|
|
120000,
|
|
);
|
|
|
|
// Anthropic model via Copilot
|
|
it.skipIf(!githubCopilotToken)(
|
|
"claude-sonnet-4 - should detect overflow via isContextOverflow",
|
|
async () => {
|
|
const model = getModel("github-copilot", "claude-sonnet-4");
|
|
const result = await testContextOverflow(model, githubCopilotToken!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/exceeds the limit of \d+/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
},
|
|
120000,
|
|
);
|
|
});
|
|
|
|
// =============================================================================
|
|
// OpenAI
|
|
// Expected pattern: "exceeds the context window"
|
|
// =============================================================================
|
|
|
|
describe.skipIf(!process.env.OPENAI_API_KEY)("OpenAI Completions", () => {
|
|
it("gpt-4o-mini - should detect overflow via isContextOverflow", async () => {
|
|
const model = { ...getModel("openai", "gpt-4o-mini") };
|
|
model.api = "openai-completions" as any;
|
|
const result = await testContextOverflow(model, process.env.OPENAI_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/maximum context length/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
describe.skipIf(!process.env.OPENAI_API_KEY)("OpenAI Responses", () => {
|
|
it("gpt-4o - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("openai", "gpt-4o");
|
|
const result = await testContextOverflow(model, process.env.OPENAI_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/exceeds the context window/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// Google
|
|
// Expected pattern: "input token count (X) exceeds the maximum"
|
|
// =============================================================================
|
|
|
|
describe.skipIf(!process.env.GEMINI_API_KEY)("Google", () => {
|
|
it("gemini-2.0-flash - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("google", "gemini-2.0-flash");
|
|
const result = await testContextOverflow(model, process.env.GEMINI_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/input token count.*exceeds the maximum/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// Google Gemini CLI (OAuth)
|
|
// Uses same API as Google, expects same error pattern
|
|
// =============================================================================
|
|
|
|
describe("Google Gemini CLI (OAuth)", () => {
|
|
it.skipIf(!geminiCliToken)(
|
|
"gemini-2.5-flash - should detect overflow via isContextOverflow",
|
|
async () => {
|
|
const model = getModel("google-gemini-cli", "gemini-2.5-flash");
|
|
const result = await testContextOverflow(model, geminiCliToken!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/input token count.*exceeds the maximum/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
},
|
|
120000,
|
|
);
|
|
});
|
|
|
|
// =============================================================================
|
|
// Google Antigravity (OAuth)
|
|
// Tests both Gemini and Anthropic models via Antigravity
|
|
// =============================================================================
|
|
|
|
describe("Google Antigravity (OAuth)", () => {
|
|
// Gemini model
|
|
it.skipIf(!antigravityToken)(
|
|
"gemini-3-flash - should detect overflow via isContextOverflow",
|
|
async () => {
|
|
const model = getModel("google-antigravity", "gemini-3-flash");
|
|
const result = await testContextOverflow(model, antigravityToken!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/input token count.*exceeds the maximum/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
},
|
|
120000,
|
|
);
|
|
|
|
// Anthropic model via Antigravity
|
|
it.skipIf(!antigravityToken)(
|
|
"claude-sonnet-4-5 - should detect overflow via isContextOverflow",
|
|
async () => {
|
|
const model = getModel("google-antigravity", "claude-sonnet-4-5");
|
|
const result = await testContextOverflow(model, antigravityToken!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
// Anthropic models return "prompt is too long" pattern
|
|
expect(result.errorMessage).toMatch(/prompt is too long/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
},
|
|
120000,
|
|
);
|
|
});
|
|
|
|
// =============================================================================
|
|
// xAI
|
|
// Expected pattern: "maximum prompt length is X but the request contains Y"
|
|
// =============================================================================
|
|
|
|
describe.skipIf(!process.env.XAI_API_KEY)("xAI", () => {
|
|
it("grok-3-fast - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("xai", "grok-3-fast");
|
|
const result = await testContextOverflow(model, process.env.XAI_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/maximum prompt length is \d+/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// Groq
|
|
// Expected pattern: "reduce the length of the messages"
|
|
// =============================================================================
|
|
|
|
describe.skipIf(!process.env.GROQ_API_KEY)("Groq", () => {
|
|
it("llama-3.3-70b-versatile - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("groq", "llama-3.3-70b-versatile");
|
|
const result = await testContextOverflow(model, process.env.GROQ_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/reduce the length of the messages/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// Cerebras
|
|
// Expected: 400/413 status code with no body
|
|
// =============================================================================
|
|
|
|
describe.skipIf(!process.env.CEREBRAS_API_KEY)("Cerebras", () => {
|
|
it("qwen-3-235b - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("cerebras", "qwen-3-235b-a22b-instruct-2507");
|
|
const result = await testContextOverflow(model, process.env.CEREBRAS_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
// Cerebras returns status code with no body (400, 413, or 429 for token rate limit)
|
|
expect(result.errorMessage).toMatch(/4(00|13|29).*\(no body\)/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// z.ai
|
|
// Special case: Sometimes accepts overflow silently, sometimes rate limits
|
|
// Detection via usage.input > contextWindow when successful
|
|
// =============================================================================
|
|
|
|
describe.skipIf(!process.env.ZAI_API_KEY)("z.ai", () => {
|
|
it("glm-4.5-flash - should detect overflow via isContextOverflow (silent overflow or rate limit)", async () => {
|
|
const model = getModel("zai", "glm-4.5-flash");
|
|
const result = await testContextOverflow(model, process.env.ZAI_API_KEY!);
|
|
logResult(result);
|
|
|
|
// z.ai behavior is inconsistent:
|
|
// - Sometimes accepts overflow and returns successfully with usage.input > contextWindow
|
|
// - Sometimes returns rate limit error
|
|
// Either way, isContextOverflow should detect it (via usage check or we skip if rate limited)
|
|
if (result.stopReason === "stop") {
|
|
expect(result.hasUsageData).toBe(true);
|
|
expect(result.usage.input).toBeGreaterThan(model.contextWindow);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
} else {
|
|
// Rate limited or other error - just log and pass
|
|
console.log(" z.ai returned error (possibly rate limited), skipping overflow detection");
|
|
}
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// Mistral
|
|
// Expected pattern: TBD - need to test actual error message
|
|
// =============================================================================
|
|
|
|
describe.skipIf(!process.env.MISTRAL_API_KEY)("Mistral", () => {
|
|
it("devstral-medium-latest - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("mistral", "devstral-medium-latest");
|
|
const result = await testContextOverflow(model, process.env.MISTRAL_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// OpenRouter - Multiple backend providers
|
|
// Expected pattern: "maximum context length is X tokens"
|
|
// =============================================================================
|
|
|
|
describe.skipIf(!process.env.OPENROUTER_API_KEY)("OpenRouter", () => {
|
|
// Anthropic backend
|
|
it("anthropic/claude-sonnet-4 via OpenRouter - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("openrouter", "anthropic/claude-sonnet-4");
|
|
const result = await testContextOverflow(model, process.env.OPENROUTER_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/maximum context length is \d+ tokens/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
|
|
// DeepSeek backend
|
|
it("deepseek/deepseek-v3.2 via OpenRouter - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("openrouter", "deepseek/deepseek-v3.2");
|
|
const result = await testContextOverflow(model, process.env.OPENROUTER_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/maximum context length is \d+ tokens/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
|
|
// Mistral backend
|
|
it("mistralai/mistral-large-2512 via OpenRouter - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("openrouter", "mistralai/mistral-large-2512");
|
|
const result = await testContextOverflow(model, process.env.OPENROUTER_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/maximum context length is \d+ tokens/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
|
|
// Google backend
|
|
it("google/gemini-2.5-flash via OpenRouter - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("openrouter", "google/gemini-2.5-flash");
|
|
const result = await testContextOverflow(model, process.env.OPENROUTER_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/maximum context length is \d+ tokens/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
|
|
// Meta/Llama backend
|
|
it("meta-llama/llama-4-maverick via OpenRouter - should detect overflow via isContextOverflow", async () => {
|
|
const model = getModel("openrouter", "meta-llama/llama-4-maverick");
|
|
const result = await testContextOverflow(model, process.env.OPENROUTER_API_KEY!);
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(result.errorMessage).toMatch(/maximum context length is \d+ tokens/i);
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// Ollama (local)
|
|
// =============================================================================
|
|
|
|
// Check if ollama is installed
|
|
let ollamaInstalled = false;
|
|
try {
|
|
execSync("which ollama", { stdio: "ignore" });
|
|
ollamaInstalled = true;
|
|
} catch {
|
|
ollamaInstalled = false;
|
|
}
|
|
|
|
describe.skipIf(!ollamaInstalled)("Ollama (local)", () => {
|
|
let ollamaProcess: ChildProcess | null = null;
|
|
let model: Model<"openai-completions">;
|
|
|
|
beforeAll(async () => {
|
|
// Check if model is available, if not pull it
|
|
try {
|
|
execSync("ollama list | grep -q 'gpt-oss:20b'", { stdio: "ignore" });
|
|
} catch {
|
|
console.log("Pulling gpt-oss:20b model for Ollama overflow 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);
|
|
});
|
|
|
|
model = {
|
|
id: "gpt-oss:20b",
|
|
api: "openai-completions",
|
|
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",
|
|
};
|
|
}, 60000);
|
|
|
|
afterAll(() => {
|
|
if (ollamaProcess) {
|
|
ollamaProcess.kill("SIGTERM");
|
|
ollamaProcess = null;
|
|
}
|
|
});
|
|
|
|
it("gpt-oss:20b - should detect overflow via isContextOverflow (ollama silently truncates)", async () => {
|
|
const result = await testContextOverflow(model, "ollama");
|
|
logResult(result);
|
|
|
|
// Ollama silently truncates input instead of erroring
|
|
// It returns stopReason "stop" with truncated usage
|
|
// We cannot detect overflow via error message, only via usage comparison
|
|
if (result.stopReason === "stop" && result.hasUsageData) {
|
|
// Ollama truncated - check if reported usage is less than what we sent
|
|
// This is a "silent overflow" - we can detect it if we know expected input size
|
|
console.log(" Ollama silently truncated input to", result.usage.input, "tokens");
|
|
// For now, we accept this behavior - Ollama doesn't give us a way to detect overflow
|
|
} else if (result.stopReason === "error") {
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}
|
|
}, 300000); // 5 min timeout for local model
|
|
});
|
|
|
|
// =============================================================================
|
|
// LM Studio (local) - Skip if not running
|
|
// =============================================================================
|
|
|
|
let lmStudioRunning = false;
|
|
try {
|
|
execSync("curl -s --max-time 1 http://localhost:1234/v1/models > /dev/null", { stdio: "ignore" });
|
|
lmStudioRunning = true;
|
|
} catch {
|
|
lmStudioRunning = false;
|
|
}
|
|
|
|
describe.skipIf(!lmStudioRunning)("LM Studio (local)", () => {
|
|
it("should detect overflow via isContextOverflow", async () => {
|
|
const model: Model<"openai-completions"> = {
|
|
id: "local-model",
|
|
api: "openai-completions",
|
|
provider: "lm-studio",
|
|
baseUrl: "http://localhost:1234/v1",
|
|
reasoning: false,
|
|
input: ["text"],
|
|
contextWindow: 8192,
|
|
maxTokens: 2048,
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
name: "LM Studio Local Model",
|
|
};
|
|
|
|
const result = await testContextOverflow(model, "lm-studio");
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
|
|
// =============================================================================
|
|
// llama.cpp server (local) - Skip if not running
|
|
// =============================================================================
|
|
|
|
let llamaCppRunning = false;
|
|
try {
|
|
execSync("curl -s --max-time 1 http://localhost:8081/health > /dev/null", { stdio: "ignore" });
|
|
llamaCppRunning = true;
|
|
} catch {
|
|
llamaCppRunning = false;
|
|
}
|
|
|
|
describe.skipIf(!llamaCppRunning)("llama.cpp (local)", () => {
|
|
it("should detect overflow via isContextOverflow", async () => {
|
|
// Using small context (4096) to match server --ctx-size setting
|
|
const model: Model<"openai-completions"> = {
|
|
id: "local-model",
|
|
api: "openai-completions",
|
|
provider: "llama.cpp",
|
|
baseUrl: "http://localhost:8081/v1",
|
|
reasoning: false,
|
|
input: ["text"],
|
|
contextWindow: 4096,
|
|
maxTokens: 2048,
|
|
cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
|
|
name: "llama.cpp Local Model",
|
|
};
|
|
|
|
const result = await testContextOverflow(model, "llama.cpp");
|
|
logResult(result);
|
|
|
|
expect(result.stopReason).toBe("error");
|
|
expect(isContextOverflow(result.response, model.contextWindow)).toBe(true);
|
|
}, 120000);
|
|
});
|
|
});
|