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This commit is contained in:
@@ -0,0 +1,327 @@
|
||||
import { ChatOpenAI, type ClientOptions } from '@langchain/openai';
|
||||
import {
|
||||
getProxyAgent,
|
||||
makeN8nLlmFailedAttemptHandler,
|
||||
N8nLlmTracing,
|
||||
getConnectionHintNoticeField,
|
||||
} from '@n8n/ai-utilities';
|
||||
|
||||
import {
|
||||
NodeConnectionTypes,
|
||||
type INodeType,
|
||||
type INodeTypeDescription,
|
||||
type ISupplyDataFunctions,
|
||||
type SupplyData,
|
||||
} from 'n8n-workflow';
|
||||
|
||||
import type { OpenAICompatibleCredential } from '../../../types/types';
|
||||
import { openAiFailedAttemptHandler } from '../../vendors/OpenAi/helpers/error-handling';
|
||||
|
||||
interface OpenAIToolCall {
|
||||
function?: { arguments?: unknown };
|
||||
}
|
||||
|
||||
interface OpenAIChoice {
|
||||
message?: { tool_calls?: OpenAIToolCall[] };
|
||||
}
|
||||
|
||||
function isOpenAIResponseWithChoices(json: unknown): json is { choices: OpenAIChoice[] } {
|
||||
return (
|
||||
typeof json === 'object' &&
|
||||
json !== null &&
|
||||
'choices' in json &&
|
||||
Array.isArray((json as { choices: unknown }).choices)
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Wraps fetch to fix empty tool call arguments in API responses.
|
||||
*
|
||||
* When Anthropic models are accessed through OpenRouter, tool calls for tools
|
||||
* with no parameters return empty string arguments ("") instead of "{}".
|
||||
* LangChain's parseToolCall does JSON.parse("") which throws, breaking the agent.
|
||||
* This wrapper normalizes empty arguments to "{}" before LangChain sees them.
|
||||
*/
|
||||
function createOpenRouterFetch(baseFetch: typeof globalThis.fetch): typeof globalThis.fetch {
|
||||
return async (input, init) => {
|
||||
const response = await baseFetch(input, init);
|
||||
|
||||
const contentType = response.headers.get('content-type') ?? '';
|
||||
if (!contentType.includes('json')) return response;
|
||||
|
||||
// Clone before reading, since .json() consumes the body. If no
|
||||
// modification is needed we return the clone with the original body intact.
|
||||
const clone = response.clone();
|
||||
const json: unknown = await response.json();
|
||||
|
||||
if (!isOpenAIResponseWithChoices(json)) return clone;
|
||||
|
||||
const isInvalidArgs = (args: unknown): boolean => typeof args !== 'string' || !args.trim();
|
||||
|
||||
const toolCallsToFix = json.choices
|
||||
.flatMap((choice) => choice.message?.tool_calls ?? [])
|
||||
.filter((tc) => tc.function && isInvalidArgs(tc.function.arguments));
|
||||
|
||||
if (toolCallsToFix.length === 0) return clone;
|
||||
|
||||
for (const tc of toolCallsToFix) {
|
||||
if (!tc.function) continue;
|
||||
const { arguments: args } = tc.function;
|
||||
// Preserve already-parsed plain objects by stringifying them.
|
||||
// Arrays and other non-object types are not valid tool args, so default to '{}'.
|
||||
const isPlainObject = typeof args === 'object' && args !== null && !Array.isArray(args);
|
||||
tc.function.arguments = isPlainObject ? JSON.stringify(args) : '{}';
|
||||
}
|
||||
|
||||
const body = JSON.stringify(json);
|
||||
return new Response(body, {
|
||||
status: response.status,
|
||||
statusText: response.statusText,
|
||||
headers: { 'content-type': contentType },
|
||||
});
|
||||
};
|
||||
}
|
||||
|
||||
export class LmChatOpenRouter implements INodeType {
|
||||
description: INodeTypeDescription = {
|
||||
displayName: 'OpenRouter Chat Model',
|
||||
name: 'lmChatOpenRouter',
|
||||
icon: { light: 'file:openrouter.svg', dark: 'file:openrouter.dark.svg' },
|
||||
group: ['transform'],
|
||||
version: [1],
|
||||
description: 'For advanced usage with an AI chain',
|
||||
defaults: {
|
||||
name: 'OpenRouter Chat Model',
|
||||
},
|
||||
codex: {
|
||||
categories: ['AI'],
|
||||
subcategories: {
|
||||
AI: ['Language Models', 'Root Nodes'],
|
||||
'Language Models': ['Chat Models (Recommended)'],
|
||||
},
|
||||
resources: {
|
||||
primaryDocumentation: [
|
||||
{
|
||||
url: 'https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatopenrouter/',
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
|
||||
inputs: [],
|
||||
|
||||
outputs: [NodeConnectionTypes.AiLanguageModel],
|
||||
outputNames: ['Model'],
|
||||
credentials: [
|
||||
{
|
||||
name: 'openRouterApi',
|
||||
required: true,
|
||||
},
|
||||
],
|
||||
requestDefaults: {
|
||||
ignoreHttpStatusErrors: true,
|
||||
baseURL: '={{ $credentials?.url }}',
|
||||
},
|
||||
properties: [
|
||||
getConnectionHintNoticeField([NodeConnectionTypes.AiChain, NodeConnectionTypes.AiAgent]),
|
||||
{
|
||||
displayName:
|
||||
'If using JSON response format, you must include word "json" in the prompt in your chain or agent. Also, make sure to select latest models released post November 2023.',
|
||||
name: 'notice',
|
||||
type: 'notice',
|
||||
default: '',
|
||||
displayOptions: {
|
||||
show: {
|
||||
'/options.responseFormat': ['json_object'],
|
||||
},
|
||||
},
|
||||
},
|
||||
{
|
||||
displayName: 'Model',
|
||||
name: 'model',
|
||||
type: 'options',
|
||||
description:
|
||||
'The model which will generate the completion. <a href="https://openrouter.ai/docs/models">Learn more</a>.',
|
||||
typeOptions: {
|
||||
loadOptions: {
|
||||
routing: {
|
||||
request: {
|
||||
method: 'GET',
|
||||
url: '/models',
|
||||
},
|
||||
output: {
|
||||
postReceive: [
|
||||
{
|
||||
type: 'rootProperty',
|
||||
properties: {
|
||||
property: 'data',
|
||||
},
|
||||
},
|
||||
{
|
||||
type: 'setKeyValue',
|
||||
properties: {
|
||||
name: '={{$responseItem.id}}',
|
||||
value: '={{$responseItem.id}}',
|
||||
},
|
||||
},
|
||||
{
|
||||
type: 'sort',
|
||||
properties: {
|
||||
key: 'name',
|
||||
},
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
},
|
||||
},
|
||||
routing: {
|
||||
send: {
|
||||
type: 'body',
|
||||
property: 'model',
|
||||
},
|
||||
},
|
||||
default: 'openai/gpt-4.1-mini',
|
||||
},
|
||||
{
|
||||
displayName: 'Options',
|
||||
name: 'options',
|
||||
placeholder: 'Add Option',
|
||||
description: 'Additional options to add',
|
||||
type: 'collection',
|
||||
default: {},
|
||||
options: [
|
||||
{
|
||||
displayName: 'Frequency Penalty',
|
||||
name: 'frequencyPenalty',
|
||||
default: 0,
|
||||
typeOptions: { maxValue: 2, minValue: -2, numberPrecision: 1 },
|
||||
description:
|
||||
"Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim",
|
||||
type: 'number',
|
||||
},
|
||||
{
|
||||
displayName: 'Maximum Number of Tokens',
|
||||
name: 'maxTokens',
|
||||
default: -1,
|
||||
description:
|
||||
'The maximum number of tokens to generate in the completion. Most models have a context length of 2048 tokens (except for the newest models, which support 32,768).',
|
||||
type: 'number',
|
||||
typeOptions: {
|
||||
maxValue: 32768,
|
||||
},
|
||||
},
|
||||
{
|
||||
displayName: 'Response Format',
|
||||
name: 'responseFormat',
|
||||
default: 'text',
|
||||
type: 'options',
|
||||
options: [
|
||||
{
|
||||
name: 'Text',
|
||||
value: 'text',
|
||||
description: 'Regular text response',
|
||||
},
|
||||
{
|
||||
name: 'JSON',
|
||||
value: 'json_object',
|
||||
description:
|
||||
'Enables JSON mode, which should guarantee the message the model generates is valid JSON',
|
||||
},
|
||||
],
|
||||
},
|
||||
{
|
||||
displayName: 'Presence Penalty',
|
||||
name: 'presencePenalty',
|
||||
default: 0,
|
||||
typeOptions: { maxValue: 2, minValue: -2, numberPrecision: 1 },
|
||||
description:
|
||||
"Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics",
|
||||
type: 'number',
|
||||
},
|
||||
{
|
||||
displayName: 'Sampling Temperature',
|
||||
name: 'temperature',
|
||||
default: 0.7,
|
||||
typeOptions: { maxValue: 2, minValue: 0, numberPrecision: 1 },
|
||||
description:
|
||||
'Controls randomness: Lowering results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive.',
|
||||
type: 'number',
|
||||
},
|
||||
{
|
||||
displayName: 'Timeout',
|
||||
name: 'timeout',
|
||||
default: 360000,
|
||||
description: 'Maximum amount of time a request is allowed to take in milliseconds',
|
||||
type: 'number',
|
||||
},
|
||||
{
|
||||
displayName: 'Max Retries',
|
||||
name: 'maxRetries',
|
||||
default: 2,
|
||||
description: 'Maximum number of retries to attempt',
|
||||
type: 'number',
|
||||
},
|
||||
{
|
||||
displayName: 'Top P',
|
||||
name: 'topP',
|
||||
default: 1,
|
||||
typeOptions: { maxValue: 1, minValue: 0, numberPrecision: 1 },
|
||||
description:
|
||||
'Controls diversity via nucleus sampling: 0.5 means half of all likelihood-weighted options are considered. We generally recommend altering this or temperature but not both.',
|
||||
type: 'number',
|
||||
},
|
||||
],
|
||||
},
|
||||
],
|
||||
};
|
||||
|
||||
async supplyData(this: ISupplyDataFunctions, itemIndex: number): Promise<SupplyData> {
|
||||
const credentials = await this.getCredentials<OpenAICompatibleCredential>('openRouterApi');
|
||||
|
||||
const modelName = this.getNodeParameter('model', itemIndex) as string;
|
||||
|
||||
const options = this.getNodeParameter('options', itemIndex, {}) as {
|
||||
frequencyPenalty?: number;
|
||||
maxTokens?: number;
|
||||
maxRetries: number;
|
||||
timeout: number;
|
||||
presencePenalty?: number;
|
||||
temperature?: number;
|
||||
topP?: number;
|
||||
responseFormat?: 'text' | 'json_object';
|
||||
};
|
||||
|
||||
const timeout = options.timeout;
|
||||
const configuration: ClientOptions = {
|
||||
baseURL: credentials.url,
|
||||
fetch: createOpenRouterFetch(globalThis.fetch),
|
||||
fetchOptions: {
|
||||
dispatcher: getProxyAgent(credentials.url, {
|
||||
headersTimeout: timeout,
|
||||
bodyTimeout: timeout,
|
||||
}),
|
||||
},
|
||||
};
|
||||
|
||||
const model = new ChatOpenAI({
|
||||
apiKey: credentials.apiKey,
|
||||
model: modelName,
|
||||
...options,
|
||||
timeout,
|
||||
maxRetries: options.maxRetries ?? 2,
|
||||
configuration,
|
||||
callbacks: [new N8nLlmTracing(this)],
|
||||
modelKwargs: options.responseFormat
|
||||
? {
|
||||
response_format: { type: options.responseFormat },
|
||||
}
|
||||
: undefined,
|
||||
onFailedAttempt: makeN8nLlmFailedAttemptHandler(this, openAiFailedAttemptHandler),
|
||||
});
|
||||
|
||||
return {
|
||||
response: model,
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1 @@
|
||||
<svg fill="white" fill-rule="evenodd" width="40" height="40" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><title>OpenRouter</title><path d="M16.804 1.957l7.22 4.105v.087L16.73 10.21l.017-2.117-.821-.03c-1.059-.028-1.611.002-2.268.11-1.064.175-2.038.577-3.147 1.352L8.345 11.03c-.284.195-.495.336-.68.455l-.515.322-.397.234.385.23.53.338c.476.314 1.17.796 2.701 1.866 1.11.775 2.083 1.177 3.147 1.352l.3.045c.694.091 1.375.094 2.825.033l.022-2.159 7.22 4.105v.087L16.589 22l.014-1.862-.635.022c-1.386.042-2.137.002-3.138-.162-1.694-.28-3.26-.926-4.881-2.059l-2.158-1.5a21.997 21.997 0 00-.755-.498l-.467-.28a55.927 55.927 0 00-.76-.43C2.908 14.73.563 14.116 0 14.116V9.888l.14.004c.564-.007 2.91-.622 3.809-1.124l1.016-.58.438-.274c.428-.28 1.072-.726 2.686-1.853 1.621-1.133 3.186-1.78 4.881-2.059 1.152-.19 1.974-.213 3.814-.138l.02-1.907z"></path></svg>
|
||||
|
After Width: | Height: | Size: 866 B |
@@ -0,0 +1 @@
|
||||
<svg fill="#94A3B8" fill-rule="evenodd" width="40" height="40" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg"><title>OpenRouter</title><path d="M16.804 1.957l7.22 4.105v.087L16.73 10.21l.017-2.117-.821-.03c-1.059-.028-1.611.002-2.268.11-1.064.175-2.038.577-3.147 1.352L8.345 11.03c-.284.195-.495.336-.68.455l-.515.322-.397.234.385.23.53.338c.476.314 1.17.796 2.701 1.866 1.11.775 2.083 1.177 3.147 1.352l.3.045c.694.091 1.375.094 2.825.033l.022-2.159 7.22 4.105v.087L16.589 22l.014-1.862-.635.022c-1.386.042-2.137.002-3.138-.162-1.694-.28-3.26-.926-4.881-2.059l-2.158-1.5a21.997 21.997 0 00-.755-.498l-.467-.28a55.927 55.927 0 00-.76-.43C2.908 14.73.563 14.116 0 14.116V9.888l.14.004c.564-.007 2.91-.622 3.809-1.124l1.016-.58.438-.274c.428-.28 1.072-.726 2.686-1.853 1.621-1.133 3.186-1.78 4.881-2.059 1.152-.19 1.974-.213 3.814-.138l.02-1.907z"></path></svg>
|
||||
|
After Width: | Height: | Size: 868 B |
+327
@@ -0,0 +1,327 @@
|
||||
/* eslint-disable n8n-nodes-base/node-filename-against-convention */
|
||||
/* eslint-disable @typescript-eslint/no-unsafe-member-access */
|
||||
/* eslint-disable @typescript-eslint/no-unsafe-assignment */
|
||||
|
||||
/* eslint-disable @typescript-eslint/unbound-method */
|
||||
import { ChatOpenAI } from '@langchain/openai';
|
||||
import { makeN8nLlmFailedAttemptHandler, N8nLlmTracing, getProxyAgent } from '@n8n/ai-utilities';
|
||||
import { createMockExecuteFunction } from 'n8n-nodes-base/test/nodes/Helpers';
|
||||
import type { INode, ISupplyDataFunctions } from 'n8n-workflow';
|
||||
|
||||
import { LmChatOpenRouter } from '../LmChatOpenRouter.node';
|
||||
|
||||
jest.mock('@langchain/openai');
|
||||
jest.mock('@n8n/ai-utilities');
|
||||
|
||||
const MockedChatOpenAI = jest.mocked(ChatOpenAI);
|
||||
const MockedN8nLlmTracing = jest.mocked(N8nLlmTracing);
|
||||
const mockedMakeN8nLlmFailedAttemptHandler = jest.mocked(makeN8nLlmFailedAttemptHandler);
|
||||
const mockedGetProxyAgent = jest.mocked(getProxyAgent);
|
||||
|
||||
describe('LmChatOpenRouter', () => {
|
||||
let node: LmChatOpenRouter;
|
||||
|
||||
const mockNodeDef: INode = {
|
||||
id: '1',
|
||||
name: 'OpenRouter Chat Model',
|
||||
typeVersion: 1,
|
||||
type: 'n8n-nodes-langchain.lmChatOpenRouter',
|
||||
position: [0, 0],
|
||||
parameters: {},
|
||||
};
|
||||
|
||||
const setupMockContext = (nodeOverrides: Partial<INode> = {}) => {
|
||||
const nodeDef = { ...mockNodeDef, ...nodeOverrides };
|
||||
const ctx = createMockExecuteFunction<ISupplyDataFunctions>(
|
||||
{},
|
||||
nodeDef,
|
||||
) as jest.Mocked<ISupplyDataFunctions>;
|
||||
|
||||
ctx.getCredentials = jest.fn().mockResolvedValue({
|
||||
apiKey: 'test-key',
|
||||
url: 'https://openrouter.ai/api/v1',
|
||||
});
|
||||
ctx.getNode = jest.fn().mockReturnValue(nodeDef);
|
||||
ctx.getNodeParameter = jest.fn().mockImplementation((paramName: string) => {
|
||||
if (paramName === 'model') return 'anthropic/claude-sonnet-4-20250514';
|
||||
if (paramName === 'options') return {};
|
||||
return undefined;
|
||||
});
|
||||
|
||||
MockedN8nLlmTracing.mockImplementation(() => ({}) as unknown as N8nLlmTracing);
|
||||
mockedMakeN8nLlmFailedAttemptHandler.mockReturnValue(jest.fn());
|
||||
mockedGetProxyAgent.mockReturnValue({} as any);
|
||||
return ctx;
|
||||
};
|
||||
|
||||
beforeEach(() => {
|
||||
node = new LmChatOpenRouter();
|
||||
jest.clearAllMocks();
|
||||
});
|
||||
|
||||
describe('node description', () => {
|
||||
it('should have correct node properties', () => {
|
||||
expect(node.description).toMatchObject({
|
||||
displayName: 'OpenRouter Chat Model',
|
||||
name: 'lmChatOpenRouter',
|
||||
group: ['transform'],
|
||||
version: [1],
|
||||
});
|
||||
});
|
||||
|
||||
it('should require openRouterApi credentials', () => {
|
||||
expect(node.description.credentials).toEqual([{ name: 'openRouterApi', required: true }]);
|
||||
});
|
||||
|
||||
it('should output ai_languageModel', () => {
|
||||
expect(node.description.outputs).toEqual(['ai_languageModel']);
|
||||
expect(node.description.outputNames).toEqual(['Model']);
|
||||
});
|
||||
});
|
||||
|
||||
describe('supplyData', () => {
|
||||
it('should create ChatOpenAI with basic configuration', async () => {
|
||||
const ctx = setupMockContext();
|
||||
|
||||
const result = await node.supplyData.call(ctx, 0);
|
||||
|
||||
expect(ctx.getCredentials).toHaveBeenCalledWith('openRouterApi');
|
||||
expect(MockedChatOpenAI).toHaveBeenCalledWith(
|
||||
expect.objectContaining({
|
||||
apiKey: 'test-key',
|
||||
model: 'anthropic/claude-sonnet-4-20250514',
|
||||
maxRetries: 2,
|
||||
callbacks: expect.arrayContaining([expect.any(Object)]),
|
||||
onFailedAttempt: expect.any(Function),
|
||||
}),
|
||||
);
|
||||
expect(result).toEqual({ response: expect.any(Object) });
|
||||
});
|
||||
|
||||
it('should pass options to ChatOpenAI', async () => {
|
||||
const ctx = setupMockContext();
|
||||
ctx.getNodeParameter = jest.fn().mockImplementation((paramName: string) => {
|
||||
if (paramName === 'model') return 'anthropic/claude-sonnet-4-20250514';
|
||||
if (paramName === 'options')
|
||||
return {
|
||||
temperature: 0.5,
|
||||
maxTokens: 2000,
|
||||
topP: 0.9,
|
||||
frequencyPenalty: 0.3,
|
||||
presencePenalty: 0.2,
|
||||
timeout: 60000,
|
||||
maxRetries: 5,
|
||||
};
|
||||
return undefined;
|
||||
});
|
||||
|
||||
await node.supplyData.call(ctx, 0);
|
||||
|
||||
expect(MockedChatOpenAI).toHaveBeenCalledWith(
|
||||
expect.objectContaining({
|
||||
temperature: 0.5,
|
||||
maxTokens: 2000,
|
||||
topP: 0.9,
|
||||
frequencyPenalty: 0.3,
|
||||
presencePenalty: 0.2,
|
||||
timeout: 60000,
|
||||
maxRetries: 5,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it('should set response_format in modelKwargs when responseFormat is provided', async () => {
|
||||
const ctx = setupMockContext();
|
||||
ctx.getNodeParameter = jest.fn().mockImplementation((paramName: string) => {
|
||||
if (paramName === 'model') return 'anthropic/claude-sonnet-4-20250514';
|
||||
if (paramName === 'options') return { responseFormat: 'json_object' };
|
||||
return undefined;
|
||||
});
|
||||
|
||||
await node.supplyData.call(ctx, 0);
|
||||
|
||||
expect(MockedChatOpenAI).toHaveBeenCalledWith(
|
||||
expect.objectContaining({
|
||||
modelKwargs: { response_format: { type: 'json_object' } },
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it('should not set modelKwargs when no responseFormat', async () => {
|
||||
const ctx = setupMockContext();
|
||||
|
||||
await node.supplyData.call(ctx, 0);
|
||||
|
||||
expect(MockedChatOpenAI).toHaveBeenCalledWith(
|
||||
expect.objectContaining({
|
||||
modelKwargs: undefined,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it('should pass a custom fetch wrapper in configuration', async () => {
|
||||
const ctx = setupMockContext();
|
||||
|
||||
await node.supplyData.call(ctx, 0);
|
||||
|
||||
const callArgs = MockedChatOpenAI.mock.calls[0][0];
|
||||
expect(callArgs?.configuration?.fetch).toEqual(expect.any(Function));
|
||||
});
|
||||
});
|
||||
|
||||
describe('fetch wrapper (empty tool call arguments fix)', () => {
|
||||
const origFetch = globalThis.fetch;
|
||||
|
||||
afterEach(() => {
|
||||
globalThis.fetch = origFetch;
|
||||
});
|
||||
|
||||
function jsonResponse(body: unknown): Response {
|
||||
const json = JSON.stringify(body);
|
||||
return new Response(json, {
|
||||
status: 200,
|
||||
headers: { 'content-type': 'application/json' },
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets up a mock fetch, calls supplyData to capture it in the wrapper,
|
||||
* and returns the wrapper function from the ChatOpenAI constructor args.
|
||||
*/
|
||||
async function setupFetchWrapper(mockFetch: jest.Mock): Promise<typeof globalThis.fetch> {
|
||||
globalThis.fetch = mockFetch;
|
||||
const ctx = setupMockContext();
|
||||
await node.supplyData.call(ctx, 0);
|
||||
return MockedChatOpenAI.mock.calls[0][0]?.configuration?.fetch as typeof fetch;
|
||||
}
|
||||
|
||||
it.each<{ input: unknown; expected: string; label: string }>([
|
||||
{ input: '', expected: '{}', label: 'empty string' },
|
||||
{ input: ' ', expected: '{}', label: 'whitespace-only string' },
|
||||
{ input: null, expected: '{}', label: 'null' },
|
||||
{ input: [], expected: '{}', label: 'empty array' },
|
||||
{ input: [1, 2], expected: '{}', label: 'non-empty array' },
|
||||
{ input: {}, expected: '{}', label: 'empty object (stringified)' },
|
||||
{
|
||||
input: { location: 'NYC' },
|
||||
expected: '{"location":"NYC"}',
|
||||
label: 'plain object (stringified)',
|
||||
},
|
||||
{
|
||||
input: '{"location":"NYC"}',
|
||||
expected: '{"location":"NYC"}',
|
||||
label: 'valid JSON string (unchanged)',
|
||||
},
|
||||
{ input: '{}', expected: '{}', label: 'empty JSON object string (unchanged)' },
|
||||
])('should normalize arguments: $label → $expected', async ({ input, expected }) => {
|
||||
const mockFetch = jest.fn().mockResolvedValue(
|
||||
jsonResponse({
|
||||
choices: [
|
||||
{
|
||||
message: {
|
||||
tool_calls: [{ id: 'call_1', function: { name: 'tool', arguments: input } }],
|
||||
},
|
||||
},
|
||||
],
|
||||
}),
|
||||
);
|
||||
|
||||
const wrappedFetch = await setupFetchWrapper(mockFetch);
|
||||
const response = await wrappedFetch('https://openrouter.ai/api/v1/chat/completions');
|
||||
const result = await response.json();
|
||||
|
||||
expect(result.choices[0].message.tool_calls[0].function.arguments).toBe(expected);
|
||||
});
|
||||
|
||||
it('should pass through non-JSON responses untouched', async () => {
|
||||
const textBody = 'plain text response';
|
||||
const mockFetch = jest.fn().mockResolvedValue(
|
||||
new Response(textBody, {
|
||||
status: 200,
|
||||
headers: { 'content-type': 'text/plain' },
|
||||
}),
|
||||
);
|
||||
|
||||
const wrappedFetch = await setupFetchWrapper(mockFetch);
|
||||
const response = await wrappedFetch('https://openrouter.ai/api/v1/chat/completions');
|
||||
|
||||
expect(await response.text()).toBe(textBody);
|
||||
});
|
||||
|
||||
it('should pass through JSON responses without choices', async () => {
|
||||
const body = { models: ['a', 'b'] };
|
||||
const mockFetch = jest.fn().mockResolvedValue(jsonResponse(body));
|
||||
|
||||
const wrappedFetch = await setupFetchWrapper(mockFetch);
|
||||
const response = await wrappedFetch('https://openrouter.ai/api/v1/models');
|
||||
|
||||
expect(await response.json()).toEqual(body);
|
||||
});
|
||||
|
||||
it('should pass through responses without tool_calls', async () => {
|
||||
const body = {
|
||||
choices: [{ message: { role: 'assistant', content: 'Hello!' } }],
|
||||
};
|
||||
const mockFetch = jest.fn().mockResolvedValue(jsonResponse(body));
|
||||
|
||||
const wrappedFetch = await setupFetchWrapper(mockFetch);
|
||||
const response = await wrappedFetch('https://openrouter.ai/api/v1/chat/completions');
|
||||
|
||||
expect(await response.json()).toEqual(body);
|
||||
});
|
||||
|
||||
it('should fix only empty arguments in a mixed set of tool calls', async () => {
|
||||
const mockFetch = jest.fn().mockResolvedValue(
|
||||
jsonResponse({
|
||||
choices: [
|
||||
{
|
||||
message: {
|
||||
tool_calls: [
|
||||
{
|
||||
id: 'call_1',
|
||||
function: { name: 'get_weather', arguments: '{"city":"NYC"}' },
|
||||
},
|
||||
{ id: 'call_2', function: { name: 'get_time', arguments: '' } },
|
||||
{
|
||||
id: 'call_3',
|
||||
function: { name: 'get_date', arguments: '{"format":"iso"}' },
|
||||
},
|
||||
],
|
||||
},
|
||||
},
|
||||
],
|
||||
}),
|
||||
);
|
||||
|
||||
const wrappedFetch = await setupFetchWrapper(mockFetch);
|
||||
const response = await wrappedFetch('https://openrouter.ai/api/v1/chat/completions');
|
||||
const result = await response.json();
|
||||
|
||||
const toolCalls = result.choices[0].message.tool_calls;
|
||||
expect(toolCalls[0].function.arguments).toBe('{"city":"NYC"}');
|
||||
expect(toolCalls[1].function.arguments).toBe('{}');
|
||||
expect(toolCalls[2].function.arguments).toBe('{"format":"iso"}');
|
||||
});
|
||||
|
||||
it('should only carry content-type header on modified responses', async () => {
|
||||
const mockFetch = jest.fn().mockResolvedValue(
|
||||
jsonResponse({
|
||||
choices: [
|
||||
{
|
||||
message: {
|
||||
tool_calls: [{ id: 'call_1', function: { name: 'get_time', arguments: '' } }],
|
||||
},
|
||||
},
|
||||
],
|
||||
}),
|
||||
);
|
||||
|
||||
const wrappedFetch = await setupFetchWrapper(mockFetch);
|
||||
const response = await wrappedFetch('https://openrouter.ai/api/v1/chat/completions');
|
||||
|
||||
expect(response.headers.get('content-type')).toBe('application/json');
|
||||
// Stale metadata headers (content-length, etag, etc.) are not carried over
|
||||
expect(response.headers.get('content-length')).toBeNull();
|
||||
});
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user