mirror of
https://github.com/danny-avila/LibreChat.git
synced 2026-02-15 06:58:10 +01:00
🎭 feat: Override Custom Endpoint Schema with Specified Params Endpoint (#11788)
* 🔧 refactor: Simplify payload parsing and enhance getSaveOptions logic - Removed unused bedrockInputSchema from payloadParser, streamlining the function. - Updated payloadParser to handle optional chaining for model parameters. - Enhanced getSaveOptions to ensure runOptions defaults to an empty object if parsing fails, improving robustness. - Adjusted the assignment of maxContextTokens to use the instance variable for consistency. * 🔧 fix: Update maxContextTokens assignment logic in initializeAgent function - Enhanced the maxContextTokens assignment to allow for user-defined values, ensuring it defaults to a calculated value only when not provided or invalid. This change improves flexibility in agent initialization. * 🧪 test: Add unit tests for initializeAgent function - Introduced comprehensive unit tests for the initializeAgent function, focusing on maxContextTokens behavior. - Tests cover scenarios for user-defined values, fallback calculations, and edge cases such as zero and negative values, enhancing overall test coverage and reliability of agent initialization logic. * refactor: default params Endpoint Configuration Handling - Integrated `getEndpointsConfig` to fetch endpoint configurations, allowing for dynamic handling of `defaultParamsEndpoint`. - Updated `buildEndpointOption` to pass `defaultParamsEndpoint` to `parseCompactConvo`, ensuring correct parameter handling based on endpoint type. - Added comprehensive unit tests for `buildDefaultConvo` and `cleanupPreset` to validate behavior with `defaultParamsEndpoint`, covering various scenarios and edge cases. - Refactored related hooks and utility functions to support the new configuration structure, improving overall flexibility and maintainability. * refactor: Centralize defaultParamsEndpoint retrieval - Introduced `getDefaultParamsEndpoint` function to streamline the retrieval of `defaultParamsEndpoint` across various hooks and middleware. - Updated multiple files to utilize the new function, enhancing code consistency and maintainability. - Removed redundant logic for fetching `defaultParamsEndpoint`, simplifying the codebase.
This commit is contained in:
Danny Avila
GitHub
parent
6cc6ee3207
commit
467df0f07a
19 changed files with 1234 additions and 45 deletions
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@ -39,7 +39,6 @@ const {
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PermissionTypes,
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isAgentsEndpoint,
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isEphemeralAgentId,
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bedrockInputSchema,
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removeNullishValues,
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} = require('librechat-data-provider');
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const { spendTokens, spendStructuredTokens } = require('~/models/spendTokens');
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@ -69,17 +68,11 @@ const omitTitleOptions = new Set([
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* @param {Agent} agent
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* @param {string} endpoint
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*/
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const payloadParser = ({ req, agent, endpoint }) => {
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const payloadParser = ({ req, endpoint }) => {
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if (isAgentsEndpoint(endpoint)) {
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return { model: undefined };
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} else if (endpoint === EModelEndpoint.bedrock) {
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const parsedValues = bedrockInputSchema.parse(agent.model_parameters);
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if (parsedValues.thinking == null) {
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parsedValues.thinking = false;
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}
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return parsedValues;
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return;
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}
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return req.body.endpointOption.model_parameters;
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return req.body?.endpointOption?.model_parameters;
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};
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function createTokenCounter(encoding) {
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@ -296,14 +289,9 @@ class AgentClient extends BaseClient {
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checkVisionRequest() {}
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getSaveOptions() {
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// TODO:
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// would need to be override settings; otherwise, model needs to be undefined
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// model: this.override.model,
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// instructions: this.override.instructions,
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// additional_instructions: this.override.additional_instructions,
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let runOptions = {};
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try {
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runOptions = payloadParser(this.options);
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runOptions = payloadParser(this.options) ?? {};
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} catch (error) {
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logger.error(
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'[api/server/controllers/agents/client.js #getSaveOptions] Error parsing options',
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@ -314,14 +302,14 @@ class AgentClient extends BaseClient {
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return removeNullishValues(
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Object.assign(
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{
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spec: this.options.spec,
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iconURL: this.options.iconURL,
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endpoint: this.options.endpoint,
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agent_id: this.options.agent.id,
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modelLabel: this.options.modelLabel,
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maxContextTokens: this.options.maxContextTokens,
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resendFiles: this.options.resendFiles,
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imageDetail: this.options.imageDetail,
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spec: this.options.spec,
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iconURL: this.options.iconURL,
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maxContextTokens: this.maxContextTokens,
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},
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// TODO: PARSE OPTIONS BY PROVIDER, MAY CONTAIN SENSITIVE DATA
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runOptions,
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@ -5,9 +5,11 @@ const {
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EModelEndpoint,
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isAgentsEndpoint,
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parseCompactConvo,
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getDefaultParamsEndpoint,
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} = require('librechat-data-provider');
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const azureAssistants = require('~/server/services/Endpoints/azureAssistants');
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const assistants = require('~/server/services/Endpoints/assistants');
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const { getEndpointsConfig } = require('~/server/services/Config');
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const agents = require('~/server/services/Endpoints/agents');
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const { updateFilesUsage } = require('~/models');
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@ -19,9 +21,24 @@ const buildFunction = {
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async function buildEndpointOption(req, res, next) {
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const { endpoint, endpointType } = req.body;
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let endpointsConfig;
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try {
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endpointsConfig = await getEndpointsConfig(req);
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} catch (error) {
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logger.error('Error fetching endpoints config in buildEndpointOption', error);
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}
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const defaultParamsEndpoint = getDefaultParamsEndpoint(endpointsConfig, endpoint);
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let parsedBody;
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try {
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parsedBody = parseCompactConvo({ endpoint, endpointType, conversation: req.body });
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parsedBody = parseCompactConvo({
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endpoint,
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endpointType,
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conversation: req.body,
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defaultParamsEndpoint,
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});
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} catch (error) {
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logger.error(`Error parsing compact conversation for endpoint ${endpoint}`, error);
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logger.debug({
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@ -55,6 +72,7 @@ async function buildEndpointOption(req, res, next) {
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endpoint,
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endpointType,
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conversation: currentModelSpec.preset,
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defaultParamsEndpoint,
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});
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if (currentModelSpec.iconURL != null && currentModelSpec.iconURL !== '') {
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parsedBody.iconURL = currentModelSpec.iconURL;
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237
api/server/middleware/buildEndpointOption.spec.js
Normal file
237
api/server/middleware/buildEndpointOption.spec.js
Normal file
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@ -0,0 +1,237 @@
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/**
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* Wrap parseCompactConvo: the REAL function runs, but jest can observe
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* calls and return values. Must be declared before require('./buildEndpointOption')
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* so the destructured reference in the middleware captures the wrapper.
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*/
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jest.mock('librechat-data-provider', () => {
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const actual = jest.requireActual('librechat-data-provider');
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return {
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...actual,
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parseCompactConvo: jest.fn((...args) => actual.parseCompactConvo(...args)),
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};
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});
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const { EModelEndpoint, parseCompactConvo } = require('librechat-data-provider');
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const mockBuildOptions = jest.fn((_endpoint, parsedBody) => ({
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...parsedBody,
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endpoint: _endpoint,
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}));
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jest.mock('~/server/services/Endpoints/azureAssistants', () => ({
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buildOptions: mockBuildOptions,
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}));
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jest.mock('~/server/services/Endpoints/assistants', () => ({
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buildOptions: mockBuildOptions,
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}));
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jest.mock('~/server/services/Endpoints/agents', () => ({
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buildOptions: mockBuildOptions,
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}));
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jest.mock('~/models', () => ({
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updateFilesUsage: jest.fn(),
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}));
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const mockGetEndpointsConfig = jest.fn();
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jest.mock('~/server/services/Config', () => ({
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getEndpointsConfig: (...args) => mockGetEndpointsConfig(...args),
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}));
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jest.mock('@librechat/api', () => ({
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handleError: jest.fn(),
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}));
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const buildEndpointOption = require('./buildEndpointOption');
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const createReq = (body, config = {}) => ({
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body,
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config,
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baseUrl: '/api/chat',
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});
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const createRes = () => ({
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status: jest.fn().mockReturnThis(),
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json: jest.fn().mockReturnThis(),
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});
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describe('buildEndpointOption - defaultParamsEndpoint parsing', () => {
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beforeEach(() => {
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jest.clearAllMocks();
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});
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it('should pass defaultParamsEndpoint to parseCompactConvo and preserve maxOutputTokens', async () => {
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mockGetEndpointsConfig.mockResolvedValue({
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AnthropicClaude: {
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type: EModelEndpoint.custom,
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customParams: {
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defaultParamsEndpoint: EModelEndpoint.anthropic,
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},
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},
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});
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const req = createReq(
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{
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endpoint: 'AnthropicClaude',
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endpointType: EModelEndpoint.custom,
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model: 'anthropic/claude-opus-4.5',
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temperature: 0.7,
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maxOutputTokens: 8192,
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topP: 0.9,
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maxContextTokens: 50000,
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},
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{ modelSpecs: null },
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);
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await buildEndpointOption(req, createRes(), jest.fn());
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expect(parseCompactConvo).toHaveBeenCalledWith(
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expect.objectContaining({
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defaultParamsEndpoint: EModelEndpoint.anthropic,
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}),
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);
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const parsedResult = parseCompactConvo.mock.results[0].value;
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expect(parsedResult.maxOutputTokens).toBe(8192);
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expect(parsedResult.topP).toBe(0.9);
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expect(parsedResult.temperature).toBe(0.7);
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expect(parsedResult.maxContextTokens).toBe(50000);
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});
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it('should strip maxOutputTokens when no defaultParamsEndpoint is configured', async () => {
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mockGetEndpointsConfig.mockResolvedValue({
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MyOpenRouter: {
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type: EModelEndpoint.custom,
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},
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});
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const req = createReq(
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{
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endpoint: 'MyOpenRouter',
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endpointType: EModelEndpoint.custom,
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model: 'gpt-4o',
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temperature: 0.7,
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maxOutputTokens: 8192,
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max_tokens: 4096,
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},
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{ modelSpecs: null },
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);
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await buildEndpointOption(req, createRes(), jest.fn());
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expect(parseCompactConvo).toHaveBeenCalledWith(
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expect.objectContaining({
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defaultParamsEndpoint: undefined,
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}),
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);
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const parsedResult = parseCompactConvo.mock.results[0].value;
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expect(parsedResult.maxOutputTokens).toBeUndefined();
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expect(parsedResult.max_tokens).toBe(4096);
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expect(parsedResult.temperature).toBe(0.7);
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});
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it('should strip bedrock region from custom endpoint without defaultParamsEndpoint', async () => {
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mockGetEndpointsConfig.mockResolvedValue({
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MyEndpoint: {
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type: EModelEndpoint.custom,
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},
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});
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const req = createReq(
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{
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endpoint: 'MyEndpoint',
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endpointType: EModelEndpoint.custom,
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model: 'gpt-4o',
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temperature: 0.7,
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region: 'us-east-1',
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},
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{ modelSpecs: null },
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);
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await buildEndpointOption(req, createRes(), jest.fn());
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const parsedResult = parseCompactConvo.mock.results[0].value;
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expect(parsedResult.region).toBeUndefined();
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expect(parsedResult.temperature).toBe(0.7);
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});
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it('should pass defaultParamsEndpoint when re-parsing enforced model spec', async () => {
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mockGetEndpointsConfig.mockResolvedValue({
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AnthropicClaude: {
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type: EModelEndpoint.custom,
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customParams: {
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defaultParamsEndpoint: EModelEndpoint.anthropic,
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},
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},
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});
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const modelSpec = {
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name: 'claude-opus-4.5',
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preset: {
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endpoint: 'AnthropicClaude',
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endpointType: EModelEndpoint.custom,
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model: 'anthropic/claude-opus-4.5',
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temperature: 0.7,
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maxOutputTokens: 8192,
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maxContextTokens: 50000,
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},
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};
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const req = createReq(
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{
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endpoint: 'AnthropicClaude',
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endpointType: EModelEndpoint.custom,
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spec: 'claude-opus-4.5',
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model: 'anthropic/claude-opus-4.5',
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},
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{
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modelSpecs: {
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enforce: true,
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list: [modelSpec],
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},
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},
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);
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await buildEndpointOption(req, createRes(), jest.fn());
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const enforcedCall = parseCompactConvo.mock.calls[1];
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expect(enforcedCall[0]).toEqual(
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expect.objectContaining({
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defaultParamsEndpoint: EModelEndpoint.anthropic,
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}),
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);
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const enforcedResult = parseCompactConvo.mock.results[1].value;
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expect(enforcedResult.maxOutputTokens).toBe(8192);
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expect(enforcedResult.temperature).toBe(0.7);
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expect(enforcedResult.maxContextTokens).toBe(50000);
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});
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it('should fall back to OpenAI schema when getEndpointsConfig fails', async () => {
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mockGetEndpointsConfig.mockRejectedValue(new Error('Config unavailable'));
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const req = createReq(
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{
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endpoint: 'AnthropicClaude',
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endpointType: EModelEndpoint.custom,
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model: 'anthropic/claude-opus-4.5',
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temperature: 0.7,
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maxOutputTokens: 8192,
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max_tokens: 4096,
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},
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{ modelSpecs: null },
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);
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await buildEndpointOption(req, createRes(), jest.fn());
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expect(parseCompactConvo).toHaveBeenCalledWith(
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expect.objectContaining({
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defaultParamsEndpoint: undefined,
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}),
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);
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const parsedResult = parseCompactConvo.mock.results[0].value;
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expect(parsedResult.maxOutputTokens).toBeUndefined();
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expect(parsedResult.max_tokens).toBe(4096);
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});
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});
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@ -1,7 +1,12 @@
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import { useCallback } from 'react';
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import { useRecoilValue } from 'recoil';
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import { useGetModelsQuery } from 'librechat-data-provider/react-query';
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import { getEndpointField, LocalStorageKeys, isAssistantsEndpoint } from 'librechat-data-provider';
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import {
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getEndpointField,
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LocalStorageKeys,
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isAssistantsEndpoint,
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getDefaultParamsEndpoint,
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} from 'librechat-data-provider';
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import type { TEndpointsConfig, EModelEndpoint, TConversation } from 'librechat-data-provider';
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import type { AssistantListItem, NewConversationParams } from '~/common';
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import useAssistantListMap from '~/hooks/Assistants/useAssistantListMap';
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@ -84,11 +89,13 @@ export default function useAddedResponse() {
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}
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const models = modelsConfig?.[defaultEndpoint ?? ''] ?? [];
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const defaultParamsEndpoint = getDefaultParamsEndpoint(endpointsConfig, defaultEndpoint);
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newConversation = buildDefaultConvo({
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conversation: newConversation,
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lastConversationSetup: preset as TConversation,
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endpoint: defaultEndpoint ?? ('' as EModelEndpoint),
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models,
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defaultParamsEndpoint,
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});
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if (preset?.title != null && preset.title !== '') {
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@ -13,6 +13,7 @@ import {
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parseCompactConvo,
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replaceSpecialVars,
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isAssistantsEndpoint,
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getDefaultParamsEndpoint,
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} from 'librechat-data-provider';
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import type {
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TMessage,
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@ -173,12 +174,14 @@ export default function useChatFunctions({
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const startupConfig = queryClient.getQueryData<TStartupConfig>([QueryKeys.startupConfig]);
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const endpointType = getEndpointField(endpointsConfig, endpoint, 'type');
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const iconURL = conversation?.iconURL;
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const defaultParamsEndpoint = getDefaultParamsEndpoint(endpointsConfig, endpoint);
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/** This becomes part of the `endpointOption` */
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const convo = parseCompactConvo({
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endpoint: endpoint as EndpointSchemaKey,
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endpointType: endpointType as EndpointSchemaKey,
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conversation: conversation ?? {},
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defaultParamsEndpoint,
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});
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const { modelDisplayLabel } = endpointsConfig?.[endpoint ?? ''] ?? {};
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|
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@ -1,5 +1,5 @@
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import { excludedKeys } from 'librechat-data-provider';
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import { useGetModelsQuery } from 'librechat-data-provider/react-query';
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import { excludedKeys, getDefaultParamsEndpoint } from 'librechat-data-provider';
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import type {
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TEndpointsConfig,
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TModelsConfig,
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@ -47,11 +47,14 @@ const useDefaultConvo = () => {
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}
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}
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const defaultParamsEndpoint = getDefaultParamsEndpoint(endpointsConfig, endpoint);
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const defaultConvo = buildDefaultConvo({
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conversation: conversation as TConversation,
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endpoint,
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lastConversationSetup: preset as TConversation,
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models,
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defaultParamsEndpoint,
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});
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if (!cleanOutput) {
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|
|
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|
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@ -1,7 +1,12 @@
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import { useRecoilValue } from 'recoil';
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import { useCallback, useRef, useEffect } from 'react';
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import { useGetModelsQuery } from 'librechat-data-provider/react-query';
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import { getEndpointField, LocalStorageKeys, isAssistantsEndpoint } from 'librechat-data-provider';
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import {
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getEndpointField,
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LocalStorageKeys,
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isAssistantsEndpoint,
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getDefaultParamsEndpoint,
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} from 'librechat-data-provider';
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import type {
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TEndpointsConfig,
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EModelEndpoint,
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|
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@ -117,11 +122,13 @@ const useGenerateConvo = ({
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}
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const models = modelsConfig?.[defaultEndpoint ?? ''] ?? [];
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const defaultParamsEndpoint = getDefaultParamsEndpoint(endpointsConfig, defaultEndpoint);
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conversation = buildDefaultConvo({
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conversation,
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lastConversationSetup: preset as TConversation,
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endpoint: defaultEndpoint ?? ('' as EModelEndpoint),
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models,
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defaultParamsEndpoint,
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});
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if (preset?.title != null && preset.title !== '') {
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|
|
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|
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@ -2,7 +2,13 @@ import { useCallback } from 'react';
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import { useSetRecoilState } from 'recoil';
|
||||
import { useNavigate } from 'react-router-dom';
|
||||
import { useQueryClient } from '@tanstack/react-query';
|
||||
import { QueryKeys, Constants, dataService, getEndpointField } from 'librechat-data-provider';
|
||||
import {
|
||||
QueryKeys,
|
||||
Constants,
|
||||
dataService,
|
||||
getEndpointField,
|
||||
getDefaultParamsEndpoint,
|
||||
} from 'librechat-data-provider';
|
||||
import type {
|
||||
TEndpointsConfig,
|
||||
TStartupConfig,
|
||||
|
|
@ -106,11 +112,13 @@ const useNavigateToConvo = (index = 0) => {
|
|||
|
||||
const models = modelsConfig?.[defaultEndpoint ?? ''] ?? [];
|
||||
|
||||
const defaultParamsEndpoint = getDefaultParamsEndpoint(endpointsConfig, defaultEndpoint);
|
||||
convo = buildDefaultConvo({
|
||||
models,
|
||||
conversation,
|
||||
endpoint: defaultEndpoint,
|
||||
lastConversationSetup: conversation,
|
||||
defaultParamsEndpoint,
|
||||
});
|
||||
}
|
||||
clearAllConversations(true);
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ import {
|
|||
LocalStorageKeys,
|
||||
isEphemeralAgentId,
|
||||
isAssistantsEndpoint,
|
||||
getDefaultParamsEndpoint,
|
||||
} from 'librechat-data-provider';
|
||||
import type {
|
||||
TPreset,
|
||||
|
|
@ -191,11 +192,13 @@ const useNewConvo = (index = 0) => {
|
|||
}
|
||||
|
||||
const models = modelsConfig?.[defaultEndpoint] ?? [];
|
||||
const defaultParamsEndpoint = getDefaultParamsEndpoint(endpointsConfig, defaultEndpoint);
|
||||
conversation = buildDefaultConvo({
|
||||
conversation,
|
||||
lastConversationSetup: activePreset as TConversation,
|
||||
endpoint: defaultEndpoint,
|
||||
models,
|
||||
defaultParamsEndpoint,
|
||||
});
|
||||
}
|
||||
|
||||
|
|
|
|||
202
client/src/utils/__tests__/buildDefaultConvo.test.ts
Normal file
202
client/src/utils/__tests__/buildDefaultConvo.test.ts
Normal file
|
|
@ -0,0 +1,202 @@
|
|||
import { EModelEndpoint } from 'librechat-data-provider';
|
||||
import type { TConversation } from 'librechat-data-provider';
|
||||
import buildDefaultConvo from '../buildDefaultConvo';
|
||||
|
||||
jest.mock('../localStorage', () => ({
|
||||
getLocalStorageItems: jest.fn(() => ({
|
||||
lastSelectedModel: {},
|
||||
lastSelectedTools: [],
|
||||
lastConversationSetup: {},
|
||||
})),
|
||||
}));
|
||||
|
||||
const baseConversation: TConversation = {
|
||||
conversationId: 'test-convo-id',
|
||||
title: 'Test Conversation',
|
||||
createdAt: '2024-01-01T00:00:00Z',
|
||||
updatedAt: '2024-01-01T00:00:00Z',
|
||||
endpoint: null,
|
||||
};
|
||||
|
||||
describe('buildDefaultConvo - defaultParamsEndpoint', () => {
|
||||
describe('custom endpoint with defaultParamsEndpoint: anthropic', () => {
|
||||
const models = ['anthropic/claude-opus-4.5', 'anthropic/claude-sonnet-4'];
|
||||
|
||||
it('should preserve maxOutputTokens from model spec preset', () => {
|
||||
const preset: TConversation = {
|
||||
...baseConversation,
|
||||
endpoint: 'AnthropicClaude' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
topP: 0.9,
|
||||
maxContextTokens: 50000,
|
||||
};
|
||||
|
||||
const result = buildDefaultConvo({
|
||||
models,
|
||||
conversation: baseConversation,
|
||||
endpoint: 'AnthropicClaude' as EModelEndpoint,
|
||||
lastConversationSetup: preset,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result.maxOutputTokens).toBe(8192);
|
||||
expect(result.topP).toBe(0.9);
|
||||
expect(result.temperature).toBe(0.7);
|
||||
expect(result.maxContextTokens).toBe(50000);
|
||||
expect(result.model).toBe('anthropic/claude-opus-4.5');
|
||||
});
|
||||
|
||||
it('should strip maxOutputTokens without defaultParamsEndpoint', () => {
|
||||
const preset: TConversation = {
|
||||
...baseConversation,
|
||||
endpoint: 'AnthropicClaude' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
};
|
||||
|
||||
const result = buildDefaultConvo({
|
||||
models,
|
||||
conversation: baseConversation,
|
||||
endpoint: 'AnthropicClaude' as EModelEndpoint,
|
||||
lastConversationSetup: preset,
|
||||
});
|
||||
|
||||
expect(result.maxOutputTokens).toBeUndefined();
|
||||
expect(result.temperature).toBe(0.7);
|
||||
});
|
||||
|
||||
it('should strip OpenAI-specific fields when using anthropic params', () => {
|
||||
const preset: TConversation = {
|
||||
...baseConversation,
|
||||
endpoint: 'AnthropicClaude' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
max_tokens: 4096,
|
||||
top_p: 0.9,
|
||||
presence_penalty: 0.5,
|
||||
frequency_penalty: 0.3,
|
||||
};
|
||||
|
||||
const result = buildDefaultConvo({
|
||||
models,
|
||||
conversation: baseConversation,
|
||||
endpoint: 'AnthropicClaude' as EModelEndpoint,
|
||||
lastConversationSetup: preset,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result.max_tokens).toBeUndefined();
|
||||
expect(result.top_p).toBeUndefined();
|
||||
expect(result.presence_penalty).toBeUndefined();
|
||||
expect(result.frequency_penalty).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
describe('custom endpoint without defaultParamsEndpoint (OpenAI default)', () => {
|
||||
const models = ['gpt-4o', 'gpt-4.1'];
|
||||
|
||||
it('should preserve OpenAI fields and strip anthropic fields', () => {
|
||||
const preset: TConversation = {
|
||||
...baseConversation,
|
||||
endpoint: 'MyOpenRouterEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
top_p: 0.9,
|
||||
maxOutputTokens: 8192,
|
||||
};
|
||||
|
||||
const result = buildDefaultConvo({
|
||||
models,
|
||||
conversation: baseConversation,
|
||||
endpoint: 'MyOpenRouterEndpoint' as EModelEndpoint,
|
||||
lastConversationSetup: preset,
|
||||
});
|
||||
|
||||
expect(result.max_tokens).toBe(4096);
|
||||
expect(result.top_p).toBe(0.9);
|
||||
expect(result.temperature).toBe(0.7);
|
||||
expect(result.maxOutputTokens).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
describe('custom endpoint with defaultParamsEndpoint: google', () => {
|
||||
const models = ['gemini-pro', 'gemini-1.5-pro'];
|
||||
|
||||
it('should preserve Google-specific fields', () => {
|
||||
const preset: TConversation = {
|
||||
...baseConversation,
|
||||
endpoint: 'MyGoogleEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'gemini-pro',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
topP: 0.9,
|
||||
topK: 40,
|
||||
};
|
||||
|
||||
const result = buildDefaultConvo({
|
||||
models,
|
||||
conversation: baseConversation,
|
||||
endpoint: 'MyGoogleEndpoint' as EModelEndpoint,
|
||||
lastConversationSetup: preset,
|
||||
defaultParamsEndpoint: EModelEndpoint.google,
|
||||
});
|
||||
|
||||
expect(result.maxOutputTokens).toBe(8192);
|
||||
expect(result.topP).toBe(0.9);
|
||||
expect(result.topK).toBe(40);
|
||||
});
|
||||
});
|
||||
|
||||
describe('cross-endpoint field isolation', () => {
|
||||
it('should not carry bedrock region to a custom endpoint', () => {
|
||||
const preset: TConversation = {
|
||||
...baseConversation,
|
||||
endpoint: 'MyChatEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
region: 'us-east-1',
|
||||
};
|
||||
|
||||
const result = buildDefaultConvo({
|
||||
models: ['gpt-4o'],
|
||||
conversation: baseConversation,
|
||||
endpoint: 'MyChatEndpoint' as EModelEndpoint,
|
||||
lastConversationSetup: preset,
|
||||
});
|
||||
|
||||
expect(result.region).toBeUndefined();
|
||||
expect(result.temperature).toBe(0.7);
|
||||
});
|
||||
|
||||
it('should not carry bedrock region even with anthropic defaultParamsEndpoint', () => {
|
||||
const preset: TConversation = {
|
||||
...baseConversation,
|
||||
endpoint: 'MyChatEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'claude-3-opus',
|
||||
region: 'us-east-1',
|
||||
maxOutputTokens: 8192,
|
||||
};
|
||||
|
||||
const result = buildDefaultConvo({
|
||||
models: ['claude-3-opus'],
|
||||
conversation: baseConversation,
|
||||
endpoint: 'MyChatEndpoint' as EModelEndpoint,
|
||||
lastConversationSetup: preset,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result.region).toBeUndefined();
|
||||
expect(result.maxOutputTokens).toBe(8192);
|
||||
});
|
||||
});
|
||||
});
|
||||
119
client/src/utils/__tests__/cleanupPreset.integration.test.ts
Normal file
119
client/src/utils/__tests__/cleanupPreset.integration.test.ts
Normal file
|
|
@ -0,0 +1,119 @@
|
|||
import { EModelEndpoint } from 'librechat-data-provider';
|
||||
import cleanupPreset from '../cleanupPreset';
|
||||
|
||||
/**
|
||||
* Integration tests for cleanupPreset — NO mocks.
|
||||
* Uses the real parseConvo to verify actual schema behavior
|
||||
* with defaultParamsEndpoint for custom endpoints.
|
||||
*/
|
||||
describe('cleanupPreset - real parsing with defaultParamsEndpoint', () => {
|
||||
it('should preserve maxOutputTokens when defaultParamsEndpoint is anthropic', () => {
|
||||
const preset = {
|
||||
presetId: 'test-id',
|
||||
title: 'Claude Opus',
|
||||
endpoint: 'AnthropicClaude',
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
topP: 0.9,
|
||||
maxContextTokens: 50000,
|
||||
};
|
||||
|
||||
const result = cleanupPreset({
|
||||
preset,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result.maxOutputTokens).toBe(8192);
|
||||
expect(result.topP).toBe(0.9);
|
||||
expect(result.temperature).toBe(0.7);
|
||||
expect(result.maxContextTokens).toBe(50000);
|
||||
expect(result.model).toBe('anthropic/claude-opus-4.5');
|
||||
});
|
||||
|
||||
it('should strip maxOutputTokens without defaultParamsEndpoint (OpenAI schema)', () => {
|
||||
const preset = {
|
||||
presetId: 'test-id',
|
||||
title: 'GPT Custom',
|
||||
endpoint: 'MyOpenRouter',
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
max_tokens: 4096,
|
||||
};
|
||||
|
||||
const result = cleanupPreset({ preset });
|
||||
|
||||
expect(result.maxOutputTokens).toBeUndefined();
|
||||
expect(result.max_tokens).toBe(4096);
|
||||
expect(result.temperature).toBe(0.7);
|
||||
});
|
||||
|
||||
it('should strip OpenAI-specific fields when using anthropic params', () => {
|
||||
const preset = {
|
||||
presetId: 'test-id',
|
||||
title: 'Claude Custom',
|
||||
endpoint: 'AnthropicClaude',
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'anthropic/claude-3-opus',
|
||||
max_tokens: 4096,
|
||||
top_p: 0.9,
|
||||
presence_penalty: 0.5,
|
||||
frequency_penalty: 0.3,
|
||||
temperature: 0.7,
|
||||
};
|
||||
|
||||
const result = cleanupPreset({
|
||||
preset,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result.max_tokens).toBeUndefined();
|
||||
expect(result.top_p).toBeUndefined();
|
||||
expect(result.presence_penalty).toBeUndefined();
|
||||
expect(result.frequency_penalty).toBeUndefined();
|
||||
expect(result.temperature).toBe(0.7);
|
||||
});
|
||||
|
||||
it('should not carry bedrock region to custom endpoint', () => {
|
||||
const preset = {
|
||||
presetId: 'test-id',
|
||||
title: 'Custom',
|
||||
endpoint: 'MyEndpoint',
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
region: 'us-east-1',
|
||||
};
|
||||
|
||||
const result = cleanupPreset({ preset });
|
||||
|
||||
expect(result.region).toBeUndefined();
|
||||
expect(result.temperature).toBe(0.7);
|
||||
});
|
||||
|
||||
it('should preserve Google-specific fields when defaultParamsEndpoint is google', () => {
|
||||
const preset = {
|
||||
presetId: 'test-id',
|
||||
title: 'Gemini Custom',
|
||||
endpoint: 'MyGoogleEndpoint',
|
||||
endpointType: EModelEndpoint.custom,
|
||||
model: 'gemini-pro',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
topP: 0.9,
|
||||
topK: 40,
|
||||
};
|
||||
|
||||
const result = cleanupPreset({
|
||||
preset,
|
||||
defaultParamsEndpoint: EModelEndpoint.google,
|
||||
});
|
||||
|
||||
expect(result.maxOutputTokens).toBe(8192);
|
||||
expect(result.topP).toBe(0.9);
|
||||
expect(result.topK).toBe(40);
|
||||
});
|
||||
});
|
||||
|
|
@ -1,12 +1,9 @@
|
|||
import { EModelEndpoint } from 'librechat-data-provider';
|
||||
import { EModelEndpoint, parseConvo } from 'librechat-data-provider';
|
||||
import cleanupPreset from '../cleanupPreset';
|
||||
import type { TPreset } from 'librechat-data-provider';
|
||||
|
||||
// Mock parseConvo since we're focusing on testing the chatGptLabel migration logic
|
||||
jest.mock('librechat-data-provider', () => ({
|
||||
...jest.requireActual('librechat-data-provider'),
|
||||
parseConvo: jest.fn((input) => {
|
||||
// Return a simplified mock that passes through most properties
|
||||
const { conversation } = input;
|
||||
return {
|
||||
...conversation,
|
||||
|
|
@ -221,4 +218,41 @@ describe('cleanupPreset', () => {
|
|||
expect(result.presetId).toBeNull();
|
||||
});
|
||||
});
|
||||
|
||||
describe('defaultParamsEndpoint threading', () => {
|
||||
it('should pass defaultParamsEndpoint to parseConvo', () => {
|
||||
const preset = {
|
||||
...basePreset,
|
||||
endpoint: 'MyCustomEndpoint',
|
||||
endpointType: EModelEndpoint.custom,
|
||||
};
|
||||
|
||||
cleanupPreset({
|
||||
preset,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(parseConvo).toHaveBeenCalledWith(
|
||||
expect.objectContaining({
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
}),
|
||||
);
|
||||
});
|
||||
|
||||
it('should pass undefined defaultParamsEndpoint when not provided', () => {
|
||||
const preset = {
|
||||
...basePreset,
|
||||
endpoint: 'MyCustomEndpoint',
|
||||
endpointType: EModelEndpoint.custom,
|
||||
};
|
||||
|
||||
cleanupPreset({ preset });
|
||||
|
||||
expect(parseConvo).toHaveBeenCalledWith(
|
||||
expect.objectContaining({
|
||||
defaultParamsEndpoint: undefined,
|
||||
}),
|
||||
);
|
||||
});
|
||||
});
|
||||
});
|
||||
|
|
|
|||
|
|
@ -14,11 +14,13 @@ const buildDefaultConvo = ({
|
|||
conversation,
|
||||
endpoint = null,
|
||||
lastConversationSetup,
|
||||
defaultParamsEndpoint,
|
||||
}: {
|
||||
models: string[];
|
||||
conversation: TConversation;
|
||||
endpoint?: EModelEndpoint | null;
|
||||
lastConversationSetup: TConversation | null;
|
||||
defaultParamsEndpoint?: string | null;
|
||||
}): TConversation => {
|
||||
const { lastSelectedModel, lastSelectedTools } = getLocalStorageItems();
|
||||
const endpointType = lastConversationSetup?.endpointType ?? conversation.endpointType;
|
||||
|
|
@ -49,6 +51,7 @@ const buildDefaultConvo = ({
|
|||
possibleValues: {
|
||||
models: possibleModels,
|
||||
},
|
||||
defaultParamsEndpoint,
|
||||
});
|
||||
|
||||
const defaultConvo = {
|
||||
|
|
|
|||
|
|
@ -4,9 +4,10 @@ import type { TPreset } from 'librechat-data-provider';
|
|||
type UIPreset = Partial<TPreset> & { presetOverride?: Partial<TPreset> };
|
||||
type TCleanupPreset = {
|
||||
preset?: UIPreset;
|
||||
defaultParamsEndpoint?: string | null;
|
||||
};
|
||||
|
||||
const cleanupPreset = ({ preset: _preset }: TCleanupPreset): TPreset => {
|
||||
const cleanupPreset = ({ preset: _preset, defaultParamsEndpoint }: TCleanupPreset): TPreset => {
|
||||
const { endpoint, endpointType } = _preset ?? ({} as UIPreset);
|
||||
if (endpoint == null || endpoint === '') {
|
||||
console.error(`Unknown endpoint ${endpoint}`, _preset);
|
||||
|
|
@ -35,8 +36,13 @@ const cleanupPreset = ({ preset: _preset }: TCleanupPreset): TPreset => {
|
|||
delete preset.chatGptLabel;
|
||||
}
|
||||
|
||||
/* @ts-ignore: endpoint can be a custom defined name */
|
||||
const parsedPreset = parseConvo({ endpoint, endpointType, conversation: preset });
|
||||
const parsedPreset = parseConvo({
|
||||
/* @ts-ignore: endpoint can be a custom defined name */
|
||||
endpoint,
|
||||
endpointType,
|
||||
conversation: preset,
|
||||
defaultParamsEndpoint,
|
||||
});
|
||||
|
||||
return {
|
||||
presetId: _preset?.presetId ?? null,
|
||||
|
|
|
|||
284
packages/api/src/agents/__tests__/initialize.test.ts
Normal file
284
packages/api/src/agents/__tests__/initialize.test.ts
Normal file
|
|
@ -0,0 +1,284 @@
|
|||
import { Providers } from '@librechat/agents';
|
||||
import { EModelEndpoint } from 'librechat-data-provider';
|
||||
import type { Agent } from 'librechat-data-provider';
|
||||
import type { ServerRequest, InitializeResultBase } from '~/types';
|
||||
import type { InitializeAgentDbMethods } from '../initialize';
|
||||
|
||||
// Mock logger
|
||||
jest.mock('winston', () => ({
|
||||
createLogger: jest.fn(() => ({
|
||||
debug: jest.fn(),
|
||||
warn: jest.fn(),
|
||||
error: jest.fn(),
|
||||
})),
|
||||
format: {
|
||||
combine: jest.fn(),
|
||||
colorize: jest.fn(),
|
||||
simple: jest.fn(),
|
||||
},
|
||||
transports: {
|
||||
Console: jest.fn(),
|
||||
},
|
||||
}));
|
||||
|
||||
const mockExtractLibreChatParams = jest.fn();
|
||||
const mockGetModelMaxTokens = jest.fn();
|
||||
const mockOptionalChainWithEmptyCheck = jest.fn();
|
||||
const mockGetThreadData = jest.fn();
|
||||
|
||||
jest.mock('~/utils', () => ({
|
||||
extractLibreChatParams: (...args: unknown[]) => mockExtractLibreChatParams(...args),
|
||||
getModelMaxTokens: (...args: unknown[]) => mockGetModelMaxTokens(...args),
|
||||
optionalChainWithEmptyCheck: (...args: unknown[]) => mockOptionalChainWithEmptyCheck(...args),
|
||||
getThreadData: (...args: unknown[]) => mockGetThreadData(...args),
|
||||
}));
|
||||
|
||||
const mockGetProviderConfig = jest.fn();
|
||||
jest.mock('~/endpoints', () => ({
|
||||
getProviderConfig: (...args: unknown[]) => mockGetProviderConfig(...args),
|
||||
}));
|
||||
|
||||
jest.mock('~/files', () => ({
|
||||
filterFilesByEndpointConfig: jest.fn(() => []),
|
||||
}));
|
||||
|
||||
jest.mock('~/prompts', () => ({
|
||||
generateArtifactsPrompt: jest.fn(() => null),
|
||||
}));
|
||||
|
||||
jest.mock('../resources', () => ({
|
||||
primeResources: jest.fn().mockResolvedValue({
|
||||
attachments: [],
|
||||
tool_resources: undefined,
|
||||
}),
|
||||
}));
|
||||
|
||||
import { initializeAgent } from '../initialize';
|
||||
|
||||
/**
|
||||
* Creates minimal mock objects for initializeAgent tests.
|
||||
*/
|
||||
function createMocks(overrides?: {
|
||||
maxContextTokens?: number;
|
||||
modelDefault?: number;
|
||||
maxOutputTokens?: number;
|
||||
}) {
|
||||
const { maxContextTokens, modelDefault = 200000, maxOutputTokens = 4096 } = overrides ?? {};
|
||||
|
||||
const agent = {
|
||||
id: 'agent-1',
|
||||
model: 'test-model',
|
||||
provider: Providers.OPENAI,
|
||||
tools: [],
|
||||
model_parameters: { model: 'test-model' },
|
||||
} as unknown as Agent;
|
||||
|
||||
const req = {
|
||||
user: { id: 'user-1' },
|
||||
config: {},
|
||||
} as unknown as ServerRequest;
|
||||
|
||||
const res = {} as unknown as import('express').Response;
|
||||
|
||||
const mockGetOptions = jest.fn().mockResolvedValue({
|
||||
llmConfig: {
|
||||
model: 'test-model',
|
||||
maxTokens: maxOutputTokens,
|
||||
},
|
||||
endpointTokenConfig: undefined,
|
||||
} satisfies InitializeResultBase);
|
||||
|
||||
mockGetProviderConfig.mockReturnValue({
|
||||
getOptions: mockGetOptions,
|
||||
overrideProvider: Providers.OPENAI,
|
||||
});
|
||||
|
||||
// extractLibreChatParams returns maxContextTokens when provided in model_parameters
|
||||
mockExtractLibreChatParams.mockReturnValue({
|
||||
resendFiles: false,
|
||||
maxContextTokens,
|
||||
modelOptions: { model: 'test-model' },
|
||||
});
|
||||
|
||||
// getModelMaxTokens returns the model's default context window
|
||||
mockGetModelMaxTokens.mockReturnValue(modelDefault);
|
||||
|
||||
// Implement real optionalChainWithEmptyCheck behavior
|
||||
mockOptionalChainWithEmptyCheck.mockImplementation(
|
||||
(...values: (string | number | undefined)[]) => {
|
||||
for (const v of values) {
|
||||
if (v !== undefined && v !== null && v !== '') {
|
||||
return v;
|
||||
}
|
||||
}
|
||||
return values[values.length - 1];
|
||||
},
|
||||
);
|
||||
|
||||
const loadTools = jest.fn().mockResolvedValue({
|
||||
tools: [],
|
||||
toolContextMap: {},
|
||||
userMCPAuthMap: undefined,
|
||||
toolRegistry: undefined,
|
||||
toolDefinitions: [],
|
||||
hasDeferredTools: false,
|
||||
});
|
||||
|
||||
const db: InitializeAgentDbMethods = {
|
||||
getFiles: jest.fn().mockResolvedValue([]),
|
||||
getConvoFiles: jest.fn().mockResolvedValue([]),
|
||||
updateFilesUsage: jest.fn().mockResolvedValue([]),
|
||||
getUserKey: jest.fn().mockResolvedValue('user-1'),
|
||||
getUserKeyValues: jest.fn().mockResolvedValue([]),
|
||||
getToolFilesByIds: jest.fn().mockResolvedValue([]),
|
||||
};
|
||||
|
||||
return { agent, req, res, loadTools, db };
|
||||
}
|
||||
|
||||
describe('initializeAgent — maxContextTokens', () => {
|
||||
beforeEach(() => {
|
||||
jest.clearAllMocks();
|
||||
});
|
||||
|
||||
it('uses user-configured maxContextTokens when provided via model_parameters', async () => {
|
||||
const userValue = 50000;
|
||||
const { agent, req, res, loadTools, db } = createMocks({
|
||||
maxContextTokens: userValue,
|
||||
modelDefault: 200000,
|
||||
maxOutputTokens: 4096,
|
||||
});
|
||||
|
||||
const result = await initializeAgent(
|
||||
{
|
||||
req,
|
||||
res,
|
||||
agent,
|
||||
loadTools,
|
||||
endpointOption: {
|
||||
endpoint: EModelEndpoint.agents,
|
||||
model_parameters: { maxContextTokens: userValue },
|
||||
},
|
||||
allowedProviders: new Set([Providers.OPENAI]),
|
||||
isInitialAgent: true,
|
||||
},
|
||||
db,
|
||||
);
|
||||
|
||||
expect(result.maxContextTokens).toBe(userValue);
|
||||
});
|
||||
|
||||
it('falls back to formula when maxContextTokens is NOT provided', async () => {
|
||||
const modelDefault = 200000;
|
||||
const maxOutputTokens = 4096;
|
||||
const { agent, req, res, loadTools, db } = createMocks({
|
||||
maxContextTokens: undefined,
|
||||
modelDefault,
|
||||
maxOutputTokens,
|
||||
});
|
||||
|
||||
const result = await initializeAgent(
|
||||
{
|
||||
req,
|
||||
res,
|
||||
agent,
|
||||
loadTools,
|
||||
endpointOption: { endpoint: EModelEndpoint.agents },
|
||||
allowedProviders: new Set([Providers.OPENAI]),
|
||||
isInitialAgent: true,
|
||||
},
|
||||
db,
|
||||
);
|
||||
|
||||
const expected = Math.round((modelDefault - maxOutputTokens) * 0.9);
|
||||
expect(result.maxContextTokens).toBe(expected);
|
||||
});
|
||||
|
||||
it('falls back to formula when maxContextTokens is 0', async () => {
|
||||
const maxOutputTokens = 4096;
|
||||
const { agent, req, res, loadTools, db } = createMocks({
|
||||
maxContextTokens: 0,
|
||||
modelDefault: 200000,
|
||||
maxOutputTokens,
|
||||
});
|
||||
|
||||
const result = await initializeAgent(
|
||||
{
|
||||
req,
|
||||
res,
|
||||
agent,
|
||||
loadTools,
|
||||
endpointOption: {
|
||||
endpoint: EModelEndpoint.agents,
|
||||
model_parameters: { maxContextTokens: 0 },
|
||||
},
|
||||
allowedProviders: new Set([Providers.OPENAI]),
|
||||
isInitialAgent: true,
|
||||
},
|
||||
db,
|
||||
);
|
||||
|
||||
// 0 is not used as-is; the formula kicks in.
|
||||
// optionalChainWithEmptyCheck(0, 200000, 18000) returns 0 (not null/undefined),
|
||||
// then Number(0) || 18000 = 18000 (the fallback default).
|
||||
expect(result.maxContextTokens).not.toBe(0);
|
||||
const expected = Math.round((18000 - maxOutputTokens) * 0.9);
|
||||
expect(result.maxContextTokens).toBe(expected);
|
||||
});
|
||||
|
||||
it('falls back to formula when maxContextTokens is negative', async () => {
|
||||
const maxOutputTokens = 4096;
|
||||
const { agent, req, res, loadTools, db } = createMocks({
|
||||
maxContextTokens: -1,
|
||||
modelDefault: 200000,
|
||||
maxOutputTokens,
|
||||
});
|
||||
|
||||
const result = await initializeAgent(
|
||||
{
|
||||
req,
|
||||
res,
|
||||
agent,
|
||||
loadTools,
|
||||
endpointOption: {
|
||||
endpoint: EModelEndpoint.agents,
|
||||
model_parameters: { maxContextTokens: -1 },
|
||||
},
|
||||
allowedProviders: new Set([Providers.OPENAI]),
|
||||
isInitialAgent: true,
|
||||
},
|
||||
db,
|
||||
);
|
||||
|
||||
// -1 is not used as-is; the formula kicks in
|
||||
expect(result.maxContextTokens).not.toBe(-1);
|
||||
});
|
||||
|
||||
it('preserves small user-configured value (e.g. 1000 from modelSpec)', async () => {
|
||||
const userValue = 1000;
|
||||
const { agent, req, res, loadTools, db } = createMocks({
|
||||
maxContextTokens: userValue,
|
||||
modelDefault: 128000,
|
||||
maxOutputTokens: 4096,
|
||||
});
|
||||
|
||||
const result = await initializeAgent(
|
||||
{
|
||||
req,
|
||||
res,
|
||||
agent,
|
||||
loadTools,
|
||||
endpointOption: {
|
||||
endpoint: EModelEndpoint.agents,
|
||||
model_parameters: { maxContextTokens: userValue },
|
||||
},
|
||||
allowedProviders: new Set([Providers.OPENAI]),
|
||||
isInitialAgent: true,
|
||||
},
|
||||
db,
|
||||
);
|
||||
|
||||
// Should NOT be overridden to Math.round((128000 - 4096) * 0.9) = 111,514
|
||||
expect(result.maxContextTokens).toBe(userValue);
|
||||
});
|
||||
});
|
||||
|
|
@ -413,7 +413,10 @@ export async function initializeAgent(
|
|||
toolContextMap: toolContextMap ?? {},
|
||||
useLegacyContent: !!options.useLegacyContent,
|
||||
tools: (tools ?? []) as GenericTool[] & string[],
|
||||
maxContextTokens: Math.round((agentMaxContextNum - maxOutputTokensNum) * 0.9),
|
||||
maxContextTokens:
|
||||
maxContextTokens != null && maxContextTokens > 0
|
||||
? maxContextTokens
|
||||
: Math.round((agentMaxContextNum - maxOutputTokensNum) * 0.9),
|
||||
};
|
||||
|
||||
return initializedAgent;
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import { replaceSpecialVars, parseCompactConvo, parseTextParts } from '../src/parsers';
|
||||
import { replaceSpecialVars, parseConvo, parseCompactConvo, parseTextParts } from '../src/parsers';
|
||||
import { specialVariables } from '../src/config';
|
||||
import { EModelEndpoint } from '../src/schemas';
|
||||
import { ContentTypes } from '../src/types/runs';
|
||||
|
|
@ -262,6 +262,257 @@ describe('parseCompactConvo', () => {
|
|||
});
|
||||
});
|
||||
|
||||
describe('parseConvo - defaultParamsEndpoint', () => {
|
||||
test('should strip maxOutputTokens for custom endpoint without defaultParamsEndpoint', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
maxContextTokens: 50000,
|
||||
};
|
||||
|
||||
const result = parseConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.temperature).toBe(0.7);
|
||||
expect(result?.maxContextTokens).toBe(50000);
|
||||
expect(result?.maxOutputTokens).toBeUndefined();
|
||||
});
|
||||
|
||||
test('should preserve maxOutputTokens when defaultParamsEndpoint is anthropic', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
topP: 0.9,
|
||||
topK: 40,
|
||||
maxContextTokens: 50000,
|
||||
};
|
||||
|
||||
const result = parseConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.model).toBe('anthropic/claude-opus-4.5');
|
||||
expect(result?.temperature).toBe(0.7);
|
||||
expect(result?.maxOutputTokens).toBe(8192);
|
||||
expect(result?.topP).toBe(0.9);
|
||||
expect(result?.topK).toBe(40);
|
||||
expect(result?.maxContextTokens).toBe(50000);
|
||||
});
|
||||
|
||||
test('should strip OpenAI-specific fields when defaultParamsEndpoint is anthropic', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
top_p: 0.9,
|
||||
presence_penalty: 0.5,
|
||||
frequency_penalty: 0.3,
|
||||
};
|
||||
|
||||
const result = parseConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.temperature).toBe(0.7);
|
||||
expect(result?.max_tokens).toBeUndefined();
|
||||
expect(result?.top_p).toBeUndefined();
|
||||
expect(result?.presence_penalty).toBeUndefined();
|
||||
expect(result?.frequency_penalty).toBeUndefined();
|
||||
});
|
||||
|
||||
test('should preserve max_tokens when defaultParamsEndpoint is not set (OpenAI default)', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
top_p: 0.9,
|
||||
};
|
||||
|
||||
const result = parseConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.max_tokens).toBe(4096);
|
||||
expect(result?.top_p).toBe(0.9);
|
||||
});
|
||||
|
||||
test('should preserve Google-specific fields when defaultParamsEndpoint is google', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'gemini-pro',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
topP: 0.9,
|
||||
topK: 40,
|
||||
};
|
||||
|
||||
const result = parseConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
defaultParamsEndpoint: EModelEndpoint.google,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.maxOutputTokens).toBe(8192);
|
||||
expect(result?.topP).toBe(0.9);
|
||||
expect(result?.topK).toBe(40);
|
||||
});
|
||||
|
||||
test('should not strip fields from non-custom endpoints that already have a schema', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
top_p: 0.9,
|
||||
};
|
||||
|
||||
const result = parseConvo({
|
||||
endpoint: EModelEndpoint.openAI,
|
||||
conversation,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.max_tokens).toBe(4096);
|
||||
expect(result?.top_p).toBe(0.9);
|
||||
});
|
||||
|
||||
test('should not carry bedrock region to custom endpoint without defaultParamsEndpoint', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
region: 'us-east-1',
|
||||
};
|
||||
|
||||
const result = parseConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.temperature).toBe(0.7);
|
||||
expect(result?.region).toBeUndefined();
|
||||
});
|
||||
|
||||
test('should fall back to endpointType schema when defaultParamsEndpoint is invalid', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'gpt-4o',
|
||||
temperature: 0.7,
|
||||
max_tokens: 4096,
|
||||
maxOutputTokens: 8192,
|
||||
};
|
||||
|
||||
const result = parseConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
defaultParamsEndpoint: 'nonexistent_endpoint',
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.max_tokens).toBe(4096);
|
||||
expect(result?.maxOutputTokens).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
describe('parseCompactConvo - defaultParamsEndpoint', () => {
|
||||
test('should strip maxOutputTokens for custom endpoint without defaultParamsEndpoint', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
};
|
||||
|
||||
const result = parseCompactConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.temperature).toBe(0.7);
|
||||
expect(result?.maxOutputTokens).toBeUndefined();
|
||||
});
|
||||
|
||||
test('should preserve maxOutputTokens when defaultParamsEndpoint is anthropic', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
temperature: 0.7,
|
||||
maxOutputTokens: 8192,
|
||||
topP: 0.9,
|
||||
maxContextTokens: 50000,
|
||||
};
|
||||
|
||||
const result = parseCompactConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.maxOutputTokens).toBe(8192);
|
||||
expect(result?.topP).toBe(0.9);
|
||||
expect(result?.maxContextTokens).toBe(50000);
|
||||
});
|
||||
|
||||
test('should strip iconURL even when defaultParamsEndpoint is set', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'anthropic/claude-opus-4.5',
|
||||
iconURL: 'https://malicious.com/track.png',
|
||||
maxOutputTokens: 8192,
|
||||
};
|
||||
|
||||
const result = parseCompactConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
defaultParamsEndpoint: EModelEndpoint.anthropic,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.['iconURL']).toBeUndefined();
|
||||
expect(result?.maxOutputTokens).toBe(8192);
|
||||
});
|
||||
|
||||
test('should fall back to endpointType when defaultParamsEndpoint is null', () => {
|
||||
const conversation: Partial<TConversation> = {
|
||||
model: 'gpt-4o',
|
||||
max_tokens: 4096,
|
||||
maxOutputTokens: 8192,
|
||||
};
|
||||
|
||||
const result = parseCompactConvo({
|
||||
endpoint: 'MyCustomEndpoint' as EModelEndpoint,
|
||||
endpointType: EModelEndpoint.custom,
|
||||
conversation,
|
||||
defaultParamsEndpoint: null,
|
||||
});
|
||||
|
||||
expect(result).not.toBeNull();
|
||||
expect(result?.max_tokens).toBe(4096);
|
||||
expect(result?.maxOutputTokens).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
describe('parseTextParts', () => {
|
||||
test('should concatenate text parts', () => {
|
||||
const parts: TMessageContentParts[] = [
|
||||
|
|
|
|||
|
|
@ -1908,3 +1908,14 @@ export function getEndpointField<
|
|||
}
|
||||
return config[property];
|
||||
}
|
||||
|
||||
/** Resolves the `defaultParamsEndpoint` for a given endpoint from its custom params config */
|
||||
export function getDefaultParamsEndpoint(
|
||||
endpointsConfig: TEndpointsConfig | undefined | null,
|
||||
endpoint: string | null | undefined,
|
||||
): string | undefined {
|
||||
if (!endpointsConfig || !endpoint) {
|
||||
return undefined;
|
||||
}
|
||||
return endpointsConfig[endpoint]?.customParams?.defaultParamsEndpoint;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -144,26 +144,25 @@ export const parseConvo = ({
|
|||
endpointType,
|
||||
conversation,
|
||||
possibleValues,
|
||||
defaultParamsEndpoint,
|
||||
}: {
|
||||
endpoint: EndpointSchemaKey;
|
||||
endpointType?: EndpointSchemaKey | null;
|
||||
conversation: Partial<s.TConversation | s.TPreset> | null;
|
||||
possibleValues?: TPossibleValues;
|
||||
// TODO: POC for default schema
|
||||
// defaultSchema?: Partial<EndpointSchema>,
|
||||
defaultParamsEndpoint?: string | null;
|
||||
}) => {
|
||||
let schema = endpointSchemas[endpoint] as EndpointSchema | undefined;
|
||||
|
||||
if (!schema && !endpointType) {
|
||||
throw new Error(`Unknown endpoint: ${endpoint}`);
|
||||
} else if (!schema && endpointType) {
|
||||
schema = endpointSchemas[endpointType];
|
||||
} else if (!schema) {
|
||||
const overrideSchema = defaultParamsEndpoint
|
||||
? endpointSchemas[defaultParamsEndpoint as EndpointSchemaKey]
|
||||
: undefined;
|
||||
schema = overrideSchema ?? (endpointType ? endpointSchemas[endpointType] : undefined);
|
||||
}
|
||||
|
||||
// if (defaultSchema && schemaCreators[endpoint]) {
|
||||
// schema = schemaCreators[endpoint](defaultSchema);
|
||||
// }
|
||||
|
||||
const convo = schema?.parse(conversation) as s.TConversation | undefined;
|
||||
const { models } = possibleValues ?? {};
|
||||
|
||||
|
|
@ -310,13 +309,13 @@ export const parseCompactConvo = ({
|
|||
endpointType,
|
||||
conversation,
|
||||
possibleValues,
|
||||
defaultParamsEndpoint,
|
||||
}: {
|
||||
endpoint?: EndpointSchemaKey;
|
||||
endpointType?: EndpointSchemaKey | null;
|
||||
conversation: Partial<s.TConversation | s.TPreset>;
|
||||
possibleValues?: TPossibleValues;
|
||||
// TODO: POC for default schema
|
||||
// defaultSchema?: Partial<EndpointSchema>,
|
||||
defaultParamsEndpoint?: string | null;
|
||||
}): Omit<s.TConversation, 'iconURL'> | null => {
|
||||
if (!endpoint) {
|
||||
throw new Error(`undefined endpoint: ${endpoint}`);
|
||||
|
|
@ -326,8 +325,11 @@ export const parseCompactConvo = ({
|
|||
|
||||
if (!schema && !endpointType) {
|
||||
throw new Error(`Unknown endpoint: ${endpoint}`);
|
||||
} else if (!schema && endpointType) {
|
||||
schema = compactEndpointSchemas[endpointType];
|
||||
} else if (!schema) {
|
||||
const overrideSchema = defaultParamsEndpoint
|
||||
? compactEndpointSchemas[defaultParamsEndpoint as EndpointSchemaKey]
|
||||
: undefined;
|
||||
schema = overrideSchema ?? (endpointType ? compactEndpointSchemas[endpointType] : undefined);
|
||||
}
|
||||
|
||||
if (!schema) {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue