LibreChat/api/server/services/Endpoints/openAI/initialize.spec.js

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const { EModelEndpoint, ErrorTypes, validateAzureGroups } = require('librechat-data-provider');
const { getUserKey, getUserKeyValues } = require('~/server/services/UserService');
const initializeClient = require('./initialize');
const { OpenAIClient } = require('~/app');
// Mock getUserKey since it's the only function we want to mock
feat(Google): Support all Text/Chat Models, Response streaming, `PaLM` -> `Google` 🤖 (#1316) * feat: update PaLM icons * feat: add additional google models * POC: formatting inputs for Vertex AI streaming * refactor: move endpoints services outside of /routes dir to /services/Endpoints * refactor: shorten schemas import * refactor: rename PALM to GOOGLE * feat: make Google editable endpoint * feat: reusable Ask and Edit controllers based off Anthropic * chore: organize imports/logic * fix(parseConvo): include examples in googleSchema * fix: google only allows odd number of messages to be sent * fix: pass proxy to AnthropicClient * refactor: change `google` altName to `Google` * refactor: update getModelMaxTokens and related functions to handle maxTokensMap with nested endpoint model key/values * refactor: google Icon and response sender changes (Codey and Google logo instead of PaLM in all cases) * feat: google support for maxTokensMap * feat: google updated endpoints with Ask/Edit controllers, buildOptions, and initializeClient * feat(GoogleClient): now builds prompt for text models and supports real streaming from Vertex AI through langchain * chore(GoogleClient): remove comments, left before for reference in git history * docs: update google instructions (WIP) * docs(apis_and_tokens.md): add images to google instructions * docs: remove typo apis_and_tokens.md * Update apis_and_tokens.md * feat(Google): use default settings map, fully support context for both text and chat models, fully support examples for chat models * chore: update more PaLM references to Google * chore: move playwright out of workflows to avoid failing tests
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jest.mock('~/server/services/UserService', () => ({
getUserKey: jest.fn(),
getUserKeyValues: jest.fn(),
feat(Google): Support all Text/Chat Models, Response streaming, `PaLM` -> `Google` 🤖 (#1316) * feat: update PaLM icons * feat: add additional google models * POC: formatting inputs for Vertex AI streaming * refactor: move endpoints services outside of /routes dir to /services/Endpoints * refactor: shorten schemas import * refactor: rename PALM to GOOGLE * feat: make Google editable endpoint * feat: reusable Ask and Edit controllers based off Anthropic * chore: organize imports/logic * fix(parseConvo): include examples in googleSchema * fix: google only allows odd number of messages to be sent * fix: pass proxy to AnthropicClient * refactor: change `google` altName to `Google` * refactor: update getModelMaxTokens and related functions to handle maxTokensMap with nested endpoint model key/values * refactor: google Icon and response sender changes (Codey and Google logo instead of PaLM in all cases) * feat: google support for maxTokensMap * feat: google updated endpoints with Ask/Edit controllers, buildOptions, and initializeClient * feat(GoogleClient): now builds prompt for text models and supports real streaming from Vertex AI through langchain * chore(GoogleClient): remove comments, left before for reference in git history * docs: update google instructions (WIP) * docs(apis_and_tokens.md): add images to google instructions * docs: remove typo apis_and_tokens.md * Update apis_and_tokens.md * feat(Google): use default settings map, fully support context for both text and chat models, fully support examples for chat models * chore: update more PaLM references to Google * chore: move playwright out of workflows to avoid failing tests
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checkUserKeyExpiry: jest.requireActual('~/server/services/UserService').checkUserKeyExpiry,
}));
describe('initializeClient', () => {
// Set up environment variables
const originalEnvironment = process.env;
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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const app = {
locals: {},
};
const validAzureConfigs = [
{
group: 'librechat-westus',
apiKey: 'WESTUS_API_KEY',
instanceName: 'librechat-westus',
version: '2023-12-01-preview',
models: {
'gpt-4-vision-preview': {
deploymentName: 'gpt-4-vision-preview',
version: '2024-02-15-preview',
},
'gpt-3.5-turbo': {
deploymentName: 'gpt-35-turbo',
},
'gpt-3.5-turbo-1106': {
deploymentName: 'gpt-35-turbo-1106',
},
'gpt-4': {
deploymentName: 'gpt-4',
},
'gpt-4-1106-preview': {
deploymentName: 'gpt-4-1106-preview',
},
},
},
{
group: 'librechat-eastus',
apiKey: 'EASTUS_API_KEY',
instanceName: 'librechat-eastus',
deploymentName: 'gpt-4-turbo',
version: '2024-02-15-preview',
models: {
'gpt-4-turbo': true,
},
baseURL: 'https://eastus.example.com',
additionalHeaders: {
'x-api-key': 'x-api-key-value',
},
},
{
group: 'mistral-inference',
apiKey: 'AZURE_MISTRAL_API_KEY',
baseURL:
'https://Mistral-large-vnpet-serverless.region.inference.ai.azure.com/v1/chat/completions',
serverless: true,
models: {
'mistral-large': true,
},
},
{
group: 'llama-70b-chat',
apiKey: 'AZURE_LLAMA2_70B_API_KEY',
baseURL:
'https://Llama-2-70b-chat-qmvyb-serverless.region.inference.ai.azure.com/v1/chat/completions',
serverless: true,
models: {
'llama-70b-chat': true,
},
},
];
const { modelNames, modelGroupMap, groupMap } = validateAzureGroups(validAzureConfigs);
beforeEach(() => {
jest.resetModules(); // Clears the cache
process.env = { ...originalEnvironment }; // Make a copy
});
afterAll(() => {
process.env = originalEnvironment; // Restore original env vars
});
test('should initialize client with OpenAI API key and default options', async () => {
process.env.OPENAI_API_KEY = 'test-openai-api-key';
process.env.DEBUG_OPENAI = 'false';
process.env.OPENAI_SUMMARIZE = 'false';
const req = {
body: { key: null, endpoint: EModelEndpoint.openAI },
user: { id: '123' },
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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app,
};
const res = {};
const endpointOption = {};
const result = await initializeClient({ req, res, endpointOption });
expect(result.openAIApiKey).toBe('test-openai-api-key');
expect(result.client).toBeInstanceOf(OpenAIClient);
});
test('should initialize client with Azure credentials when endpoint is azureOpenAI', async () => {
process.env.AZURE_API_KEY = 'test-azure-api-key';
(process.env.AZURE_OPENAI_API_INSTANCE_NAME = 'some-value'),
(process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME = 'some-value'),
(process.env.AZURE_OPENAI_API_VERSION = 'some-value'),
(process.env.AZURE_OPENAI_API_COMPLETIONS_DEPLOYMENT_NAME = 'some-value'),
(process.env.AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME = 'some-value'),
(process.env.OPENAI_API_KEY = 'test-openai-api-key');
process.env.DEBUG_OPENAI = 'false';
process.env.OPENAI_SUMMARIZE = 'false';
const req = {
body: { key: null, endpoint: 'azureOpenAI' },
user: { id: '123' },
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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app,
};
const res = {};
const endpointOption = { modelOptions: { model: 'test-model' } };
const client = await initializeClient({ req, res, endpointOption });
expect(client.openAIApiKey).toBe('test-azure-api-key');
expect(client.client).toBeInstanceOf(OpenAIClient);
});
test('should use the debug option when DEBUG_OPENAI is enabled', async () => {
process.env.OPENAI_API_KEY = 'test-openai-api-key';
process.env.DEBUG_OPENAI = 'true';
const req = {
body: { key: null, endpoint: EModelEndpoint.openAI },
user: { id: '123' },
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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app,
};
const res = {};
const endpointOption = {};
const client = await initializeClient({ req, res, endpointOption });
expect(client.client.options.debug).toBe(true);
});
test('should set contextStrategy to summarize when OPENAI_SUMMARIZE is enabled', async () => {
process.env.OPENAI_API_KEY = 'test-openai-api-key';
process.env.OPENAI_SUMMARIZE = 'true';
const req = {
body: { key: null, endpoint: EModelEndpoint.openAI },
user: { id: '123' },
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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app,
};
const res = {};
const endpointOption = {};
const client = await initializeClient({ req, res, endpointOption });
expect(client.client.options.contextStrategy).toBe('summarize');
});
test('should set reverseProxyUrl and proxy when they are provided in the environment', async () => {
process.env.OPENAI_API_KEY = 'test-openai-api-key';
process.env.OPENAI_REVERSE_PROXY = 'http://reverse.proxy';
process.env.PROXY = 'http://proxy';
const req = {
body: { key: null, endpoint: EModelEndpoint.openAI },
user: { id: '123' },
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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app,
};
const res = {};
const endpointOption = {};
const client = await initializeClient({ req, res, endpointOption });
expect(client.client.options.reverseProxyUrl).toBe('http://reverse.proxy');
expect(client.client.options.proxy).toBe('http://proxy');
});
test('should throw an error if the user-provided key has expired', async () => {
process.env.OPENAI_API_KEY = 'user_provided';
process.env.AZURE_API_KEY = 'user_provided';
process.env.DEBUG_OPENAI = 'false';
process.env.OPENAI_SUMMARIZE = 'false';
const expiresAt = new Date(Date.now() - 10000).toISOString(); // Expired
const req = {
body: { key: expiresAt, endpoint: EModelEndpoint.openAI },
user: { id: '123' },
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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app,
};
const res = {};
const endpointOption = {};
await expect(initializeClient({ req, res, endpointOption })).rejects.toThrow(
/expired_user_key/,
);
});
test('should throw an error if no API keys are provided in the environment', async () => {
// Clear the environment variables for API keys
delete process.env.OPENAI_API_KEY;
delete process.env.AZURE_API_KEY;
const req = {
body: { key: null, endpoint: EModelEndpoint.openAI },
user: { id: '123' },
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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app,
};
const res = {};
const endpointOption = {};
await expect(initializeClient({ req, res, endpointOption })).rejects.toThrow(
`${EModelEndpoint.openAI} API Key not provided.`,
);
});
it('should handle user-provided keys and check expiry', async () => {
// Set up the req.body to simulate user-provided key scenario
const req = {
body: {
key: new Date(Date.now() + 10000).toISOString(),
endpoint: EModelEndpoint.openAI,
},
user: {
id: '123',
},
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
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app,
};
const res = {};
const endpointOption = {};
// Ensure the environment variable is set to 'user_provided' to match the isUserProvided condition
process.env.OPENAI_API_KEY = 'user_provided';
// Mock getUserKey to return the expected key
getUserKeyValues.mockResolvedValue({ apiKey: 'test-user-provided-openai-api-key' });
// Call the initializeClient function
const result = await initializeClient({ req, res, endpointOption });
// Assertions
expect(result.openAIApiKey).toBe('test-user-provided-openai-api-key');
});
test('should throw an error if the user-provided key is invalid', async () => {
const invalidKey = new Date(Date.now() - 100000).toISOString();
const req = {
body: { key: invalidKey, endpoint: EModelEndpoint.openAI },
user: { id: '123' },
🅰️ feat: Azure Config to Allow Different Deployments per Model (#1863) * wip: first pass for azure endpoint schema * refactor: azure config to return groupMap and modelConfigMap * wip: naming and schema changes * refactor(errorsToString): move to data-provider * feat: rename to azureGroups, add additional tests, tests all expected outcomes, return errors * feat(AppService): load Azure groups * refactor(azure): use imported types, write `mapModelToAzureConfig` * refactor: move `extractEnvVariable` to data-provider * refactor(validateAzureGroups): throw on duplicate groups or models; feat(mapModelToAzureConfig): throw if env vars not present, add tests * refactor(AppService): ensure each model is properly configured on startup * refactor: deprecate azureOpenAI environment variables in favor of librechat.yaml config * feat: use helper functions to handle and order enabled/default endpoints; initialize azureOpenAI from config file * refactor: redefine types as well as load azureOpenAI models from config file * chore(ci): fix test description naming * feat(azureOpenAI): use validated model grouping for request authentication * chore: bump data-provider following rebase * chore: bump config file version noting significant changes * feat: add title options and switch azure configs for titling and vision requests * feat: enable azure plugins from config file * fix(ci): pass tests * chore(.env.example): mark `PLUGINS_USE_AZURE` as deprecated * fix(fetchModels): early return if apiKey not passed * chore: fix azure config typing * refactor(mapModelToAzureConfig): return baseURL and headers as well as azureOptions * feat(createLLM): use `azureOpenAIBasePath` * feat(parsers): resolveHeaders * refactor(extractBaseURL): handle invalid input * feat(OpenAIClient): handle headers and baseURL for azureConfig * fix(ci): pass `OpenAIClient` tests * chore: extract env var for azureOpenAI group config, baseURL * docs: azureOpenAI config setup docs * feat: safe check of potential conflicting env vars that map to unique placeholders * fix: reset apiKey when model switches from originally requested model (vision or title) * chore: linting * docs: CONFIG_PATH notes in custom_config.md
2024-02-26 14:12:25 -05:00
app,
};
const res = {};
const endpointOption = {};
// Ensure the environment variable is set to 'user_provided' to match the isUserProvided condition
process.env.OPENAI_API_KEY = 'user_provided';
// Mock getUserKey to return an invalid key
getUserKey.mockResolvedValue(invalidKey);
await expect(initializeClient({ req, res, endpointOption })).rejects.toThrow(
/expired_user_key/,
);
});
test('should throw an error when user-provided values are not valid JSON', async () => {
process.env.OPENAI_API_KEY = 'user_provided';
const req = {
body: { key: new Date(Date.now() + 10000).toISOString(), endpoint: EModelEndpoint.openAI },
user: { id: '123' },
app,
};
const res = {};
const endpointOption = {};
// Mock getUserKey to return a non-JSON string
getUserKey.mockResolvedValue('not-a-json');
getUserKeyValues.mockImplementation(() => {
let userValues = getUserKey();
try {
userValues = JSON.parse(userValues);
} catch (e) {
throw new Error(
JSON.stringify({
type: ErrorTypes.INVALID_USER_KEY,
}),
);
}
return userValues;
});
await expect(initializeClient({ req, res, endpointOption })).rejects.toThrow(
/invalid_user_key/,
);
});
test('should initialize client correctly for Azure OpenAI with valid configuration', async () => {
const req = {
body: {
key: null,
endpoint: EModelEndpoint.azureOpenAI,
model: modelNames[0],
},
user: { id: '123' },
app: {
locals: {
[EModelEndpoint.azureOpenAI]: {
modelNames,
modelGroupMap,
groupMap,
},
},
},
};
const res = {};
const endpointOption = {};
const client = await initializeClient({ req, res, endpointOption });
expect(client.client.options.azure).toBeDefined();
});
test('should initialize client with default options when certain env vars are not set', async () => {
delete process.env.DEBUG_OPENAI;
delete process.env.OPENAI_SUMMARIZE;
process.env.OPENAI_API_KEY = 'some-api-key';
const req = {
body: { key: null, endpoint: EModelEndpoint.openAI },
user: { id: '123' },
app,
};
const res = {};
const endpointOption = {};
const client = await initializeClient({ req, res, endpointOption });
expect(client.client.options.debug).toBe(false);
expect(client.client.options.contextStrategy).toBe(null);
});
test('should correctly use user-provided apiKey and baseURL when provided', async () => {
process.env.OPENAI_API_KEY = 'user_provided';
process.env.OPENAI_REVERSE_PROXY = 'user_provided';
const req = {
body: {
key: new Date(Date.now() + 10000).toISOString(),
endpoint: EModelEndpoint.openAI,
},
user: {
id: '123',
},
app,
};
const res = {};
const endpointOption = {};
getUserKeyValues.mockResolvedValue({
apiKey: 'test',
baseURL: 'https://user-provided-url.com',
});
const result = await initializeClient({ req, res, endpointOption });
expect(result.openAIApiKey).toBe('test');
expect(result.client.options.reverseProxyUrl).toBe('https://user-provided-url.com');
});
});