Feat/add.azure.and.other.providers (#607)
* fix: claude-4 not having the right max_tokens * feat: add bedrock support * chore: fix package-lock.json * fix: rename baseUrl to baseURL * feat: add azure support * fix: final touches of azure integration * feat: add google vertex provider * chore: fix tests and refactor task-manager.test.js * chore: move task 92 to 94
This commit is contained in:
Ralph Khreish
@@ -4,9 +4,9 @@
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* Implementation for interacting with Anthropic models (e.g., Claude)
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* using the Vercel AI SDK.
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*/
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import { createAnthropic } from '@ai-sdk/anthropic';
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import { generateText, streamText, generateObject } from 'ai';
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import { log } from '../../scripts/modules/utils.js'; // Assuming utils is accessible
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import { BaseAIProvider } from './base-provider.js';
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// TODO: Implement standardized functions for generateText, streamText, generateObject
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@@ -17,207 +17,38 @@ import { log } from '../../scripts/modules/utils.js'; // Assuming utils is acces
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// Remove the global variable and caching logic
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// let anthropicClient;
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function getClient(apiKey, baseUrl) {
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if (!apiKey) {
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// In a real scenario, this would use the config resolver.
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// Throwing error here if key isn't passed for simplicity.
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// Keep the error check for the passed key
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throw new Error('Anthropic API key is required.');
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export class AnthropicAIProvider extends BaseAIProvider {
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constructor() {
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super();
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this.name = 'Anthropic';
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}
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// Remove the check for anthropicClient
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// if (!anthropicClient) {
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// TODO: Explore passing options like default headers if needed
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// Create and return a new instance directly with standard version header
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return createAnthropic({
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apiKey: apiKey,
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...(baseUrl && { baseURL: baseUrl }),
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// Use standard version header instead of beta
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headers: {
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'anthropic-beta': 'output-128k-2025-02-19'
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/**
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* Creates and returns an Anthropic client instance.
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* @param {object} params - Parameters for client initialization
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* @param {string} params.apiKey - Anthropic API key
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* @param {string} [params.baseURL] - Optional custom API endpoint
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* @returns {Function} Anthropic client function
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* @throws {Error} If API key is missing or initialization fails
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*/
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getClient(params) {
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try {
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const { apiKey, baseURL } = params;
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if (!apiKey) {
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throw new Error('Anthropic API key is required.');
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}
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return createAnthropic({
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apiKey,
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...(baseURL && { baseURL }),
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headers: {
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'anthropic-beta': 'output-128k-2025-02-19'
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}
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});
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} catch (error) {
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this.handleError('client initialization', error);
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}
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});
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}
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// --- Standardized Service Function Implementations ---
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/**
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* Generates text using an Anthropic model.
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*
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* @param {object} params - Parameters for the text generation.
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* @param {string} params.apiKey - The Anthropic API key.
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* @param {string} params.modelId - The specific Anthropic model ID.
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* @param {Array<object>} params.messages - The messages array (e.g., [{ role: 'user', content: '...' }]).
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* @param {number} [params.maxTokens] - Maximum tokens for the response.
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* @param {number} [params.temperature] - Temperature for generation.
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* @param {string} [params.baseUrl] - The base URL for the Anthropic API.
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* @returns {Promise<object>} The generated text content and usage.
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* @throws {Error} If the API call fails.
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*/
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export async function generateAnthropicText({
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apiKey,
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modelId,
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messages,
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maxTokens,
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temperature,
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baseUrl
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}) {
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log('debug', `Generating Anthropic text with model: ${modelId}`);
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try {
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const client = getClient(apiKey, baseUrl);
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const result = await generateText({
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model: client(modelId),
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messages: messages,
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maxTokens: maxTokens,
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temperature: temperature
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// Beta header moved to client initialization
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// TODO: Add other relevant parameters like topP, topK if needed
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});
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log(
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'debug',
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`Anthropic generateText result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
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);
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// Return both text and usage
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return {
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text: result.text,
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usage: {
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inputTokens: result.usage.promptTokens,
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outputTokens: result.usage.completionTokens
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}
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};
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} catch (error) {
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log('error', `Anthropic generateText failed: ${error.message}`);
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// Consider more specific error handling or re-throwing a standardized error
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throw error;
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}
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}
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/**
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* Streams text using an Anthropic model.
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*
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* @param {object} params - Parameters for the text streaming.
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* @param {string} params.apiKey - The Anthropic API key.
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* @param {string} params.modelId - The specific Anthropic model ID.
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* @param {Array<object>} params.messages - The messages array.
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* @param {number} [params.maxTokens] - Maximum tokens for the response.
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* @param {number} [params.temperature] - Temperature for generation.
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* @param {string} [params.baseUrl] - The base URL for the Anthropic API.
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* @returns {Promise<object>} The full stream result object from the Vercel AI SDK.
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* @throws {Error} If the API call fails to initiate the stream.
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*/
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export async function streamAnthropicText({
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apiKey,
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modelId,
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messages,
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maxTokens,
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temperature,
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baseUrl
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}) {
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log('debug', `Streaming Anthropic text with model: ${modelId}`);
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try {
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const client = getClient(apiKey, baseUrl);
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log(
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'debug',
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'[streamAnthropicText] Parameters received by streamText:',
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JSON.stringify(
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{
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modelId: modelId,
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messages: messages,
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maxTokens: maxTokens,
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temperature: temperature
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},
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null,
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2
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)
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);
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const stream = await streamText({
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model: client(modelId),
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messages: messages,
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maxTokens: maxTokens,
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temperature: temperature
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// TODO: Add other relevant parameters
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});
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// *** RETURN THE FULL STREAM OBJECT, NOT JUST stream.textStream ***
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return stream;
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} catch (error) {
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log('error', `Anthropic streamText failed: ${error.message}`, error.stack);
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throw error;
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}
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}
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/**
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* Generates a structured object using an Anthropic model.
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* NOTE: Anthropic's tool/function calling support might have limitations
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* compared to OpenAI, especially regarding complex schemas or enforcement.
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* The Vercel AI SDK attempts to abstract this.
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*
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* @param {object} params - Parameters for object generation.
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* @param {string} params.apiKey - The Anthropic API key.
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* @param {string} params.modelId - The specific Anthropic model ID.
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* @param {Array<object>} params.messages - The messages array.
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* @param {import('zod').ZodSchema} params.schema - The Zod schema for the object.
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* @param {string} params.objectName - A name for the object/tool.
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* @param {number} [params.maxTokens] - Maximum tokens for the response.
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* @param {number} [params.temperature] - Temperature for generation.
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* @param {number} [params.maxRetries] - Max retries for validation/generation.
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* @param {string} [params.baseUrl] - The base URL for the Anthropic API.
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* @returns {Promise<object>} The generated object matching the schema and usage.
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* @throws {Error} If generation or validation fails.
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*/
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export async function generateAnthropicObject({
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apiKey,
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modelId,
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messages,
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schema,
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objectName = 'generated_object',
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maxTokens,
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temperature,
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maxRetries = 3,
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baseUrl
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}) {
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log(
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'debug',
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`Generating Anthropic object ('${objectName}') with model: ${modelId}`
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);
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try {
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const client = getClient(apiKey, baseUrl);
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log(
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'debug',
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`Using maxTokens: ${maxTokens}, temperature: ${temperature}, model: ${modelId}`
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);
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const result = await generateObject({
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model: client(modelId),
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mode: 'tool',
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schema: schema,
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messages: messages,
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tool: {
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name: objectName,
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description: `Generate a ${objectName} based on the prompt.`
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},
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maxTokens: maxTokens,
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temperature: temperature,
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maxRetries: maxRetries
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});
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log(
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'debug',
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`Anthropic generateObject result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
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);
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// Return both object and usage
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return {
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object: result.object,
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usage: {
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inputTokens: result.usage.promptTokens,
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outputTokens: result.usage.completionTokens
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}
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};
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} catch (error) {
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log(
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'error',
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`Anthropic generateObject ('${objectName}') failed: ${error.message}`
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);
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throw error;
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}
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}
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52
src/ai-providers/azure.js
Normal file
52
src/ai-providers/azure.js
Normal file
@@ -0,0 +1,52 @@
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/**
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* azure.js
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* AI provider implementation for Azure OpenAI models using Vercel AI SDK.
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*/
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import { createAzure } from '@ai-sdk/azure';
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import { BaseAIProvider } from './base-provider.js';
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export class AzureProvider extends BaseAIProvider {
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constructor() {
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super();
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this.name = 'Azure OpenAI';
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}
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/**
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* Validates Azure-specific authentication parameters
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* @param {object} params - Parameters to validate
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* @throws {Error} If required parameters are missing
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*/
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validateAuth(params) {
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if (!params.apiKey) {
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throw new Error('Azure API key is required');
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}
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if (!params.baseURL) {
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throw new Error(
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'Azure endpoint URL is required. Set it in .taskmasterconfig global.azureBaseURL or models.[role].baseURL'
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);
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}
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}
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/**
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* Creates and returns an Azure OpenAI client instance.
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* @param {object} params - Parameters for client initialization
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* @param {string} params.apiKey - Azure OpenAI API key
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* @param {string} params.baseURL - Azure OpenAI endpoint URL (from .taskmasterconfig global.azureBaseURL or models.[role].baseURL)
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* @returns {Function} Azure OpenAI client function
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* @throws {Error} If required parameters are missing or initialization fails
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*/
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getClient(params) {
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try {
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const { apiKey, baseURL } = params;
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return createAzure({
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apiKey,
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baseURL
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});
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} catch (error) {
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this.handleError('client initialization', error);
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}
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}
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}
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214
src/ai-providers/base-provider.js
Normal file
214
src/ai-providers/base-provider.js
Normal file
@@ -0,0 +1,214 @@
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import { generateText, streamText, generateObject } from 'ai';
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import { log } from '../../scripts/modules/index.js';
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/**
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* Base class for all AI providers
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*/
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export class BaseAIProvider {
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constructor() {
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if (this.constructor === BaseAIProvider) {
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throw new Error('BaseAIProvider cannot be instantiated directly');
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||||
}
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// Each provider must set their name
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this.name = this.constructor.name;
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||||
}
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||||
/**
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||||
* Validates authentication parameters - can be overridden by providers
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* @param {object} params - Parameters to validate
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||||
*/
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validateAuth(params) {
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||||
// Default: require API key (most providers need this)
|
||||
if (!params.apiKey) {
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||||
throw new Error(`${this.name} API key is required`);
|
||||
}
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||||
}
|
||||
|
||||
/**
|
||||
* Validates common parameters across all methods
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||||
* @param {object} params - Parameters to validate
|
||||
*/
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||||
validateParams(params) {
|
||||
// Validate authentication (can be overridden by providers)
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||||
this.validateAuth(params);
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||||
|
||||
// Validate required model ID
|
||||
if (!params.modelId) {
|
||||
throw new Error(`${this.name} Model ID is required`);
|
||||
}
|
||||
|
||||
// Validate optional parameters
|
||||
this.validateOptionalParams(params);
|
||||
}
|
||||
|
||||
/**
|
||||
* Validates optional parameters like temperature and maxTokens
|
||||
* @param {object} params - Parameters to validate
|
||||
*/
|
||||
validateOptionalParams(params) {
|
||||
if (
|
||||
params.temperature !== undefined &&
|
||||
(params.temperature < 0 || params.temperature > 1)
|
||||
) {
|
||||
throw new Error('Temperature must be between 0 and 1');
|
||||
}
|
||||
if (params.maxTokens !== undefined && params.maxTokens <= 0) {
|
||||
throw new Error('maxTokens must be greater than 0');
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Validates message array structure
|
||||
*/
|
||||
validateMessages(messages) {
|
||||
if (!messages || !Array.isArray(messages) || messages.length === 0) {
|
||||
throw new Error('Invalid or empty messages array provided');
|
||||
}
|
||||
|
||||
for (const msg of messages) {
|
||||
if (!msg.role || !msg.content) {
|
||||
throw new Error(
|
||||
'Invalid message format. Each message must have role and content'
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Common error handler
|
||||
*/
|
||||
handleError(operation, error) {
|
||||
const errorMessage = error.message || 'Unknown error occurred';
|
||||
log('error', `${this.name} ${operation} failed: ${errorMessage}`, {
|
||||
error
|
||||
});
|
||||
throw new Error(
|
||||
`${this.name} API error during ${operation}: ${errorMessage}`
|
||||
);
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates and returns a client instance for the provider
|
||||
* @abstract
|
||||
*/
|
||||
getClient(params) {
|
||||
throw new Error('getClient must be implemented by provider');
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates text using the provider's model
|
||||
*/
|
||||
async generateText(params) {
|
||||
try {
|
||||
this.validateParams(params);
|
||||
this.validateMessages(params.messages);
|
||||
|
||||
log(
|
||||
'debug',
|
||||
`Generating ${this.name} text with model: ${params.modelId}`
|
||||
);
|
||||
|
||||
const client = this.getClient(params);
|
||||
const result = await generateText({
|
||||
model: client(params.modelId),
|
||||
messages: params.messages,
|
||||
maxTokens: params.maxTokens,
|
||||
temperature: params.temperature
|
||||
});
|
||||
|
||||
log(
|
||||
'debug',
|
||||
`${this.name} generateText completed successfully for model: ${params.modelId}`
|
||||
);
|
||||
|
||||
return {
|
||||
text: result.text,
|
||||
usage: {
|
||||
inputTokens: result.usage?.promptTokens,
|
||||
outputTokens: result.usage?.completionTokens,
|
||||
totalTokens: result.usage?.totalTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
this.handleError('text generation', error);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Streams text using the provider's model
|
||||
*/
|
||||
async streamText(params) {
|
||||
try {
|
||||
this.validateParams(params);
|
||||
this.validateMessages(params.messages);
|
||||
|
||||
log('debug', `Streaming ${this.name} text with model: ${params.modelId}`);
|
||||
|
||||
const client = this.getClient(params);
|
||||
const stream = await streamText({
|
||||
model: client(params.modelId),
|
||||
messages: params.messages,
|
||||
maxTokens: params.maxTokens,
|
||||
temperature: params.temperature
|
||||
});
|
||||
|
||||
log(
|
||||
'debug',
|
||||
`${this.name} streamText initiated successfully for model: ${params.modelId}`
|
||||
);
|
||||
|
||||
return stream;
|
||||
} catch (error) {
|
||||
this.handleError('text streaming', error);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates a structured object using the provider's model
|
||||
*/
|
||||
async generateObject(params) {
|
||||
try {
|
||||
this.validateParams(params);
|
||||
this.validateMessages(params.messages);
|
||||
|
||||
if (!params.schema) {
|
||||
throw new Error('Schema is required for object generation');
|
||||
}
|
||||
if (!params.objectName) {
|
||||
throw new Error('Object name is required for object generation');
|
||||
}
|
||||
|
||||
log(
|
||||
'debug',
|
||||
`Generating ${this.name} object ('${params.objectName}') with model: ${params.modelId}`
|
||||
);
|
||||
|
||||
const client = this.getClient(params);
|
||||
const result = await generateObject({
|
||||
model: client(params.modelId),
|
||||
messages: params.messages,
|
||||
schema: params.schema,
|
||||
mode: 'tool',
|
||||
maxTokens: params.maxTokens,
|
||||
temperature: params.temperature
|
||||
});
|
||||
|
||||
log(
|
||||
'debug',
|
||||
`${this.name} generateObject completed successfully for model: ${params.modelId}`
|
||||
);
|
||||
|
||||
return {
|
||||
object: result.object,
|
||||
usage: {
|
||||
inputTokens: result.usage?.promptTokens,
|
||||
outputTokens: result.usage?.completionTokens,
|
||||
totalTokens: result.usage?.totalTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
this.handleError('object generation', error);
|
||||
}
|
||||
}
|
||||
}
|
||||
41
src/ai-providers/bedrock.js
Normal file
41
src/ai-providers/bedrock.js
Normal file
@@ -0,0 +1,41 @@
|
||||
import { createAmazonBedrock } from '@ai-sdk/amazon-bedrock';
|
||||
import { fromNodeProviderChain } from '@aws-sdk/credential-providers';
|
||||
import { BaseAIProvider } from './base-provider.js';
|
||||
|
||||
export class BedrockAIProvider extends BaseAIProvider {
|
||||
constructor() {
|
||||
super();
|
||||
this.name = 'Bedrock';
|
||||
}
|
||||
|
||||
/**
|
||||
* Override auth validation - Bedrock uses AWS credentials instead of API keys
|
||||
* @param {object} params - Parameters to validate
|
||||
*/
|
||||
validateAuth(params) {}
|
||||
|
||||
/**
|
||||
* Creates and returns a Bedrock client instance.
|
||||
* See https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-envvars.html
|
||||
* for AWS SDK environment variables and configuration options.
|
||||
*/
|
||||
getClient(params) {
|
||||
try {
|
||||
const {
|
||||
profile = process.env.AWS_PROFILE || 'default',
|
||||
region = process.env.AWS_DEFAULT_REGION || 'us-east-1',
|
||||
baseURL
|
||||
} = params;
|
||||
|
||||
const credentialProvider = fromNodeProviderChain({ profile });
|
||||
|
||||
return createAmazonBedrock({
|
||||
region,
|
||||
credentialProvider,
|
||||
...(baseURL && { baseURL })
|
||||
});
|
||||
} catch (error) {
|
||||
this.handleError('client initialization', error);
|
||||
}
|
||||
}
|
||||
}
|
||||
150
src/ai-providers/google-vertex.js
Normal file
150
src/ai-providers/google-vertex.js
Normal file
@@ -0,0 +1,150 @@
|
||||
/**
|
||||
* google-vertex.js
|
||||
* AI provider implementation for Google Vertex AI models using Vercel AI SDK.
|
||||
*/
|
||||
|
||||
import { createVertex } from '@ai-sdk/google-vertex';
|
||||
import { BaseAIProvider } from './base-provider.js';
|
||||
import { resolveEnvVariable } from '../../scripts/modules/utils.js';
|
||||
import { log } from '../../scripts/modules/utils.js';
|
||||
|
||||
// Vertex-specific error classes
|
||||
class VertexAuthError extends Error {
|
||||
constructor(message) {
|
||||
super(message);
|
||||
this.name = 'VertexAuthError';
|
||||
this.code = 'vertex_auth_error';
|
||||
}
|
||||
}
|
||||
|
||||
class VertexConfigError extends Error {
|
||||
constructor(message) {
|
||||
super(message);
|
||||
this.name = 'VertexConfigError';
|
||||
this.code = 'vertex_config_error';
|
||||
}
|
||||
}
|
||||
|
||||
class VertexApiError extends Error {
|
||||
constructor(message, statusCode) {
|
||||
super(message);
|
||||
this.name = 'VertexApiError';
|
||||
this.code = 'vertex_api_error';
|
||||
this.statusCode = statusCode;
|
||||
}
|
||||
}
|
||||
|
||||
export class VertexAIProvider extends BaseAIProvider {
|
||||
constructor() {
|
||||
super();
|
||||
this.name = 'Google Vertex AI';
|
||||
}
|
||||
|
||||
/**
|
||||
* Validates Vertex AI-specific authentication parameters
|
||||
* @param {object} params - Parameters to validate
|
||||
* @throws {Error} If required parameters are missing
|
||||
*/
|
||||
validateAuth(params) {
|
||||
const { apiKey, projectId, location, credentials } = params;
|
||||
|
||||
// Check for API key OR service account credentials
|
||||
if (!apiKey && !credentials) {
|
||||
throw new VertexAuthError(
|
||||
'Either Google API key (GOOGLE_API_KEY) or service account credentials (GOOGLE_APPLICATION_CREDENTIALS) is required for Vertex AI'
|
||||
);
|
||||
}
|
||||
|
||||
// Project ID is required for Vertex AI
|
||||
if (!projectId) {
|
||||
throw new VertexConfigError(
|
||||
'Google Cloud project ID is required for Vertex AI. Set VERTEX_PROJECT_ID environment variable.'
|
||||
);
|
||||
}
|
||||
|
||||
// Location is required for Vertex AI
|
||||
if (!location) {
|
||||
throw new VertexConfigError(
|
||||
'Google Cloud location is required for Vertex AI. Set VERTEX_LOCATION environment variable (e.g., "us-central1").'
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Creates and returns a Google Vertex AI client instance.
|
||||
* @param {object} params - Parameters for client initialization
|
||||
* @param {string} [params.apiKey] - Google API key
|
||||
* @param {string} params.projectId - Google Cloud project ID
|
||||
* @param {string} params.location - Google Cloud location (e.g., "us-central1")
|
||||
* @param {object} [params.credentials] - Service account credentials object
|
||||
* @param {string} [params.baseURL] - Optional custom API endpoint
|
||||
* @returns {Function} Google Vertex AI client function
|
||||
* @throws {Error} If required parameters are missing or initialization fails
|
||||
*/
|
||||
getClient(params) {
|
||||
try {
|
||||
// Validate required parameters
|
||||
this.validateAuth(params);
|
||||
|
||||
const { apiKey, projectId, location, credentials, baseURL } = params;
|
||||
|
||||
// Configure auth options - either API key or service account
|
||||
const authOptions = {};
|
||||
if (apiKey) {
|
||||
authOptions.apiKey = apiKey;
|
||||
} else if (credentials) {
|
||||
authOptions.googleAuthOptions = credentials;
|
||||
}
|
||||
|
||||
// Return Vertex AI client
|
||||
return createVertex({
|
||||
...authOptions,
|
||||
projectId,
|
||||
location,
|
||||
...(baseURL && { baseURL })
|
||||
});
|
||||
} catch (error) {
|
||||
this.handleError('client initialization', error);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Handle errors from Vertex AI
|
||||
* @param {string} operation - Description of the operation that failed
|
||||
* @param {Error} error - The error object
|
||||
* @throws {Error} Rethrows the error with additional context
|
||||
*/
|
||||
handleError(operation, error) {
|
||||
log('error', `Vertex AI ${operation} error:`, error);
|
||||
|
||||
// Handle known error types
|
||||
if (
|
||||
error.name === 'VertexAuthError' ||
|
||||
error.name === 'VertexConfigError' ||
|
||||
error.name === 'VertexApiError'
|
||||
) {
|
||||
throw error;
|
||||
}
|
||||
|
||||
// Handle network/API errors
|
||||
if (error.response) {
|
||||
const statusCode = error.response.status;
|
||||
const errorMessage = error.response.data?.error?.message || error.message;
|
||||
|
||||
// Categorize by status code
|
||||
if (statusCode === 401 || statusCode === 403) {
|
||||
throw new VertexAuthError(`Authentication failed: ${errorMessage}`);
|
||||
} else if (statusCode === 400) {
|
||||
throw new VertexConfigError(`Invalid request: ${errorMessage}`);
|
||||
} else {
|
||||
throw new VertexApiError(
|
||||
`API error (${statusCode}): ${errorMessage}`,
|
||||
statusCode
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// Generic error handling
|
||||
throw new Error(`Vertex AI ${operation} failed: ${error.message}`);
|
||||
}
|
||||
}
|
||||
@@ -1,181 +1,39 @@
|
||||
/**
|
||||
* google.js
|
||||
* AI provider implementation for Google AI models (e.g., Gemini) using Vercel AI SDK.
|
||||
* AI provider implementation for Google AI models using Vercel AI SDK.
|
||||
*/
|
||||
|
||||
// import { GoogleGenerativeAI } from '@ai-sdk/google'; // Incorrect import
|
||||
import { createGoogleGenerativeAI } from '@ai-sdk/google'; // Correct import for customization
|
||||
import { generateText, streamText, generateObject } from 'ai'; // Import from main 'ai' package
|
||||
import { log } from '../../scripts/modules/utils.js'; // Import logging utility
|
||||
import { createGoogleGenerativeAI } from '@ai-sdk/google';
|
||||
import { BaseAIProvider } from './base-provider.js';
|
||||
|
||||
// Consider making model configurable via config-manager.js later
|
||||
const DEFAULT_MODEL = 'gemini-2.5-pro-exp-03-25'; // Or a suitable default
|
||||
const DEFAULT_TEMPERATURE = 0.2; // Or a suitable default
|
||||
|
||||
function getClient(apiKey, baseUrl) {
|
||||
if (!apiKey) {
|
||||
throw new Error('Google API key is required.');
|
||||
export class GoogleAIProvider extends BaseAIProvider {
|
||||
constructor() {
|
||||
super();
|
||||
this.name = 'Google';
|
||||
}
|
||||
return createGoogleGenerativeAI({
|
||||
apiKey: apiKey,
|
||||
...(baseUrl && { baseURL: baseUrl })
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates text using a Google AI model.
|
||||
*
|
||||
* @param {object} params - Parameters for the generation.
|
||||
* @param {string} params.apiKey - Google API Key.
|
||||
* @param {string} params.modelId - Specific model ID to use (overrides default).
|
||||
* @param {number} params.temperature - Generation temperature.
|
||||
* @param {Array<object>} params.messages - The conversation history (system/user prompts).
|
||||
* @param {number} [params.maxTokens] - Optional max tokens.
|
||||
* @returns {Promise<string>} The generated text content.
|
||||
* @throws {Error} If API key is missing or API call fails.
|
||||
*/
|
||||
async function generateGoogleText({
|
||||
apiKey,
|
||||
modelId = DEFAULT_MODEL,
|
||||
temperature = DEFAULT_TEMPERATURE,
|
||||
messages,
|
||||
maxTokens,
|
||||
baseUrl
|
||||
}) {
|
||||
if (!apiKey) {
|
||||
throw new Error('Google API key is required.');
|
||||
}
|
||||
log('info', `Generating text with Google model: ${modelId}`);
|
||||
/**
|
||||
* Creates and returns a Google AI client instance.
|
||||
* @param {object} params - Parameters for client initialization
|
||||
* @param {string} params.apiKey - Google API key
|
||||
* @param {string} [params.baseURL] - Optional custom API endpoint
|
||||
* @returns {Function} Google AI client function
|
||||
* @throws {Error} If API key is missing or initialization fails
|
||||
*/
|
||||
getClient(params) {
|
||||
try {
|
||||
const { apiKey, baseURL } = params;
|
||||
|
||||
try {
|
||||
const googleProvider = getClient(apiKey, baseUrl);
|
||||
const model = googleProvider(modelId);
|
||||
const result = await generateText({
|
||||
model,
|
||||
messages,
|
||||
temperature,
|
||||
maxOutputTokens: maxTokens
|
||||
});
|
||||
|
||||
// Assuming result structure provides text directly or within a property
|
||||
// return result.text; // Adjust based on actual SDK response
|
||||
// Return both text and usage
|
||||
return {
|
||||
text: result.text,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
if (!apiKey) {
|
||||
throw new Error('Google API key is required.');
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Error generating text with Google (${modelId}): ${error.message}`
|
||||
);
|
||||
throw error;
|
||||
|
||||
return createGoogleGenerativeAI({
|
||||
apiKey,
|
||||
...(baseURL && { baseURL })
|
||||
});
|
||||
} catch (error) {
|
||||
this.handleError('client initialization', error);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Streams text using a Google AI model.
|
||||
*
|
||||
* @param {object} params - Parameters for the streaming.
|
||||
* @param {string} params.apiKey - Google API Key.
|
||||
* @param {string} params.modelId - Specific model ID to use (overrides default).
|
||||
* @param {number} params.temperature - Generation temperature.
|
||||
* @param {Array<object>} params.messages - The conversation history.
|
||||
* @param {number} [params.maxTokens] - Optional max tokens.
|
||||
* @returns {Promise<ReadableStream>} A readable stream of text deltas.
|
||||
* @throws {Error} If API key is missing or API call fails.
|
||||
*/
|
||||
async function streamGoogleText({
|
||||
apiKey,
|
||||
modelId = DEFAULT_MODEL,
|
||||
temperature = DEFAULT_TEMPERATURE,
|
||||
messages,
|
||||
maxTokens,
|
||||
baseUrl
|
||||
}) {
|
||||
if (!apiKey) {
|
||||
throw new Error('Google API key is required.');
|
||||
}
|
||||
log('info', `Streaming text with Google model: ${modelId}`);
|
||||
|
||||
try {
|
||||
const googleProvider = getClient(apiKey, baseUrl);
|
||||
const model = googleProvider(modelId);
|
||||
const stream = await streamText({
|
||||
model,
|
||||
messages,
|
||||
temperature,
|
||||
maxOutputTokens: maxTokens
|
||||
});
|
||||
return stream;
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Error streaming text with Google (${modelId}): ${error.message}`
|
||||
);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates a structured object using a Google AI model.
|
||||
*
|
||||
* @param {object} params - Parameters for the object generation.
|
||||
* @param {string} params.apiKey - Google API Key.
|
||||
* @param {string} params.modelId - Specific model ID to use (overrides default).
|
||||
* @param {number} params.temperature - Generation temperature.
|
||||
* @param {Array<object>} params.messages - The conversation history.
|
||||
* @param {import('zod').ZodSchema} params.schema - Zod schema for the expected object.
|
||||
* @param {string} params.objectName - Name for the object generation context.
|
||||
* @param {number} [params.maxTokens] - Optional max tokens.
|
||||
* @returns {Promise<object>} The generated object matching the schema.
|
||||
* @throws {Error} If API key is missing or API call fails.
|
||||
*/
|
||||
async function generateGoogleObject({
|
||||
apiKey,
|
||||
modelId = DEFAULT_MODEL,
|
||||
temperature = DEFAULT_TEMPERATURE,
|
||||
messages,
|
||||
schema,
|
||||
objectName, // Note: Vercel SDK might use this differently or not at all
|
||||
maxTokens,
|
||||
baseUrl
|
||||
}) {
|
||||
if (!apiKey) {
|
||||
throw new Error('Google API key is required.');
|
||||
}
|
||||
log('info', `Generating object with Google model: ${modelId}`);
|
||||
|
||||
try {
|
||||
const googleProvider = getClient(apiKey, baseUrl);
|
||||
const model = googleProvider(modelId);
|
||||
const result = await generateObject({
|
||||
model,
|
||||
schema,
|
||||
messages,
|
||||
temperature,
|
||||
maxOutputTokens: maxTokens
|
||||
});
|
||||
|
||||
// return object; // Return the parsed object
|
||||
// Return both object and usage
|
||||
return {
|
||||
object: result.object,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Error generating object with Google (${modelId}): ${error.message}`
|
||||
);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
export { generateGoogleText, streamGoogleText, generateGoogleObject };
|
||||
|
||||
15
src/ai-providers/index.js
Normal file
15
src/ai-providers/index.js
Normal file
@@ -0,0 +1,15 @@
|
||||
/**
|
||||
* src/ai-providers/index.js
|
||||
* Central export point for all AI provider classes
|
||||
*/
|
||||
|
||||
export { AnthropicAIProvider } from './anthropic.js';
|
||||
export { PerplexityAIProvider } from './perplexity.js';
|
||||
export { GoogleAIProvider } from './google.js';
|
||||
export { OpenAIProvider } from './openai.js';
|
||||
export { XAIProvider } from './xai.js';
|
||||
export { OpenRouterAIProvider } from './openrouter.js';
|
||||
export { OllamaAIProvider } from './ollama.js';
|
||||
export { BedrockAIProvider } from './bedrock.js';
|
||||
export { AzureProvider } from './azure.js';
|
||||
export { VertexAIProvider } from './google-vertex.js';
|
||||
@@ -4,160 +4,39 @@
|
||||
*/
|
||||
|
||||
import { createOllama } from 'ollama-ai-provider';
|
||||
import { log } from '../../scripts/modules/utils.js'; // Import logging utility
|
||||
import { generateObject, generateText, streamText } from 'ai';
|
||||
import { BaseAIProvider } from './base-provider.js';
|
||||
|
||||
// Consider making model configurable via config-manager.js later
|
||||
const DEFAULT_MODEL = 'llama3'; // Or a suitable default for Ollama
|
||||
const DEFAULT_TEMPERATURE = 0.2;
|
||||
export class OllamaAIProvider extends BaseAIProvider {
|
||||
constructor() {
|
||||
super();
|
||||
this.name = 'Ollama';
|
||||
}
|
||||
|
||||
function getClient(baseUrl) {
|
||||
// baseUrl is optional, defaults to http://localhost:11434
|
||||
return createOllama({
|
||||
baseUrl: baseUrl || undefined
|
||||
});
|
||||
}
|
||||
/**
|
||||
* Override auth validation - Ollama doesn't require API keys
|
||||
* @param {object} params - Parameters to validate
|
||||
*/
|
||||
validateAuth(_params) {
|
||||
// Ollama runs locally and doesn't require API keys
|
||||
// No authentication validation needed
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates text using an Ollama model.
|
||||
*
|
||||
* @param {object} params - Parameters for the generation.
|
||||
* @param {string} params.modelId - Specific model ID to use (overrides default).
|
||||
* @param {number} params.temperature - Generation temperature.
|
||||
* @param {Array<object>} params.messages - The conversation history (system/user prompts).
|
||||
* @param {number} [params.maxTokens] - Optional max tokens.
|
||||
* @param {string} [params.baseUrl] - Optional Ollama base URL.
|
||||
* @returns {Promise<string>} The generated text content.
|
||||
* @throws {Error} If API call fails.
|
||||
*/
|
||||
async function generateOllamaText({
|
||||
modelId = DEFAULT_MODEL,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature = DEFAULT_TEMPERATURE,
|
||||
baseUrl
|
||||
}) {
|
||||
log('info', `Generating text with Ollama model: ${modelId}`);
|
||||
/**
|
||||
* Creates and returns an Ollama client instance.
|
||||
* @param {object} params - Parameters for client initialization
|
||||
* @param {string} [params.baseURL] - Optional Ollama base URL (defaults to http://localhost:11434)
|
||||
* @returns {Function} Ollama client function
|
||||
* @throws {Error} If initialization fails
|
||||
*/
|
||||
getClient(params) {
|
||||
try {
|
||||
const { baseURL } = params;
|
||||
|
||||
try {
|
||||
const client = getClient(baseUrl);
|
||||
const result = await generateText({
|
||||
model: client(modelId),
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature
|
||||
});
|
||||
log('debug', `Ollama generated text: ${result.text}`);
|
||||
return {
|
||||
text: result.text,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Error generating text with Ollama (${modelId}): ${error.message}`
|
||||
);
|
||||
throw error;
|
||||
return createOllama({
|
||||
...(baseURL && { baseURL })
|
||||
});
|
||||
} catch (error) {
|
||||
this.handleError('client initialization', error);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Streams text using an Ollama model.
|
||||
*
|
||||
* @param {object} params - Parameters for the streaming.
|
||||
* @param {string} params.modelId - Specific model ID to use (overrides default).
|
||||
* @param {number} params.temperature - Generation temperature.
|
||||
* @param {Array<object>} params.messages - The conversation history.
|
||||
* @param {number} [params.maxTokens] - Optional max tokens.
|
||||
* @param {string} [params.baseUrl] - Optional Ollama base URL.
|
||||
* @returns {Promise<ReadableStream>} A readable stream of text deltas.
|
||||
* @throws {Error} If API call fails.
|
||||
*/
|
||||
async function streamOllamaText({
|
||||
modelId = DEFAULT_MODEL,
|
||||
temperature = DEFAULT_TEMPERATURE,
|
||||
messages,
|
||||
maxTokens,
|
||||
baseUrl
|
||||
}) {
|
||||
log('info', `Streaming text with Ollama model: ${modelId}`);
|
||||
|
||||
try {
|
||||
const ollama = getClient(baseUrl);
|
||||
const stream = await streamText({
|
||||
model: modelId,
|
||||
messages,
|
||||
temperature,
|
||||
maxTokens
|
||||
});
|
||||
return stream;
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Error streaming text with Ollama (${modelId}): ${error.message}`
|
||||
);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates a structured object using an Ollama model using the Vercel AI SDK's generateObject.
|
||||
*
|
||||
* @param {object} params - Parameters for the object generation.
|
||||
* @param {string} params.modelId - Specific model ID to use (overrides default).
|
||||
* @param {number} params.temperature - Generation temperature.
|
||||
* @param {Array<object>} params.messages - The conversation history.
|
||||
* @param {import('zod').ZodSchema} params.schema - Zod schema for the expected object.
|
||||
* @param {string} params.objectName - Name for the object generation context.
|
||||
* @param {number} [params.maxTokens] - Optional max tokens.
|
||||
* @param {number} [params.maxRetries] - Max retries for validation/generation.
|
||||
* @param {string} [params.baseUrl] - Optional Ollama base URL.
|
||||
* @returns {Promise<object>} The generated object matching the schema.
|
||||
* @throws {Error} If generation or validation fails.
|
||||
*/
|
||||
async function generateOllamaObject({
|
||||
modelId = DEFAULT_MODEL,
|
||||
temperature = DEFAULT_TEMPERATURE,
|
||||
messages,
|
||||
schema,
|
||||
objectName = 'generated_object',
|
||||
maxTokens,
|
||||
maxRetries = 3,
|
||||
baseUrl
|
||||
}) {
|
||||
log('info', `Generating object with Ollama model: ${modelId}`);
|
||||
try {
|
||||
const ollama = getClient(baseUrl);
|
||||
const result = await generateObject({
|
||||
model: ollama(modelId),
|
||||
mode: 'tool',
|
||||
schema: schema,
|
||||
messages: messages,
|
||||
tool: {
|
||||
name: objectName,
|
||||
description: `Generate a ${objectName} based on the prompt.`
|
||||
},
|
||||
maxOutputTokens: maxTokens,
|
||||
temperature: temperature,
|
||||
maxRetries: maxRetries
|
||||
});
|
||||
return {
|
||||
object: result.object,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Ollama generateObject ('${objectName}') failed: ${error.message}`
|
||||
);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
export { generateOllamaText, streamOllamaText, generateOllamaObject };
|
||||
|
||||
@@ -1,199 +1,39 @@
|
||||
import { createOpenAI } from '@ai-sdk/openai'; // Using openai provider from Vercel AI SDK
|
||||
import { generateObject, generateText } from 'ai'; // Import necessary functions from 'ai'
|
||||
import { log } from '../../scripts/modules/utils.js';
|
||||
|
||||
function getClient(apiKey, baseUrl) {
|
||||
if (!apiKey) {
|
||||
throw new Error('OpenAI API key is required.');
|
||||
}
|
||||
return createOpenAI({
|
||||
apiKey: apiKey,
|
||||
...(baseUrl && { baseURL: baseUrl })
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates text using OpenAI models via Vercel AI SDK.
|
||||
*
|
||||
* @param {object} params - Parameters including apiKey, modelId, messages, maxTokens, temperature, baseUrl.
|
||||
* @returns {Promise<object>} The generated text content and usage.
|
||||
* @throws {Error} If API call fails.
|
||||
* openai.js
|
||||
* AI provider implementation for OpenAI models using Vercel AI SDK.
|
||||
*/
|
||||
export async function generateOpenAIText(params) {
|
||||
const { apiKey, modelId, messages, maxTokens, temperature, baseUrl } = params;
|
||||
log('debug', `generateOpenAIText called with model: ${modelId}`);
|
||||
|
||||
if (!apiKey) {
|
||||
throw new Error('OpenAI API key is required.');
|
||||
}
|
||||
if (!modelId) {
|
||||
throw new Error('OpenAI Model ID is required.');
|
||||
}
|
||||
if (!messages || !Array.isArray(messages) || messages.length === 0) {
|
||||
throw new Error('Invalid or empty messages array provided for OpenAI.');
|
||||
import { createOpenAI } from '@ai-sdk/openai';
|
||||
import { BaseAIProvider } from './base-provider.js';
|
||||
|
||||
export class OpenAIProvider extends BaseAIProvider {
|
||||
constructor() {
|
||||
super();
|
||||
this.name = 'OpenAI';
|
||||
}
|
||||
|
||||
const openaiClient = getClient(apiKey, baseUrl);
|
||||
/**
|
||||
* Creates and returns an OpenAI client instance.
|
||||
* @param {object} params - Parameters for client initialization
|
||||
* @param {string} params.apiKey - OpenAI API key
|
||||
* @param {string} [params.baseURL] - Optional custom API endpoint
|
||||
* @returns {Function} OpenAI client function
|
||||
* @throws {Error} If API key is missing or initialization fails
|
||||
*/
|
||||
getClient(params) {
|
||||
try {
|
||||
const { apiKey, baseURL } = params;
|
||||
|
||||
try {
|
||||
const result = await generateText({
|
||||
model: openaiClient(modelId),
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature
|
||||
});
|
||||
|
||||
if (!result || !result.text) {
|
||||
log(
|
||||
'warn',
|
||||
'OpenAI generateText response did not contain expected content.',
|
||||
{ result }
|
||||
);
|
||||
throw new Error('Failed to extract content from OpenAI response.');
|
||||
}
|
||||
log(
|
||||
'debug',
|
||||
`OpenAI generateText completed successfully for model: ${modelId}`
|
||||
);
|
||||
return {
|
||||
text: result.text.trim(),
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
if (!apiKey) {
|
||||
throw new Error('OpenAI API key is required.');
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Error in generateOpenAIText (Model: ${modelId}): ${error.message}`,
|
||||
{ error }
|
||||
);
|
||||
throw new Error(
|
||||
`OpenAI API error during text generation: ${error.message}`
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Streams text using OpenAI models via Vercel AI SDK.
|
||||
*
|
||||
* @param {object} params - Parameters including apiKey, modelId, messages, maxTokens, temperature, baseUrl.
|
||||
* @returns {Promise<ReadableStream>} A readable stream of text deltas.
|
||||
* @throws {Error} If API call fails.
|
||||
*/
|
||||
export async function streamOpenAIText(params) {
|
||||
const { apiKey, modelId, messages, maxTokens, temperature, baseUrl } = params;
|
||||
log('debug', `streamOpenAIText called with model: ${modelId}`);
|
||||
|
||||
if (!apiKey) {
|
||||
throw new Error('OpenAI API key is required.');
|
||||
}
|
||||
if (!modelId) {
|
||||
throw new Error('OpenAI Model ID is required.');
|
||||
}
|
||||
if (!messages || !Array.isArray(messages) || messages.length === 0) {
|
||||
throw new Error(
|
||||
'Invalid or empty messages array provided for OpenAI streaming.'
|
||||
);
|
||||
}
|
||||
|
||||
const openaiClient = getClient(apiKey, baseUrl);
|
||||
|
||||
try {
|
||||
const stream = await openaiClient.chat.stream(messages, {
|
||||
model: modelId,
|
||||
max_tokens: maxTokens,
|
||||
temperature
|
||||
});
|
||||
|
||||
log(
|
||||
'debug',
|
||||
`OpenAI streamText initiated successfully for model: ${modelId}`
|
||||
);
|
||||
return stream;
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Error initiating OpenAI stream (Model: ${modelId}): ${error.message}`,
|
||||
{ error }
|
||||
);
|
||||
throw new Error(
|
||||
`OpenAI API error during streaming initiation: ${error.message}`
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates structured objects using OpenAI models via Vercel AI SDK.
|
||||
*
|
||||
* @param {object} params - Parameters including apiKey, modelId, messages, schema, objectName, maxTokens, temperature, baseUrl.
|
||||
* @returns {Promise<object>} The generated object matching the schema and usage.
|
||||
* @throws {Error} If API call fails or object generation fails.
|
||||
*/
|
||||
export async function generateOpenAIObject(params) {
|
||||
const {
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
schema,
|
||||
objectName,
|
||||
maxTokens,
|
||||
temperature,
|
||||
baseUrl
|
||||
} = params;
|
||||
log(
|
||||
'debug',
|
||||
`generateOpenAIObject called with model: ${modelId}, object: ${objectName}`
|
||||
);
|
||||
|
||||
if (!apiKey) throw new Error('OpenAI API key is required.');
|
||||
if (!modelId) throw new Error('OpenAI Model ID is required.');
|
||||
if (!messages || !Array.isArray(messages) || messages.length === 0)
|
||||
throw new Error('Invalid messages array for OpenAI object generation.');
|
||||
if (!schema)
|
||||
throw new Error('Schema is required for OpenAI object generation.');
|
||||
if (!objectName)
|
||||
throw new Error('Object name is required for OpenAI object generation.');
|
||||
|
||||
const openaiClient = getClient(apiKey, baseUrl);
|
||||
|
||||
try {
|
||||
const result = await generateObject({
|
||||
model: openaiClient(modelId),
|
||||
schema: schema,
|
||||
messages: messages,
|
||||
mode: 'tool',
|
||||
maxTokens: maxTokens,
|
||||
temperature: temperature
|
||||
});
|
||||
|
||||
log(
|
||||
'debug',
|
||||
`OpenAI generateObject completed successfully for model: ${modelId}`
|
||||
);
|
||||
if (!result || typeof result.object === 'undefined') {
|
||||
log(
|
||||
'warn',
|
||||
'OpenAI generateObject response did not contain expected object.',
|
||||
{ result }
|
||||
);
|
||||
throw new Error('Failed to extract object from OpenAI response.');
|
||||
return createOpenAI({
|
||||
apiKey,
|
||||
...(baseURL && { baseURL })
|
||||
});
|
||||
} catch (error) {
|
||||
this.handleError('client initialization', error);
|
||||
}
|
||||
return {
|
||||
object: result.object,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Error in generateOpenAIObject (Model: ${modelId}, Object: ${objectName}): ${error.message}`,
|
||||
{ error }
|
||||
);
|
||||
throw new Error(
|
||||
`OpenAI API error during object generation: ${error.message}`
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,246 +1,39 @@
|
||||
/**
|
||||
* openrouter.js
|
||||
* AI provider implementation for OpenRouter models using Vercel AI SDK.
|
||||
*/
|
||||
|
||||
import { createOpenRouter } from '@openrouter/ai-sdk-provider';
|
||||
import { generateText, streamText, generateObject } from 'ai';
|
||||
import { log } from '../../scripts/modules/utils.js'; // Assuming utils.js is in scripts/modules
|
||||
import { BaseAIProvider } from './base-provider.js';
|
||||
|
||||
function getClient(apiKey, baseUrl) {
|
||||
if (!apiKey) throw new Error('OpenRouter API key is required.');
|
||||
return createOpenRouter({
|
||||
apiKey,
|
||||
...(baseUrl && { baseURL: baseUrl })
|
||||
});
|
||||
}
|
||||
export class OpenRouterAIProvider extends BaseAIProvider {
|
||||
constructor() {
|
||||
super();
|
||||
this.name = 'OpenRouter';
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates text using an OpenRouter chat model.
|
||||
*
|
||||
* @param {object} params - Parameters for the text generation.
|
||||
* @param {string} params.apiKey - OpenRouter API key.
|
||||
* @param {string} params.modelId - The OpenRouter model ID (e.g., 'anthropic/claude-3.5-sonnet').
|
||||
* @param {Array<object>} params.messages - Array of message objects (system, user, assistant).
|
||||
* @param {number} [params.maxTokens] - Maximum tokens to generate.
|
||||
* @param {number} [params.temperature] - Sampling temperature.
|
||||
* @param {string} [params.baseUrl] - Base URL for the OpenRouter API.
|
||||
* @returns {Promise<string>} The generated text content.
|
||||
* @throws {Error} If the API call fails.
|
||||
*/
|
||||
async function generateOpenRouterText({
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
baseUrl,
|
||||
...rest // Capture any other Vercel AI SDK compatible parameters
|
||||
}) {
|
||||
if (!apiKey) throw new Error('OpenRouter API key is required.');
|
||||
if (!modelId) throw new Error('OpenRouter model ID is required.');
|
||||
if (!messages || messages.length === 0)
|
||||
throw new Error('Messages array cannot be empty.');
|
||||
/**
|
||||
* Creates and returns an OpenRouter client instance.
|
||||
* @param {object} params - Parameters for client initialization
|
||||
* @param {string} params.apiKey - OpenRouter API key
|
||||
* @param {string} [params.baseURL] - Optional custom API endpoint
|
||||
* @returns {Function} OpenRouter client function
|
||||
* @throws {Error} If API key is missing or initialization fails
|
||||
*/
|
||||
getClient(params) {
|
||||
try {
|
||||
const { apiKey, baseURL } = params;
|
||||
|
||||
try {
|
||||
const openrouter = getClient(apiKey, baseUrl);
|
||||
const model = openrouter.chat(modelId); // Assuming chat model
|
||||
|
||||
// Capture the full result from generateText
|
||||
const result = await generateText({
|
||||
model,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
...rest // Pass any additional parameters
|
||||
});
|
||||
|
||||
// Check if text and usage are present
|
||||
if (!result || typeof result.text !== 'string') {
|
||||
log(
|
||||
'warn',
|
||||
`OpenRouter generateText for model ${modelId} did not return expected text.`,
|
||||
{ result }
|
||||
);
|
||||
throw new Error('Failed to extract text from OpenRouter response.');
|
||||
}
|
||||
if (!result.usage) {
|
||||
log(
|
||||
'warn',
|
||||
`OpenRouter generateText for model ${modelId} did not return usage data.`,
|
||||
{ result }
|
||||
);
|
||||
// Decide if this is critical. For now, let it pass but telemetry will be incomplete.
|
||||
}
|
||||
|
||||
log('debug', `OpenRouter generateText completed for model ${modelId}`);
|
||||
// Return text and usage
|
||||
return {
|
||||
text: result.text,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
if (!apiKey) {
|
||||
throw new Error('OpenRouter API key is required.');
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
let detailedMessage = `OpenRouter generateText failed for model ${modelId}: ${error.message}`;
|
||||
if (error.cause) {
|
||||
detailedMessage += `\n\nCause:\n\n ${typeof error.cause === 'string' ? error.cause : JSON.stringify(error.cause)}`;
|
||||
|
||||
return createOpenRouter({
|
||||
apiKey,
|
||||
...(baseURL && { baseURL })
|
||||
});
|
||||
} catch (error) {
|
||||
this.handleError('client initialization', error);
|
||||
}
|
||||
// Vercel AI SDK sometimes wraps the actual API error response in error.data
|
||||
if (error.data) {
|
||||
detailedMessage += `\n\nData:\n\n ${JSON.stringify(error.data)}`;
|
||||
}
|
||||
// Log the original error object for full context if needed for deeper debugging
|
||||
log('error', detailedMessage, { originalErrorObject: error });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Streams text using an OpenRouter chat model.
|
||||
*
|
||||
* @param {object} params - Parameters for the text streaming.
|
||||
* @param {string} params.apiKey - OpenRouter API key.
|
||||
* @param {string} params.modelId - The OpenRouter model ID (e.g., 'anthropic/claude-3.5-sonnet').
|
||||
* @param {Array<object>} params.messages - Array of message objects (system, user, assistant).
|
||||
* @param {number} [params.maxTokens] - Maximum tokens to generate.
|
||||
* @param {number} [params.temperature] - Sampling temperature.
|
||||
* @param {string} [params.baseUrl] - Base URL for the OpenRouter API.
|
||||
* @returns {Promise<ReadableStream<string>>} A readable stream of text deltas.
|
||||
* @throws {Error} If the API call fails.
|
||||
*/
|
||||
async function streamOpenRouterText({
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
baseUrl,
|
||||
...rest
|
||||
}) {
|
||||
if (!apiKey) throw new Error('OpenRouter API key is required.');
|
||||
if (!modelId) throw new Error('OpenRouter model ID is required.');
|
||||
if (!messages || messages.length === 0)
|
||||
throw new Error('Messages array cannot be empty.');
|
||||
|
||||
try {
|
||||
const openrouter = getClient(apiKey, baseUrl);
|
||||
const model = openrouter.chat(modelId);
|
||||
|
||||
// Directly return the stream from the Vercel AI SDK function
|
||||
const stream = await streamText({
|
||||
model,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
...rest
|
||||
});
|
||||
return stream;
|
||||
} catch (error) {
|
||||
let detailedMessage = `OpenRouter streamText failed for model ${modelId}: ${error.message}`;
|
||||
if (error.cause) {
|
||||
detailedMessage += `\n\nCause:\n\n ${typeof error.cause === 'string' ? error.cause : JSON.stringify(error.cause)}`;
|
||||
}
|
||||
if (error.data) {
|
||||
detailedMessage += `\n\nData:\n\n ${JSON.stringify(error.data)}`;
|
||||
}
|
||||
log('error', detailedMessage, { originalErrorObject: error });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates a structured object using an OpenRouter chat model.
|
||||
*
|
||||
* @param {object} params - Parameters for object generation.
|
||||
* @param {string} params.apiKey - OpenRouter API key.
|
||||
* @param {string} params.modelId - The OpenRouter model ID.
|
||||
* @param {import('zod').ZodSchema} params.schema - The Zod schema for the expected object.
|
||||
* @param {Array<object>} params.messages - Array of message objects.
|
||||
* @param {string} [params.objectName='generated_object'] - Name for object/tool.
|
||||
* @param {number} [params.maxRetries=3] - Max retries for object generation.
|
||||
* @param {number} [params.maxTokens] - Maximum tokens.
|
||||
* @param {number} [params.temperature] - Temperature.
|
||||
* @param {string} [params.baseUrl] - Base URL for the OpenRouter API.
|
||||
* @returns {Promise<object>} The generated object matching the schema.
|
||||
* @throws {Error} If the API call fails or validation fails.
|
||||
*/
|
||||
async function generateOpenRouterObject({
|
||||
apiKey,
|
||||
modelId,
|
||||
schema,
|
||||
messages,
|
||||
objectName = 'generated_object',
|
||||
maxRetries = 3,
|
||||
maxTokens,
|
||||
temperature,
|
||||
baseUrl,
|
||||
...rest
|
||||
}) {
|
||||
if (!apiKey) throw new Error('OpenRouter API key is required.');
|
||||
if (!modelId) throw new Error('OpenRouter model ID is required.');
|
||||
if (!schema) throw new Error('Zod schema is required for object generation.');
|
||||
if (!messages || messages.length === 0)
|
||||
throw new Error('Messages array cannot be empty.');
|
||||
|
||||
try {
|
||||
const openrouter = getClient(apiKey, baseUrl);
|
||||
const model = openrouter.chat(modelId);
|
||||
|
||||
// Capture the full result from generateObject
|
||||
const result = await generateObject({
|
||||
model,
|
||||
schema,
|
||||
mode: 'tool',
|
||||
tool: {
|
||||
name: objectName,
|
||||
description: `Generate an object conforming to the ${objectName} schema.`,
|
||||
parameters: schema
|
||||
},
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
maxRetries,
|
||||
...rest
|
||||
});
|
||||
|
||||
// Check if object and usage are present
|
||||
if (!result || typeof result.object === 'undefined') {
|
||||
log(
|
||||
'warn',
|
||||
`OpenRouter generateObject for model ${modelId} did not return expected object.`,
|
||||
{ result }
|
||||
);
|
||||
throw new Error('Failed to extract object from OpenRouter response.');
|
||||
}
|
||||
if (!result.usage) {
|
||||
log(
|
||||
'warn',
|
||||
`OpenRouter generateObject for model ${modelId} did not return usage data.`,
|
||||
{ result }
|
||||
);
|
||||
}
|
||||
|
||||
log('debug', `OpenRouter generateObject completed for model ${modelId}`);
|
||||
// Return object and usage
|
||||
return {
|
||||
object: result.object,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
let detailedMessage = `OpenRouter generateObject failed for model ${modelId}: ${error.message}`;
|
||||
if (error.cause) {
|
||||
detailedMessage += `\n\nCause:\n\n ${typeof error.cause === 'string' ? error.cause : JSON.stringify(error.cause)}`;
|
||||
}
|
||||
if (error.data) {
|
||||
detailedMessage += `\n\nData:\n\n ${JSON.stringify(error.data)}`;
|
||||
}
|
||||
log('error', detailedMessage, { originalErrorObject: error });
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
export {
|
||||
generateOpenRouterText,
|
||||
streamOpenRouterText,
|
||||
generateOpenRouterObject
|
||||
};
|
||||
|
||||
@@ -1,181 +1,39 @@
|
||||
/**
|
||||
* src/ai-providers/perplexity.js
|
||||
*
|
||||
* Implementation for interacting with Perplexity models
|
||||
* using the Vercel AI SDK.
|
||||
* perplexity.js
|
||||
* AI provider implementation for Perplexity models using Vercel AI SDK.
|
||||
*/
|
||||
|
||||
import { createPerplexity } from '@ai-sdk/perplexity';
|
||||
import { generateText, streamText, generateObject, streamObject } from 'ai';
|
||||
import { log } from '../../scripts/modules/utils.js';
|
||||
import { BaseAIProvider } from './base-provider.js';
|
||||
|
||||
// --- Client Instantiation ---
|
||||
// Similar to Anthropic, this expects the resolved API key to be passed in.
|
||||
function getClient(apiKey, baseUrl) {
|
||||
if (!apiKey) {
|
||||
throw new Error('Perplexity API key is required.');
|
||||
export class PerplexityAIProvider extends BaseAIProvider {
|
||||
constructor() {
|
||||
super();
|
||||
this.name = 'Perplexity';
|
||||
}
|
||||
return createPerplexity({
|
||||
apiKey: apiKey,
|
||||
...(baseUrl && { baseURL: baseUrl })
|
||||
});
|
||||
}
|
||||
|
||||
// --- Standardized Service Function Implementations ---
|
||||
/**
|
||||
* Creates and returns a Perplexity client instance.
|
||||
* @param {object} params - Parameters for client initialization
|
||||
* @param {string} params.apiKey - Perplexity API key
|
||||
* @param {string} [params.baseURL] - Optional custom API endpoint
|
||||
* @returns {Function} Perplexity client function
|
||||
* @throws {Error} If API key is missing or initialization fails
|
||||
*/
|
||||
getClient(params) {
|
||||
try {
|
||||
const { apiKey, baseURL } = params;
|
||||
|
||||
/**
|
||||
* Generates text using a Perplexity model.
|
||||
*
|
||||
* @param {object} params - Parameters for the text generation.
|
||||
* @param {string} params.apiKey - The Perplexity API key.
|
||||
* @param {string} params.modelId - The specific Perplexity model ID.
|
||||
* @param {Array<object>} params.messages - The messages array.
|
||||
* @param {number} [params.maxTokens] - Maximum tokens for the response.
|
||||
* @param {number} [params.temperature] - Temperature for generation.
|
||||
* @param {string} [params.baseUrl] - Base URL for the Perplexity API.
|
||||
* @returns {Promise<string>} The generated text content.
|
||||
* @throws {Error} If the API call fails.
|
||||
*/
|
||||
export async function generatePerplexityText({
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
baseUrl
|
||||
}) {
|
||||
log('debug', `Generating Perplexity text with model: ${modelId}`);
|
||||
try {
|
||||
const client = getClient(apiKey, baseUrl);
|
||||
const result = await generateText({
|
||||
model: client(modelId),
|
||||
messages: messages,
|
||||
maxTokens: maxTokens,
|
||||
temperature: temperature
|
||||
});
|
||||
log(
|
||||
'debug',
|
||||
`Perplexity generateText result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
|
||||
);
|
||||
|
||||
return {
|
||||
text: result.text,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
if (!apiKey) {
|
||||
throw new Error('Perplexity API key is required.');
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log('error', `Perplexity generateText failed: ${error.message}`);
|
||||
throw error;
|
||||
|
||||
return createPerplexity({
|
||||
apiKey,
|
||||
baseURL: baseURL || 'https://api.perplexity.ai'
|
||||
});
|
||||
} catch (error) {
|
||||
this.handleError('client initialization', error);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Streams text using a Perplexity model.
|
||||
*
|
||||
* @param {object} params - Parameters for the text streaming.
|
||||
* @param {string} params.apiKey - The Perplexity API key.
|
||||
* @param {string} params.modelId - The specific Perplexity model ID.
|
||||
* @param {Array<object>} params.messages - The messages array.
|
||||
* @param {number} [params.maxTokens] - Maximum tokens for the response.
|
||||
* @param {number} [params.temperature] - Temperature for generation.
|
||||
* @param {string} [params.baseUrl] - Base URL for the Perplexity API.
|
||||
* @returns {Promise<object>} The full stream result object from the Vercel AI SDK.
|
||||
* @throws {Error} If the API call fails to initiate the stream.
|
||||
*/
|
||||
export async function streamPerplexityText({
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
baseUrl
|
||||
}) {
|
||||
log('debug', `Streaming Perplexity text with model: ${modelId}`);
|
||||
try {
|
||||
const client = getClient(apiKey, baseUrl);
|
||||
const stream = await streamText({
|
||||
model: client(modelId),
|
||||
messages: messages,
|
||||
maxTokens: maxTokens,
|
||||
temperature: temperature
|
||||
});
|
||||
return stream;
|
||||
} catch (error) {
|
||||
log('error', `Perplexity streamText failed: ${error.message}`);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates a structured object using a Perplexity model.
|
||||
* Note: Perplexity API might not directly support structured object generation
|
||||
* in the same way as OpenAI or Anthropic. This function might need
|
||||
* adjustments or might not be feasible depending on the model's capabilities
|
||||
* and the Vercel AI SDK's support for Perplexity in this context.
|
||||
*
|
||||
* @param {object} params - Parameters for object generation.
|
||||
* @param {string} params.apiKey - The Perplexity API key.
|
||||
* @param {string} params.modelId - The specific Perplexity model ID.
|
||||
* @param {Array<object>} params.messages - The messages array.
|
||||
* @param {import('zod').ZodSchema} params.schema - The Zod schema for the object.
|
||||
* @param {string} params.objectName - A name for the object/tool.
|
||||
* @param {number} [params.maxTokens] - Maximum tokens for the response.
|
||||
* @param {number} [params.temperature] - Temperature for generation.
|
||||
* @param {number} [params.maxRetries] - Max retries for validation/generation.
|
||||
* @param {string} [params.baseUrl] - Base URL for the Perplexity API.
|
||||
* @returns {Promise<object>} The generated object matching the schema.
|
||||
* @throws {Error} If generation or validation fails or is unsupported.
|
||||
*/
|
||||
export async function generatePerplexityObject({
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
schema,
|
||||
objectName = 'generated_object',
|
||||
maxTokens,
|
||||
temperature,
|
||||
maxRetries = 1,
|
||||
baseUrl
|
||||
}) {
|
||||
log(
|
||||
'debug',
|
||||
`Attempting to generate Perplexity object ('${objectName}') with model: ${modelId}`
|
||||
);
|
||||
log(
|
||||
'warn',
|
||||
'generateObject support for Perplexity might be limited or experimental.'
|
||||
);
|
||||
try {
|
||||
const client = getClient(apiKey, baseUrl);
|
||||
const result = await generateObject({
|
||||
model: client(modelId),
|
||||
schema: schema,
|
||||
messages: messages,
|
||||
maxTokens: maxTokens,
|
||||
temperature: temperature,
|
||||
maxRetries: maxRetries
|
||||
});
|
||||
log(
|
||||
'debug',
|
||||
`Perplexity generateObject result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
|
||||
);
|
||||
return {
|
||||
object: result.object,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`Perplexity generateObject ('${objectName}') failed: ${error.message}`
|
||||
);
|
||||
throw new Error(
|
||||
`Failed to generate object with Perplexity: ${error.message}. Structured output might not be fully supported.`
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Implement streamPerplexityObject if needed and feasible.
|
||||
|
||||
@@ -1,178 +1,39 @@
|
||||
/**
|
||||
* src/ai-providers/xai.js
|
||||
*
|
||||
* Implementation for interacting with xAI models (e.g., Grok)
|
||||
* using the Vercel AI SDK.
|
||||
* xai.js
|
||||
* AI provider implementation for xAI models using Vercel AI SDK.
|
||||
*/
|
||||
|
||||
import { createXai } from '@ai-sdk/xai';
|
||||
import { generateText, streamText, generateObject } from 'ai'; // Only import what's used
|
||||
import { log } from '../../scripts/modules/utils.js'; // Assuming utils is accessible
|
||||
import { BaseAIProvider } from './base-provider.js';
|
||||
|
||||
// --- Client Instantiation ---
|
||||
function getClient(apiKey, baseUrl) {
|
||||
if (!apiKey) {
|
||||
throw new Error('xAI API key is required.');
|
||||
export class XAIProvider extends BaseAIProvider {
|
||||
constructor() {
|
||||
super();
|
||||
this.name = 'xAI';
|
||||
}
|
||||
return createXai({
|
||||
apiKey: apiKey,
|
||||
...(baseUrl && { baseURL: baseUrl })
|
||||
});
|
||||
}
|
||||
|
||||
// --- Standardized Service Function Implementations ---
|
||||
/**
|
||||
* Creates and returns an xAI client instance.
|
||||
* @param {object} params - Parameters for client initialization
|
||||
* @param {string} params.apiKey - xAI API key
|
||||
* @param {string} [params.baseURL] - Optional custom API endpoint
|
||||
* @returns {Function} xAI client function
|
||||
* @throws {Error} If API key is missing or initialization fails
|
||||
*/
|
||||
getClient(params) {
|
||||
try {
|
||||
const { apiKey, baseURL } = params;
|
||||
|
||||
/**
|
||||
* Generates text using an xAI model.
|
||||
*
|
||||
* @param {object} params - Parameters for the text generation.
|
||||
* @param {string} params.apiKey - The xAI API key.
|
||||
* @param {string} params.modelId - The specific xAI model ID (e.g., 'grok-3').
|
||||
* @param {Array<object>} params.messages - The messages array (e.g., [{ role: 'user', content: '...' }]).
|
||||
* @param {number} [params.maxTokens] - Maximum tokens for the response.
|
||||
* @param {number} [params.temperature] - Temperature for generation.
|
||||
* @param {string} [params.baseUrl] - The base URL for the xAI API.
|
||||
* @returns {Promise<object>} The generated text content and usage.
|
||||
* @throws {Error} If the API call fails.
|
||||
*/
|
||||
export async function generateXaiText({
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
baseUrl
|
||||
}) {
|
||||
log('debug', `Generating xAI text with model: ${modelId}`);
|
||||
try {
|
||||
const client = getClient(apiKey, baseUrl);
|
||||
const result = await generateText({
|
||||
model: client(modelId),
|
||||
messages: messages,
|
||||
maxTokens: maxTokens,
|
||||
temperature: temperature
|
||||
});
|
||||
log(
|
||||
'debug',
|
||||
`xAI generateText result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
|
||||
);
|
||||
// Return text and usage
|
||||
return {
|
||||
text: result.text,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
if (!apiKey) {
|
||||
throw new Error('xAI API key is required.');
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log('error', `xAI generateText failed: ${error.message}`);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Streams text using an xAI model.
|
||||
*
|
||||
* @param {object} params - Parameters for the text streaming.
|
||||
* @param {string} params.apiKey - The xAI API key.
|
||||
* @param {string} params.modelId - The specific xAI model ID.
|
||||
* @param {Array<object>} params.messages - The messages array.
|
||||
* @param {number} [params.maxTokens] - Maximum tokens for the response.
|
||||
* @param {number} [params.temperature] - Temperature for generation.
|
||||
* @param {string} [params.baseUrl] - The base URL for the xAI API.
|
||||
* @returns {Promise<object>} The full stream result object from the Vercel AI SDK.
|
||||
* @throws {Error} If the API call fails to initiate the stream.
|
||||
*/
|
||||
export async function streamXaiText({
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
maxTokens,
|
||||
temperature,
|
||||
baseUrl
|
||||
}) {
|
||||
log('debug', `Streaming xAI text with model: ${modelId}`);
|
||||
try {
|
||||
const client = getClient(apiKey, baseUrl);
|
||||
const stream = await streamText({
|
||||
model: client(modelId),
|
||||
messages: messages,
|
||||
maxTokens: maxTokens,
|
||||
temperature: temperature
|
||||
});
|
||||
return stream;
|
||||
} catch (error) {
|
||||
log('error', `xAI streamText failed: ${error.message}`, error.stack);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates a structured object using an xAI model.
|
||||
* Note: Based on search results, xAI models do not currently support Object Generation.
|
||||
* This function is included for structural consistency but will likely fail if called.
|
||||
*
|
||||
* @param {object} params - Parameters for object generation.
|
||||
* @param {string} params.apiKey - The xAI API key.
|
||||
* @param {string} params.modelId - The specific xAI model ID.
|
||||
* @param {Array<object>} params.messages - The messages array.
|
||||
* @param {import('zod').ZodSchema} params.schema - The Zod schema for the object.
|
||||
* @param {string} params.objectName - A name for the object/tool.
|
||||
* @param {number} [params.maxTokens] - Maximum tokens for the response.
|
||||
* @param {number} [params.temperature] - Temperature for generation.
|
||||
* @param {number} [params.maxRetries] - Max retries for validation/generation.
|
||||
* @param {string} [params.baseUrl] - The base URL for the xAI API.
|
||||
* @returns {Promise<object>} The generated object matching the schema and its usage.
|
||||
* @throws {Error} If generation or validation fails.
|
||||
*/
|
||||
export async function generateXaiObject({
|
||||
apiKey,
|
||||
modelId,
|
||||
messages,
|
||||
schema,
|
||||
objectName = 'generated_xai_object',
|
||||
maxTokens,
|
||||
temperature,
|
||||
maxRetries = 3,
|
||||
baseUrl
|
||||
}) {
|
||||
log(
|
||||
'warn',
|
||||
`Attempting to generate xAI object ('${objectName}') with model: ${modelId}. This may not be supported by the provider.`
|
||||
);
|
||||
try {
|
||||
const client = getClient(apiKey, baseUrl);
|
||||
const result = await generateObject({
|
||||
model: client(modelId),
|
||||
// Note: mode might need adjustment if xAI ever supports object generation differently
|
||||
mode: 'tool',
|
||||
schema: schema,
|
||||
messages: messages,
|
||||
tool: {
|
||||
name: objectName,
|
||||
description: `Generate a ${objectName} based on the prompt.`,
|
||||
parameters: schema
|
||||
},
|
||||
maxTokens: maxTokens,
|
||||
temperature: temperature,
|
||||
maxRetries: maxRetries
|
||||
});
|
||||
log(
|
||||
'debug',
|
||||
`xAI generateObject result received. Tokens: ${result.usage.completionTokens}/${result.usage.promptTokens}`
|
||||
);
|
||||
// Return object and usage
|
||||
return {
|
||||
object: result.object,
|
||||
usage: {
|
||||
inputTokens: result.usage.promptTokens,
|
||||
outputTokens: result.usage.completionTokens
|
||||
}
|
||||
};
|
||||
} catch (error) {
|
||||
log(
|
||||
'error',
|
||||
`xAI generateObject ('${objectName}') failed: ${error.message}. (Likely unsupported by provider)`
|
||||
);
|
||||
throw error;
|
||||
return createXai({
|
||||
apiKey,
|
||||
baseURL: baseURL || 'https://api.x.ai/v1'
|
||||
});
|
||||
} catch (error) {
|
||||
this.handleError('client initialization', error);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user