Files
Momento/memento-note/lib/ai/providers/openrouter.ts
Antigravity e09ea3a145
All checks were successful
Deploy to Production / Build and Deploy (push) Successful in 5s
fix: switch embedding dimension from 1536 to 2560 for qwen-embedding-4b
2026-05-12 09:07:55 +00:00

173 lines
5.4 KiB
TypeScript

import { createOpenAI } from '@ai-sdk/openai';
import { generateObject, generateText as aiGenerateText, stepCountIs } from 'ai';
import { z } from 'zod';
import { AIProvider, TagSuggestion, TitleSuggestion, ToolUseOptions, ToolCallResult } from '../types';
export class OpenRouterProvider implements AIProvider {
private model: any;
private apiKey: string;
private baseUrl: string;
private embeddingModelName: string;
constructor(apiKey: string, modelName: string = 'anthropic/claude-3-haiku', embeddingModelName: string = 'openai/text-embedding-3-small') {
this.apiKey = apiKey;
this.baseUrl = 'https://openrouter.ai/api/v1';
this.embeddingModelName = embeddingModelName;
// Create OpenAI-compatible client for OpenRouter
const openrouter = createOpenAI({
baseURL: this.baseUrl,
apiKey: apiKey,
});
this.model = openrouter.chat(modelName);
}
private async fetchWithTimeout(url: string, options: RequestInit, timeoutMs: number = 60_000): Promise<Response> {
const controller = new AbortController()
const timer = setTimeout(() => controller.abort(), timeoutMs)
try {
return await fetch(url, { ...options, signal: controller.signal })
} finally {
clearTimeout(timer)
}
}
async generateTags(content: string): Promise<TagSuggestion[]> {
try {
const { object } = await generateObject({
model: this.model,
schema: z.object({
tags: z.array(z.object({
tag: z.string().describe('Short tag name in lowercase'),
confidence: z.number().min(0).max(1).describe('Confidence level between 0 and 1')
}))
}),
prompt: `Analyze the following note and suggest 1 to 5 relevant tags.
Note content: "${content}"`,
});
return object.tags;
} catch (e) {
console.error('Error generating tags (OpenRouter):', e);
return [];
}
}
async getEmbeddings(text: string): Promise<number[]> {
try {
const response = await this.fetchWithTimeout(`${this.baseUrl}/embeddings`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer ${this.apiKey}`,
'HTTP-Referer': 'https://localhost:3000',
'X-Title': 'Memento AI',
},
body: JSON.stringify({
model: this.embeddingModelName,
input: text,
}),
});
if (!response.ok) {
const errText = await response.text();
throw new Error(`OpenRouter embeddings error ${response.status}: ${errText}`);
}
const data = await response.json();
// OpenRouter returns { data: [{ embedding: number[] }] }
if (data.data && Array.isArray(data.data) && data.data[0]?.embedding) {
return data.data[0].embedding;
}
// Fallback: some OpenAI-compatible providers return { embedding: number[] }
if (data.embedding && Array.isArray(data.embedding)) {
return data.embedding;
}
throw new Error(`Unexpected OpenRouter embeddings response shape: ${JSON.stringify(data)}`);
} catch (e) {
console.error('Error generating embeddings (OpenRouter):', e);
throw e;
}
}
async generateTitles(prompt: string): Promise<TitleSuggestion[]> {
try {
const { object } = await generateObject({
model: this.model,
schema: z.object({
titles: z.array(z.object({
title: z.string().describe('Suggested title'),
confidence: z.number().min(0).max(1).describe('Confidence level between 0 and 1')
}))
}),
prompt: prompt,
});
return object.titles;
} catch (e) {
console.error('Error generating titles (OpenRouter):', e);
return [];
}
}
async generateText(prompt: string): Promise<string> {
try {
const { text } = await aiGenerateText({
model: this.model,
prompt: prompt,
});
return text.trim();
} catch (e) {
console.error('Error generating text (OpenRouter):', e);
throw e;
}
}
async chat(messages: any[], systemPrompt?: string): Promise<any> {
try {
const { text } = await aiGenerateText({
model: this.model,
system: systemPrompt,
messages: messages,
});
return { text: text.trim() };
} catch (e) {
console.error('Error in chat (OpenRouter):', e);
throw e;
}
}
async generateWithTools(options: ToolUseOptions): Promise<ToolCallResult> {
const { tools, maxSteps = 10, systemPrompt, messages, prompt } = options
const opts: Record<string, any> = {
model: this.model,
tools,
stopWhen: stepCountIs(maxSteps),
}
if (systemPrompt) opts.system = systemPrompt
if (messages) opts.messages = messages
else if (prompt) opts.prompt = prompt
const result = await aiGenerateText(opts as any)
return {
toolCalls: result.toolCalls?.map((tc: any) => ({ toolName: tc.toolName, input: tc.input })) || [],
toolResults: result.toolResults?.map((tr: any) => ({ toolName: tr.toolName, input: tr.input, output: tr.output })) || [],
text: result.text,
steps: result.steps?.map((step: any) => ({
text: step.text,
toolCalls: step.toolCalls?.map((tc: any) => ({ toolName: tc.toolName, input: tc.input })) || [],
toolResults: step.toolResults?.map((tr: any) => ({ toolName: tr.toolName, input: tr.input, output: tr.output })) || []
})) || []
}
}
getModel() {
return this.model;
}
}