chore: clean up repo for public release
- Remove BMAD framework, IDE configs, dev screenshots, test files, internal docs, and backup files - Rename keep-notes/ to memento-note/ - Update all references from keep-notes to memento-note - Add Apache 2.0 license with Commons Clause (non-commercial restriction) - Add clean .gitignore and .env.docker.example
This commit is contained in:
Sepehr Ramezani
147
memento-note/lib/ai/providers/custom-openai.ts
Normal file
147
memento-note/lib/ai/providers/custom-openai.ts
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@@ -0,0 +1,147 @@
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import { createOpenAI } from '@ai-sdk/openai';
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import { generateObject, generateText as aiGenerateText, embed, stepCountIs } from 'ai';
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import { z } from 'zod';
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import { AIProvider, TagSuggestion, TitleSuggestion, ToolUseOptions, ToolCallResult } from '../types';
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export class CustomOpenAIProvider implements AIProvider {
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private model: any;
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private embeddingModel: any;
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private apiKey: string;
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private baseUrl: string;
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constructor(
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apiKey: string,
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baseUrl: string,
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modelName: string = 'gpt-4o-mini',
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embeddingModelName: string = 'text-embedding-3-small'
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) {
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this.apiKey = apiKey;
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this.baseUrl = baseUrl.endsWith('/') ? baseUrl.slice(0, -1) : baseUrl;
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// Create OpenAI-compatible client with custom base URL
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// Use .chat() to force /chat/completions endpoint (avoids Responses API)
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const customClient = createOpenAI({
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baseURL: baseUrl,
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apiKey: apiKey,
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fetch: async (url, options) => {
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const headers = new Headers(options?.headers);
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headers.set('HTTP-Referer', 'https://localhost:3000');
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headers.set('X-Title', 'Memento AI');
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return fetch(url, { ...options, headers });
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}
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});
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this.model = customClient.chat(modelName);
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this.embeddingModel = customClient.embedding(embeddingModelName);
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}
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async generateTags(content: string): Promise<TagSuggestion[]> {
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try {
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const { object } = await generateObject({
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model: this.model,
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schema: z.object({
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tags: z.array(z.object({
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tag: z.string().describe('Le nom du tag, court et en minuscules'),
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confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
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}))
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}),
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prompt: `Analyse la note suivante et suggère entre 1 et 5 tags pertinents.
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Contenu de la note: "${content}"`,
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});
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return object.tags;
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} catch (e) {
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console.error('Erreur génération tags Custom OpenAI:', e);
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return [];
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}
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}
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async getEmbeddings(text: string): Promise<number[]> {
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try {
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const { embedding } = await embed({
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model: this.embeddingModel,
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value: text,
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});
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return embedding;
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} catch (e) {
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console.error('Erreur embeddings Custom OpenAI:', e);
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return [];
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}
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}
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async generateTitles(prompt: string): Promise<TitleSuggestion[]> {
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try {
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const { object } = await generateObject({
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model: this.model,
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schema: z.object({
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titles: z.array(z.object({
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title: z.string().describe('Le titre suggéré'),
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confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
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}))
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}),
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prompt: prompt,
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});
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return object.titles;
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} catch (e) {
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console.error('Erreur génération titres Custom OpenAI:', e);
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return [];
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}
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}
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async generateText(prompt: string): Promise<string> {
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try {
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const { text } = await aiGenerateText({
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model: this.model,
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prompt: prompt,
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});
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return text.trim();
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} catch (e) {
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console.error('Erreur génération texte Custom OpenAI:', e);
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throw e;
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}
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}
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async chat(messages: any[], systemPrompt?: string): Promise<any> {
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try {
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const { text } = await aiGenerateText({
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model: this.model,
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system: systemPrompt,
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messages: messages,
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});
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return { text: text.trim() };
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} catch (e) {
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console.error('Erreur chat Custom OpenAI:', e);
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throw e;
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}
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}
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async generateWithTools(options: ToolUseOptions): Promise<ToolCallResult> {
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const { tools, maxSteps = 10, systemPrompt, messages, prompt } = options
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const opts: Record<string, any> = {
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model: this.model,
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tools,
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stopWhen: stepCountIs(maxSteps),
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}
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if (systemPrompt) opts.system = systemPrompt
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if (messages) opts.messages = messages
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else if (prompt) opts.prompt = prompt
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const result = await aiGenerateText(opts as any)
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return {
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toolCalls: result.toolCalls?.map((tc: any) => ({ toolName: tc.toolName, input: tc.input })) || [],
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toolResults: result.toolResults?.map((tr: any) => ({ toolName: tr.toolName, input: tr.input, output: tr.output })) || [],
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text: result.text,
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steps: result.steps?.map((step: any) => ({
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text: step.text,
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toolCalls: step.toolCalls?.map((tc: any) => ({ toolName: tc.toolName, input: tc.input })) || [],
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toolResults: step.toolResults?.map((tr: any) => ({ toolName: tr.toolName, input: tr.input, output: tr.output })) || []
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})) || []
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}
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}
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getModel() {
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return this.model;
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}
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}
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131
memento-note/lib/ai/providers/deepseek.ts
Normal file
131
memento-note/lib/ai/providers/deepseek.ts
Normal file
@@ -0,0 +1,131 @@
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import { createOpenAI } from '@ai-sdk/openai';
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import { generateObject, generateText as aiGenerateText, embed, stepCountIs } from 'ai';
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import { z } from 'zod';
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import { AIProvider, TagSuggestion, TitleSuggestion, ToolUseOptions, ToolCallResult } from '../types';
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export class DeepSeekProvider implements AIProvider {
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private model: any;
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private embeddingModel: any;
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constructor(apiKey: string, modelName: string = 'deepseek-chat', embeddingModelName: string = 'deepseek-embedding') {
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// Create OpenAI-compatible client for DeepSeek
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const deepseek = createOpenAI({
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baseURL: 'https://api.deepseek.com/v1',
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apiKey: apiKey,
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});
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this.model = deepseek.chat(modelName);
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this.embeddingModel = deepseek.embedding(embeddingModelName);
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}
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async generateTags(content: string): Promise<TagSuggestion[]> {
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try {
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const { object } = await generateObject({
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model: this.model,
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schema: z.object({
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tags: z.array(z.object({
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tag: z.string().describe('Le nom du tag, court et en minuscules'),
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confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
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}))
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}),
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prompt: `Analyse la note suivante et suggère entre 1 et 5 tags pertinents.
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Contenu de la note: "${content}"`,
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});
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return object.tags;
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} catch (e) {
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console.error('Erreur génération tags DeepSeek:', e);
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return [];
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}
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}
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async getEmbeddings(text: string): Promise<number[]> {
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try {
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const { embedding } = await embed({
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model: this.embeddingModel,
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value: text,
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});
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return embedding;
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} catch (e) {
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console.error('Erreur embeddings DeepSeek:', e);
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return [];
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}
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}
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async generateTitles(prompt: string): Promise<TitleSuggestion[]> {
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try {
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const { object } = await generateObject({
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model: this.model,
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schema: z.object({
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titles: z.array(z.object({
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title: z.string().describe('Le titre suggéré'),
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confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
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}))
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}),
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prompt: prompt,
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});
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return object.titles;
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} catch (e) {
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console.error('Erreur génération titres DeepSeek:', e);
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return [];
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}
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}
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async generateText(prompt: string): Promise<string> {
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try {
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const { text } = await aiGenerateText({
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model: this.model,
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prompt: prompt,
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});
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return text.trim();
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} catch (e) {
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console.error('Erreur génération texte DeepSeek:', e);
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throw e;
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}
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}
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|
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async chat(messages: any[], systemPrompt?: string): Promise<any> {
|
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try {
|
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const { text } = await aiGenerateText({
|
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model: this.model,
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system: systemPrompt,
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messages: messages,
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});
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return { text: text.trim() };
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} catch (e) {
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console.error('Erreur chat DeepSeek:', e);
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throw e;
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}
|
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}
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|
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async generateWithTools(options: ToolUseOptions): Promise<ToolCallResult> {
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const { tools, maxSteps = 10, systemPrompt, messages, prompt } = options
|
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const opts: Record<string, any> = {
|
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model: this.model,
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tools,
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stopWhen: stepCountIs(maxSteps),
|
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}
|
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if (systemPrompt) opts.system = systemPrompt
|
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if (messages) opts.messages = messages
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else if (prompt) opts.prompt = prompt
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const result = await aiGenerateText(opts as any)
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return {
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toolCalls: result.toolCalls?.map((tc: any) => ({ toolName: tc.toolName, input: tc.input })) || [],
|
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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 })) || []
|
||||
})) || []
|
||||
}
|
||||
}
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||||
|
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getModel() {
|
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return this.model;
|
||||
}
|
||||
}
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222
memento-note/lib/ai/providers/ollama.ts
Normal file
222
memento-note/lib/ai/providers/ollama.ts
Normal file
@@ -0,0 +1,222 @@
|
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import { createOpenAI } from '@ai-sdk/openai';
|
||||
import { generateText as aiGenerateText, stepCountIs } from 'ai';
|
||||
import { AIProvider, TagSuggestion, TitleSuggestion, ToolUseOptions, ToolCallResult } from '../types';
|
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|
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export class OllamaProvider implements AIProvider {
|
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private baseUrl: string;
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private modelName: string;
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||||
private embeddingModelName: string;
|
||||
private model: any;
|
||||
|
||||
constructor(baseUrl: string, modelName: string = 'llama3', embeddingModelName?: string) {
|
||||
if (!baseUrl) {
|
||||
throw new Error('baseUrl is required for OllamaProvider')
|
||||
}
|
||||
// Ensure baseUrl ends with /api for Ollama API
|
||||
this.baseUrl = baseUrl.endsWith('/api') ? baseUrl : `${baseUrl}/api`;
|
||||
this.modelName = modelName;
|
||||
this.embeddingModelName = embeddingModelName || modelName;
|
||||
|
||||
// Create OpenAI-compatible model for streaming support
|
||||
// Ollama exposes /v1/chat/completions which is compatible with the OpenAI SDK
|
||||
const cleanUrl = this.baseUrl.replace(/\/api$/, '');
|
||||
const ollamaClient = createOpenAI({
|
||||
baseURL: `${cleanUrl}/v1`,
|
||||
apiKey: 'ollama',
|
||||
});
|
||||
this.model = ollamaClient.chat(modelName);
|
||||
}
|
||||
|
||||
async generateTags(content: string, language: string = "en"): Promise<TagSuggestion[]> {
|
||||
try {
|
||||
const promptText = language === 'fa'
|
||||
? `متن زیر را تحلیل کن و مفاهیم کلیدی را به عنوان برچسب استخراج کن (حداکثر ۱-۳ کلمه).
|
||||
قوانین:
|
||||
- کلمات ربط را حذف کن.
|
||||
- عبارات ترکیبی را حفظ کن.
|
||||
- حداکثر ۵ برچسب.
|
||||
پاسخ فقط به صورت لیست JSON با فرمت [{"tag": "string", "confidence": number}]
|
||||
متن: "${content}"`
|
||||
: language === 'fr'
|
||||
? `Analyse la note suivante et extrais les concepts clés sous forme de tags courts (1-3 mots max).
|
||||
Règles:
|
||||
- Pas de mots de liaison.
|
||||
- Garde les expressions composées ensemble.
|
||||
- Normalise en minuscules sauf noms propres.
|
||||
- Maximum 5 tags.
|
||||
Réponds UNIQUEMENT sous forme de liste JSON d'objets : [{"tag": "string", "confidence": number}].
|
||||
Contenu de la note: "${content}"`
|
||||
: `Analyze the following note and extract key concepts as short tags (1-3 words max).
|
||||
Rules:
|
||||
- No stop words.
|
||||
- Keep compound expressions together.
|
||||
- Lowercase unless proper noun.
|
||||
- Max 5 tags.
|
||||
Respond ONLY as a JSON list of objects: [{"tag": "string", "confidence": number}].
|
||||
Note content: "${content}"`;
|
||||
|
||||
const response = await fetch(`${this.baseUrl}/generate`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
model: this.modelName,
|
||||
prompt: promptText,
|
||||
stream: false,
|
||||
}),
|
||||
});
|
||||
|
||||
if (!response.ok) throw new Error(`Ollama error: ${response.statusText}`);
|
||||
|
||||
const data = await response.json();
|
||||
const text = data.response;
|
||||
|
||||
const jsonMatch = text.match(/\[\s*\{[\s\S]*\}\s*\]/);
|
||||
if (jsonMatch) {
|
||||
return JSON.parse(jsonMatch[0]);
|
||||
}
|
||||
|
||||
// Support pour le format { "tags": [...] }
|
||||
const objectMatch = text.match(/\{\s*"tags"\s*:\s*(\[[\s\S]*\])\s*\}/);
|
||||
if (objectMatch && objectMatch[1]) {
|
||||
return JSON.parse(objectMatch[1]);
|
||||
}
|
||||
|
||||
return [];
|
||||
} catch (e) {
|
||||
console.error('Erreur API directe Ollama:', e);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
async getEmbeddings(text: string): Promise<number[]> {
|
||||
try {
|
||||
const response = await fetch(`${this.baseUrl}/embeddings`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
model: this.embeddingModelName,
|
||||
prompt: text,
|
||||
}),
|
||||
});
|
||||
|
||||
if (!response.ok) throw new Error(`Ollama error: ${response.statusText}`);
|
||||
|
||||
const data = await response.json();
|
||||
return data.embedding;
|
||||
} catch (e) {
|
||||
console.error('Erreur embeddings directs Ollama:', e);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
async generateTitles(prompt: string): Promise<TitleSuggestion[]> {
|
||||
try {
|
||||
const response = await fetch(`${this.baseUrl}/generate`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
model: this.modelName,
|
||||
prompt: `${prompt}\n\nRéponds UNIQUEMENT sous forme de tableau JSON : [{"title": "string", "confidence": number}]`,
|
||||
stream: false,
|
||||
}),
|
||||
});
|
||||
|
||||
if (!response.ok) throw new Error(`Ollama error: ${response.statusText}`);
|
||||
|
||||
const data = await response.json();
|
||||
const text = data.response;
|
||||
|
||||
// Extraire le JSON de la réponse
|
||||
const jsonMatch = text.match(/\[\s*\{[\s\S]*\}\s*\]/);
|
||||
if (jsonMatch) {
|
||||
return JSON.parse(jsonMatch[0]);
|
||||
}
|
||||
|
||||
return [];
|
||||
} catch (e) {
|
||||
console.error('Erreur génération titres Ollama:', e);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
async generateText(prompt: string): Promise<string> {
|
||||
try {
|
||||
const response = await fetch(`${this.baseUrl}/generate`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
model: this.modelName,
|
||||
prompt: prompt,
|
||||
stream: false,
|
||||
}),
|
||||
});
|
||||
|
||||
if (!response.ok) throw new Error(`Ollama error: ${response.statusText}`);
|
||||
|
||||
const data = await response.json();
|
||||
return data.response.trim();
|
||||
} catch (e) {
|
||||
console.error('Erreur génération texte Ollama:', e);
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
|
||||
async chat(messages: any[], systemPrompt?: string): Promise<any> {
|
||||
try {
|
||||
const ollamaMessages = messages.map(m => ({
|
||||
role: m.role,
|
||||
content: m.content
|
||||
}));
|
||||
|
||||
if (systemPrompt) {
|
||||
ollamaMessages.unshift({ role: 'system', content: systemPrompt });
|
||||
}
|
||||
|
||||
const response = await fetch(`${this.baseUrl}/chat`, {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify({
|
||||
model: this.modelName,
|
||||
messages: ollamaMessages,
|
||||
stream: false,
|
||||
}),
|
||||
});
|
||||
|
||||
if (!response.ok) throw new Error(`Ollama error: ${response.statusText}`);
|
||||
|
||||
const data = await response.json();
|
||||
return { text: data.message?.content?.trim() || '' };
|
||||
} catch (e) {
|
||||
console.error('Erreur chat Ollama:', e);
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
|
||||
getModel() {
|
||||
return this.model;
|
||||
}
|
||||
|
||||
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 })) || []
|
||||
})) || []
|
||||
}
|
||||
}
|
||||
}
|
||||
131
memento-note/lib/ai/providers/openai.ts
Normal file
131
memento-note/lib/ai/providers/openai.ts
Normal file
@@ -0,0 +1,131 @@
|
||||
import { createOpenAI } from '@ai-sdk/openai';
|
||||
import { generateObject, generateText as aiGenerateText, embed, stepCountIs } from 'ai';
|
||||
import { z } from 'zod';
|
||||
import { AIProvider, TagSuggestion, TitleSuggestion, ToolUseOptions, ToolCallResult } from '../types';
|
||||
|
||||
export class OpenAIProvider implements AIProvider {
|
||||
private model: any;
|
||||
private embeddingModel: any;
|
||||
|
||||
constructor(apiKey: string, modelName: string = 'gpt-4o-mini', embeddingModelName: string = 'text-embedding-3-small') {
|
||||
// Create OpenAI client with API key
|
||||
// Use .chat() to force /chat/completions endpoint (avoids Responses API)
|
||||
const openaiClient = createOpenAI({
|
||||
apiKey: apiKey,
|
||||
});
|
||||
|
||||
this.model = openaiClient.chat(modelName);
|
||||
this.embeddingModel = openaiClient.embedding(embeddingModelName);
|
||||
}
|
||||
|
||||
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('Le nom du tag, court et en minuscules'),
|
||||
confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
|
||||
}))
|
||||
}),
|
||||
prompt: `Analyse la note suivante et suggère entre 1 et 5 tags pertinents.
|
||||
Contenu de la note: "${content}"`,
|
||||
});
|
||||
|
||||
return object.tags;
|
||||
} catch (e) {
|
||||
console.error('Erreur génération tags OpenAI:', e);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
async getEmbeddings(text: string): Promise<number[]> {
|
||||
try {
|
||||
const { embedding } = await embed({
|
||||
model: this.embeddingModel,
|
||||
value: text,
|
||||
});
|
||||
return embedding;
|
||||
} catch (e) {
|
||||
console.error('Erreur embeddings OpenAI:', e);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
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('Le titre suggéré'),
|
||||
confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
|
||||
}))
|
||||
}),
|
||||
prompt: prompt,
|
||||
});
|
||||
|
||||
return object.titles;
|
||||
} catch (e) {
|
||||
console.error('Erreur génération titres OpenAI:', 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('Erreur génération texte OpenAI:', 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('Erreur chat OpenAI:', 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;
|
||||
}
|
||||
}
|
||||
131
memento-note/lib/ai/providers/openrouter.ts
Normal file
131
memento-note/lib/ai/providers/openrouter.ts
Normal file
@@ -0,0 +1,131 @@
|
||||
import { createOpenAI } from '@ai-sdk/openai';
|
||||
import { generateObject, generateText as aiGenerateText, embed, stepCountIs } from 'ai';
|
||||
import { z } from 'zod';
|
||||
import { AIProvider, TagSuggestion, TitleSuggestion, ToolUseOptions, ToolCallResult } from '../types';
|
||||
|
||||
export class OpenRouterProvider implements AIProvider {
|
||||
private model: any;
|
||||
private embeddingModel: any;
|
||||
|
||||
constructor(apiKey: string, modelName: string = 'anthropic/claude-3-haiku', embeddingModelName: string = 'openai/text-embedding-3-small') {
|
||||
// Create OpenAI-compatible client for OpenRouter
|
||||
const openrouter = createOpenAI({
|
||||
baseURL: 'https://openrouter.ai/api/v1',
|
||||
apiKey: apiKey,
|
||||
});
|
||||
|
||||
this.model = openrouter.chat(modelName);
|
||||
this.embeddingModel = openrouter.embedding(embeddingModelName);
|
||||
}
|
||||
|
||||
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('Le nom du tag, court et en minuscules'),
|
||||
confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
|
||||
}))
|
||||
}),
|
||||
prompt: `Analyse la note suivante et suggère entre 1 et 5 tags pertinents.
|
||||
Contenu de la note: "${content}"`,
|
||||
});
|
||||
|
||||
return object.tags;
|
||||
} catch (e) {
|
||||
console.error('Erreur génération tags OpenRouter:', e);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
async getEmbeddings(text: string): Promise<number[]> {
|
||||
try {
|
||||
const { embedding } = await embed({
|
||||
model: this.embeddingModel,
|
||||
value: text,
|
||||
});
|
||||
return embedding;
|
||||
} catch (e) {
|
||||
console.error('Erreur embeddings OpenRouter:', e);
|
||||
return [];
|
||||
}
|
||||
}
|
||||
|
||||
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('Le titre suggéré'),
|
||||
confidence: z.number().min(0).max(1).describe('Le niveau de confiance entre 0 et 1')
|
||||
}))
|
||||
}),
|
||||
prompt: prompt,
|
||||
});
|
||||
|
||||
return object.titles;
|
||||
} catch (e) {
|
||||
console.error('Erreur génération titres 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('Erreur génération texte 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('Erreur 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;
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user