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 DeepSeekProvider implements AIProvider { private model: any; private embeddingModel: any; constructor(apiKey: string, modelName: string = 'deepseek-chat', embeddingModelName: string = 'deepseek-embedding') { // Create OpenAI-compatible client for DeepSeek const deepseek = createOpenAI({ baseURL: 'https://api.deepseek.com/v1', apiKey: apiKey, }); this.model = deepseek.chat(modelName); this.embeddingModel = deepseek.embedding(embeddingModelName); } async generateTags(content: string): Promise { 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 DeepSeek:', e); return []; } } async getEmbeddings(text: string): Promise { try { const { embedding } = await embed({ model: this.embeddingModel, value: text, }); return embedding; } catch (e) { console.error('Erreur embeddings DeepSeek:', e); return []; } } async generateTitles(prompt: string): Promise { 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 DeepSeek:', e); return []; } } async generateText(prompt: string): Promise { try { const { text } = await aiGenerateText({ model: this.model, prompt: prompt, }); return text.trim(); } catch (e) { console.error('Erreur génération texte DeepSeek:', e); throw e; } } async chat(messages: any[], systemPrompt?: string): Promise { try { const { text } = await aiGenerateText({ model: this.model, system: systemPrompt, messages: messages, }); return { text: text.trim() }; } catch (e) { console.error('Erreur chat DeepSeek:', e); throw e; } } async generateWithTools(options: ToolUseOptions): Promise { const { tools, maxSteps = 10, systemPrompt, messages, prompt } = options const opts: Record = { 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; } }