Adaptateurs Personnalisés

import type { LLMAdapter, LLMGenerateOptions, LLMResult } from 'orkajs';
//ou
import { LLMAdapter, LLMGenerateOptions, LLMResult } from 'orkajs/types';
 
//--
class MyLLMAdapter implements LLMAdapter {
  readonly name = 'my-llm';
 
  async generate(prompt: string, options?: LLMGenerateOptions): Promise<LLMResult> {
    const response = await fetch('https://my-llm-api.com/generate', {
      method: 'POST',
      body: JSON.stringify({
        prompt,
        temperature: options?.temperature ?? 0.7,
        max_tokens: options?.maxTokens ?? 1024,
      }),
    });
 
    const data = await response.json();
 
    return {
      content: data.text,
      usage: {
        promptTokens: data.usage.input_tokens,
        completionTokens: data.usage.output_tokens,
        totalTokens: data.usage.total_tokens,
      },
      model: 'my-model-v1',
      finishReason: 'stop',
    };
  }
 
  async embed(texts: string | string[]): Promise<number[][]> {
    const input = Array.isArray(texts) ? texts: [texts];
    const response = await fetch('https://my-llm-api.com/embed', {
      method: 'POST',
      body: JSON.stringify({ texts: input }),
    });
    const data = await response.json();
    return data.embeddings;
  }
}

import type { VectorDBAdapter, VectorRecord, VectorSearchResult } from 'orkajs';
//ou
import { VectorDBAdapter, VectorRecord, VectorSearchResult } from 'orkajs/types';
 
//---
class MyVectorAdapter implements VectorDBAdapter {
  readonly name = 'my-vectordb';
  private collections: Map<string, VectorRecord[]> = new Map();
 
  async createCollection(name: string): Promise<void> {
    this.collections.set(name, []);
  }
 
  async deleteCollection(name: string): Promise<void> {
    this.collections.delete(name);
  }
 
  async upsert(collection: string, vectors: VectorRecord[]): Promise<void> {
    const existing = this.collections.get(collection) ?? [];
    for (const vector of vectors) {
      const idx = existing.findIndex(v => v.id === vector.id);
      if (idx >= 0) existing[idx] = vector;
      else existing.push(vector);
    }
    this.collections.set(collection, existing);
  }
 
  async search(collection: string, vector: number[]): Promise<VectorSearchResult[]> {
    // Implement your similarity search logic
    return [];
  }
 
  async delete(collection: string, ids: string[]): Promise<void> {
    const existing = this.collections.get(collection) ?? [];
    this.collections.set(collection, existing.filter(v => !ids.includes(v.id)));
  }
}