Doclea MCP

import type { EmbeddingConfig } from "@/types"; import { TransformersEmbeddingClient } from "./transformers"; export interface EmbeddingClient { embed(text: string): Promise<number[]>; embedBatch(texts: string[]): Promise<number[][]>; } export { CachedEmbeddingClient } from "./cached"; export type { TransformersEmbeddingConfig, TransformersModelName, } from "./transformers"; export { TRANSFORMERS_MODELS, TransformersEmbeddingClient, } from "./transformers"; export function createEmbeddingClient( config: EmbeddingConfig, ): EmbeddingClient { switch (config.provider) { case "local": return new LocalTEIClient(config.endpoint); case "openai": return new OpenAIClient(config.apiKey, config.model); case "nomic": return new NomicClient(config.apiKey, config.model); case "voyage": return new VoyageClient(config.apiKey, config.model); case "ollama": return new OllamaClient(config.endpoint, config.model); case "transformers": return new TransformersEmbeddingClient({ model: config.model, cacheDir: config.cacheDir, dimensions: config.dimensions, }); } } // Local HuggingFace Text Embeddings Inference class LocalTEIClient implements EmbeddingClient { constructor(private endpoint: string) {} async embed(text: string): Promise<number[]> { const response = await fetch(`${this.endpoint}/embed`, { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ inputs: text }), }); if (!response.ok) { throw new Error(`TEI embed failed: ${response.statusText}`); } const result = await response.json(); // TEI returns [[...embedding...]] for single input return Array.isArray(result[0]) ? result[0] : result; } async embedBatch(texts: string[]): Promise<number[][]> { const response = await fetch(`${this.endpoint}/embed`, { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ inputs: texts }), }); if (!response.ok) { throw new Error(`TEI embed batch failed: ${response.statusText}`); } return response.json(); } } // OpenAI Embeddings class OpenAIClient implements EmbeddingClient { constructor( private apiKey: string, private model: string, ) {} async embed(text: string): Promise<number[]> { const response = await fetch("https://api.openai.com/v1/embeddings", { method: "POST", headers: { "Content-Type": "application/json", Authorization: `Bearer ${this.apiKey}`, }, body: JSON.stringify({ model: this.model, input: text, }), }); if (!response.ok) { throw new Error(`OpenAI embed failed: ${response.statusText}`); } const result = await response.json(); return result.data[0].embedding; } async embedBatch(texts: string[]): Promise<number[][]> { const response = await fetch("https://api.openai.com/v1/embeddings", { method: "POST", headers: { "Content-Type": "application/json", Authorization: `Bearer ${this.apiKey}`, }, body: JSON.stringify({ model: this.model, input: texts, }), }); if (!response.ok) { throw new Error(`OpenAI embed batch failed: ${response.statusText}`); } const result = await response.json(); return result.data.map((d: { embedding: number[] }) => d.embedding); } } // Nomic Embeddings class NomicClient implements EmbeddingClient { constructor( private apiKey: string, private model: string, ) {} async embed(text: string): Promise<number[]> { const response = await fetch( "https://api-atlas.nomic.ai/v1/embedding/text", { method: "POST", headers: { "Content-Type": "application/json", Authorization: `Bearer ${this.apiKey}`, }, body: JSON.stringify({ model: this.model, texts: [text], task_type: "search_document", }), }, ); if (!response.ok) { throw new Error(`Nomic embed failed: ${response.statusText}`); } const result = await response.json(); return result.embeddings[0]; } async embedBatch(texts: string[]): Promise<number[][]> { const response = await fetch( "https://api-atlas.nomic.ai/v1/embedding/text", { method: "POST", headers: { "Content-Type": "application/json", Authorization: `Bearer ${this.apiKey}`, }, body: JSON.stringify({ model: this.model, texts, task_type: "search_document", }), }, ); if (!response.ok) { throw new Error(`Nomic embed batch failed: ${response.statusText}`); } const result = await response.json(); return result.embeddings; } } // Voyage AI Embeddings class VoyageClient implements EmbeddingClient { constructor( private apiKey: string, private model: string, ) {} async embed(text: string): Promise<number[]> { const response = await fetch("https://api.voyageai.com/v1/embeddings", { method: "POST", headers: { "Content-Type": "application/json", Authorization: `Bearer ${this.apiKey}`, }, body: JSON.stringify({ model: this.model, input: text, }), }); if (!response.ok) { throw new Error(`Voyage embed failed: ${response.statusText}`); } const result = await response.json(); return result.data[0].embedding; } async embedBatch(texts: string[]): Promise<number[][]> { const response = await fetch("https://api.voyageai.com/v1/embeddings", { method: "POST", headers: { "Content-Type": "application/json", Authorization: `Bearer ${this.apiKey}`, }, body: JSON.stringify({ model: this.model, input: texts, }), }); if (!response.ok) { throw new Error(`Voyage embed batch failed: ${response.statusText}`); } const result = await response.json(); return result.data.map((d: { embedding: number[] }) => d.embedding); } } // Ollama Embeddings class OllamaClient implements EmbeddingClient { constructor( private endpoint: string, private model: string, ) {} async embed(text: string): Promise<number[]> { const response = await fetch(`${this.endpoint}/api/embeddings`, { method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify({ model: this.model, prompt: text, }), }); if (!response.ok) { throw new Error(`Ollama embed failed: ${response.statusText}`); } const result = await response.json(); return result.embedding; } async embedBatch(texts: string[]): Promise<number[][]> { // Ollama doesn't support batch, so we do sequential return Promise.all(texts.map((text) => this.embed(text))); } }