本地运行性能超越 OpenAI Text-Embedding-Ada-002 的 Embedding 服务，太方便了！

ollama pull nomic-embed-text

ollama serve

curl http://localhost:11434/api/embeddings -d '{"model": "nomic-embed-text","prompt": "The sky is blue because of Rayleigh scattering"}'

import { TextLoader } from "langchain/document_loaders/fs/text";async function load(path: string) {const loader = new TextLoader(path);const docs = await loader.load();return docs;}

import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";import { Document } from "langchain/document";function split(documents: Document[]) {const splitter = new RecursiveCharacterTextSplitter({chunkSize: 500,chunkOverlap: 20,});return splitter.splitDocuments(documents);}

const EMBEDDINGS_URL = "http://127.0.0.1:11434/api/embeddings";async function embedding(path: string) {const docs = await load(path);const splittedDocs = await split(docs);for (let doc of splittedDocs) {const embedding = await sendRequest(EMBEDDINGS_URL, {model: "nomic-embed-text",prompt: doc.pageContent,});console.dir(embedding.embedding);}}

async function sendRequest(url: string, data: Record) {try {const response = await fetch(url, {method: "POST",body: JSON.stringify(data),headers: {"Content-Type": "application/json",},});if (!response.ok) {throw new Error(`HTTP error! status: ${response.status}`);}const responseData = await response.json();return responseData;} catch (error) {console.error("Error:", error);}}

async function embedTxtFile(path: string) {try {embedding(path);} catch (error) {console.dir(error);}}embedTxtFile("langchain.txt")

import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";const embeddings = new OllamaEmbeddings({model: "nomic-embed-text", baseUrl: "http://127.0.0.1:11434",requestOptions: {useMMap: true,numThread: 6,numGpu: 1,},});const documents = ["Hello World!", "Bye Bye"];const documentEmbeddings = await embeddings.embedDocuments(documents);console.log(documentEmbeddings);