推荐订阅源

nomic-embed-text 是一个用于生成高质量文本嵌入（embeddings）的工具或模型

将文本转换为固定长度的向量表示，这些向量可以用于语义搜索、文本分类、聚类等任务

使用本地 ollama 部署的 nomic-embed-text

import { OllamaEmbeddings } from "@langchain/ollama";

const embeddings = new OllamaEmbeddings({
  model: "nomic-embed-text:latest",
  baseUrl: "http://192.168.0.220:11434", // Default value
  requestOptions: {
    useMMap: true,
    numThread: 6,
    numGpu: 1,
  },
});

const documents = ["Hello!", "abc"];
const embeddings = await embeddings.embedDocuments(documents);
console.log(embeddings);

对本地的文本文件进行embeddings 操作

1.文档加载

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

2.对文本进行分片

import { CharacterTextSplitter } from "langchain/text_splitter";async function split(documents) {
  const splitter = new CharacterTextSplitter({
    chunkSize: 500,
    chunkOverlap: 20,
  });
  return splitter.splitDocuments(documents);
}

3.对文本块进行embeddings 

import { OllamaEmbeddings } from "@langchain/ollama";
const embeddings = new OllamaEmbeddings({
  model: "nomic-embed-text:latest",
  baseUrl: "http://192.168.0.220:11434", // Default value
  requestOptions: {
    useMMap: true,
    numThread: 6,
    numGpu: 1,
  },
});

const docs = await load("说明.txt")
const splittedDocs = await split(docs);
for (let doc of splittedDocs) {
  const embedding = await embeddings.embedDocuments(doc.pageContent)
  console.dir(embedding);
}