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Typesense

利用 Typesense 搜索引擎的向量存储。

基本用法

npm install @langchain/openai @langchain/community @langchain/core
import {
Typesense,
TypesenseConfig,
} from "@lanchain/community/vectorstores/typesense";
import { OpenAIEmbeddings } from "@langchain/openai";
import { Client } from "typesense";
import { Document } from "@langchain/core/documents";

const vectorTypesenseClient = new Client({
nodes: [
{
// Ideally should come from your .env file
host: "...",
port: 123,
protocol: "https",
},
],
// Ideally should come from your .env file
apiKey: "...",
numRetries: 3,
connectionTimeoutSeconds: 60,
});

const typesenseVectorStoreConfig = {
// Typesense client
typesenseClient: vectorTypesenseClient,
// Name of the collection to store the vectors in
schemaName: "your_schema_name",
// Optional column names to be used in Typesense
columnNames: {
// "vec" is the default name for the vector column in Typesense but you can change it to whatever you want
vector: "vec",
// "text" is the default name for the text column in Typesense but you can change it to whatever you want
pageContent: "text",
// Names of the columns that you will save in your typesense schema and need to be retrieved as metadata when searching
metadataColumnNames: ["foo", "bar", "baz"],
},
// Optional search parameters to be passed to Typesense when searching
searchParams: {
q: "*",
filter_by: "foo:[fooo]",
query_by: "",
},
// You can override the default Typesense import function if you want to do something more complex
// Default import function:
// async importToTypesense<
// T extends Record<string, unknown> = Record<string, unknown>
// >(data: T[], collectionName: string) {
// const chunkSize = 2000;
// for (let i = 0; i < data.length; i += chunkSize) {
// const chunk = data.slice(i, i + chunkSize);

// await this.caller.call(async () => {
// await this.client
// .collections<T>(collectionName)
// .documents()
// .import(chunk, { action: "emplace", dirty_values: "drop" });
// });
// }
// }
import: async (data, collectionName) => {
await vectorTypesenseClient
.collections(collectionName)
.documents()
.import(data, { action: "emplace", dirty_values: "drop" });
},
} satisfies TypesenseConfig;

/**
* Creates a Typesense vector store from a list of documents.
* Will update documents if there is a document with the same id, at least with the default import function.
* @param documents list of documents to create the vector store from
* @returns Typesense vector store
*/
const createVectorStoreWithTypesense = async (documents: Document[] = []) =>
Typesense.fromDocuments(
documents,
new OpenAIEmbeddings(),
typesenseVectorStoreConfig
);

/**
* Returns a Typesense vector store from an existing index.
* @returns Typesense vector store
*/
const getVectorStoreWithTypesense = async () =>
new Typesense(new OpenAIEmbeddings(), typesenseVectorStoreConfig);

// Do a similarity search
const vectorStore = await getVectorStoreWithTypesense();
const documents = await vectorStore.similaritySearch("hello world");

// Add filters based on metadata with the search parameters of Typesense
// will exclude documents with author:JK Rowling, so if Joe Rowling & JK Rowling exists, only Joe Rowling will be returned
vectorStore.similaritySearch("Rowling", undefined, {
filter_by: "author:!=JK Rowling",
});

// Delete a document
vectorStore.deleteDocuments(["document_id_1", "document_id_2"]);

构造函数

在开始之前,在 Typesense 中创建一个模式,包含 id,向量字段和文本字段。根据元数据需要添加任意数量的其他字段。

  • constructor(embeddings: Embeddings, config: TypesenseConfig):构造 Typesense 类的新实例。
    • embeddingsEmbeddings 类的实例,用于嵌入文档。
    • config:Typesense 向量存储的配置对象。
      • typesenseClient:Typesense 客户端实例。
      • schemaName:Typesense 模式的名称,文档将存储和搜索在该模式中。
      • searchParams (可选):Typesense 搜索参数。默认为 { q: '*', per_page: 5, query_by: '' }
      • columnNames (可选):列名配置。
        • vector (可选):向量列名。默认为 'vec'
        • pageContent (可选):页面内容列名。默认为 'text'
        • metadataColumnNames (可选):元数据列名。默认为空数组 []
      • import (可选):替换用于将数据导入 Typesense 的默认导入函数。 这可能会影响更新文档的功能。

方法

  • async addDocuments(documents: Document[]): Promise<void>:将文档添加到向量存储。 如果存在具有相同 ID 的文档,则将更新文档。
  • static async fromDocuments(docs: Document[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>:从文档列表创建 Typesense 向量存储。 文档在构造期间添加到向量存储。
  • static async fromTexts(texts: string[], metadatas: object[], embeddings: Embeddings, config: TypesenseConfig): Promise<Typesense>:从文本列表和关联的元数据创建 Typesense 向量存储。 文本转换为文档并在构造期间添加到向量存储。
  • async similaritySearch(query: string, k?: number, filter?: Record<string, unknown>): Promise<Document[]>:根据查询搜索相似的文档。 返回相似文档的数组。
  • async deleteDocuments(documentIds: string[]): Promise<void>:根据文档 ID 从向量存储中删除文档。

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