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Convex

LangChain.js 支持将 Convex 作为 向量存储，并支持标准相似性搜索。

¥LangChain.js supports Convex as a vector store, and supports the standard similarity search.

设置​

¥Setup

创建项目​

¥Create project

设置一个可运行的 Convex 项目，例如使用以下方法：

¥Get a working Convex project set up, for example by using:

npm create convex@latest

添加数据库访问器​

¥Add database accessors

向 convex/langchain/db.ts 添加查询和变异助手：

¥Add query and mutation helpers to convex/langchain/db.ts:

convex/langchain/db.ts
export * from "@langchain/community/utils/convex";

配置你的模式​

¥Configure your schema

设置模式（用于向量索引）：

¥Set up your schema (for vector indexing):

convex/schema.ts
import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";

export default defineSchema({
  documents: defineTable({
    embedding: v.array(v.number()),
    text: v.string(),
    metadata: v.any(),
  }).vectorIndex("byEmbedding", {
    vectorField: "embedding",
    dimensions: 1536,
  }),
});

用法​

¥Usage

tip

请参阅 此部分包含有关安装集成包的一般说明。

¥See this section for general instructions on installing integration packages.

npm install @langchain/openai @langchain/community @langchain/core

提取​

¥Ingestion

convex/myActions.ts
"use node";

import { ConvexVectorStore } from "@langchain/community/vectorstores/convex";
import { OpenAIEmbeddings } from "@langchain/openai";
import { action } from "./_generated/server.js";

export const ingest = action({
  args: {},
  handler: async (ctx) => {
    await ConvexVectorStore.fromTexts(
      ["Hello world", "Bye bye", "What's this?"],
      [{ prop: 2 }, { prop: 1 }, { prop: 3 }],
      new OpenAIEmbeddings(),
      { ctx }
    );
  },
});

API Reference:

¥Search

convex/myActions.ts
"use node";

import { ConvexVectorStore } from "@langchain/community/vectorstores/convex";
import { OpenAIEmbeddings } from "@langchain/openai";
import { v } from "convex/values";
import { action } from "./_generated/server.js";

export const search = action({
  args: {
    query: v.string(),
  },
  handler: async (ctx, args) => {
    const vectorStore = new ConvexVectorStore(new OpenAIEmbeddings(), { ctx });

    const resultOne = await vectorStore.similaritySearch(args.query, 1);
    console.log(resultOne);
  },
});

API Reference:

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