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

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设置

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创建项目

设置一个工作的 Convex 项目，例如使用： 
npm create convex@latest

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添加数据库访问器

将查询和突变助手添加到 convex/langchain/db.ts：
convex/langchain/db.ts
export * from "@langchain/community/utils/convex";

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配置您的模式

设置您的模式（用于向量索引）：
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,
  }),
});

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用法

请参阅 此部分 以获取有关安装 LangChain 包的一般说明。
npm
npm install @langchain/openai @langchain/community @langchain/core

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摄取

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 }
    );
  },
});

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搜索

"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);
  },
});

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