chat-api/langChain.mjs

import { ChatOpenAI } from 'langchain/chat_models/openai'
import { HumanMessage, ChatMessage, SystemMessage } from 'langchain/schema'
import { PromptTemplate } from 'langchain/prompts'
import { LLMChain } from 'langchain/chains'
import { PDFLoader } from 'langchain/document_loaders/fs/pdf'
import { UnstructuredLoader } from 'langchain/document_loaders/fs/unstructured'
import { config } from 'dotenv'
import { BufferMemory } from 'langchain/memory'
import { RedisChatMessageHistory } from 'langchain/stores/message/ioredis'
import { ConversationChain } from 'langchain/chains'
import { OpenAIEmbeddings } from 'langchain/embeddings/openai'
import { TypeORMVectorStore } from 'langchain/vectorstores/typeorm'

config()

const loader1 = new UnstructuredLoader('/Users/drew/Downloads/客服的副本.pdf', {
    apiUrl: 'http://192.168.6.19:8000/general/v0/general'
})
const docs1 = await loader1.load()
console.log(docs1)

const embeddings = new OpenAIEmbeddings({
    azureOpenAIApiKey: 'beb32e4625a94b65ba8bc0ba1688c4d2',
    azureOpenAIApiInstanceName: 'zouma',
    azureOpenAIApiDeploymentName: 'embedding',
    azureOpenAIApiVersion: '2023-03-15-preview',
    verbose: true
})

const typeormVectorStore = await TypeORMVectorStore.fromDataSource(embeddings, {
    postgresConnectionOptions: {
        type: 'postgres',
        host: process.env.PG_HOST,
        port: process.env.PG_PORT,
        username: process.env.PG_USERNAME,
        password: process.env.PG_PASSWORD,
        database: process.env.PG_DATABASE
    },
    verbose: true
})

await typeormVectorStore.ensureTableInDatabase()

await typeormVectorStore.addDocuments(docs1)

const results = await typeormVectorStore.similaritySearch('包邮', 2)
console.log(results)