实例详解 | 借助 Langchain 和 Gemma 2 构建 RAG 应用

本文将为您介绍如何使用 LangChain、NestJS 和 Gemma 2 构建关于 PDF 格式 Angular 书籍的 RAG 应用。接着，HTMX 和 Handlebar 模板引擎将响应呈现为列表。应用使用 LangChain 及其内置的 PDF 加载器来加载 PDF 书籍，并将文档拆分为小块。然后，LangChain 使用 Gemini 嵌入文本模型将文档表示为向量，并将向量持久化存储到向量数据库中。向量存储检索器为大语言模型 (LLM) 提供上下文，以便在其数据中查找信息，从而生成正确的响应。

设置环境变量

访问 https://aistudio.google.com/app/apikey，登录帐号，创建新的 API 密钥。将 API 密钥替换为 GEMINI_API_KEY。

访问 Groq Cloud: https://console.groq.com/，注册帐号并新建一个 API 密钥。将 API 密钥替换为 GROQ_API_KEY。

访问 Huggingface: https://huggingface.co/join，注册帐号，创建新的访问令牌。将访问令牌替换为 HUGGINGFACE_API_KEY。

访问 Qdrant: https://cloud.qdrant.io/，注册帐号，创建 Qdrant 空间。将网址替换为 QDRANT_URL。将 API 密钥替换为 QDRANT_API_KEY。

安装依赖项

定义应用的配置

创建 src/configs 文件夹并在其中添加 configuration.ts 文件。

export default () => ({
 port: parseInt(process.env.PORT, 10) || 3000, 
 groq: {
   apiKey: process.env.GROQ_API_KEY || '',   
   model: process.env.GROQ_MODEL || 'gemma2-9b-it', 
}, 
gemini: {
  apiKey: process.env.GEMINI_API_KEY || '',   
  embeddingModel: process.env.GEMINI_TEXT_EMBEDDING_MODEL || 'text-embedding-004', 
}, 
huggingface: {
  apiKey: process.env.HUGGINGFACE_API_KEY || '',   
  embeddingModel: process.env.HUGGINGFACE_EMBEDDING_MODEL || 'BAAI/bge-small-en-v1.5', 
}, 
qdrant: {
  url: process.env.QDRANT_URL || 'http://localhost:6333',   
  apiKey: process.env.QDRANT_APK_KEY || '', 
 },
});

创建 Groq 模块

生成 Groq 模块、控制器和服务。

添加一个聊天模型

在模块中定义 Groq 配置类型，文件路径为 application/types/groq-config.type.ts。配置服务将配置值转换为自定义对象。

添加自定义提供程序以提供 GroqChatModel 的实例。在 application/constants 文件夹下创建 groq.constant.ts 文件。

在控制器中测试 Groq 聊天模型

GroqService 服务有一个方法，用于执行查询并要求模型生成文本响应。

从模块导出聊天模型

import { Module } from '@nestjs/common';
import { GroqChatModelProvider } from './application/providers/groq-chat-model.provider';
import { GroqService } from './application/groq.service';
import { GroqController } from './presenters/http/groq.controller';

@Module({
 providers: [GroqChatModelProvider, GroqService], 
 controllers: [GroqController], 
 exports: [GroqChatModelProvider],
})
export class GroqModule {}

创建向量存储模块

nest g mo vectorStore
nest g s application/vectorStore --flat

添加配置类型

在 application/types 文件夹下定义配置类型。

这是嵌入模型的配置类型。此应用同时支持 Gemini 文本嵌入模型和 Huggingface 推理嵌入模型。

应用支持内存向量存储和 Qdrant 向量存储。因此，应用具有 Qdrant 配置。

此配置中存储了拆分后的文档、向量数据库类型和嵌入模型。

export type VectorDatabasesType = 'MEMORY' | 'QDRANT';

创建可配置的嵌入模型

export type EmbeddingModels = 'GEMINI_AI' | 'HUGGINGFACE_INFERENCE';

import { TaskType } from '@google/generative-ai';
import { HuggingFaceInferenceEmbeddings } from '@langchain/community/embeddings/hf';
import { Embeddings } from '@langchain/core/embeddings';
import { GoogleGenerativeAIEmbeddings } from '@langchain/google-genai';
import { InternalServerErrorException } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import { EmbeddingModelConfig } from '../types/embedding-model-config.type';
import { EmbeddingModels } from '../types/embedding-models.type';

function createGeminiTextEmbeddingModel(configService: ConfigService) {
 const { apiKey, embeddingModel: model } = configService.get<EmbeddingModelConfig>('gemini'); 
 return new GoogleGenerativeAIEmbeddings({
   apiKey,   
   model,   
   taskType: TaskType.RETRIEVAL_DOCUMENT,   
   title: 'Angular Book', 
 });
}

function createHuggingfaceInferenceEmbeddingModel(configService: ConfigService) {
 const { apiKey, embeddingModel: model } = configService.get<EmbeddingModelConfig>('huggingface'); 
 return new HuggingFaceInferenceEmbeddings({
   apiKey,   
   model, 
 });
}

export function createTextEmbeddingModel(configService: ConfigService, embeddingModel: EmbeddingModels): Embeddings {
 if (embeddingModel === 'GEMINI_AI') {
   return createGeminiTextEmbeddingModel(configService); 
 } else if (embeddingModel === 'HUGGINGFACE_INFERENCE') {  
   return createHuggingfaceInferenceEmbeddingModel(configService); 
 } else { 
   throw new InternalServerErrorException('Invalid type of embedding model.'); 
 }
}

createGeminiTextEmbeddingModel 函数将实例化并返回 Gemini 文本嵌入模型。类似地，createHuggingfaceInferenceEmbeddingModel 将实例化并返回 Huggingface 推理嵌入模型。最后，createTextEmbeddingModel 函数是一个工厂方法，根据嵌入模型标志创建嵌入模型。

创建可配置的向量存储检索器

定义向量数据库服务接口

// application/interfaces/vector-database.interface.ts

import { VectorStore, VectorStoreRetriever } from '@langchain/core/vectorstores';
import { DatabaseConfig } from '../types/vector-store-config.type';

export interface VectorDatabase {
 init(config: DatabaseConfig): Promise<void>; 
 asRetriever(): VectorStoreRetriever<VectorStore>;
}

import { VectorStore, VectorStoreRetriever } from '@langchain/core/vectorstores';
import { Injectable, Logger } from '@nestjs/common';
import { MemoryVectorStore } from 'langchain/vectorstores/memory';
import { VectorDatabase } from '../interfaces/vector-database.interface';
import { DatabaseConfig } from '../types/vector-store-config.type';

@Injectable()
export class MemoryVectorDBService implements VectorDatabase {
 private readonly logger = new Logger(MemoryVectorDBService.name); 
 private vectorStore: VectorStore; 
 
 async init({ docs, embeddings }: DatabaseConfig): Promise<void> {
   this.logger.log('MemoryVectorStoreService init called');   
   this.vectorStore = await MemoryVectorStore.fromDocuments(docs, embeddings);
 } 
 
 asRetriever(): VectorStoreRetriever<VectorStore> { 
   return this.vectorStore.asRetriever(); 
 }
}

MemoryVectorDBService 实现了接口，将向量持久化存储到内存存储中，并返回向量存储检索器。

import { VectorStore, VectorStoreRetriever } from '@langchain/core/vectorstores';
import { QdrantVectorStore } from '@langchain/qdrant';
import { Injectable, InternalServerErrorException, Logger } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import { QdrantClient } from '@qdrant/js-client-rest';
import { VectorDatabase } from '../interfaces/vector-database.interface';
import { QdrantDatabaseConfig } from '../types/qdrant-database-config.type';
import { DatabaseConfig } from '../types/vector-store-config.type';

const COLLECTION_NAME = 'angular_evolution_collection';

@Injectable()
export class QdrantVectorDBService implements VectorDatabase {
 private readonly logger = new Logger(QdrantVectorDBService.name); 
 private vectorStore: VectorStore; 
 
 constructor(private configService: ConfigService) {} 
 
 async init({ docs, embeddings }: DatabaseConfig): Promise<void> {
   this.logger.log('QdrantVectorStoreService init called');   
   const { url, apiKey } = this.configService.get<QdrantDatabaseConfig>('qdrant');   
   const client = new QdrantClient({ url, apiKey });   
   const { exists: isCollectionExists } = await client.collectionExists(COLLECTION_NAME);   
   if (isCollectionExists) {   
     const isDeleted = await client.deleteCollection(COLLECTION_NAME);     
     if (!isDeleted) {     
       throw new InternalServerErrorException(`Unable to delete ${COLLECTION_NAME}`);     
     }    
     this.logger.log(`QdrantVectorStoreService deletes ${COLLECTION_NAME}. Result -> ${isDeleted}`);   
   }   
   
   const size = (await embeddings.embedQuery('test')).length;   
   const isSuccess = await client.createCollection(COLLECTION_NAME, {   
     vectors: { size, distance: 'Cosine' },   
   });   
   
   if (!isSuccess) {  
     throw new InternalServerErrorException(`Unable to create collection ${COLLECTION_NAME}`);   
   }   
   
   this.vectorStore = await QdrantVectorStore.fromDocuments(docs, embeddings, {    
     client,     
     collectionName: COLLECTION_NAME,   
   }); 
 } 

 asRetriever(): VectorStoreRetriever<VectorStore> {
   return this.vectorStore.asRetriever(); 
 }
}

QdrantVectorDBService 实现了接口，将向量持久化存储到 Qdrant 向量数据库中，并返回向量存储检索器。

// application/vector-databases/create-vector-database.t

import { InternalServerErrorException } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import { VectorDatabasesType } from '../types/vector-databases.type';
import { MemoryVectorDBService } from './memory-vector-db.service';
import { QdrantVectorDBService } from './qdrant-vector-db.service';

export function createVectorDatabase(type: VectorDatabasesType, configService: ConfigService) {
 if (type === 'MEMORY') {  
   return new MemoryVectorDBService(); 
 } else if (type === 'QDRANT') { 
   return new QdrantVectorDBService(configService); 
 } 
 throw new InternalServerErrorException(`Invalid vector store type: ${type}`);
}

函数将根据数据库类型实例化数据库服务。

从 Angular PDF 书籍创建文档块

将书籍复制到 assets 文件夹

loadPdf 函数使用 PDF 加载器来加载 PDF 文件，并将文档拆分为多个小块。

import { Embeddings } from '@langchain/core/embeddings';
import { VectorStore, VectorStoreRetriever } from '@langchain/core/vectorstores';
import { Inject, Injectable, Logger } from '@nestjs/common';
import path from 'path';
import { appConfig } from '~configs/root-path.config';
import { ANGULAR_EVOLUTION_BOOK, TEXT_EMBEDDING_MODEL, VECTOR_DATABASE } from './constants/rag.constant';
import { VectorDatabase } from './interfaces/vector-database.interface';
import { loadPdf } from './loaders/pdf-loader';

@Injectable()
export class VectorStoreService {
 private readonly logger = new Logger(VectorStoreService.name);
 
 constructor( 
   @Inject(TEXT_EMBEDDING_MODEL) embeddings: Embeddings,   
   @Inject(VECTOR_DATABASE) private dbService: VectorDatabase,
) { 
   this.createDatabase(embeddings, this.dbService); 
 } 
 
 private async createDatabase(embeddings: Embeddings, dbService: VectorDatabase) { 
   const docs = await this.loadDocuments();   
   await dbService.init({ docs, embeddings }); 
 } 
 
 private async loadDocuments() {
   const bookFullPath = path.join(appConfig.rootPath, ANGULAR_EVOLUTION_BOOK);   
   const docs = await loadPdf(bookFullPath);   
   this.logger.log(`number of docs -> ${docs.length}`);   
   return docs; 
 } 
 
 asRetriever(): VectorStoreRetriever<VectorStore> {
   this.logger.log(`return vector retriever`);   
   return this.dbService.asRetriever(); 
 }
}

VectorStoreService 将 PDF 书籍存储到向量数据库中，并返回向量存储检索器。

将模块设为动态模块

import { DynamicModule, Module } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import { TEXT_EMBEDDING_MODEL, VECTOR_DATABASE, VECTOR_STORE_TYPE } from './application/constants/rag.constant';
import { createTextEmbeddingModel } from './application/embeddings/create-embedding-model';
import { EmbeddingModels } from './application/types/embedding-models.type';
import { VectorDatabasesType } from './application/types/vector-databases.type';
import { createVectorDatabase, MemoryVectorDBService, QdrantVectorDBService } from './application/vector-databases';
import { VectorStoreTestService } from './application/vector-store-test.service';
import { VectorStoreService } from './application/vector-store.service';
import { VectorStoreController } from './presenters/http/vector-store.controller';

@Module({
 providers: [VectorStoreService, VectorStoreTestService, MemoryVectorDBService, QdrantVectorDBService], 
 controllers: [VectorStoreController], 
 exports: [VectorStoreService],
})
export class VectorStoreModule {
 static register(embeddingModel: EmbeddingModels, vectorStoreType: VectorDatabasesType): DynamicModule { 
   return {   
     module: VectorStoreModule,     
     providers: [     
       {      
         provide: TEXT_EMBEDDING_MODEL,         
         useFactory: (configService: ConfigService) => createTextEmbeddingModel(configService, embeddingModel),         
         inject: [ConfigService],       
       },       
       {        
         provide: VECTOR_STORE_TYPE,         
         useValue: vectorStoreType,       
       },       
       {      
         provide: VECTOR_DATABASE,         
         useFactory: (type: VectorDatabasesType, configService: ConfigService) =>       
           createVectorDatabase(type, configService),         
         inject: [VECTOR_STORE_TYPE, ConfigService],      
       },     
     ],   
   }; 
 }
}

VectorStoreModule 是一个动态模块。嵌入模型和向量数据库均可自行配置。注册静态方法根据配置创建文本嵌入模块和向量数据库。

创建 RAG 模块

RAG 模块负责创建 LangChain 链，该链请求模型生成响应。

创建 RAG 服务

// application/constants/prompts.constant.ts

import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts';

const qaSystemPrompt = `You are an assistant for question-answering tasks.
Use the following pieces of retrieved context to answer the question.
If you don't know the answer, just say that you don't know.

{context}`;

export const qaPrompt = ChatPromptTemplate.fromMessages([
 ['system', qaSystemPrompt], 
 new MessagesPlaceholder('chat_history'), 
 ['human', '{question}'],
]);

const contextualizeQSystemPrompt = `Given a chat history and the latest user question
which might reference context in the chat history, formulate a standalone question
which can be understood without the chat history. Do NOT answer the question,
just reformulate it if needed and otherwise return it as is.`;

export const contextualizeQPrompt = ChatPromptTemplate.fromMessages([
 ['system', contextualizeQSystemPrompt], 
 new MessagesPlaceholder('chat_history'), 
 ['human', '{question}'],
]);

此常量文件存储了 LangChain 链的一些提示词。

import { StringOutputParser } from '@langchain/core/output_parsers';
import { ChatGroq } from '@langchain/groq';
import { contextualizeQPrompt } from '../constants/prompts.constant';

export function createContextualizedQuestion(llm: ChatGroq) {
 const contextualizeQChain = contextualizeQPrompt.pipe(llm).pipe(new StringOutputParser()); 

 return (input: Record<string, unknown>) => {
   if ('chat_history' in input) {  
     return contextualizeQChain;   
   }   
   return input.question; 
 };
}

该函数会创建一个链，该链可以在不依赖聊天历史记录的情况下提出问题。

import { BaseMessage } from '@langchain/core/messages';
import { Runnable, RunnablePassthrough, RunnableSequence } from '@langchain/core/runnables';
import { ChatGroq } from '@langchain/groq';
import { Inject, Injectable } from '@nestjs/common';
import { formatDocumentsAsString } from 'langchain/util/document';
import { GROQ_CHAT_MODEL } from '~groq/application/constants/groq.constant';
import { VectorStoreService } from '~vector-store/application/vector-store.service';
import { createContextualizedQuestion } from './chain-with-history/create-contextual-chain';
import { qaPrompt } from './constants/prompts.constant';
import { ConversationContent } from './types/conversation-content.type';

@Injectable()
export class RagService {
 private chat_history: BaseMessage[] = []; 
 
 constructor(
   @Inject(GROQ_CHAT_MODEL) private model: ChatGroq,   
   private vectorStoreService: VectorStoreService,
) {} 
 
 async ask(question: string): Promise<ConversationContent[]> { 
   const contextualizedQuestion = createContextualizedQuestion(this.model);   
   const retriever = this.vectorStoreService.asRetriever();   
   
   try {  
     const ragChain = RunnableSequence.from([     
       RunnablePassthrough.assign({       
         context: (input: Record<string, unknown>) => {         
           if ('chat_history' in input) {         
             const chain = contextualizedQuestion(input);             
             return (chain as Runnable).pipe(retriever).pipe(formatDocumentsAsString);           
           }           
           return '';         
         },       
       }),       
       qaPrompt,       
       this.model,     
     ]);     
     
     const aiMessage = await ragChain.invoke({ question, chat_history: this.chat_history });     
     this.chat_history = this.chat_history.concat(aiMessage);     
     if (this.chat_history.length > 10) {     
       this.chat_history.shift();     
     }     
     
     return [    
       {       
         role: 'Human',         
         content: question,       
       },       
       {       
         role: 'Assistant',         
         content: (aiMessage.content as string) || '',       
       },     
     ];   
   } catch (ex) {    
     console.error(ex);     
     throw ex;   
   } 
 }
}

RagService 服务非常简单。ask 方法将输入提交给链并输出响应。该方法从响应中提取内容，将聊天历史记录中人类和 AI 之间的聊天消息存储在内存中，并将对话返回给模板引擎进行渲染。

添加 RAG 控制器

RAG 控制器将查询提交给链，获取结果，并将 HTML 代码发送回模板引擎进行渲染。

将模块导入 RAG 模块

将 RagModule 导入 AppModule

修改应用控制器以渲染 Handlebar 模板

HTMX 和 Handlebar 模板引擎

这是一个用于显示对话的简单界面。

default.hbs
<!DOCTYPE html>
<html lang="en"> 
 <head>  
   <meta charset="utf-8" />   
   <meta name="description" content="Angular tech book RAG powed by gemma 2 LLM." />   
   <meta name="author" content="Connie Leung" />   
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />   
   <title>{{{ title }}}</title>   
   <style>  
     *, *::before, *::after {   
         padding: 0;         
         margin: 0;         
         box-sizing: border-box;     
     }
</style> 
   <script src="https://cdn.tailwindcss.com?plugins=forms,typography"></script> 
 </head> 
 <body class="p-4 w-screen h-screen min-h-full"> 
   <script src="https://unpkg.com/htmx.org@2.0.1" integrity="sha384-QWGpdj554B4ETpJJC9z+ZHJcA/i59TyjxEPXiiUgN2WmTyV5OEZWCD6gQhgkdpB/" crossorigin="anonymous"></script>   
   <div class="h-full grid grid-rows-[auto_1fr_40px] grid-cols-[1fr]">   
     {{> header }}     
     {{{ body }}}     
     {{> footer }}   
   </div> 
 </body>
</html>

以上是具有页眉、页脚和正文的默认布局。正文最终显示的是 AI 与人类之间的对话。页眉部分则导入 Tailwind，用于设置 HTML 元素的样式，并导入 HTMX 来与服务器交互。

<div>
   <div class="mb-2 p-1 border border-solid border-[#464646] rounded-lg">  
       <p class="text-[1.25rem] mb-2 text-[#464646] underline">Architecture</p>       
       <ul>       
           <li class="text-[1rem]">Chat Model: Groq</li>           
           <li class="text-[1rem]">LLM: Gemma 2</li>           
           <li class="text-[1rem]">Embeddings: Gemini AI Embedding / HuggingFace Embedding</li>           
           <li class="text-[1rem]">Vector Store: Memory Vector Store / Qdrant Vector Store</li>           
           <li class="text-[1rem]">Retriever: Vector Store Retriever</li>       
       </ul>   
   </div>   
   <div id="chat-list" class="mb-4 h-[300px] overflow-y-auto overflow-x-auto"> 
       <div class="flex text-[#464646] text-[1.25rem] italic underline">       
           <span class="w-1/5 p-1 border border-solid border-[#464646]">Role</span>           
           <span class="w-4/5 p-1 border border-solid border-[#464646]">Result</span>      
       </div>   
   </div>   
   <form id="rag-form" hx-post="/rag" hx-target="#chat-list" hx-swap="beforeend swap:1s">
       <div>           
           <label>            
               <span class="text-[1rem] mr-1 w-1/5 mb-2 text-[#464646]">Question: </span>               
               <input type="text" name="query" class="mb-4 w-4/5 rounded-md p-2"      
                   placeholder="Ask me something"                   
                   aria-placeholder="Placeholder to ask question to RAG"></input>          
           </label>      
       </div>       
       <button type="submit" class="bg-blue-500 hover:bg-blue-700 text-white p-2 text-[1rem] flex justify-center items-center rounded-lg">         
           <span class="mr-1">Send</span><img class="w-4 h-4 htmx-indicator" src="/images/spinner.gif">       
       </button>  
   </form>
</div>

用户可以在文本框中输入问题，然后点击 “发送” 按钮。该按钮向 /rag 发出 POST 请求并将对话附加到列表中。

这个 LangChain RAG 应用到此就创建完成了，创建该应用时采用了 Gemma 2 模型，以生成响应。

​

如何学习AI大模型？

作为一名热心肠的互联网老兵，我决定把宝贵的AI知识分享给大家。 至于能学习到多少就看你的学习毅力和能力了 。我已将重要的AI大模型资料包括AI大模型入门学习思维导图、精品AI大模型学习书籍手册、视频教程、实战学习等录播视频免费分享出来。

这份完整版的大模型 AI 学习资料已经上传优快云，朋友们如果需要可以微信扫描下方优快云官方认证二维码免费领取【保证100%免费】

一、全套AGI大模型学习路线

AI大模型时代的学习之旅：从基础到前沿，掌握人工智能的核心技能！

二、640套AI大模型报告合集

这套包含640份报告的合集，涵盖了AI大模型的理论研究、技术实现、行业应用等多个方面。无论您是科研人员、工程师，还是对AI大模型感兴趣的爱好者，这套报告合集都将为您提供宝贵的信息和启示。

三、AI大模型经典PDF籍

随着人工智能技术的飞速发展，AI大模型已经成为了当今科技领域的一大热点。这些大型预训练模型，如GPT-3、BERT、XLNet等，以其强大的语言理解和生成能力，正在改变我们对人工智能的认识。 那以下这些PDF籍就是非常不错的学习资源。

四、AI大模型商业化落地方案

作为普通人，入局大模型时代需要持续学习和实践，不断提高自己的技能和认知水平，同时也需要有责任感和伦理意识，为人工智能的健康发展贡献力量。