逐步解析 | 如何使用 LangChain、NestJS 和 Gemma 2 构建一个 Agentic RAG 应用

privateasynccreateFormRetrieverTool: Promise<DynamicStructuredTool< any>> { constretriever = awaitthis.createFormRetriever; returncreateRetrieverTool(retriever, { name: 'angular_form_search', deion: `Search for information about Angular reactive, typed reactive, template-drive, and dynamic forms.For any questions about Angular Forms, you must use this tool! Please return the answer in markdown.If you do not know the answer, please say you don't know.`, });}

asynccreateRetrieverTools: Promise<DynamicStructuredTool< any>[]> { returnPromise.all([ this.createSignalRetrieverTool, this.createFormRetrieverTool]); }

createSignalRetrieverTool函数调用 createRetrieverTool方法从 Angular Signal 检索器创建工具。createFormRetrieverTool从 Angular Form 检索器创建工具。最后，createRetrieverTools函数调用 createSignalRetrieverTool和 createFormRetrieverTool来返回检索工具数组。

创建 Agent 模块

智能体模块负责创建一个 LangChain 智能体，该智能体执行各种工具以生成响应。

创建常量

// agent.constant.ts

exportconstAGENT_EXECUTOR = 'AGENT_EXECUTOR';

// groq-chat-model.constant.ts

exportconstGROQ_CHAT_MODEL = 'GROQ_CHAT_MODEL';

// tools.constant.ts

exportconstTOOLS = 'TOOLS';

定义常量是为了在 NestJS 应用中注入自定义资源。

Providers

GROQ_CHAT_MODEL创建一个应用了 Gemma 2 模型的 Groq 聊天模型。

// groq-chat-model.provider.ts

import{ ChatGroq } from'@langchain/groq'; import{ Inject, Provider } from'@nestjs/common'; import{ ConfigService } from'@nestjs/config'; import{ GroqConfig } from'~configs/types/groq-config.type'; import{ GROQ_CHAT_MODEL } from'../constants/groq-chat-model.constant';

exportfunctionInjectChatModel( ) { returnInject(GROQ_CHAT_MODEL); }

exportconstGroqChatModelProvider: Provider<ChatGroq> = { provide: GROQ_CHAT_MODEL, useFactory: ( configService: ConfigService) => { const{ apiKey, model } = configService.get<GroqConfig>( 'groq'); returnnewChatGroq({ apiKey,model,temperature: 0.3, maxTokens: 2048, streaming: false, });},inject: [ConfigService], };

TOOLS注入了一组工具，供智能体执行并生成结果。

// tool.provider.ts

import{ DuckDuckGoSearch } from'@langchain/community/tools/duckduckgo_search'; import{ Tool } from'@langchain/core/tools'; import{ Provider } from'@nestjs/common'; import{ ConfigService } from'@nestjs/config'; import{ AngularDocsService } from'~angular-docs/application/angular-docs.service'; import{ DuckDuckGoConfig } from'~configs/types/duck-config.type'; import{ TOOLS } from'../constants/tools.constant'; import{ DragonBallService } from'../dragon-ball.service';

exportconstToolsProvider: Provider<Tool[]> = { provide: TOOLS, useFactory: async(service: ConfigService, dragonBallService: DragonBallService, docsService: AngularDocsService) => { const{ maxResults } = service.get<DuckDuckGoConfig>( 'duckDuckGo'); constduckTool = newDuckDuckGoSearch({ maxResults }); constcharacterFiltertool = dragonBallService.createCharactersFilterTool; constretrieverTools = awaitdocsService.createRetrieverTools; return[duckTool, characterFiltertool, ...retrieverTools]; },inject: [ConfigService, DragonBallService, AngularDocsService], };

DuckDuckGoSearch是一款用于在互联网上搜索信息的 LangChain 工具。characterFilterTool是一款自定义工具，可调用 Dragon Ball API 根据给定条件筛选角色。retrieverTools是一组工具，用于返回 Angular Signal 和 Angular Form 的知识。ToolsProvider提供程序会返回一个工具列表，智能体可以执行这些工具来获取信息。

import{ ChatPromptTemplate } from'@langchain/core/prompts'; import{ Tool } from'@langchain/core/tools'; import{ ChatGroq } from'@langchain/groq'; import{ Inject, Provider } from'@nestjs/common'; import{ AgentExecutor, createToolCallingAgent } from'langchain/agents'; import{ AGENT_EXECUTOR } from'../constants/agent.constant'; import{ GROQ_CHAT_MODEL } from'../constants/groq-chat-model.constant'; import{ TOOLS } from'../constants/tools.constant';

constprompt = ChatPromptTemplate.fromMessages([ [ 'system', 'You are a helpful assistant.'], [ 'placeholder', '{chat_history}'], [ 'human', '{input}'], [ 'placeholder', '{agent_scratchpad}'], ]);

exportfunctionInjectAgent( ) { returnInject(AGENT_EXECUTOR); }

exportconstAgentExecutorProvider: Provider<AgentExecutor> = { provide: AGENT_EXECUTOR, useFactory: async(llm: ChatGroq, tools: Tool[]) => { constagent = awaitcreateToolCallingAgent({ llm, tools, prompt, streamRunnable: false}); console.log( 'tools', tools);

returnAgentExecutor.fromAgentAndTools({ agent,tools,verbose: true, });},inject: [GROQ_CHAT_MODEL, TOOLS], };

AgentExecutorProvider提供程序使用智能体和工具创建智能体执行器。智能体执行器生成函数调用，智能体负责调用工具以生成相关响应。

在 DragonBall Service 中

创建自定义工具

import{ DynamicStructuredTool, tool } from'@langchain/core/tools'; import{ HttpService } from'@nestjs/axios'; import{ Injectable } from'@nestjs/common'; import{ z } from'zod'; import{ CharacterFilter } from'./types/character-filter.type'; import{ Character } from'./types/character.type';

exportconstcharacterFilterSchema = z.object({ name: z.string.optional.describe( 'Name of a Dragon Ball Z character.'), gender: z.enum([ 'Male', 'Female', 'Unknown']).optional.describe( 'Gender of a Dragon Ball Z caracter.'), race: z.enum([ 'Human', 'Saiyan']) .optional.describe( 'Race of a Dragon Ball Z character'), affiliation: z.enum([ 'Z Fighter', 'Red Ribbon Army', 'Namekian Warrior']) .optional.describe( 'Affiliation of a Dragon Ball Z character.'), });

@InjectableexportclassDragonBallService { constructor( privatereadonly httpService: HttpService ) {}

asyncgetCharacters(characterFilter: CharacterFilter): Promise< string> { constfilter = this.buildFilter(characterFilter);

if(!filter) { returnthis.generateMarkdownList([]); }

constcharacters = awaitthis.httpService.axiosRef .get<Character[]>( `https://dragonball-api.com/api/characters? ${filter}` ) .then( ( { data }) => data);

returnthis.generateMarkdownList(characters); }

createCharactersFilterTool: DynamicStructuredTool< any> { returntool( async(input: CharacterFilter): Promise< string> => this.getCharacters(input), { name: 'dragonBallCharacters', deion: `Call Dragon Ball filter characters API to retrieve characters by name, race, affiliation, or gender.`, schema: characterFilterSchema,});}

getCharacters方法接受姓名、性别、种族和隶属关系等可选条件。然后，它将查询参数附加到 Dragon Ball URL 以检索字符并生成 markdown 文件。createCharactersFilterTool从 LangChain 导入工具，用于创建可供智能体使用的自定义工具。

创建 Agent 执行器服务

import{ AIMessage, HumanMessage } from'@langchain/core/messages'; import{ Injectable } from'@nestjs/common'; import{ AgentExecutor } from'langchain/agents'; import{ ToolExecutor } from'./interfaces/tool.interface'; import{ InjectAgent } from'./providers/agent-executor.provider'; import{ AgentContent } from'./types/agent-content.type';

@InjectableexportclassAgentExecutorService implementsToolExecutor { privatechatHistory = [];

constructor( @InjectAgentprivateagentExecutor: AgentExecutor ) {}

asyncexecute(input: string): Promise<AgentContent[]> { const{ output } = awaitthis.agentExecutor.invoke({ input, chat_history: this.chatHistory });

this.chatHistory = this.chatHistory.concat([ newHumanMessage(input), newAIMessage(output)]); if( this.chatHistory.length > 10) { // remove the oldest Human and AI Messagesthis.chatHistory.splice( 0, 2); }return[ {role: 'Human', content: input,},{role: 'Assistant', content: output,},];}}

AgentExecutorService服务非常简单。它注入 AgentExecutor的实例，调用 invoke方法，将输入提交到链上并输出一个字符串。该方法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。

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添加 Agent 控制器