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Copy file name to clipboardExpand all lines: website/blog/2025-04-24-rag-chatbot/index.md
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---
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title: Building a RAG Chatbot with VoltAgent
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description: Learn how to enhance your chatbot with external knowledge using Retrieval-Augmented Generation (RAG) in VoltAgent.
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description: Build RAG chatbots with VoltAgent to use external knowledge.
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slug: rag-chatbot
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tags: [rag, tutorial]
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authors: necatiozmen
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import TabItem from '@theme/TabItem';
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import RetrieverMethodHelper from '@site/src/components/blog-widgets/RetrieverMethodHelper';
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Chatbots have become incredibly useful, haven't they? From customer support to personal assistants, they handle conversations pretty well. But sometimes, standard chatbots hit a wall – their knowledge is limited to what they were trained on. What if you want your chatbot to answer questions based on specific documents, recent data, or a private knowledge base?
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## Introduction
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Chatbots have become incredibly useful, haven't they? From customer support to personal assistants, they handle conversations pretty well. But sometimes, standard chatbots hit a wall – their knowledge is limited to what they were trained on.
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What if you want your chatbot to answer questions based on specific documents, recent data, or a private knowledge base?
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That's where **Retrieval-Augmented Generation (RAG)** comes in.
-[What RAG (Retrieval-Augmented Generation) is and why it's useful.](#what-is-rag-and-why-use-it)
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-[How VoltAgent's Retriever system facilitates RAG.](#voltagents-retriever-system)
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-[Setting up a VoltAgent project.](#setting-up-the-project)
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-[Implementing a custom `BaseRetriever` with a simple knowledge base.](#implementing-the-retriever-and-agent)
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-[Creating a VoltAgent `Agent` that uses the retriever directly.](#implementing-the-retriever-and-agent)
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-[Running and testing the RAG chatbot using the VoltAgent Console.](#running-the-agent)
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## What is RAG, and Why Use It?
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At its core, RAG is a technique that helps Large Language Models (LLMs) like the ones powering chatbots become smarter by giving them access to external information _before_ they generate a response.
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Think of it like this:
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:::info Think of it like this:
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1.**Retrieval:** When you ask a RAG-enabled chatbot a question, it first _retrieves_ relevant snippets of information from a predefined data source (like documents, databases, or websites).
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2.**Augmentation:** This retrieved information (the "context") is then _added_ to your original question.
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3.**Generation:** Finally, the LLM receives the combined prompt (your question + the retrieved context) and _generates_ an answer that's grounded in that specific information.
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-**Retrieval:** When you ask a RAG-enabled chatbot a question, it first _retrieves_ relevant snippets of information from a predefined data source (like documents, databases, or websites).
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-**Augmentation:** This retrieved information (the "context") is then _added_ to your original question.
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-**Generation:** Finally, the LLM receives the combined prompt (your question + the retrieved context) and _generates_ an answer that's grounded in that specific information.
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:::
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The result? Chatbots that can provide more accurate, up-to-date, and contextually relevant answers, going beyond their built-in knowledge. I find this incredibly powerful for building bots that need to know about specific product documentation, internal company policies, or recent news.
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## Introducing VoltAgent
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Before we dive into building, let me briefly mention **VoltAgent**. It's a TypeScript framework I've been working with that makes building sophisticated AI agents (like our RAG chatbot) much simpler. It handles a lot of the boilerplate, letting me focus on the core logic of my agents, including how they retrieve and use information.
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Before we dive into building, let me briefly mention [**VoltAgent**](https://github.com/VoltAgent/voltagent). It's a TypeScript framework I've been working with that makes building sophisticated AI agents (like our RAG chatbot) much simpler. It handles a lot of the boilerplate, letting me focus on the core logic of my agents, including how they retrieve and use information.
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## VoltAgent's Retriever System
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-**Direct Attachment (`agent.retriever`):** The retriever runs automatically _before every_ LLM call for that agent. Simple, ensures context is always fetched.
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-**As a Tool (`agent.tools`):** The LLM decides _when_ to call the retriever tool based on the conversation. More efficient and flexible, especially if retrieval isn't always needed.
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<br/>
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<RetrieverMethodHelper />
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For this tutorial, we'll use the **direct attachment** method for simplicity. Our agent will always try to fetch context from its small knowledge base before answering.
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## Let's Build a Simple RAG Chatbot!
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## Let's Build a Simple RAG Chatbot
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Okay, theory's great, but let's get hands-on. I'll show you how I built a basic RAG chatbot using VoltAgent that answers questions based on a small, hardcoded set of facts about VoltAgent itself.
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```
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with-rag-chatbot/
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├── src/
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│ └── index.ts # Our main agent logic will go here
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│ └── index.ts # Our main agent logic will go here
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├── package.json # Project dependencies
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├── tsconfig.json # TypeScript config
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├── .gitignore # Git ignore rules
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Before running, you need an API key for your chosen LLM provider (like OpenAI).
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**Create `.env` file:** In the root of your `with-rag-chatbot` project folder, create a file named `.env`.
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1.**Create `.env` file:** In the root of your `with-rag-chatbot` project folder, create a file named `.env`.
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**Add API Key:** Add your key like this:
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`bash title=".env"
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OPENAI_API_KEY=your_openai_api_key_here
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`
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(Replace `your_openai_api_key_here` with your actual key).
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2.**Add API Key:** Add your key like this:
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`bash title=".env"
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OPENAI_API_KEY=your_openai_api_key_here
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`
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(Replace `your_openai_api_key_here` with your actual key).
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**Install Dependencies:** Open your terminal _inside_ the `with-rag-chatbot` directory and run:
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<Tabs>
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<TabItemvalue="npm"label="npm"default>
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3.**Install Dependencies:** Open your terminal _inside_ the `with-rag-chatbot` directory and run:
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<Tabs>
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<TabItemvalue="npm"label="npm"default>
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```bash
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npm install
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```
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```bash
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npm install
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```
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</TabItem>
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<TabItem value="yarn" label="yarn">
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</TabItem>
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<TabItem value="yarn" label="yarn">
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```bash
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yarn install
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```
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```bash
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yarn install
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```
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</TabItem>
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<TabItem value="pnpm" label="pnpm">
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</TabItem>
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<TabItem value="pnpm" label="pnpm">
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```bash
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pnpm install
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```
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```bash
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pnpm install
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```
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</TabItem>
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</Tabs>
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</TabItem>
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</Tabs>
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**Start the Agent:** Run the development server:
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<Tabs>
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<TabItemvalue="npm"label="npm"default>
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4.**Start the Agent:** Run the development server:
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<Tabs>
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<TabItemvalue="npm"label="npm"default>
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```bash
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npm run dev
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```
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```bash
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npm run dev
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```
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</TabItem>
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<TabItem value="yarn" label="yarn">
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</TabItem>
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<TabItem value="yarn" label="yarn">
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```bash
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yarn dev
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```
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```bash
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yarn dev
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```
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</TabItem>
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<TabItem value="pnpm" label="pnpm">
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</TabItem>
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<TabItem value="pnpm" label="pnpm">
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```bash
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pnpm dev
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```
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```bash
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pnpm dev
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```
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</TabItem>
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</Tabs>
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</TabItem>
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</Tabs>
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You should see the VoltAgent server startup message, including a link to the Developer Console:
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As you can see, implementing a basic RAG system with VoltAgent is quite straightforward. By creating a custom `BaseRetriever` and attaching it to an `Agent`, I could quickly build a chatbot grounded in specific external knowledge.
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This simple example uses hardcoded data, but you could easily adapt the `KnowledgeBaseRetriever` to fetch data from a real database, API, or vector store for much more powerful applications. RAG opens up a lot of possibilities for creating smarter, more knowledgeable AI agents, and I think VoltAgent makes it significantly easier to get started.
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