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This guide walks through building a complete RAG (Retrieval Augmented Generation) system with RadarOS. The agent will search a knowledge base of documents to answer questions.

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Overview

User Question → Agent → KnowledgeBase Tool → Vector Search → Context → LLM → Answer
1

Set up embeddings and vector store

Choose an embedding provider and vector store for your documents.
2

Create a KnowledgeBase

Initialize the KnowledgeBase with your vector store.
3

Add documents

Index your content into the knowledge base.
4

Create an agent with the KB tool

Use kb.asTool() to give the agent retrieval capabilities.
5

Query

Ask questions and get grounded answers.

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Complete Example

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In-Memory RAG

import {
  Agent,
  openai,
  KnowledgeBase,
  InMemoryVectorStore,
  OpenAIEmbedding,
} from "@radaros/core";

const embedder = new OpenAIEmbedding();
const vectorStore = new InMemoryVectorStore(1536);

const kb = new KnowledgeBase({
  name: "company-docs",
  vectorStore,
});

await kb.initialize();

// Add documents
await kb.addDocuments([
  {
    id: "pricing",
    content: "Our Pro plan costs $49/month and includes unlimited agents, 100k API calls, and priority support.",
    metadata: { category: "pricing" },
  },
  {
    id: "features",
    content: "RadarOS supports multi-agent teams, workflows, RAG, and multi-modal inputs including images and audio.",
    metadata: { category: "features" },
  },
  {
    id: "setup",
    content: "Install with npm install @radaros/core. Set your OPENAI_API_KEY environment variable to get started.",
    metadata: { category: "getting-started" },
  },
]);

// Create agent with KB as a tool
const agent = new Agent({
  name: "support-bot",
  model: openai("gpt-4o"),
  instructions: "You are a support agent. Use the search tool to find answers from the knowledge base. Always cite sources.",
  tools: [kb.asTool()],
  logLevel: "info",
});

const result = await agent.run("How much does the Pro plan cost?");
console.log(result.text);
// "The Pro plan costs $49/month and includes unlimited agents, 100k API calls, and priority support."

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RAG with Qdrant

import {
  Agent,
  openai,
  KnowledgeBase,
  QdrantVectorStore,
  OpenAIEmbedding,
} from "@radaros/core";

const embedder = new OpenAIEmbedding();

const vectorStore = new QdrantVectorStore({
  url: "http://localhost:6334",
  collectionName: "docs",
  dimensions: 1536,
});

const kb = new KnowledgeBase({
  name: "docs",
  vectorStore,
});

await kb.initialize();

// Add documents (only needed once)
await kb.addDocuments([
  { id: "doc-1", content: "Your document content here..." },
  { id: "doc-2", content: "Another document..." },
]);

const agent = new Agent({
  name: "rag-agent",
  model: openai("gpt-4o"),
  instructions: "Answer questions using the knowledge base.",
  tools: [kb.asTool({ topK: 3, description: "Search the documentation" })],
});

const result = await agent.run("What does the documentation say about X?");
console.log(result.text);

​
RAG with MongoDB Atlas

import {
  Agent,
  openai,
  KnowledgeBase,
  MongoDBVectorStore,
  OpenAIEmbedding,
} from "@radaros/core";

const embedder = new OpenAIEmbedding();

const vectorStore = new MongoDBVectorStore({
  uri: "mongodb+srv://user:pass@cluster.mongodb.net",
  dbName: "myapp",
  collectionName: "documents",
  indexName: "vector_index",
  dimensions: 1536,
});

const kb = new KnowledgeBase({
  name: "knowledge",
  vectorStore,
});

await kb.initialize();

// Add and query as above
For the best retrieval accuracy, enable hybrid search. It combines semantic (vector) and keyword (BM25) matching: 
import {
  Agent, openai, KnowledgeBase,
  InMemoryVectorStore, OpenAIEmbedding,
} from "@radaros/core";

const kb = new KnowledgeBase({
  name: "Product Docs",
  vectorStore: new InMemoryVectorStore(new OpenAIEmbedding()),
  searchMode: "hybrid",
});

await kb.initialize();
await kb.addDocuments([
  { id: "pricing", content: "Our Pro plan costs $49/month with unlimited API calls." },
  { id: "sla", content: "Enterprise SLA guarantees 99.9% uptime with 4-hour response time." },
]);

const agent = new Agent({
  name: "support-bot",
  model: openai("gpt-4o"),
  tools: [kb.asTool({ topK: 3, searchMode: "hybrid" })],
  instructions: "Answer using the knowledge base. Cite sources.",
});

const result = await agent.run("What's the SLA uptime guarantee?");
See Hybrid Search for tuning weights, BM25 details, and comparison of all three modes.

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Customizing the Tool

The asTool() method accepts configuration to customize how the knowledge base is exposed to the agent: 
kb.asTool({
  toolName: "search_docs",                   // Custom tool name
  description: "Search company documentation", // Custom description
  topK: 5,                                    // Number of results
  minScore: 0.7,                              // Minimum relevance score
  formatResults: (results) => {               // Custom result formatting
    return results
      .map((r) => `[${r.document.id}] ${r.document.content}`)
      .join("\n\n");
  },
});
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toolName
string
Name of the generated tool. Defaults to search_<collection>.
​
description
string
Description shown to the LLM. Auto-generated if not provided.
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topK
number
default:"5"
Maximum number of results to return.
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minScore
number
Minimum similarity score threshold.
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formatResults
(results: VectorSearchResult[]) => string
Custom function to format search results into a string for the LLM.