Praison AI

RAG Agent

Build Retrieval-Augmented Generation agents with knowledge bases

Build agents that retrieve and cite information from knowledge bases.

Diagram

User

RAG Agent

Knowledge Base

Response

Quick Start

  1. Simple Usage
    typescript
    import { Agent, createMemoryVectorStore } from 'praisonai-ts';

// Create a vector store for RAG
const vectorStore = createMemoryVectorStore();

// Add documents to the knowledge base
await vectorStore.addDocuments([
  { id: '1', content: 'PraisonAI is an AI agent framework', metadata: { source: 'docs' } },
  { id: '2', content: 'Agents can use tools to accomplish tasks', metadata: { source: 'docs' } },
]);

// Create an agent with RAG capabilities
const agent = new Agent({
  name: 'RAGAgent',
  instructions: 'You answer questions using the knowledge base. Always cite sources.',
  knowledge: vectorStore,
});

const response = await agent.chat('What is PraisonAI?');
console.log(response);
  2. With Configuration

    Use ragConfig, production vector stores, and reranking — see Configuration Options below.

Configuration Options

typescript
import { Agent, createPineconeStore } from 'praisonai-ts';

const vectorStore = await createPineconeStore({
  apiKey: process.env.PINECONE_API_KEY!,
  index: 'my-knowledge-base',
  namespace: 'docs',
});

const agent = new Agent({
  name: 'RAGAgent',
  instructions: 'You are a helpful assistant with access to a knowledge base.',
  knowledge: vectorStore,
  ragConfig: {
    topK: 5,                    // Number of results to retrieve
    minScore: 0.7,              // Minimum similarity score
    includeMetadata: true,      // Include document metadata
    rerank: true,               // Enable reranking
    citationFormat: 'inline',   // Citation format: inline, footnote, none
  },
});

Vector Store Options

Memory Vector Store (Development)

typescript
import { createMemoryVectorStore } from 'praisonai-ts';

const store = createMemoryVectorStore();

Pinecone

typescript
import { createPineconeStore } from 'praisonai-ts';

const store = await createPineconeStore({
  apiKey: process.env.PINECONE_API_KEY!,
  index: 'my-index',
  namespace: 'my-namespace',
});

Weaviate

typescript
import { createWeaviateStore } from 'praisonai-ts';

const store = await createWeaviateStore({
  host: process.env.WEAVIATE_HOST!,
  apiKey: process.env.WEAVIATE_API_KEY,
  className: 'Documents',
});

Qdrant

typescript
import { createQdrantStore } from 'praisonai-ts';

const store = await createQdrantStore({
  url: process.env.QDRANT_URL!,
  apiKey: process.env.QDRANT_API_KEY,
  collectionName: 'my-collection',
});

ChromaDB

typescript
import { createChromaStore } from 'praisonai-ts';

const store = await createChromaStore({
  path: './chroma-data',
  collectionName: 'my-collection',
});

Adding Documents

typescript
// Add single document
await vectorStore.addDocument({
  id: 'doc-1',
  content: 'Document content here',
  metadata: { source: 'manual', category: 'guide' },
});

// Add multiple documents
await vectorStore.addDocuments([
  { id: 'doc-2', content: 'First document', metadata: {} },
  { id: 'doc-3', content: 'Second document', metadata: {} },
]);

// Add from file (PDF, TXT, MD)
await vectorStore.addFromFile('./document.pdf');

Reranking

Enable reranking for better retrieval quality:

typescript
import { Agent, createCohereReranker } from 'praisonai-ts';

const reranker = createCohereReranker({
  apiKey: process.env.COHERE_API_KEY!,
  model: 'rerank-english-v3.0',
});

const agent = new Agent({
  name: 'RAGAgent',
  instructions: 'Answer questions using the knowledge base.',
  knowledge: vectorStore,
  reranker: reranker,
});

Graph RAG

For complex relationships between documents:

typescript
import { createGraphRAG } from 'praisonai-ts';

const graphRAG = await createGraphRAG({
  vectorStore: vectorStore,
  extractEntities: true,
  extractRelationships: true,
});

const agent = new Agent({
  name: 'GraphRAGAgent',
  instructions: 'Answer questions using the knowledge graph.',
  knowledge: graphRAG,
});

Best Practices

Chunk documents appropriately

Split large documents into meaningful chunks.

Use metadata

Add source, date, and category metadata for filtering.

Enable reranking

Improves retrieval quality for complex queries.

Set appropriate topK

Balance between context size and relevance.

Environment Variables

VariableRequiredDescription
OPENAI_API_KEYYesFor embeddings and LLM
PINECONE_API_KEYFor PineconePinecone API key
COHERE_API_KEYFor rerankingCohere API key

Related

Knowledge Base

Managing knowledge bases

 Graph RAG

Graph-based RAG