RAG Agent - PraisonAI

Build agents that retrieve and cite information from knowledge bases.

Quick Start

Simple Usage

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);

With Configuration

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

Configuration Options

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)

import { createMemoryVectorStore } from 'praisonai-ts';

const store = createMemoryVectorStore();

Pinecone

import { createPineconeStore } from 'praisonai-ts';

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

Weaviate

import { createWeaviateStore } from 'praisonai-ts';

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

Qdrant

import { createQdrantStore } from 'praisonai-ts';

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

ChromaDB

import { createChromaStore } from 'praisonai-ts';

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

Adding Documents

// 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:

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:

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

Variable	Required	Description
`OPENAI_API_KEY`	Yes	For embeddings and LLM
`PINECONE_API_KEY`	For Pinecone	Pinecone API key
`COHERE_API_KEY`	For reranking	Cohere API key

Knowledge Base

Managing knowledge bases

Graph RAG

Graph-based RAG

​Quick Start

​Configuration Options

​Vector Store Options

​Memory Vector Store (Development)

​Pinecone

​Weaviate

​Qdrant

​ChromaDB

​Adding Documents

​Reranking

​Graph RAG

​Best Practices

​Environment Variables

​Related

Knowledge Base

Graph RAG

Quick Start

Configuration Options

Vector Store Options

Memory Vector Store (Development)

Pinecone

Weaviate

Qdrant

ChromaDB

Adding Documents

Reranking

Graph RAG

Best Practices

Environment Variables

Related