Skip to main content
Vector stores give agents the ability to store and retrieve knowledge from large document collections — enabling RAG (Retrieval Augmented Generation) where agents answer questions grounded in your data.

​
Quick Start

1

Simple Usage

import { Agent } from 'praisonai';

const agent = new Agent({
  name: 'Knowledge Assistant',
  instructions: 'Answer questions using the provided knowledge base.',
  knowledge: ['./docs/', './manuals/product.pdf']
});

const response = await agent.chat('What is our refund policy?');
console.log(response);
2

With Configuration

import { Agent, createPineconeStore } from 'praisonai';

const vectorStore = createPineconeStore({
  apiKey: process.env.PINECONE_API_KEY
});

const agent = new Agent({
  name: 'Research Assistant',
  instructions: 'Search the knowledge base and answer accurately.',
  knowledge: {
    vectorStore,
    indexName: 'company-docs'
  }
});

await agent.chat('Find information about our pricing plans');
3

In-Memory Store (Testing)

import { Agent, createMemoryVectorStore } from 'praisonai';

const vectorStore = createMemoryVectorStore();

const agent = new Agent({
  instructions: 'Answer questions from the knowledge base.',
  knowledge: { vectorStore, indexName: 'test-docs' }
});

​
How It Works

​
Supported Vector Stores

StoreUse Case
PineconeProduction, large-scale
WeaviateHybrid search, self-hosted
QdrantHigh performance, self-hosted
ChromaDevelopment, local
MemoryTesting, ephemeral

​
Configuration Options

Vector Store API Reference

TypeScript vector store configuration options

​
Common Patterns

​
Agent with Search Tool

import { Agent, createTool, createPineconeStore } from 'praisonai';

const vectorStore = createPineconeStore({
  apiKey: process.env.PINECONE_API_KEY
});

const searchDocs = createTool({
  name: 'search_docs',
  description: 'Search the knowledge base for relevant information',
  parameters: {
    type: 'object',
    properties: {
      query: { type: 'string', description: 'Search query' }
    },
    required: ['query']
  },
  execute: async ({ query }) => {
    const results = await vectorStore.query({
      indexName: 'documents',
      queryText: query,
      topK: 5
    });
    return results.map(r => r.content).join('\n\n');
  }
});

const agent = new Agent({
  name: 'Research Assistant',
  instructions: 'Use the search tool to find accurate information.',
  tools: [searchDocs]
});

​
Multi-Agent with Shared Knowledge

import { Agent, createPineconeStore } from 'praisonai';

const sharedStore = createPineconeStore({
  apiKey: process.env.PINECONE_API_KEY
});

const researcher = new Agent({
  name: 'Researcher',
  instructions: 'Find relevant information from the knowledge base.',
  knowledge: { vectorStore: sharedStore, indexName: 'docs' }
});

const writer = new Agent({
  name: 'Writer',
  instructions: 'Write clear responses based on the research.',
  knowledge: { vectorStore: sharedStore, indexName: 'docs' }
});

​
Setup Pinecone Index

import { createPineconeStore } from 'praisonai';

const pinecone = createPineconeStore({
  apiKey: process.env.PINECONE_API_KEY
});

await pinecone.createIndex({
  indexName: 'agent-knowledge',
  dimension: 1536,
  metric: 'cosine'
});

​
Best Practices

createMemoryVectorStore() requires no external dependencies. Switch to Pinecone or Qdrant before going to production.
Split large documents into 500–1000 token chunks before indexing. Smaller chunks retrieve more precisely.
A top_k of 3–5 gives the agent focused context. Higher values can dilute relevance.
Use the same embedding model for indexing and querying. Mixing models produces incorrect similarity scores.

Knowledge Base

Higher-level RAG for agents

Embeddings

Create and manage embeddings