Vector Store - PraisonAI

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

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);

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');

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

Store	Use Case
Pinecone	Production, large-scale
Weaviate	Hybrid search, self-hosted
Qdrant	High performance, self-hosted
Chroma	Development, local
Memory	Testing, 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

Use memory store for development

createMemoryVectorStore() requires no external dependencies. Switch to Pinecone or Qdrant before going to production.

Chunk large documents

Split large documents into 500–1000 token chunks before indexing. Smaller chunks retrieve more precisely.

Set top_k carefully

A top_k of 3–5 gives the agent focused context. Higher values can dilute relevance.

Keep embeddings consistent

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

​Quick Start

​How It Works

​Supported Vector Stores

​Configuration Options

Vector Store API Reference

​Common Patterns

​Agent with Search Tool

​Multi-Agent with Shared Knowledge

​Setup Pinecone Index

​Best Practices

​Related