Praison AI

Vector Store

Give your agents long-term knowledge with vector database integrations

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.

Diagram

Vector Store

📄 Documents

🧠 Embed

💾 Vector Store

❓ Query

🧠 Embed

✅ Top Results

🤖 Agent

Quick Start

  1. Simple Usage
    typescript
    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
    typescript
    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)
    typescript
    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

Diagram
Vector StoreEmbedderAgentUserVector StoreEmbedderAgentUser“What is our refund policy?”Embed questionQuery vectorSearch top_k=5Relevant documentsGenerate answer with contextGrounded response

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

typescript
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

typescript
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

typescript
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.

Related

Knowledge Base

Higher-level RAG for agents

 Embeddings

Create and manage embeddings