Praison AI

Knowledge Base

Give agents documents to answer from

Give an Agent a Knowledge store and it searches it before answering.

Diagram

🤖 Agent

📚 Knowledge

✅ Answer

Quick Start

  1. Simple Usage
    typescript
    import { Agent, Knowledge } from 'praisonai';

const kb = new Knowledge();
await kb.store('Returns are accepted within 30 days.', { userId: 'u1' });
await kb.add('faqs.pdf', { userId: 'u1' });

const agent = new Agent({
  instructions: 'Answer support questions using the knowledge base.',
  knowledge: kb,
});

await agent.start('What is the return policy?');
  2. With Configuration
    typescript
    import { Agent, Knowledge, createEmbeddingProvider } from 'praisonai';

const kb = new Knowledge({
  embeddingProvider: createEmbeddingProvider({ model: 'text-embedding-3-small' }),
});

await kb.store('Returns are accepted within 30 days.', { userId: 'u1' });

const agent = new Agent({
  instructions: 'Answer support questions using the knowledge base.',
  knowledge: kb,
  verbose: true,
});

Store or Add

Use store() for raw text and add() for files, directories, or URLs.

Diagram

yes

no

yes

What are you loading?

Raw text?

kb.store(text, options)

File / directory / URL?

kb.add(pathOrUrl, options)

typescript
import { Knowledge } from 'praisonai';

const kb = new Knowledge();

await kb.store('Our return policy allows returns within 30 days.', { userId: 'u1' });

await kb.add('faqs.pdf', { userId: 'u1' });
await kb.add(['a.pdf', 'b.txt'], { userId: 'u1' });
await kb.add('https://example.com/help', { userId: 'u1' });

How Search Works

search() returns an envelope, not a bare array.

Diagram

🔎 query

Knowledge.search

{ query, results, totalCount }

results[i].text

typescript
const found = await kb.search('return policy', { userId: 'u1', limit: 5 });

console.log(found.query);        // 'return policy'
console.log(found.totalCount);   // number of matches before limit
console.log(found.results[0].text);

Each item in results:

FieldTypeDescription
idstringUnique identifier
textstringThe stored content
scorenumberRelevance score
metadataobjectStored metadata (always an object)
source`string \null`Source identifier (e.g. URL)
filename`string \null`Origin filename, if any
createdAt`string \null`Creation timestamp
updatedAt`string \null`Update timestamp

Configuration Options

Knowledge takes a Python-parity config object as its first argument.

Knowledge API Reference

Full KnowledgeStoreConfig options

Common Patterns

Scope by user, agent, or run

Every operation isolates memories by userId, agentId, and runId.

typescript
await kb.store('Refund promised: $49 by Aug 12.', {
  userId: 'customer_42',
  agentId: 'support_bot_v1',
  runId: 'session_2026_09_03',
});

const scoped = await kb.search('refund promise', {
  userId: 'customer_42',
  agentId: 'support_bot_v1',
});

Search from a tool

typescript
import { Agent, Knowledge, createTool } from 'praisonai';

const kb = new Knowledge();
await kb.store('Important company information...', { userId: 'u1' });

const searchKB = createTool({
  name: 'search_knowledge',
  description: 'Search the knowledge base for relevant information',
  parameters: {
    type: 'object',
    properties: { query: { type: 'string', description: 'Search query' } },
    required: ['query'],
  },
  execute: async ({ query }) => {
    const found = await kb.search(query, { userId: 'u1', limit: 3 });
    return found.results.map(r => r.text).join('\n');
  },
});

const agent = new Agent({
  instructions: 'Use search_knowledge before answering.',
  tools: [searchKB],
});

await agent.start('What do you know about the company?');

Manage stored items

typescript
const item = kb.get('memory-id');
const all = kb.getAll({ userId: 'u1' });
await kb.update('memory-id', 'Updated content');
kb.delete('memory-id');
kb.deleteAll({ userId: 'u1' });
kb.reset();

Best Practices

Always pass a scope

Supply at least one of userId, agentId, or runId on every store, add, and search call so tenants never read each other’s data.

Use add() for files, store() for text

add() reads and chunks files, directories, and URLs. store() takes text you already hold in memory.

Read results[i].text

search() returns { query, results, totalCount }. Read found.results[i].text, and page with limit.

Enable embeddings for semantic search

Pass embeddingProvider in the config to rank results by vector similarity instead of keyword overlap.

Related

Knowledge Base CLI

CLI knowledge commands

 Chunking

Split documents before storing