Knowledge Bases & Memory

Osiris provides intelligent knowledge management and memory systems that enable AI agents and humans to build, share, and monetize collective intelligence. Transform any content into structured knowledge that can be accessed everywhere.

🧠 Knowledge Architecture

Knowledge Hierarchy

Knowledge in Osiris follows a structured hierarchy:

Knowledge Base
├── Knowledge Sources (Documents, Videos, URLs, etc.)
	├── Knowledge Unit (Specific insights, mechanisms, processes)
    ├── Knowledge Unit (Frameworks, analysis, data)
    └── Knowledge Unit (Connections, implications)
Memory Systems (Short-term, Long-term, Shared)

LLM-Powered Knowledge Extraction

Every piece of content is processed by advanced LLMs to extract high-value knowledge:

• Mechanism Identification: How systems work and why they behave certain ways
• Insight Extraction: Non-obvious patterns and counterintuitive findings
• Evidence Integration: Specific data, case studies, and supporting examples
• Framework Recognition: Complete methodologies and problem-solving approaches
• Connection Mapping: Relationships between concepts and domains

📚 Knowledge Bases

Content Sources

Create knowledge bases from diverse content types:

API Integration

import { createKnowledgeBase, addKnowledgeSource } from '@osiris-ai/agent-sdk';

// Create a new knowledge base
const knowledgeBase = await createKnowledgeBase({
  name: "AI Research Compendium",
  description: "Cutting-edge AI research papers and insights",
  tags: ["artificial-intelligence", "research", "machine-learning"],
  isPublic: true,
  publicMetadata: {
    price: 50 // Osiris credits
  }
});

// Add various content sources
await addKnowledgeSource(knowledgeBase.id, {
  sourceType: 'url',
  source: 'https://arxiv.org/abs/2023.12345'
});

await addKnowledgeSource(knowledgeBase.id, {
  sourceType: 'youtube_url', 
  source: 'https://youtube.com/watch?v=ai-lecture'
});

await addKnowledgeSource(knowledgeBase.id, {
  sourceType: 'file',
  file: researchPaper.pdf
});

Processing Pipeline

1. Content Ingestion: Files, URLs, and text are processed and normalized
2. LLM Analysis: Advanced prompts extract high-value knowledge units
3. Knowledge Units: Structured insights with names, content, tags, and types
4. Indexing: Full-text search and semantic indexing for retrieval
5. Ready for Use: Knowledge becomes available across all Osiris tools

Knowledge Unit Types

The LLM automatically categorizes extracted knowledge:

• 🔧 Mechanism: How systems or processes work internally
• 🏗️ Framework: Complete methodologies and approaches
• 💡 Insight: Non-obvious patterns and discoveries
• 📋 Process: Step-by-step procedures and workflows
• 📊 Analysis: Deep examinations revealing patterns and causes

Example Knowledge Extraction

Source: Research paper on distributed systems

Generated Knowledge Units:

[
  {
    "name": "CAP Theorem Trade-offs in Practice",
    "content": "In distributed systems, the CAP theorem forces choosing between consistency and availability during network partitions. However, practical systems like Cassandra use tunable consistency levels, allowing applications to choose CP or AP behavior per operation. This enables optimizing for different use cases within the same system.",
    "tags": ["distributed-systems", "cap-theorem", "consistency", "availability"],
    "type": "mechanism"
  },
  {
    "name": "Leader Election Consensus Algorithm",
    "content": "Raft consensus algorithm achieves leader election through randomized timeouts. Followers wait random intervals (150-300ms) before becoming candidates. This prevents split votes and ensures exactly one leader emerges. The randomization is crucial - deterministic timeouts lead to perpetual splits.",
    "tags": ["consensus", "raft", "leader-election", "algorithms"],
    "type": "process"
  }
]

🧠 Memory Systems

Osiris implements three types of memory for comprehensive AI agent cognition:

1. Short-Term Memory

Purpose: Immediate context and working memory for current tasks

• Conversation History: Full dialogue context with users
• Active Tasks: Current objectives and progress tracking
• Session State: Variables, preferences, and temporary data
• Recent Actions: Cache of recent operations and results

// Short-term memory automatically managed
const conversation = await getConversationHistory("chat-id");
const activeTasks = await getActiveTasks("task-id");

2. Long-Term Memory

Purpose: Persistent knowledge and experience accumulation

Knowledge Graphs

• Semantic relationships between concepts
• Cross-domain connections and patterns
• Hierarchical concept organization
• Knowledge graph mapping through Auth Hub connectors
• Cross-platform behavior tracking via native clients

Factual Memory

• Verified facts and data points
• User preferences and behavior patterns
• Domain-specific expertise accumulation

Episodic Memory

• Significant events and experiences
• Problem-solving histories
• Learning milestones and insights

// Long-term memory integration
const userProfile = await getLongTermProfile("user-id");
const domainExpertise = await getKnowledgeGraph("machine-learning");
const pastSolutions = await getEpisodicMemory({
  domain: "software-architecture",
  problemType: "scalability"
});

3. Shared Contextual Memory

Purpose: Collaborative intelligence and collective learning

• Team Memory: Shared knowledge bases and experiences
• Organizational Intelligence: Company-wide insights and practices
• Community Wisdom: Crowdsourced solutions and patterns
• Cross-User Learning: Anonymized patterns and optimizations
• Cross Agent Collaboration: Shared knowledge and insights across agents

// Shared memory access
const teamKnowledge = await getSharedMemory({
  scope: "team",
  teamId: "team-id"
});

const communityInsights = await getSharedMemory({
  scope: "community", 
  domain: "defi-trading",
  filter: "high-confidence"
});

🌐 Osiris Extension

Universal Knowledge Access

The upcoming Osiris browser extension will bring your knowledge bases and memory everywhere you work.

Key Features:

• Context-Aware Suggestions: Relevant knowledge appears based on current webpage
• Universal Search: Access all knowledge bases from any browser tab
• Smart Capture: One-click knowledge extraction from any content
• Memory Integration: Short and long term memory integration through LLM clients
• Cross-Platform Sync: Same knowledge available across all devices

Installation & Setup

# Will be available from browser extension stores
Chrome Web Store: "Osiris Knowledge Extension" (Coming Soon)
Firefox Add-ons: "Osiris Universal Knowledge" (Coming Soon)  
Safari Extensions: "Osiris Memory Assistant" (Coming Soon)

💰 Knowledge Marketplace

Monetizing Knowledge

Transform your expertise into revenue streams:

Public Knowledge Bases

• Set prices in Osiris credits
• Automatic licensing and access control
• Revenue sharing with quality contributors
• Usage analytics and earnings tracking

Subscription Models

• Premium knowledge tiers
• Regular content updates
• Exclusive access to specialized domains
• Community features and discussions

Marketplace Features

// Create marketable knowledge base
const commercialBase = await createKnowledgeBase({
  name: "Advanced DeFi Strategies",
  description: "Professional trading strategies and risk management",
  tags: ["defi", "trading", "professional"],
  isPublic: true,
  publicMetadata: {
    price: 100, // Osiris credits
    category: "finance",
    difficulty: "advanced",
    estimatedValue: "high"
  }
});

🚀 Getting Started

Quick Start Guide (Coming Soon)

1. Create Knowledge Base: Upload your first documents or URLs
2. Wait for Processing: LLM extracts knowledge units automatically
3. Explore Results: Browse generated insights and knowledge units
4. Install Extension: Get universal access across all your tools
5. Share & Monetize: Make valuable knowledge public for credits

Best Practices

Content Selection

• Choose high-quality, authoritative sources
• Include diverse perspectives and use cases
• Focus on actionable insights and mechanisms
• Provide sufficient context and examples

Knowledge Base Organization

• Use descriptive, searchable names and tags
• Group related content in logical hierarchies
• Regular updates and content refreshes
• Clear documentation and usage guidelines

Monetization Strategy

• Price based on value and uniqueness
• Provide preview samples and descriptions
• Build reputation through quality content
• Engage with community feedback and requests

Ready to build your knowledge empire? Start with our Getting Started Guide and transform your expertise into intelligent, accessible knowledge that works everywhere.