Meta Knowledge Base

Self-building knowledge management system that learns and grows automatically.

Features

1. Auto-Capture

• Conversation Learning: Extract key information from chats
• Document Parsing: Extract from PDFs, docs, emails
• Web Scraping: Learn from visited pages
• File Watch: Monitor folders for new content

2. Knowledge Organization

• Auto-Tagging: Automatic topic categorization
• Entity Extraction: People, companies, concepts
• Relationship Mapping: Connect related ideas
• Version History: Track knowledge evolution

3. Semantic Search

• Vector Embeddings: Semantic similarity search
• Hybrid Search: Combine keyword + semantic
• Filtering: Filter by date, tags, source
• Ranking: Relevance-based results

4. Intelligent Q&A

• RAG Pipeline: Retrieve + Generate answers
• Context-Aware: Understand conversation context
• Citing Sources: Reference original knowledge
• Confidence Scoring: Show answer confidence

5. Continuous Learning

• User Feedback: Learn from corrections
• Implicit Learning: Learn from interactions
• Knowledge Updates: Keep information fresh
• Gap Identification: Find missing knowledge

Installation

pip install numpy faiss-cpu sentence-transformers

Usage

Initialize Knowledge Base

from meta_knowledge import KnowledgeBase

kb = KnowledgeBase(
    name="my_knowledge",
    embedding_model="paraphrase-multilingual-MiniLM-L12-v2"
)

Add Knowledge

# From text
kb.add(
    content="Python is a high-level programming language...",
    tags=["programming", "python"],
    metadata={"source": "user", "date": "2026-03-22"}
)

# From document
kb.add_from_file("document.pdf", tags=["research"])

# From URL
kb.add_from_url("https://example.com/article", tags=["news"])

Search

# Semantic search
results = kb.search(
    query="What is machine learning?",
    top_k=5
)

for r in results:
    print(f"{r.score:.2f} | {r.content[:100]}...")

Q&A

# Ask questions
answer = kb.ask(
    question="What do I know about AI?",
    include_sources=True
)

print(answer['answer'])
print("Sources:", answer['sources'])

Knowledge Graph

# Get entity relationships
graph = kb.get_knowledge_graph()

# Find related concepts
related = kb.find_related("Python", depth=2)

API Reference

Adding Knowledge

Searching

Q&A

Management

Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Sources   │────▶│  Ingestion  │────▶│   Storage    │
│ - Chat      │     │ - Parser    │     │ - Vector DB  │
│ - Docs      │     │ - Embedder  │     │ - Graph DB   │
│ - Web       │     │ - Indexer   │     │ - Document   │
└─────────────┘     └─────────────┘     └─────────────┘
                                           │
                    ┌──────────────────────┘
                    ▼
┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Query     │────▶│   Retrieve  │────▶│   Generate  │
│ - Search    │     │ - Vector    │     │ - LLM       │
│ - Ask       │     │ - Graph     │     │ - Cite      │
└─────────────┘     └─────────────┘     └─────────────┘

Embedding Models

Use Cases

• Personal Assistant: Remember everything
• Team Wiki: Shared knowledge base
• Customer Support: Q&A automation
• Research: Paper search & summarization
• Codebase: Documentation search

Best Practices

1. Regular Updates: Keep knowledge fresh
2. Quality over Quantity: Clean data matters
3. Use Tags: Organize for better retrieval
4. User Feedback: Improve with corrections
5. Backup: Export regularly

Integration

With OpenClaw

# Auto-capture from conversations
@hookimpl
def after_message(message, response):
    kb.add(
        content=f"User asked about: {extract_topics(message)}",
        tags=["conversation", extract_topics(message)]
    )

With Skills

# Use knowledge in skills
def my_skill(query):
    context = kb.search(query, top_k=3)
    return generate_response(query, context)

Future Capabilities

• Multi-modal knowledge (images, audio)
• Real-time sync across devices
• Collaborative knowledge base
• Automatic knowledge validation