π¦ ClawHub
Meta Knowledge Base
by @jason-aka-chen
AI-powered knowledge base builder that automatically captures, organizes, and retrieves information. Learns from conversations, documents, and interactions t...
TERMINAL
clawhub install meta-knowledge-baseπ About This Skill
name: meta-knowledge-base description: AI-powered knowledge base builder that automatically captures, organizes, and retrieves information. Learns from conversations, documents, and interactions to build a personalized knowledge graph. Enables semantic search and intelligent Q&A. tags: - meta - knowledge - knowledge-base - rag - embedding - vector-search version: 1.0.0 author: chenq
Meta Knowledge Base
Self-building knowledge management system that learns and grows automatically.
Features
1. Auto-Capture
2. Knowledge Organization
3. Semantic Search
4. Intelligent Q&A
5. Continuous Learning
Installation
pip install numpy faiss-cpu sentence-transformers
Usage
Initialize Knowledge Base
from meta_knowledge import KnowledgeBasekb = 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
| Method | Description | |--------|-------------| |add(content, ...) | Add single piece of knowledge |
| add_batch(contents) | Add multiple items |
| add_from_file(path) | Parse and add file |
| add_from_url(url) | Fetch and add web content |
| add_from_email(email) | Parse email content |Searching
| Method | Description | |--------|-------------| |search(query, top_k) | Semantic search |
| hybrid_search(query, ...) | Keyword + semantic |
| filter_search(query, filters) | Search with filters |
| find_similar(content) | Find similar items |Q&A
| Method | Description | |--------|-------------| |ask(question, ...) | Get answer with RAG |
| get_context(question) | Get relevant context |
| generate_summary(topic) | Generate topic summary |Management
| Method | Description | |--------|-------------| |get_knowledge_graph() | Get entity relationships |
| list_tags() | List all tags |
| export(format) | Export knowledge |
| import_(data) | Import knowledge |Architecture
βββββββββββββββ βββββββββββββββ βββββββββββββββ
β Sources ββββββΆβ Ingestion ββββββΆβ Storage β
β - Chat β β - Parser β β - Vector DB β
β - Docs β β - Embedder β β - Graph DB β
β - Web β β - Indexer β β - Document β
βββββββββββββββ βββββββββββββββ βββββββββββββββ
β
ββββββββββββββββββββββββ
βΌ
βββββββββββββββ βββββββββββββββ βββββββββββββββ
β Query ββββββΆβ Retrieve ββββββΆβ Generate β
β - Search β β - Vector β β - LLM β
β - Ask β β - Graph β β - Cite β
βββββββββββββββ βββββββββββββββ βββββββββββββββ
Embedding Models
| Model | Dimensions | Languages | Use Case | |-------|------------|-----------|----------| | paraphrase-multilingual-MiniLM-L12-v2 | 384 | 50+ | General | | bge-small-zh-v1.5 | 512 | Chinese | Chinese | | text-embedding-ada-002 | 1536 | EN | Production |
Use Cases
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
β‘ When to Use
π‘ Examples
Initialize Knowledge Base
from meta_knowledge import KnowledgeBasekb = 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)
π Tips & 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