Knowledge Vault (Powered by TiDB Zero)
Overview
Knowledge Vault is a Long-Term Memory module for AI Agents, powered by TiDB Vector Search (RAG).
Traditional agent memory (context window) is ephemeral and limited. Knowledge Vault allows agents to:
- Store: Ingest documents, notes, and facts as vector embeddings.
- Retrieve: Semantically search for relevant information based on user queries ("RAG").
- Remember: Access unlimited historical context without overflowing the LLM prompt.
Why use this?
- Infinite Recall: Store millions of documents without confusing the agent.
- Contextual Relevance: Find *exact* paragraphs related to a question, not just keywords.
- Privacy: Keep your knowledge base private in your own TiDB Cloud instance.
Prerequisites
- TiDB Cloud (Serverless): With Vector Search enabled.
- Embedding Model: Requires
GEMINI_API_KEY(or compatible).
🔐 Security & Provisioning
This skill operates in two modes:
- Bring Your Own Database (Recommended): Set
TIDB_HOST,TIDB_USER,TIDB_PASSWORDenvironment variables. The skill will use your existing database. - Auto-Provisioning (Fallback): If no credentials are found, the skill calls the TiDB Zero API to create a temporary, ephemeral database for you. It caches the connection string locally (
~/.openclaw_knowledge_vault_dsn) to persist memory across runs.
Installation
1. Add to TOOLS.md
- **knowledge-vault**: Store and retrieve knowledge using vector search.
- **Location:** `{baseDir}/skills/knowledge_vault/SKILL.md`
- **Command:** `python {baseDir}/skills/knowledge_vault/run.py --action search --query "<QUESTION>"`2. Add to AGENTS.md (Protocol)
Copy PROTOCOL.md.
Usage
- Add Knowledge:
python {baseDir}/run.py --action add --content "The user prefers spicy food but is allergic to peanuts." - Search (RAG):
python {baseDir}/run.py --action search --query "What are the user's dietary restrictions?"