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---
name: knowledge-vault
description: Long-term RAG memory storage for your agent, powered by TiDB Vector.
metadata:
openclaw:
emoji: π
requires:
bins: ["python3", "curl"]
env: ["TIDB_HOST", "TIDB_PORT", "TIDB_USER", "TIDB_PASSWORD", "GEMINI_API_KEY"]
---
# 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:
1. **Store:** Ingest documents, notes, and facts as vector embeddings.
2. **Retrieve:** Semantically search for relevant information based on user queries ("RAG").
3. **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).
### π Security & Provisioning
This skill operates in two modes:
1. **Bring Your Own Database (Recommended):** Set `TIDB_HOST`, `TIDB_USER`, `TIDB_PASSWORD` environment variables. The skill will use your existing database.
2. **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`
```markdown
- **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](PROTOCOL.md).
## Usage
* **Add Knowledge:**
```bash
python {baseDir}/run.py --action add --content "The user prefers spicy food but is allergic to peanuts."
```
* **Search (RAG):**
```bash
python {baseDir}/run.py --action search --query "What are the user's dietary restrictions?"
```