| name | Build SQL and vector retrieval context layers with TiDB |
| slug | build-sql-and-vector-retrieval-context-layers-with-tidb |
| description | Use TiDB when an agent needs one transactional SQL store that can also hold embeddings and serve vector retrieval for RAG, memory, or app-context workflows. |
| github_stars | 40235 |
| verification | listed |
| source | https://github.com/pingcap/tidb |
| author | PingCAP |
| publisher_type | organization |
| category | Data Extraction & Transformation |
| framework | Multi-Framework |
| tool_ecosystem | {"github_repo":"pingcap/tidb","github_stars":40235} |
Build SQL and vector retrieval context layers with TiDB
Use TiDB when an agent needs one transactional SQL store that can also hold embeddings and serve vector retrieval for RAG, memory, or app-context workflows.
Prerequisites
TiDB or TiDB Cloud, an embedding model, SQL client or application connector, source documents or application records
Installation
No source-backed install or usage instructions could be extracted automatically. Review the upstream project before running this skill in a sensitive workflow.
Documentation
Source
