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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.

Build SQL and vector retrieval context layers with TiDB とは？

Build SQL and vector retrieval context layers with TiDB is a Claude Code agent skill that 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.

対応~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/build-sql-and-vector-retrieval-context-layers-with-tidb
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ドキュメント

Build SQL and vector retrieval context layers with TiDB は何をしますか？

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

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

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