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name: pytidb description: PyTiDB (pytidb) setup and usage for TiDB from Python. Covers connecting, table modeling (TableModel), CRUD, raw SQL, transactions, vector/full-text/hybrid search, auto-embedding, custom embedding functions, and reference templates/snippets (vector/hybrid/image) plus agent-oriented examples (RAG/memory/text2sql). allowed-tools: ["bash", "write", "read_file"]

PyTiDB (pytidb)

Use this skill to connect to TiDB from Python via pytidb, define tables, and build search / AI features on top.

When to Use This Skill

• You want a Python ORM-like experience on TiDB via pytidb (built on SQLAlchemy).
• You want vector search / full-text search / hybrid search on TiDB with high-level APIs.
• You want runnable starter templates (scripts + small examples) you can adapt.

Need to provision a TiDB Cloud cluster first? Use tidbx (TiDB X) for cluster lifecycle guidance.

Code Generation Rules (Python)

• Never hardcode credentials; use env vars (.env) and document required variables.
• Prefer python -m venv .venv and pinned deps for reproducibility.
• When editing requirements.txt, do not invent pytidb versions, use an unpinned pytidb by default unless the user explicitly requests it and the version has been verified to exist.
• Keep examples minimal and runnable; avoid framework-specific assumptions unless the user asks.
• Use parameterized SQL for any dynamic value (SQL injection safety).
• For interactive environments, avoid “table already defined” errors (use extend_existing / open_table / if rows()==0 patterns).

Available Guides

Each guide is a self-contained walkthrough with a checklist and phases:

• guides/quickstart.md — one-file “connect → create table → insert → vector search”
• guides/search.md — vector / full-text / hybrid: when to use which, plus gotchas
• guides/demos.md — examples playbook (vector/hybrid/image)
• guides/agent-apps.md — agent-ish examples (RAG / memory / text2sql)
• guides/troubleshooting.md — connection, TLS, embedding, and index/search issues
• guides/custom-embedding.md — implement a custom embedding function (example: BGE-M3)

I’ll infer your intent (CRUD vs search vs “agent app”), then point you to the smallest guide and template set that gets you running.

Templates & Scripts

Each template is a complete file you can copy into your project. Choose the smallest one that matches your goal.

Core usage

• templates/quickstart.py — minimal end-to-end: connect → create table → insert → vector search
• templates/crud.py — basic table modeling + CRUD lifecycle (create/truncate/insert/query/update/delete)
• templates/auto_embedding.py — auto embedding with pluggable providers (env-driven)
• templates/vector_search.py — vector search example (optional metadata filter + threshold)
• templates/hybrid_search.py — hybrid search example (FullTextField + vector field) with fused scoring

Image search

• templates/image_search.py — image-to-image or text-to-image search (requires multimodal embedding + Pillow)
• templates/image_search_data_loader.py — loads Oxford Pets dataset into TiDB (used by image_search.py)

Custom embeddings

• templates/custom_embedding_function.py — example BaseEmbeddingFunction implementation (BGE-M3 via FlagEmbedding)
• templates/custom_embedding.py — uses the custom embedder with auto embedding + vector search

Agent-ish examples

• templates/rag.py — minimal RAG: retrieve via vector search, then generate via local LLM (Ollama via LiteLLM)
• templates/memory_lib.py — reusable “memory” library (extract facts → store → retrieve)
• templates/memory.py — CLI memory chat example using memory_lib.py
• templates/text2sql.py — interactive Text2SQL (generates SQL via OpenAI; asks before executing)

Scripts

• scripts/validate_connection.py — quick connection + SELECT 1 smoke test (supports params or DATABASE_URL)

Related Skills

• tidbx — provision/manage TiDB Cloud (TiDB X) clusters

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Workflow

I will:

1. Confirm your TiDB deployment (Cloud Starter vs self-managed) and how you want to connect (params vs DATABASE_URL).
2. Help you set env vars, validate the connection, and choose the right path:
   • CRUD/table modeling
   • vector/full-text/hybrid search (and embedding provider)
   • example templates
3. Generate the minimal set of files and commands to get you running.