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pingcap/pytidb

pingcap/pytidb

TiDB AI SDK for apps and agents
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collected from the abordage/awesome-mcp list · type: MCP server
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first seen 2026-10-14 · last seen 2026-10-16 · seen 4× · listed by 3 sources
Score
score 303 · 3 sources · mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
first catalogued 2026-10-14;last seen 2026-10-16。There is no official certification or quality rating here — we record sources and dates only.
Third-party quality signals

TiDB Python AI SDK

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

Quick Start
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Documentation
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Examples
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Roadmap
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Introduction
**Python SDK for TiDB AI**: A unified data platform empowering developers to build next-generation AI applications.
- 🔍 **Unified Search Modes**: Vector · Full‑Text · Hybrid
- 🎭 **Auto‑Embedding & Multi‑Modal Storage**: Support for text, images, and more
- 🖼️ **Image Search Support**: Text‑to‑image and image‑to‑image retrieval capabilities
- 🎯 **Advanced Filtering & Reranking**: Flexible filters with optional reranker models to fine-tune result relevance
- 💱 **Transaction Support**: Full transaction management including commit/rollback to ensure consistency
Installation
> [!NOTE]
> This Python package is under rapid development and its API may change. It is recommended to use a **fixed version** when installing, e.g., pytidb==0.0.14.
Connect to TiDB Cloud
Create a free TiDB cluster at tidbcloud.com.
Highlights
🤖 Automatic Embedding
PyTiDB automatically embeds text fields (e.g., text) and stores the vector embedding in a vector field (e.g., text_vec).
**Create a table with an embedding function:**
**Bulk insert data:**
🔍 Search
**Vector Search**
Vector search finds the most relevant records based on **semantic similarity**, so you don't need to include all keywords explicitly in your query.
See the Vector Search example for more details.
**Full-text Search**
Full-text search tokenizes the query and finds the most relevant records by matching exact keywords.
See the Full-text Search example for more details.
**Hybrid Search**
Hybrid search combines **exact matching** from full-text search with **semantic understanding** from vector search, delivering more relevant and reliable results.
See the Hybrid Search example for more details.
**Image Search**
Image search lets you find visually similar images using natural language descriptions or another image as a reference.
See the Image Search example for more details.
Advanced Filtering
PyTiDB supports a variety of operators for flexible filtering:
| Operator | Description | Example |
| -------- | --------------------- | ------------------------------------------ |
| $eq | Equal to | {"field": {"$eq": "hello"}} |
| $gt | Greater than | {"field": {"$gt": 1}} |
| $gte | Greater than or equal | {"field": {"$gte": 1}} |
| $lt | Less than | {"field": {"$lt": 1}} |
| $lte | Less than or equal | {"field": {"$lte": 1}} |
| $in | In array | {"field": {"$in": [1, 2, 3]}} |
Source
awesome · https://github.com/pingcap/pytidb
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