简介

通过利用OI-Wiki内容的向量搜索，增强大型语言模型的竞争编程知识，使模型能够检索相关的算法和技术。

简介

通过利用OI-Wiki内容的向量搜索，增强大型语言模型的竞争编程知识，使模型能够检索相关的算法和技术。

mcp-oi-wiki

Empower large models with the OI-Wiki boost!

How does it work?

We use Deepseek-V3 to generate summaries of the current 462 pages on OI-wiki, embed these summaries as semantic vectors, and establish a vector database.

During queries, the closest vector in the database is found, and the corresponding wiki markdown is returned.

Usage

Ensure you have uv.

First, download this repository:

cd git clone --recurse-submodules https://github.com/ShwStone/mcp-oi-wiki.git

Then, open your MCP configuration file (mcpo or claude):

json { "mcpServers": { "oi-wiki": { "command": "uv", "args": [ "--directory", "/mcp-oi-wiki", "run", "python", "main.py" ] } } }

Update

You can generate your own db/oi-wiki.db.

Place your Silicon flow API key in the api.key file.

Then run:

sh uv run script/request.py

Download the summary results to result.jsonl from the batch inference page.

Finally, run:

sh uv run script/gendb.py

This will generate a new db/oi-wiki.db.

Thanks

• milvus-io/milvus-lite: A lightweight version of Milvus for the vector database
• OI-wiki/OI-wiki: :star2: Wiki of OI / ICPC for everyone. (A comprehensive online guide for a popular game, featuring cool arithmetic magic) for OI-wiki
• qdrant/fastembed: Fast, Accurate, Lightweight Python library to make State of the Art Embedding for CPU-based vector embedding

工具列表

• search: nOI Wiki 致力于成为一个免费开放且持续更新的编程竞赛知识整合站点,大家可以在这里获取与竞赛相关的、有趣又实用的知识。本工具能够在 OI-wiki 中搜索相关的知识点。nnquery 应该比较详细,与要实现的算法/目标相关。比如：“求一个图的最小生成树”,“维护区间加/区间求和”nn@ param query 描述要实现的需求n

服务配置

[{'mcpServers': {'oi-wiki': {'args': ['--directory', '<path of MCP servers>/mcp-oi-wiki', 'run', 'python', 'main.py'], 'command': 'uv'}}}]

来源

• 来源：github
• 链接：https://github.com/ShwStone/mcp-oi-wiki