| # Monkey patch the graphrag package to support local models deployed via Ollama. Only tested for graphrag version 0.3.2. | |
| # See https://chishengliu.com/posts/graphrag-local-ollama/ for details. | |
| # | |
| # Usage: | |
| # * How to patch the `graphrag.index` CLI: | |
| # * Save https://github.com/microsoft/graphrag/blob/v0.3.2/graphrag/index/__main__.py as "index.py" | |
| # * Replace line 8 with: | |
| # ```python | |
| # from graphrag.index.cli import index_cli | |
| # from graphrag_monkey_patch import patch_graphrag |
| #include <iostream> | |
| #include <array> | |
| #include <type_traits> | |
| #include <iterator> | |
| template<int... ns> | |
| struct mylist | |
| { | |
| constexpr static std::array<int, sizeof...(ns)> value{ns...}; | |
| }; |
This is a fork of and builds upon the work of Eddie Webb's search and Matthew Daly's search explorations.
It's built for the Hugo static site generator, but could be adopted to function with any json index.
To see it in action, go to craigmod.com and press CMD-/ and start typing.
| # link: https://sujingjhong.com/posts/how-to-deploy-hugo-automatically-with-github-actions/ | |
| # author: lisez <mm4324@gmail.com> | |
| name: Auto publish to public site | |
| # 只有推送到 master 才自動化 | |
| on: | |
| push: | |
| branches: | |
| - master |
| package main | |
| import ( | |
| "encoding/json" | |
| "testing" | |
| ) | |
| type foo struct { | |
| ID string `json:"_id"` | |
| Index int `json:"index"` |
| #!/usr/bin/env node | |
| // http://toddhayton.com/2018/08/01/scraping-with-puppeteer/ | |
| // airtw-scraper.js https://www.cwb.gov.tw/V7/observe/real/46757.htm > a.htm | |
| const homedir = require('os').homedir(); | |
| const puppeteer = require(homedir + '/node_modules/puppeteer'); | |
| const url = 'https://airtw.epa.gov.tw/'; | |
| async function main() { | |
| const browser = await puppeteer.launch(); |
This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).
Matrix multiplication is a mathematical operation that defines the product of
