nextjs-openai-doc-search VS readability

https://supabase.com/blog/openai-embeddings-postgres-vector

https://supabase.com/blog/chatgpt-supabase-docs

There is already AI built into the docs - just hit cmd+k and ask a question. we were one of the first to do this: https://supabase.com/blog/chatgpt-supabase-docs

We were inspired by what supabase did with the creation of their own ai powered assistant here: https://supabase.com/blog/chatgpt-supabase-docs but we wanted to make one that used a more standard backend in redis and ruby.

https://release.com/blog/training-chatgpt-with-custom-librar...

We were inspired by what supabase did with the creation of their own ai powered assistant here: https://supabase.com/blog/chatgpt-supabase-docs but we wanted to make one that used a more standard backend in redis and ruby.

Gromit is super new; please give it a shot and make pull requests, leave comments, we would love to chat with you about it!

This is the same approach that Supabase Clippy took: https://supabase.com/blog/chatgpt-supabase-docs

They called it "context injection" but the OpenAI community appears to call it "retrieval-augmented generation".

(Tangent) I will go to the grave continuing to call it Supabase Clippy even though presumably this prediction from the Supabase blog post became true:

> Today, we're doing our part to support the momentum by releasing “Supabase Clippy” for our docs (and we don't expect this name to last long before the lawyers catch on).

> the trick where you search for relevant content and paste that into a prompt

Supabase Clippy was the first docs site to ship this experience to production as far as I can tell: https://supabase.com/blog/chatgpt-supabase-docs

I believe they called it "context injection" and I have been following suit in my own writing on the topic.

I am prototyping experiences like Supabase Clippy and am also very interested in fine-tuning for docs Q&A. But my main question is: what exactly would the fine-tuning inputs and outputs look like for docs Q&A?

From my blog:

> AI is all about prediction. Given this temperature, this wind, this day of the year, what is the chance of rain? Temperature, wind, and date are your inputs. Chance of rain is your desired output. Now, try to apply this same type of thinking towards documentation. What are your inputs? What’s your output? The page title and code block could be your inputs. Whether or not the code builds could be your output. Or maybe the code block should be the output? This is why I keep saying that applying fine-tuning to docs is tricky. What are the inputs and outputs?

https://technicalwriting.tools/posts/ten-principles-response...

(I am an AI n00b and have not looked deeply into how fine-tuning works but it's high on my list to experiment with OpenAI's fine-tuning API. Please LMK if I am getting any fundamentals wrong.)

Make sure to check out https://supabase.com/blog/chatgpt-supabase-docs!

Can try this: https://github.com/supabase-community/nextjs-openai-doc-search

Why not just use something like https://github.com/mozilla/readability

And not pay $0.01 per request?

There’s a node version too https://www.npmjs.com/package/@mozilla/readability

I'm working with the Mozilla's readability library https://github.com/mozilla/readability to get the "readable" text from articles and now I want to style the extracted text in a readable way.

Do you remember the Firefox Reader View? It's a feature that removes all unnecessary components like buttons, menus, images, and so on, from a website, focusing on the readable content of the page. The library powering this feature is called Readability.js, which is open source.

I wonder if Firefox "reader mode as a utility" might be a viable alternative for Pinboard like "content oriented" archiving?

https://github.com/mozilla/readability

Depending upon the type of content, one might want to look into using the Readability (Browder's reader view) to parse the webpage. It will give you all the useful info without the junk. Then you can put it in the DB as needed.

https://github.com/mozilla/readability

Btw, readability, is also available in few other languages like Kotlin:

https://github.com/dankito/Readability4J

Use Mozilla's Readability to extract that sweet, sweet text content from webpages.

Same as above but the ability to grab the entire article text (you can use the Readability library for that: https://github.com/mozilla/readability)

// Original script // https://gist.github.com/kepano/90c05f162c37cf730abb8ff027987ca3 // Bookmarklet Converter // https://caiorss.github.io/bookmarklet-maker/ // Libraries // https://github.com/mixmark-io/turndown // https://github.com/mozilla/readability javascript: Promise.all([import('https://unpkg.com/[email protected]?module'), import('https://unpkg.com/@tehshrike/[email protected]'), ]).then(async ([{ default: Turndown }, { default: Readability }]) => { /* Optional vault name */ const vault = ""; /* Optional folder name such as "Clippings/" */ const folder = "Clippings/"; /* Optional tags */ const tags = ""; function getSelectionHtml() { var html = ""; if (typeof window.getSelection != "undefined") { var sel = window.getSelection(); if (sel.rangeCount) { var container = document.createElement("div"); for (var i = 0, len = sel.rangeCount; i < len; ++i) { container.appendChild(sel.getRangeAt(i).cloneContents()); } html = container.innerHTML; } } else if (typeof document.selection != "undefined") { if (document.selection.type == "Text") { html = document.selection.createRange().htmlText; } } return html; } const selection = getSelectionHtml(); const { title, byline, content } = new Readability(document.cloneNode(true)).parse(); function getFileName(fileName) { var userAgent = window.navigator.userAgent, platform = window.navigator.platform, windowsPlatforms = ['Win32', 'Win64', 'Windows', 'WinCE']; if (windowsPlatforms.indexOf(platform) !== -1) { fileName = fileName.replace(':', '').replace(/[/\\?%*|"<>]/g, '-'); } else { fileName = fileName.replace(':', '').replace(/\//g, '-').replace(/\\/g, '-'); } return fileName; } const fileName = getFileName(title); if (selection) { var markdownify = selection; } else { var markdownify = content; } if (vault) { var vaultName = '&vault=' + encodeURIComponent(`${vault}`); } else { var vaultName = ''; } const markdownBody = new Turndown({ headingStyle: 'atx', hr: '---', bulletListMarker: '-', codeBlockStyle: 'fenced', emDelimiter: '*', }).turndown(markdownify); var date = new Date(); function convertDate(date) { var yyyy = date.getFullYear().toString(); var mm = (date.getMonth()+1).toString(); var dd = date.getDate().toString(); var mmChars = mm.split(''); var ddChars = dd.split(''); return yyyy + '-' + (mmChars[1]?mm:"0"+mmChars[0]) + '-' + (ddChars[1]?dd:"0"+ddChars[0]); } const today = convertDate(date); // This is the output template // It is similar to an Obsidian core template // except to insert a value we use: ${value} instead of {{value}} const fileContent =`--- type: clipping date_added: ${today} aliases: [] tags: [${tags}] --- author:: ${byline.toString().split('\n')[0].trim()} source:: [${title}](${document.URL}) ${markdownBody} `; // This copies your text to the clipboard navigator.clipboard.writeText(fileContent); // This creates a new document in Obsidian containing your clipping // I commented it out as this isn't what you asked for /* document.location.href = "obsidian://new?" + "file=" + encodeURIComponent(folder + fileName) + "&content=" + encodeURIComponent(fileContent) + vaultName; */ })