From bc41cf06edd7edb9df0ca2da4ab06e937a603e9c Mon Sep 17 00:00:00 2001 From: vtempest Date: Tue, 29 Oct 2024 13:10:44 -0700 Subject: [PATCH] integrated seektopic, cite format, and improved readme --- .github/workflows/update.yml | 36 - data/wiki-phrases-model-240k.json | 2 +- docs/assets/navigation.js | 2 +- docs/assets/search.js | 2 +- docs/functions/convertEmbeddingsToUMAP.html | 10 +- docs/functions/convertTextToTokens.html | 2 +- .../{extract.html => extractContent.html} | 40 +- ...L.html => extractMainContentFromHTML.html} | 4 +- ....html => extractMainContentFromHTML2.html} | 4 +- docs/functions/searchWeb.html | 3 +- docs/index.html | 63 +- docs/modules.html | 6 +- docs/sitemap.xml | 86 +- extension/.gitignore | 15 + extension/components.json | 14 + extension/index.html | 12 + extension/package.json | 88 + extension/postcss.config.cjs | 13 + extension/src/assets/icons/logo-128.png | Bin 0 -> 16273 bytes extension/src/assets/icons/logo-48.png | Bin 0 -> 3510 bytes extension/src/manifest.config.ts | 58 + extension/src/pages/app/app.pcss | 105 + extension/src/pages/app/index.ts | 10 + extension/src/pages/app/tailwind.min.css | 3753 ++++++++++++++ extension/src/pages/background/allow-cors.js | 71 + .../src/pages/background/context-menu.js | 56 + .../src/pages/background/service-worker.js | 80 + extension/src/pages/background/tabs-api.js | 85 + extension/src/pages/content/index.js | 4 + extension/src/pages/sidepanel/app.pcss | 70 + extension/src/pages/sidepanel/index.html | 11 + extension/src/pages/sidepanel/index.ts | 10 + extension/svelte.config.js | 14 + extension/tailwind.config.ts | 22 + extension/tsconfig.json | 19 + extension/tsconfig.node.json | 10 + extension/vite.config.ts | 34 + index.js | 37 +- package.json | 4 +- pnpm-lock.yaml | 3793 -------------- readme.md | 65 +- src/crawler/compose.yml | 85 +- src/crawler/crawler.js | 66 +- src/crawler/package.json | 2 +- src/datasets/compile-topic-model.js | 54 +- src/datasets/metadata-stats.js | 2 + src/extractor/html-to-cite/extract-author.js | 12 +- src/extractor/html-to-cite/extract-cite.js | 10 +- .../html-to-cite/human-names-recognize.js | 66 +- .../extract-content/extractor1-content.js | 4 +- .../extractor2-content-utils.js | 104 +- .../extract-content/extractor2-content.js | 4 +- .../html-to-content/html-to-content.js | 15 +- src/extractor/url-to-content/scrape-url.js | 3 +- .../url-to-content/url-to-content.js | 39 +- src/generate/languagereply-to-json.js | 38 + src/search/ip-to-lang.js | 2 +- src/search/search-stream.js | 6 +- src/search/search-web.js | 59 +- src/search/wiki-news.js | 50 + src/similarity/embeddings-to-graph.js | 4 +- src/topics/ngrams.js | 2 +- src/topics/seektopic-keyphrases.js | 6 +- test/data/___airesearch_js_org.json | 211 + test/data/hat_you_can_talk_to_.json | 118 - test/data/v_org_pdf_1706_03762.json | 341 +- test/reply-to-json.test.js | 37 + test/seektopic-keyphrase.test.js | 9 +- test/topic-distribution.test.js | 4 +- test/trafilatura-extractor/readability.js | 2 +- .../trafilatura-extractor/readability_lxml.js | 2 +- web-app/.env.example | 3 + web-app/package.json | 15 +- web-app/pnpm-lock.yaml | 4599 +++++++++++++++++ web-app/src/app.html | 24 + .../src/components/Home/ActionsPanel.svelte | 21 +- web-app/src/components/Home/Footer.svelte | 10 +- web-app/src/components/Home/Home.svelte | 159 +- .../src/components/Home/PricingPlan.svelte | 13 +- web-app/src/components/Home/ReadView.svelte | 105 +- .../src/components/Home/SearchInput.svelte | 7 +- web-app/src/hooks.server.js | 24 - web-app/src/lib/config/config.js | 25 +- web-app/src/routes/api/+page.svelte | 39 + web-app/src/routes/api/docs/+page.svelte | 39 - web-app/src/routes/api/extract/+server.js | 4 +- web-app/src/routes/api/search/+server.js | 7 +- .../routes/legal/privacy-policy/+page.svelte | 271 +- web-app/src/routes/legal/terms/+page.svelte | 8 +- web-app/static/Untitled-1.csv | 33 - web-app/static/openapi.yaml | 9 +- web-app/vite.config.js | 2 +- 92 files changed, 10654 insertions(+), 4833 deletions(-) delete mode 100644 .github/workflows/update.yml rename docs/functions/{extract.html => extractContent.html} (62%) rename docs/functions/{extractContentHTML.html => extractMainContentFromHTML.html} (74%) rename docs/functions/{extractContentHTML2.html => extractMainContentFromHTML2.html} (73%) create mode 100644 extension/.gitignore create mode 100644 extension/components.json create mode 100644 extension/index.html create mode 100644 extension/package.json create mode 100644 extension/postcss.config.cjs create mode 100644 extension/src/assets/icons/logo-128.png create mode 100644 extension/src/assets/icons/logo-48.png create mode 100644 extension/src/manifest.config.ts create mode 100644 extension/src/pages/app/app.pcss create mode 100644 extension/src/pages/app/index.ts create mode 100644 extension/src/pages/app/tailwind.min.css create mode 100644 extension/src/pages/background/allow-cors.js create mode 100644 extension/src/pages/background/context-menu.js create mode 100644 extension/src/pages/background/service-worker.js create mode 100644 extension/src/pages/background/tabs-api.js create mode 100644 extension/src/pages/content/index.js create mode 100644 extension/src/pages/sidepanel/app.pcss create mode 100644 extension/src/pages/sidepanel/index.html create mode 100644 extension/src/pages/sidepanel/index.ts create mode 100644 extension/svelte.config.js create mode 100644 extension/tailwind.config.ts create mode 100644 extension/tsconfig.json create mode 100644 extension/tsconfig.node.json create mode 100644 extension/vite.config.ts delete mode 100644 pnpm-lock.yaml create mode 100644 src/generate/languagereply-to-json.js create mode 100644 src/search/wiki-news.js create mode 100644 test/data/___airesearch_js_org.json delete mode 100644 test/data/hat_you_can_talk_to_.json create mode 100644 test/reply-to-json.test.js create mode 100644 web-app/.env.example create mode 100644 web-app/pnpm-lock.yaml delete mode 100644 web-app/src/hooks.server.js create mode 100644 web-app/src/routes/api/+page.svelte delete mode 100644 web-app/src/routes/api/docs/+page.svelte delete mode 100644 web-app/static/Untitled-1.csv diff --git a/.github/workflows/update.yml b/.github/workflows/update.yml deleted file mode 100644 index 51856e4..0000000 --- a/.github/workflows/update.yml +++ /dev/null @@ -1,36 +0,0 @@ -name: Auto-Green - -on: - workflow_dispatch: - inputs: - log: - description: "Commit Log" - required: true - default: "a commit a day keeps your girlfriend away" - schedule: - - cron: "0 0 * * 0-5" - -jobs: - autogreen: - runs-on: ubuntu-latest - steps: - - name: Checkout - uses: actions/checkout@v2 - - - name: Pull - run: | - git config --local user.name "${{ github.actor }}" - git config --local user.email "${{ github.actor }}@gmail.com" - git remote set-url origin https://${{ github.repository_owner }}:${{ secrets.GITHUB_TOKEN }}@github.com/${{ github.repository }} - git pull --rebase - - - name: Commit (default) - if: github.event.inputs.log == 0 - run: git commit --allow-empty -m "a commit a day keeps your girlfriend away" - - - name: Commit (input) - if: github.event.inputs.log != 0 - run: git commit --allow-empty -m "${{ github.event.inputs.log }}" - - - name: Push - run: git push \ No newline at end of file diff --git a/data/wiki-phrases-model-240k.json b/data/wiki-phrases-model-240k.json index 62e3e80..afdc247 100644 --- a/data/wiki-phrases-model-240k.json +++ b/data/wiki-phrases-model-240k.json @@ -1 +1 @@ -{"0":{"0":[[0,0,5]]},"1":{"1":[["night in paris",50,3],["lig",50,8],["fc kaiserslautern",50,8],["fc union berlin",50,3],["august",50,2],["de agosto",50,9],["fc heidenheim",50,9],["fsv mainz 05",50,7],[0,0,5],["chronicles",0,5],["esdras",0,10],["kings",0,4],["maccabees",0,9],["samuel",0,4]]},"2":{"2":[["girls 1 cup",50,3],["broke girls",50,4],["bundesliga",50,6],["fast 2 furious",50,5],["days 1 night episodes",50,3],["days 1 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rogne",0,7],["prosopis",0,6],["prosopium",0,3],["protea",0,6],["proteles",0,3],["proterochampsa",0,3],["proteus",0,6],["protium",0,7],["protoavis",0,3],["protoceratops",0,7],["protohippus",0,3],["prumnopitys",0,7],["prunella",0,6],["prunus",0,6],["psaltriparus",0,3],["psenes",0,3],["psephurus",0,3],["psetta",0,3],["psettichthys",0,3],["pseudacris",0,7],["pseudaletia",0,3],["pseudechis",0,3],["pseudemys",0,3],["pseudobombax",0,3],["pseudococcus",0,3],["pseudocolus",0,3],["pseudolarix",0,3],["pseudomonas",0,6],["pseudopleuronectes",0,3],["pseudoryx",0,3],["pseudotaxus",0,3],["pseudotsuga",0,6],["pseudowintera",0,3],["psidium",0,7],["psilophyton",0,3],["psilotum",0,3],["psithyrus",0,3],["psittacosaurus",0,7],["psittacula",0,7],["psittacus",0,7],["psophia",0,3],["psophocarpus",0,3],["psoralea",0,7],["psychopsis",0,3],["psychotria",0,6],["pteretis",0,3],["pteridium",0,7],["pteris",0,7],["pternohyla",0,3],["pterocarpus",0,7],["pterocarya",0,7],["pterocles",0,3],["pterocnemia",0,3],["pterodactylus",0,7],["pterois",0,7],["pteropogon",0,3],["pteropus",0,7],["pterospermum",0,3],["pterostylis",0,6],["ptilocercus",0,3],["ptilocrinus",0,3],["ptilonorhynchus",0,3],["ptloris",0,3],["ptyas",0,3],["ptychozoon",0,3],["puccinia",0,7],["pueraria",0,7],["puffinus",0,7],["pulex",0,7],["pulicaria",0,7],["pulsatilla",0,7],["punica",0,7],["pycnanthemum",0,3],["pygopus",0,3],["pygoscelis",0,3],["pylodictus",0,3],["pyracantha",0,7],["pyralis",0,7],["pyrausta",0,6],["pyrethrum",0,7],["pyrocephalus",0,3],["pyrola",0,7],["pyrophorus",0,3],["pyrrhula",0,3],["pyrrhuloxia",0,3],["pyrrosia",0,3],["pyrularia",0,3],["pyrus",0,6],["pythium",0,7],["python",0,5],["pyxidanthera",0,3],["quamassia",0,3],["quassia",0,3],["quercus",0,5],["quiscalus",0,3],["rachycentron",0,3],["radiigera",0,3],["radyera",0,3],["raffia",0,6],["raja",0,4],["ramalina",0,7],["ramphomicron",0,3],["rana",0,5],["ranatra",0,3],["rangifer",0,7],["ranunculus",0,6],["raoulia",0,3],["raphanus",0,7],["raphia",0,7],["raphicerus",0,3],["raphus",0,3],["ratibida",0,3],["rattus",0,6],["rauvolfia",0,7],["rauwolfia",0,7],["ravenala",0,3],["recurvirostra",0,3],["regalecus",0,3],["regnellidium",0,3],["regulus",0,6],["reithrodontomys",0,7],["remilegia",0,3],["reseda",0,6],["retama",0,7],["reticulitermes",0,3],["retrophyllum",0,3],["rhagoletis",0,7],["rhamnus",0,6],["rhapis",0,3],["rhea",0,6],["rheum",0,7],["rhexia",0,3],["rhincodon",0,3],["rhinoceros",0,5],["rhinonicteris",0,3],["rhinoptera",0,3],["rhipsalis",0,3],["rhizobium",0,7],["rhizoctinia",0,3],["rhizophora",0,7],["rhizopogon",0,3],["rhizopus",0,7],["rhodanthe",0,3],["rhododendron",0,6],["rhodosphaera",0,3],["rhodymenia",0,3],["rhus",0,6],["rhyacotriton",0,3],["rhynchoelaps",0,3],["rhyncostylis",0,3],["rhynia",0,3],["ribes",0,6],["richea",0,3],["richmondena",0,3],["ricinus",0,7],["rickettsia",0,7],["riparia",0,6],["rissa",0,7],["rivina",0,3],["rivulus",0,3],["robinia",0,7],["roccella",0,7],["roccus",0,3],["rodolia",0,3],["romneya",0,3],["roridula",0,3],["rorippa",0,7],["rosa",0,4],["rosellinia",0,3],["rosmarinus",0,7],["roystonea",0,7],["rubia",0,7],["rubus",0,6],["rudbeckia",0,7],["rumex",0,6],["rumohra",0,3],["rupicapra",0,7],["rupicola",0,7],["ruptiliocarpon",0,3],["ruscus",0,7],["russula",0,7],["ruta",0,6],["rutilus",0,7],["rynchops",0,3],["rypticus",0,3],["sabal",0,7],["sabbatia",0,3],["sabinea",0,3],["saccharomyces",0,6],["saccharum",0,6],["sadleria",0,3],["sagina",0,7],["sagitta",0,7],["sagittaria",0,7],["sagittarius",0,6],["saiga",0,7],["saimiri",0,7],["saintpaulia",0,3],["salamandra",0,7],["salicornia",0,7],["salix",0,6],["salmo",0,6],["salmonella",0,6],["salpa",0,7],["salpichroa",0,3],["salpiglossis",0,3],["salpinctes",0,3],["salsola",0,7],["salvadora",0,7],["salvelinus",0,7],["salvia",0,6],["salvinia",0,7],["sambucus",0,7],["samia",0,6],["samolus",0,3],["sanguinaria",0,3],["sanicula",0,7],["sansevieria",0,7],["santalum",0,7],["santolina",0,7],["sanvitalia",0,3],["sapindus",0,7],["saponaria",0,7],["saprolegnia",0,3],["sarcobatus",0,3],["sarcocephalus",0,3],["sarcochilus",0,3],["sarcocystis",0,3],["sarcodes",0,3],["sarcophaga",0,7],["sarcophilus",0,3],["sarcoptes",0,3],["sarcorhamphus",0,3],["sarcostemma",0,3],["sarda",0,6],["sardina",0,7],["sardinia",0,5],["sardinops",0,3],["sargassum",0,7],["sarracenia",0,7],["sassafras",0,6],["satureia",0,3],["satureja",0,3],["saturnia",0,7],["sauromalus",0,7],["saurosuchus",0,3],["saururus",0,3],["saussurea",0,7],["saxe-gothea",0,6],["saxegothea",0,3],["saxicola",0,7],["saxifraga",0,6],["sayornis",0,3],["scabiosa",0,7],["scaphiopus",0,3],["scaphosepalum",0,3],["scarabaeus",0,7],["scardinius",0,3],["scartella",0,3],["sceliphron",0,3],["sceloglaux",0,3],["sceloporus",0,7],["schaffneria",0,3],["schefflera",0,7],["schinus",0,7],["schistosoma",0,7],["schizachyrium",0,7],["schizaea",0,3],["schizanthus",0,3],["schizopetalon",0,3],["schizophragma",0,3],["schizosaccharomyces",0,7],["schlumbergera",0,3],["schomburgkia",0,3],["sciadopitys",0,3],["sciaena",0,3],["sciaenops",0,3],["sciara",0,7],["sci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"; 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"; \ No newline at end of file diff --git a/docs/functions/convertEmbeddingsToUMAP.html b/docs/functions/convertEmbeddingsToUMAP.html index a30b906..cde4c15 100644 --- a/docs/functions/convertEmbeddingsToUMAP.html +++ b/docs/functions/convertEmbeddingsToUMAP.html @@ -30,17 +30,17 @@

Understanding UMAP
-UMAP Algorithm Overview
- -

-

Parameters

Settings

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On This Page

UMAP: Convert Embeddings to 2D or 3D Graph
diff --git a/docs/functions/convertTextToTokens.html b/docs/functions/convertTextToTokens.html index 9abdc75..a3ccaa5 100644 --- a/docs/functions/convertTextToTokens.html +++ b/docs/functions/convertTextToTokens.html @@ -16,7 +16,7 @@

Author

ai-research-agent (2024)

-

Settings

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On This Page

Convert Text Query to Topic Phrase Tokens
diff --git a/docs/functions/extract.html b/docs/functions/extractContent.html similarity index 62% rename from docs/functions/extract.html rename to docs/functions/extractContent.html index ee0b075..83cfc73 100644 --- a/docs/functions/extract.html +++ b/docs/functions/extractContent.html @@ -1,12 +1,34 @@ -extract | ai-research-agent

Function extract

  • extract(urlOrDoc, options?): {
        title: string;
        author_cite: string;
        author_short: string;
        author: string;
        date: string;
        source: string;
        html: string;
        word_count: number;
    }

  • 🚜📜 Tractor the Text Extractor

      -
    1. Extract URL or HTML to main content, based on Readability with improved version -using 100+ custom adapters for major websites.
      2. -
    2. Strips to basic HTML for reading mode or saving research notes.
      3. -
    3. Youtube - get full transcript for video if detected a youtube video.
      4. -
    4. PDF - Extracts formatted text from PDF with page headers, -footnotes, linebreaks, and adding headings based on standard deviation of range text height.
      5. +extractContent | ai-research-agent

      Function extractContent

      • extractContent(urlOrDoc, options?): {
            title: string;
            author_cite: string;
            author_short: string;
            author: string;
            date: string;
            source: string;
            html: string;
            word_count: number;
        }

      • 🚜📜 Tractor the Text Extractor

        +
          +
        1. Main Content Detection: Extract the main content from a URL by combining +Mozilla Readability and Postlight Mercury algorithms, utilizing over 100 +custom adapters for major sites for article, author, date HTML classes.
          2. +
        2. Basic HTML Standardization: Transform complex HTML into a simplified +reading-mode format of basic HTML, making it ideal for research note archival +and focused reading, with headings, images and links.
          3. +
        3. YouTube Transcript Processing: When a YouTube video URL is detected, +retrieve the complete video transcript including both manual captions and +auto-generated subtitles, maintaining proper timestamp synchronization and +speaker identification where available.
          4. +
        4. PDF Text Extraction and Structure: Process PDF documents by extracting +formatted text while intelligently handling line breaks, page headers, +footnotes. The system analyzes text height statistics to automatically +infer heading levels, creating a properly structured document hierarchy +based on standard deviation from mean text size.
          5. +
        5. Citation Information Extraction: Identify and extract citation metadata +including author names, publication dates, sources, and titles using HTML +meta tags and common class name patterns. The system validates author names +against a comprehensive database of 90,000 first and last names, +distinguishing between personal and organizational authors to properly +format citations.
          6. +
        6. Author Name Formatting: Process author names by checking against +known name databases, handling affixes and titles correctly, and determining +whether to reverse the name order based on whether it's a personal or +organizational author, ensuring proper citation formatting.
          7. +
        7. Content Validation: Verify the extracted content's accuracy by comparing +results from multiple extraction methods, ensuring all essential elements +are preserved and properly formatted for the intended use case.
        -


        Parameters

        • urlOrDoc: string | Document

          url or dom object with article content

        • Optionaloptions: {
              images: boolean;
              links: boolean;
              formatting: boolean;
              absoluteURLs: boolean;
              timeout: number;
          } = {}
          • @@ -29,7 +51,7 @@
          • word_count - The word count of the full text (without HTML tags)
          • title: string
          • author_cite: string
          • author_short: string
          • author: string
          • date: string
          • source: string
          • html: string
          • word_count: number

          Author

          ai-research-agent (2024)

          -

      Settings

      Member Visibility

      On This Page

      🚜📜 Tractor the Text Extractor
      diff --git a/docs/functions/extractContentHTML.html b/docs/functions/extractMainContentFromHTML.html similarity index 74% rename from docs/functions/extractContentHTML.html rename to docs/functions/extractMainContentFromHTML.html index 39bf9c6..4cab449 100644 --- a/docs/functions/extractContentHTML.html +++ b/docs/functions/extractMainContentFromHTML.html @@ -1,4 +1,4 @@ -extractContentHTML | ai-research-agent

      Function extractContentHTML

      • extractContentHTML(html, options?): Element

      • HTML-to-Main-Content Extractor #1

        The function extracts main content with regex patterns, cleaning HTML, scoring nodes +extractMainContentFromHTML | ai-research-agent

        Function extractMainContentFromHTML

        • extractMainContentFromHTML(html, options?): Element

        • HTML-to-Main-Content Extractor #1

          The function extracts main content with regex patterns, cleaning HTML, scoring nodes based on content indicators like paragraphs and id/class names, selecting the top candidate, extracting it, and cleaning up content around it.

            @@ -67,7 +67,7 @@
        • minTextLength: number

          default=25 - Minimum length of text to be considered valid

        • retryLength: number

          default=250 - Length to retry content extraction if initial attempt fails

      Returns Element

      Extracted HTML element of main content such as article body

      -

      Example

      var url = "https://www.nytimes.com/2024/08/28/business/telegram-ceo-pavel-durov-charged.html"
      const html = await (await fetch(url)).text();
      var articleContent = extractContentHTML(html);
+
      Example
      var url = "https://www.nytimes.com/2024/08/28/business/telegram-ceo-pavel-durov-charged.html"
      const html = await (await fetch(url)).text();
      var articleContent = extractMainContentFromHTML(html);
 
      
 
 
      Author
      Based on Mozilla Readability (2015), Arc90 (2010)
      
diff --git a/docs/functions/extractContentHTML2.html b/docs/functions/extractMainContentFromHTML2.html
similarity index 73%
rename from docs/functions/extractContentHTML2.html
rename to docs/functions/extractMainContentFromHTML2.html
index e455057..e9bb09e 100644
--- a/docs/functions/extractContentHTML2.html
+++ b/docs/functions/extractMainContentFromHTML2.html
@@ -1,4 +1,4 @@
-extractContentHTML2 | ai-research-agent
      Function extractContentHTML2
      • extractContentHTML2(html, opts?): string
      • HTML-to-Main-Content Extractor #2
          
+extractMainContentFromHTML2 | ai-research-agent
          Function extractMainContentFromHTML2
          • extractMainContentFromHTML2(html, opts?): string
          • HTML-to-Main-Content Extractor #2
              
 
            1. The algorithm starts by loading the HTML content using linkedom, a lightweight DOM parser for Node.js.
              2. 
 
            2. It then applies a series of cleaning and scoring techniques to identify the main content of
 the page, starting with stripping unlikely candidates (e.g., elements with class names like "comment"
@@ -32,7 +32,7 @@
 quality content. On each failure, retry with slightly more lax options.
              
 
      Returns string
      The extracted content as an HTML string, or null if extraction fails.
      
 
      Author
      Based on Postlight Mercury Parser (2017-)
      
-
      Example
      var url =  "https://en.wikipedia.org/wiki/David_Hilbert"
      var html = await (await fetch(url)).text();
      var content = extractContentHTML(html);
      console.log(content); // HTML content of main article body
+
      Example
      var url =  "https://en.wikipedia.org/wiki/David_Hilbert"
      var html = await (await fetch(url)).text();
      var content = extractMainContentFromHTML(html);
      console.log(content); // HTML content of main article body

Settings

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On This Page

HTML-to-Main-Content Extractor #2
diff --git a/docs/index.html b/docs/index.html index 1605527..d726cfb 100644 --- a/docs/index.html +++ b/docs/index.html @@ -1,6 +1,13 @@ ai-research-agent

ai-research-agent

+

+ Being is Becoming
+ Whatever Research Can Be,
+ That is What It Must Become.
+ If AI is Humanity's Last Invention,
+ Then Vector Space is the Final Frontier.
+


@@ -37,12 +44,8 @@

Javascript Docs (airesearch.js.org) Live Demo (qwksearch.com)

-

🧠💻 Reimagine the Internet as 3D Mind Map

- Being is Becoming
- Whatever Research Can Be,
- That is What It Must Become.
- If AI is Humanity's Last Invention,
- Then Vector Space is the Final Frontier.
+

🧠💻 Reimagine the Internet as Self-Organizing Mind Map

+

Critical times call for critical thinkers to create a crowdsourced argument reasoning dataset, for AI models to recommend research quotes, evolve crowdsourced chain-of-thought reasoning, unlock faster ways to read long articles, monitor developments by topic modeling a knowledge base graph, and provide a public service of answers to research.

Language Models can distill the essence of collective thought into a vector space where every point has a weighted value representing its contribution to the overall decision-making process, leading to direct democratic AI economy where public votes reward influence. AI will show its reasoning based on what sentences and cites it used from the collective research, so that people can see it is aligned with our interests. Research Agents recommend articles for human researchers working alongside AI to develop a summarized topic outline as a public service. The agents monitor for any related articles via web searches for keywords associated with that Topic Model. Users can upload research PDFs, then the app finds the citations full text and creates topic model and keyword summaries, then monitors that literature base and stores highlights. People will make personal knowledge bases of what influences them to create AI assistants cloning their mind-uploaded perspective. Similar apps are Anthropic Claude, Obsidian, SciSpace and Perplexity, showing that people need this new way of self-organizing information.

@@ -58,7 +61,7 @@

Javascript Docs (airesea
  • Research Agent prompt with key sentences from relevant sources to answer via Groq Llama, OpenAI, or Anthropic API Key and suggest follow-ups

    • 🔤📊 SEEKTOPIC: Summarization by Extracting Entities, Keyword Tokens, and Outline Phrases Important to Context

    -

    extractSEEKTOPIC Docs

    SEEKTOPIC Sample Output

    @@ -105,15 +108,20 @@

    Javascript Docs (airesea

    extract Docs

      -
    1. Extract URL or HTML to main content, improved version combining Mozilla Readability and Postlight Mercury using 100+ custom adapters for major websites.
      2. -
    2. Strips to basic HTML for reading mode or saving research notes
      3. -
    3. Youtube - get full transcript for video if detected a youtube video
      4. -
    4. PDF - Extracts formatted text from PDF with parsing of linebreaks, page headers, footnotes, and adding infer headings based on standard deviation from average text height
      5. -
    5. Cite - Extract author, date, source, and title from HTML using meta tags and common class names. Validates human name from author string to check against common list of 90k first names, last names, and organizations to infer if last name should be reversed starting by author last name (accounting for affixes/titles), since organizations are not reversed.
      6. +
    6. Main Content Detection: Extract the main content from a URL by combining Mozilla Readability and Postlight Mercury algorithms, utilizing over 100 custom adapters for major sites for article, author, date HTML classes.
      7. +
    7. Basic HTML Standardization: Transform complex HTML into a simplified reading-mode format of basic HTML, making it ideal for research note archival and focused reading, with headings, images and links.
      8. +
    8. YouTube Transcript Processing: When a YouTube video URL is detected, retrieve the complete video transcript including both manual captions and auto-generated subtitles, maintaining proper timestamp synchronization and speaker identification where available.
      9. +
    9. PDF Text Extraction and Structure: Process PDF documents by extracting formatted text while intelligently handling line breaks, page headers, and footnotes. The system analyzes text height statistics to automatically infer heading levels, creating a properly structured document hierarchy based on standard deviation from mean text size.
      10. +
    10. Citation Information Extraction: Identify and extract citation metadata including author names, publication dates, sources, and titles using HTML meta tags and common class name patterns. The system validates author names against a comprehensive database of 90,000 first and last names, distinguishing between personal and organizational authors to properly format citations.
      11. +
    11. Author Name Formatting: Process author names by checking against known name databases, handling affixes and titles correctly, and determining whether to reverse the name order based on whether it's a personal or organizational author, ensuring proper citation formatting.
      12. +
    12. Content Validation: Verify the extracted content's completeness and accuracy by comparing results from multiple extraction methods, ensuring all essential elements are preserved and properly formatted for the intended use case.
    -

    Tardigrade the Web Crawler

    +

    🕸️🖥️ Tardigrade the Web Crawler

    + +

    +

    scrapeURL Docs

      -
    1. Use Fetch API, check for bot detection. Scrape any domain's URL to get its HTML, JSON, or arraybuffer. +
    2. First Use Fetch API, check for bot detection. Scrape any domain's URL to get its HTML, JSON, or Binary Buffer. Scraping internet pages is a free speech right globally.
    3. Features: timeout, redirects, default UA, referer as google, and bot @@ -141,27 +149,12 @@

      Javascript Docs (airesea

      weighRelevanceTermFrequency Docs

      +

      $$\text{score}(D,Q) = \sum_{i=1}^{N} \text{Wiki-IDF}(q_i) \times \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \frac{|D|}{\text{avgdl}}\right)}$$

      Calculate term specificity for a single doc with BM25 formula by using Wikipedia term frequencies as the baseline Inverse Frequency across Documents. WikiBM25 solves the need to pass in all docs to compute against all documents in a database. The problem with BM25 and TF-IDF is that a large set of documents is needed to find the words that are repeated often across all. These overused words are often the same list of words, so using Wikipedia's term frequencies ensures a common sense baseline against a neutral corpus.

      -

      Use this list to Replace or Combine with All Documents IDF - Many websites may have less than a hundred pages to search through and that is not enough to find which terms are domain-specific. They can score a single doc at a time to find the weight each word in query gets. Wikipedia IDf can be a baseline IDF to average with the All Docs IDF for uniqueness across the average public and the specific domain.

      +

      All words in English Wikipedia are sorted by number of pages they are in for 325K words with frequencies of at least 32 wikipages, between 3 to 23 characters of Latin alphanumerics like az09, punctuation like .-, and diacritics like éï, but filtering out numbers and foreign language. +Use this list to Replace or Combine with All Documents IDF - Many websites may have less than a hundred pages to search through and that is not enough to find which terms are domain-specific. They can score a single doc at a time to find the weight each word in query gets. Wikipedia IDf can be a baseline IDF to average with the All Docs IDF for uniqueness across the average public and the specific domain.

      Example: Given a query "Superbowl wins by year" we do not want to simply return docs filled with common words like year, but rather recognize Superbowl is more domains-specific. This requires precomputing IDF values across all docs, and for websites that may not have that many docs to start with may consider averaging their precomputed score with wikiIDF values to ensure most unique words get a score.

      -

      LLM RAG Chunk to Query Similarity - When we chunk a document into parts to find which to pass into a LLM prompt, they need to be weighted to relevance to the query. Semantic Embedding with a LLM not only takes resources to compute & store the vectors, it also performs worse than BM25 on its own. Hybrid BM25 & Embeddings RAG is best, but there may not be time to compute BM25 idf scores across all doc chunks. We need a fast way to distinguish more unique words to give them more weight rather than common short words that get repeated a lot in an edge case paragraph. WikiBM25 is the best in use cases like realtime web search where chunking the text cannot be done beforehand.

      -

      $$\text{score}(D,Q) = \sum_{i=1}^{N} \text{Wiki-IDF}(q_i) \times \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \frac{|D|}{\text{avgdl}}\right)}$$

      -

      WikiIDF Term Specificity Statistics Metadata

      -
        -
      • -

        Phrase Starter Words and Single Words: 104556

        -
        • -
      • -

        Total Terms: 204169

        -
        • -
      • -

        Dict single words: 84493

        -
        • -
      • -

        Dict phrases: 67444

        -
        • -
      -

      All words in English Wikipedia are sorted by number of pages they are in for 325K words with frequencies of at least 32 wikipages, between 3 to 23 characters of Latin alphanumerics like az09, punctuation like .-, and diacritics like éï, but filtering out numbers and foreign language.

      +

      Use Case: LLM RAG Chunk to Query Similarity - When we chunk a document into parts to find which to pass into a LLM prompt, they need to be weighted to relevance to the query. Semantic Embedding with a LLM not only takes resources to compute & store the vectors, it also performs worse than BM25 on its own. Hybrid BM25 & Embeddings RAG is best, but there may not be time to compute BM25 idf scores across all doc chunks. We need a fast way to distinguish more unique words to give them more weight rather than common short words that get repeated a lot in an edge case paragraph. WikiBM25 is the best in use cases like realtime web search where chunking the text cannot be done beforehand.

      🧩🔍 Autocomplete & Query To Topic Phrase Tokenization

      @@ -184,9 +177,9 @@

      Javascript Docs (airesea
    4. Transformers Explained Visually (Part 3)
    5. Can LLMs Generate Novel Research Ideas?
    6. Graph Algorithms Playground
      7. +
    7. CommonCrawl C4 Download
      8. -

      PRs Welcome

      -

    Settings

    Member Visibility

    On This Page

    🧠💻 Reimagine the Internet as 3D Mind MapTardigrade the Web CrawlerFurther Research
    diff --git a/docs/modules.html b/docs/modules.html index bb8adbc..3952aa6 100644 --- a/docs/modules.html +++ b/docs/modules.html @@ -1,10 +1,10 @@ ai-research-agent

    ai-research-agent

    Index

    Extract

    extractCite extractFavicon -extractContentHTML -extractContentHTML2 +extractMainContentFromHTML +extractMainContentFromHTML2 convertPDFToHTML scrapeURL -extract +extractContent convertYoutubeToText

    Generate

    generateLanguageModelReply

    HTML Utilities

    convertHTMLToBasicHTML diff --git a/docs/sitemap.xml b/docs/sitemap.xml index bb9ada1..d93d55b 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -2,162 +2,162 @@ https://airesearch.js.org/modules.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/index.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/extractCite.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/extractFavicon.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z - https://airesearch.js.org/functions/extractContentHTML.html - 2024-10-20T22:49:14.973Z + https://airesearch.js.org/functions/extractMainContentFromHTML.html + 2024-10-25T20:21:12.886Z - https://airesearch.js.org/functions/extractContentHTML2.html - 2024-10-20T22:49:14.973Z + https://airesearch.js.org/functions/extractMainContentFromHTML2.html + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertHTMLToBasicHTML.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertMathLaTexToImage.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertHTMLSpecialChars.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertMarkdownToHTML.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/copyHTMLToClipboard.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertPDFToHTML.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/scrapeURL.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z - https://airesearch.js.org/functions/extract.html - 2024-10-20T22:49:14.973Z + https://airesearch.js.org/functions/extractContent.html + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/embedYoutubePlayer.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertYoutubeToText.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/generateLanguageModelReply.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/weighSimilarityByCharacter.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/matchQUASAR.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/weighRelevanceTermFrequency.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/searchSTREAM.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/searchWeb.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/searchWikipedia.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertEmbeddingsToUMAP.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/weighRelevanceConceptVectorAPI.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertTextToEmbedding.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/getEmbeddingModel.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/addEmbeddingVectorsToIndex.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/searchVectorIndex.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/getAllEmbeddings.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/exportEmbeddingsIndex.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/importVectorIndexFromString.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/splitSentences.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/suggestNextWordCompletions.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/splitTextSemanticChars.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/convertTextToTokens.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/stemWordToRoot.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/extractSEEKTOPIC.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/functions/extractTopicTermGroupsLDA.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z https://airesearch.js.org/classes/torch.html - 2024-10-20T22:49:14.973Z + 2024-10-25T20:21:12.886Z diff --git a/extension/.gitignore b/extension/.gitignore new file mode 100644 index 0000000..6f913d8 --- /dev/null +++ b/extension/.gitignore @@ -0,0 +1,15 @@ +.DS_Store +node_modules +/build +/dist +/.svelte-kit +/package +.env +.env.* +!.env.example +vite.config.js.timestamp-* +vite.config.ts.timestamp-* +.cloudflare +.wrangler +bun.lockb +web-app/data/* \ No newline at end of file diff --git a/extension/components.json b/extension/components.json new file mode 100644 index 0000000..1e00f53 --- /dev/null +++ b/extension/components.json @@ -0,0 +1,14 @@ +{ + "$schema": "https://shadcn-svelte.com/schema.json", + "style": "default", + "tailwind": { + "config": "../web-app/tailwind.config.ts", + "css": "../web-app/src/app.pcss", + "baseColor": "slate" + }, + "aliases": { + "components": "../web-app/src/lib/components", + "utils": "../web-app/src/lib/utils" + }, + "typescript": true +} \ No newline at end of file diff --git a/extension/index.html b/extension/index.html new file mode 100644 index 0000000..b670f34 --- /dev/null +++ b/extension/index.html @@ -0,0 +1,12 @@ + + + + + Debate AI + + + +
    + + + diff --git a/extension/package.json b/extension/package.json new file mode 100644 index 0000000..66e493c --- /dev/null +++ b/extension/package.json @@ -0,0 +1,88 @@ +{ + "name": "debate-ai-chrome-extension", + "version": "6.0.0", + "scripts": { + "css": "tailwindcss -i ./src/pages/app/app.pcss -o ./src/pages/app/tailwind.min.css", + + "dev": "vite dev", + "build": "vite build; ", + "preview": "vite preview", + "zip": "powershell -Command \"Compress-Archive -Path (Join-Path (Get-Location) 'dist\\*') -DestinationPath (Join-Path (Get-Location) 'app.zip') -Force\"", + "check": "svelte-kit sync && svelte-check --tsconfig ./jsconfig.json", + "generate": "drizzle-kit generate", + "push": "drizzle-kit push", + "pull": "drizzle-kit pull" + }, + "peerDependencies": { + "svelte": "^4.0.0" + }, + "devDependencies": { + "@crxjs/vite-plugin": "2.0.0-beta.25", + "@sveltejs/adapter-auto": "^3.2.2", + "@sveltejs/kit": "^2.5.18", + "@sveltejs/package": "^2.3.2", + "@sveltejs/vite-plugin-svelte": "^3.1.1", + "@tsconfig/svelte": "^5.0.4", + "@types/better-sqlite3": "^7.6.11", + "publint": "^0.2.9", + "svelte": "^4.2.18", + "svelte-check": "^3.8.4", + "tslib": "^2.6.3", + "typescript": "^5.5.3", + "vite": "^5.3.4" + }, + "type": "module", + "dependencies": { + "@icons-pack/react-simple-icons": "^9.6.0", + "@langchain/groq": "^0.0.15", + "@postlight/parser": "^2.2.3", + "@sveltejs/adapter-static": "^3.0.2", + "@syfxlin/tiptap-starter-kit": "^1.0.3", + "@tiptap/core": "^2.5.4", + "@tiptap/starter-kit": "^2.5.4", + "@tsparticles/slim": "^3.5.0", + "@tsparticles/svelte": "^3.1.1", + "ai-research-agent": "^0.9.3", + "autoprefixer": "^10.4.19", + "better-docs": "^2.7.3", + "better-sqlite3": "^11.1.2", + "bits-ui": "^0.21.12", + "chrono-node": "^2.7.6", + "clsx": "^2.1.1", + "dotenv": "^16.4.5", + "drizzle-kit": "^0.23.0", + "drizzle-orm": "^0.32.0", + "file-type": "^19.2.0", + "formsnap": "^1.0.1", + "htmlparser2": "^9.1.0", + "jszip": "^3.10.1", + "langchain": "^0.2.10", + "lexical": "^0.16.1", + "linkedom": "^0.18.4", + "lucide-svelte": "^0.411.0", + "meilisearch": "^0.41.0", + "parse-full-name": "^1.2.6", + "postcss": "^8.4.39", + "sortablejs": "^1.15.2", + "svelte-lexical": "^0.4.1", + "svelte-lightbox": "^1.1.3", + "svelte-markdown": "^0.4.1", + "svelte-routing": "^2.13.0", + "svelte-sonner": "^0.3.27", + "svelte-spa-router": "^4.0.1", + "svelte-splitpanes": "^8.0.5", + "svelte-textarea-autosize": "^1.0.0", + "svelte-tiptap": "^1.1.3", + "sveltekit-superforms": "^2.16.1", + "tailwind": "^4.0.0", + "tailwind-merge": "^2.4.0", + "tailwind-variants": "^0.2.1", + "tailwindcss": "^3.4.6", + "tiptap": "^1.32.2", + "typewriter-editor": "^0.12.6", + "unpdf": "^0.11.0", + "vite-plugin-top-level-await": "^1.4.3", + "youtube-transcript": "^1.2.1", + "zod": "^3.23.8" + } +} diff --git a/extension/postcss.config.cjs b/extension/postcss.config.cjs new file mode 100644 index 0000000..e48cff5 --- /dev/null +++ b/extension/postcss.config.cjs @@ -0,0 +1,13 @@ +const tailwindcss = require("tailwindcss"); +const autoprefixer = require("autoprefixer"); + +const config = { + plugins: [ + //Some plugins, like tailwindcss/nesting, need to run before Tailwind, + tailwindcss(), + //But others, like autoprefixer, need to run after, + autoprefixer, + ], +}; + +module.exports = config; diff --git a/extension/src/assets/icons/logo-128.png b/extension/src/assets/icons/logo-128.png new file mode 100644 index 0000000000000000000000000000000000000000..cefa201d3c9b6034bc4d92315d88fd23d196e3ff GIT binary patch literal 16273 zcmV;CKW@N@P)P_1f3NB4j3-PDZ0*8Rnr;+?Wf}q-1zquuaIcn* z#)XfBy@m`Ze693Yjb9X}YW$(t2nXlDu6nvt4IFD=Ep23lM}hGKNBD!fPg5dfU*f1{9Hf?Yg|B4exL>jTgU<= zGnqZJyjk9I_wKIx&N)@p-S@s_-c07@<(X7+=iQ}mci%dv&iX&6gfRvl828^HSNaAX z7%Mv-V1WDicz^-!=i>nexSx+;u@8k1D|TaVxb}u19dy@#i8t2A>&?#W^w}W<>!b<- zBm2EX4fcgf9gWpsI!UW{zy5VU*I%)ly^oK@41KZahxalAT>XDP*%;K!C1Y{zQUGzfbyG!tIiob4FD%fGN^sA{6U($pAH?F$!XD9E~8^4c_ 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"src/pages/sidepanel/index.html", + }, + + options_page: "index.html", + + action: { + default_title: "Search Tools", + }, + + icons: { + "48": "src/assets/icons/logo-48.png", + "128": "src/assets/icons/logo-128.png", + }, + commands: { + _execute_action: { + suggested_key: { + default: "Ctrl+Q", + mac: "Command+B", + }, + }, + }, + content_scripts: [ + { + matches: [""], + js: ["src/pages/content/index.js"], + }, + ], + + content_security_policy: { + extension_pages: "script-src 'self'; object-src 'self'", + }, + web_accessible_resources: [ + { + resources: ["src/*", "*.svg"], + matches: [""], + }, + ], +})); diff --git a/extension/src/pages/app/app.pcss b/extension/src/pages/app/app.pcss new file mode 100644 index 0000000..7f67139 --- /dev/null +++ b/extension/src/pages/app/app.pcss @@ -0,0 +1,105 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + + + + /* Hide outline on contenteditable elements */ + [contenteditable] { + outline: none; + } + + /* Optional: Add a custom focus style for accessibility */ + [contenteditable]:focus { + box-shadow: 0 0 0 0px black; + } + + /* Ensure the text cursor is visible */ + [contenteditable]:focus::selection { + background: transparent; + } + + /* Hide the resize handle in Firefox */ + [contenteditable] { + resize: none; + } + + /* Remove the default focus ring in Firefox */ + [contenteditable]::-moz-focus-inner { + border: 0; + } + + /* Optional: Custom styling for placeholder text */ + [contenteditable]:empty:before { + /* content: attr(placeholder); */ + /* color: #888; */ + /* font-style: italic; */ + } + + +@layer base { + :root { + --background: 0 0% 100%; + --foreground: 222.2 84% 4.9%; + + --muted: 210 40% 96.1%; + --muted-foreground: 215.4 16.3% 46.9%; + + --popover: 0 0% 100%; + --popover-foreground: 222.2 84% 4.9%; + + --card: 0 0% 100%; + --card-foreground: 222.2 84% 4.9%; + + --border: 214.3 31.8% 91.4%; + --input: 214.3 31.8% 91.4%; + + --primary: 222.2 47.4% 11.2%; + --primary-foreground: 210 40% 98%; + + --secondary: 210 40% 96.1%; + --secondary-foreground: 222.2 47.4% 11.2%; + + --accent: 210 40% 96.1%; + --accent-foreground: 222.2 47.4% 11.2%; + + --destructive: 0 72.2% 50.6%; + --destructive-foreground: 210 40% 98%; + + --ring: 222.2 84% 4.9%; + + --radius: 0.5rem; + } + + .dark { + --background: 222.2 84% 4.9%; + --foreground: 210 40% 98%; + + --muted: 217.2 32.6% 17.5%; + --muted-foreground: 215 20.2% 65.1%; + + --popover: 222.2 84% 4.9%; + --popover-foreground: 210 40% 98%; + + --card: 222.2 84% 4.9%; + --card-foreground: 210 40% 98%; + + --border: 217.2 32.6% 17.5%; + --input: 217.2 32.6% 17.5%; + + --primary: 210 40% 98%; + --primary-foreground: 222.2 47.4% 11.2%; + + --secondary: 217.2 32.6% 17.5%; + --secondary-foreground: 210 40% 98%; + + --accent: 217.2 32.6% 17.5%; + --accent-foreground: 210 40% 98%; + + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 210 40% 98%; + + --ring: hsl(212.7,26.8%,83.9); + } +} + \ No newline at end of file diff --git a/extension/src/pages/app/index.ts b/extension/src/pages/app/index.ts new file mode 100644 index 0000000..8113f37 --- /dev/null +++ b/extension/src/pages/app/index.ts @@ -0,0 +1,10 @@ +import Main from '$components/DebateApp/DebateApp.svelte'; +import "tailwindcss/tailwind.css"; +import "./app.pcss"; + + +document.addEventListener('DOMContentLoaded', () => + new Main({ + target: document.body, + }) +); diff --git a/extension/src/pages/app/tailwind.min.css b/extension/src/pages/app/tailwind.min.css new file mode 100644 index 0000000..dd4b694 --- /dev/null +++ b/extension/src/pages/app/tailwind.min.css @@ -0,0 +1,3753 @@ +/* +! tailwindcss v3.4.6 | MIT License | https://tailwindcss.com +*/ + +/* +1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4) +2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116) +*/ + +*, +::before, +::after { + box-sizing: border-box; + /* 1 */ + border-width: 0; + /* 2 */ + border-style: solid; + /* 2 */ + border-color: #e5e7eb; + /* 2 */ +} + +::before, +::after { + --tw-content: ''; +} + +/* +1. Use a consistent sensible line-height in all browsers. +2. Prevent adjustments of font size after orientation changes in iOS. +3. Use a more readable tab size. +4. Use the user's configured `sans` font-family by default. +5. Use the user's configured `sans` font-feature-settings by default. +6. Use the user's configured `sans` font-variation-settings by default. +7. Disable tap highlights on iOS +*/ + +html, +:host { + line-height: 1.5; + /* 1 */ + -webkit-text-size-adjust: 100%; + /* 2 */ + -moz-tab-size: 4; + /* 3 */ + -o-tab-size: 4; + tab-size: 4; + /* 3 */ + font-family: ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji"; + /* 4 */ + font-feature-settings: normal; + /* 5 */ + font-variation-settings: normal; + /* 6 */ + -webkit-tap-highlight-color: transparent; + /* 7 */ +} + +/* +1. Remove the margin in all browsers. +2. Inherit line-height from `html` so users can set them as a class directly on the `html` element. +*/ + +body { + margin: 0; + /* 1 */ + line-height: inherit; + /* 2 */ +} + +/* +1. Add the correct height in Firefox. +2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655) +3. Ensure horizontal rules are visible by default. +*/ + +hr { + height: 0; + /* 1 */ + color: inherit; + /* 2 */ + border-top-width: 1px; + /* 3 */ +} + +/* +Add the correct text decoration in Chrome, Edge, and Safari. +*/ + +abbr:where([title]) { + -webkit-text-decoration: underline dotted; + text-decoration: underline dotted; +} + +/* +Remove the default font size and weight for headings. +*/ + +h1, +h2, +h3, +h4, +h5, +h6 { + font-size: inherit; + font-weight: inherit; +} + +/* +Reset links to optimize for opt-in styling instead of opt-out. +*/ + +a { + color: inherit; + text-decoration: inherit; +} + +/* +Add the correct font weight in Edge and Safari. +*/ + +b, +strong { + font-weight: bolder; +} + +/* +1. Use the user's configured `mono` font-family by default. +2. Use the user's configured `mono` font-feature-settings by default. +3. Use the user's configured `mono` font-variation-settings by default. +4. Correct the odd `em` font sizing in all browsers. +*/ + +code, +kbd, +samp, +pre { + font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; + /* 1 */ + font-feature-settings: normal; + /* 2 */ + font-variation-settings: normal; + /* 3 */ + font-size: 1em; + /* 4 */ +} + +/* +Add the correct font size in all browsers. +*/ + +small { + font-size: 80%; +} + +/* +Prevent `sub` and `sup` elements from affecting the line height in all browsers. +*/ + +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline; +} + +sub { + bottom: -0.25em; +} + +sup { + top: -0.5em; +} + +/* +1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297) +2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016) +3. Remove gaps between table borders by default. +*/ + +table { + text-indent: 0; + /* 1 */ + border-color: inherit; + /* 2 */ + border-collapse: collapse; + /* 3 */ +} + +/* +1. Change the font styles in all browsers. +2. Remove the margin in Firefox and Safari. +3. Remove default padding in all browsers. +*/ + +button, +input, +optgroup, +select, +textarea { + font-family: inherit; + /* 1 */ + font-feature-settings: inherit; + /* 1 */ + font-variation-settings: inherit; + /* 1 */ + font-size: 100%; + /* 1 */ + font-weight: inherit; + /* 1 */ + line-height: inherit; + /* 1 */ + letter-spacing: inherit; + /* 1 */ + color: inherit; + /* 1 */ + margin: 0; + /* 2 */ + padding: 0; + /* 3 */ +} + +/* +Remove the inheritance of text transform in Edge and Firefox. +*/ + +button, +select { + text-transform: none; +} + +/* +1. Correct the inability to style clickable types in iOS and Safari. +2. Remove default button styles. +*/ + +button, +input:where([type='button']), +input:where([type='reset']), +input:where([type='submit']) { + -webkit-appearance: button; + /* 1 */ + background-color: transparent; + /* 2 */ + background-image: none; + /* 2 */ +} + +/* +Use the modern Firefox focus style for all focusable elements. +*/ + +:-moz-focusring { + outline: auto; +} + +/* +Remove the additional `:invalid` styles in Firefox. (https://github.com/mozilla/gecko-dev/blob/2f9eacd9d3d995c937b4251a5557d95d494c9be1/layout/style/res/forms.css#L728-L737) +*/ + +:-moz-ui-invalid { + box-shadow: none; +} + +/* +Add the correct vertical alignment in Chrome and Firefox. +*/ + +progress { + vertical-align: baseline; +} + +/* +Correct the cursor style of increment and decrement buttons in Safari. +*/ + +::-webkit-inner-spin-button, +::-webkit-outer-spin-button { + height: auto; +} + +/* +1. Correct the odd appearance in Chrome and Safari. +2. Correct the outline style in Safari. +*/ + +[type='search'] { + -webkit-appearance: textfield; + /* 1 */ + outline-offset: -2px; + /* 2 */ +} + +/* +Remove the inner padding in Chrome and Safari on macOS. +*/ + +::-webkit-search-decoration { + -webkit-appearance: none; +} + +/* +1. Correct the inability to style clickable types in iOS and Safari. +2. Change font properties to `inherit` in Safari. +*/ + +::-webkit-file-upload-button { + -webkit-appearance: button; + /* 1 */ + font: inherit; + /* 2 */ +} + +/* +Add the correct display in Chrome and Safari. +*/ + +summary { + display: list-item; +} + +/* +Removes the default spacing and border for appropriate elements. +*/ + +blockquote, +dl, +dd, +h1, +h2, +h3, +h4, +h5, +h6, +hr, +figure, +p, +pre { + margin: 0; +} + +fieldset { + margin: 0; + padding: 0; +} + +legend { + padding: 0; +} + +ol, +ul, +menu { + list-style: none; + margin: 0; + padding: 0; +} + +/* +Reset default styling for dialogs. +*/ + +dialog { + padding: 0; +} + +/* +Prevent resizing textareas horizontally by default. +*/ + +textarea { + resize: vertical; +} + +/* +1. Reset the default placeholder opacity in Firefox. (https://github.com/tailwindlabs/tailwindcss/issues/3300) +2. Set the default placeholder color to the user's configured gray 400 color. +*/ + +input::-moz-placeholder, textarea::-moz-placeholder { + opacity: 1; + /* 1 */ + color: #9ca3af; + /* 2 */ +} + +input::placeholder, +textarea::placeholder { + opacity: 1; + /* 1 */ + color: #9ca3af; + /* 2 */ +} + +/* +Set the default cursor for buttons. +*/ + +button, +[role="button"] { + cursor: pointer; +} + +/* +Make sure disabled buttons don't get the pointer cursor. +*/ + +:disabled { + cursor: default; +} + +/* +1. Make replaced elements `display: block` by default. (https://github.com/mozdevs/cssremedy/issues/14) +2. Add `vertical-align: middle` to align replaced elements more sensibly by default. (https://github.com/jensimmons/cssremedy/issues/14#issuecomment-634934210) + This can trigger a poorly considered lint error in some tools but is included by design. +*/ + +img, +svg, +video, +canvas, +audio, +iframe, +embed, +object { + display: block; + /* 1 */ + vertical-align: middle; + /* 2 */ +} + +/* +Constrain images and videos to the parent width and preserve their intrinsic aspect ratio. (https://github.com/mozdevs/cssremedy/issues/14) +*/ + +img, +video { + max-width: 100%; + height: auto; +} + +/* Make elements with the HTML hidden attribute stay hidden by default */ + +[hidden] { + display: none; +} + +:root { + --background: 0 0% 100%; + --foreground: 222.2 84% 4.9%; + --muted: 210 40% 96.1%; + --muted-foreground: 215.4 16.3% 46.9%; + --popover: 0 0% 100%; + --popover-foreground: 222.2 84% 4.9%; + --card: 0 0% 100%; + --card-foreground: 222.2 84% 4.9%; + --border: 214.3 31.8% 91.4%; + --input: 214.3 31.8% 91.4%; + --primary: 222.2 47.4% 11.2%; + --primary-foreground: 210 40% 98%; + --secondary: 210 40% 96.1%; + --secondary-foreground: 222.2 47.4% 11.2%; + --accent: 210 40% 96.1%; + --accent-foreground: 222.2 47.4% 11.2%; + --destructive: 0 72.2% 50.6%; + --destructive-foreground: 210 40% 98%; + --ring: 222.2 84% 4.9%; + --radius: 0.5rem; +} + +.dark { + --background: 222.2 84% 4.9%; + --foreground: 210 40% 98%; + --muted: 217.2 32.6% 17.5%; + --muted-foreground: 215 20.2% 65.1%; + --popover: 222.2 84% 4.9%; + --popover-foreground: 210 40% 98%; + --card: 222.2 84% 4.9%; + --card-foreground: 210 40% 98%; + --border: 217.2 32.6% 17.5%; + --input: 217.2 32.6% 17.5%; + --primary: 210 40% 98%; + --primary-foreground: 222.2 47.4% 11.2%; + --secondary: 217.2 32.6% 17.5%; + --secondary-foreground: 210 40% 98%; + --accent: 217.2 32.6% 17.5%; + --accent-foreground: 210 40% 98%; + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 210 40% 98%; + --ring: hsl(212.7,26.8%,83.9); +} + +*, ::before, ::after { + --tw-border-spacing-x: 0; + --tw-border-spacing-y: 0; + --tw-translate-x: 0; + --tw-translate-y: 0; + --tw-rotate: 0; + --tw-skew-x: 0; + --tw-skew-y: 0; + --tw-scale-x: 1; + --tw-scale-y: 1; + --tw-pan-x: ; + --tw-pan-y: ; + --tw-pinch-zoom: ; + --tw-scroll-snap-strictness: proximity; + --tw-gradient-from-position: ; + --tw-gradient-via-position: ; + --tw-gradient-to-position: ; + --tw-ordinal: ; + --tw-slashed-zero: ; + --tw-numeric-figure: ; + --tw-numeric-spacing: ; + --tw-numeric-fraction: ; + --tw-ring-inset: ; + --tw-ring-offset-width: 0px; + --tw-ring-offset-color: #fff; + --tw-ring-color: rgb(59 130 246 / 0.5); + --tw-ring-offset-shadow: 0 0 #0000; + --tw-ring-shadow: 0 0 #0000; + --tw-shadow: 0 0 #0000; + --tw-shadow-colored: 0 0 #0000; + --tw-blur: ; + --tw-brightness: ; + --tw-contrast: ; + --tw-grayscale: ; + --tw-hue-rotate: ; + --tw-invert: ; + --tw-saturate: ; + --tw-sepia: ; + --tw-drop-shadow: ; + --tw-backdrop-blur: ; + --tw-backdrop-brightness: ; + --tw-backdrop-contrast: ; + --tw-backdrop-grayscale: ; + --tw-backdrop-hue-rotate: ; + --tw-backdrop-invert: ; + --tw-backdrop-opacity: ; + --tw-backdrop-saturate: ; + --tw-backdrop-sepia: ; + --tw-contain-size: ; + --tw-contain-layout: ; + --tw-contain-paint: ; + --tw-contain-style: ; +} + +::backdrop { + --tw-border-spacing-x: 0; + --tw-border-spacing-y: 0; + --tw-translate-x: 0; + --tw-translate-y: 0; + --tw-rotate: 0; + --tw-skew-x: 0; + --tw-skew-y: 0; + --tw-scale-x: 1; + --tw-scale-y: 1; + --tw-pan-x: ; + --tw-pan-y: ; + --tw-pinch-zoom: ; + --tw-scroll-snap-strictness: proximity; + --tw-gradient-from-position: ; + --tw-gradient-via-position: ; + --tw-gradient-to-position: ; + --tw-ordinal: ; + --tw-slashed-zero: ; + --tw-numeric-figure: ; + --tw-numeric-spacing: ; + --tw-numeric-fraction: ; + --tw-ring-inset: ; + --tw-ring-offset-width: 0px; + --tw-ring-offset-color: #fff; + --tw-ring-color: rgb(59 130 246 / 0.5); + --tw-ring-offset-shadow: 0 0 #0000; + --tw-ring-shadow: 0 0 #0000; + --tw-shadow: 0 0 #0000; + --tw-shadow-colored: 0 0 #0000; + --tw-blur: ; + --tw-brightness: ; + --tw-contrast: ; + --tw-grayscale: ; + --tw-hue-rotate: ; + --tw-invert: ; + --tw-saturate: ; + --tw-sepia: ; + --tw-drop-shadow: ; + --tw-backdrop-blur: ; + --tw-backdrop-brightness: ; + --tw-backdrop-contrast: ; + --tw-backdrop-grayscale: ; + --tw-backdrop-hue-rotate: ; + --tw-backdrop-invert: ; + --tw-backdrop-opacity: ; + --tw-backdrop-saturate: ; + --tw-backdrop-sepia: ; + --tw-contain-size: ; + --tw-contain-layout: ; + --tw-contain-paint: ; + --tw-contain-style: ; +} + +.container { + width: 100%; +} + +@media (min-width: 640px) { + .container { + max-width: 640px; + } +} + +@media (min-width: 768px) { + .container { + max-width: 768px; + } +} + +@media (min-width: 1024px) { + .container { + max-width: 1024px; + } +} + +@media (min-width: 1280px) { + .container { + max-width: 1280px; + } +} + +@media (min-width: 1536px) { + .container { + max-width: 1536px; + } +} + +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; +} + +.pointer-events-none { + pointer-events: none; +} + +.visible { + visibility: visible; +} + +.invisible { + visibility: hidden; +} + +.collapse { + visibility: collapse; +} + +.static { + position: static; +} + +.fixed { + position: fixed; +} + +.absolute { + position: absolute; +} + +.relative { + position: relative; +} + +.sticky { + position: sticky; +} + +.inset-0 { + inset: 0px; +} + +.inset-x-0 { + left: 0px; + right: 0px; +} + +.inset-y-0 { + top: 0px; + bottom: 0px; +} + +.bottom-0 { + bottom: 0px; +} + +.bottom-1 { + bottom: 0.25rem; +} + +.bottom-24 { + bottom: 6rem; +} + +.bottom-4 { + bottom: 1rem; +} + +.left-0 { + left: 0px; +} + +.left-1\/2 { + left: 50%; +} + +.left-2 { + left: 0.5rem; +} + +.left-3 { + left: 0.75rem; +} + +.left-4 { + left: 1rem; +} + +.left-\[50\%\] { + left: 50%; +} + +.right-0 { + right: 0px; +} + +.right-1 { + right: 0.25rem; +} + +.right-10 { + right: 2.5rem; +} + +.right-2 { + right: 0.5rem; +} + +.right-4 { + right: 1rem; +} + +.top-0 { + top: 0px; +} + +.top-1 { + top: 0.25rem; +} + +.top-1\/2 { + top: 50%; +} + +.top-2 { + top: 0.5rem; +} + +.top-4 { + top: 1rem; +} + +.top-7 { + top: 1.75rem; +} + +.top-\[50\%\] { + top: 50%; +} + +.top-full { + top: 100%; +} + +.isolate { + isolation: isolate; +} + +.-z-10 { + z-index: -10; +} + +.z-10 { + z-index: 10; +} + +.z-20 { + z-index: 20; +} + +.z-30 { + z-index: 30; +} + +.z-40 { + z-index: 40; +} + +.z-50 { + z-index: 50; +} + +.z-\[9999\] { + z-index: 9999; +} + +.col-span-1 { + grid-column: span 1 / span 1; +} + +.m-0 { + margin: 0px; +} + +.m-1 { + margin: 0.25rem; +} + +.m-\[2px\] { + margin: 2px; +} + +.m-\[5px\] { + margin: 5px; +} + +.-mx-1 { + margin-left: -0.25rem; + margin-right: -0.25rem; +} + +.-mx-2 { + margin-left: -0.5rem; + margin-right: -0.5rem; +} + +.mx-1 { + margin-left: 0.25rem; + margin-right: 0.25rem; +} + +.mx-6 { + margin-left: 1.5rem; + margin-right: 1.5rem; +} + +.mx-auto { + margin-left: auto; + margin-right: auto; +} + +.my-1 { + margin-top: 0.25rem; + margin-bottom: 0.25rem; +} + +.my-2 { + margin-top: 0.5rem; + margin-bottom: 0.5rem; +} + +.my-3 { + margin-top: 0.75rem; + margin-bottom: 0.75rem; +} + +.my-6 { + margin-top: 1.5rem; + margin-bottom: 1.5rem; +} + +.my-8 { + margin-top: 2rem; + margin-bottom: 2rem; +} + +.\!mt-2 { + margin-top: 0.5rem !important; +} + +.-mr-2 { + margin-right: -0.5rem; +} + +.-mt-4 { + margin-top: -1rem; +} + +.mb-0 { + margin-bottom: 0px; +} + +.mb-1 { + margin-bottom: 0.25rem; +} + +.mb-11 { + margin-bottom: 2.75rem; +} + +.mb-2 { + margin-bottom: 0.5rem; +} + +.mb-4 { + margin-bottom: 1rem; +} + +.mb-8 { + margin-bottom: 2rem; +} + +.mb-\[var\(--gap\)\] { + margin-bottom: var(--gap); +} + +.ml-1 { + margin-left: 0.25rem; +} + +.ml-2 { + margin-left: 0.5rem; +} + +.ml-6 { + margin-left: 1.5rem; +} + +.ml-auto { + margin-left: auto; +} + +.mr-1 { + margin-right: 0.25rem; +} + +.mr-2 { + margin-right: 0.5rem; +} + +.mr-4 { + margin-right: 1rem; +} + +.mt-1 { + margin-top: 0.25rem; +} + +.mt-2 { + margin-top: 0.5rem; +} + +.mt-4 { + margin-top: 1rem; +} + +.mt-6 { + margin-top: 1.5rem; +} + +.mt-8 { + margin-top: 2rem; +} + +.mt-auto { + margin-top: auto; +} + +.box-border { + box-sizing: border-box; +} + +.box-content { + box-sizing: content-box; +} + +.block { + display: block; +} + +.inline-block { + display: inline-block; +} + +.inline { + display: inline; +} + +.flex { + display: flex; +} + +.inline-flex { + display: inline-flex; +} + +.table { + display: table; +} + +.table-caption { + display: table-caption; +} + +.table-cell { + display: table-cell; +} + +.table-row { + display: table-row; +} + +.grid { + display: grid; +} + +.contents { + display: contents; +} + +.hidden { + display: none; +} + +.aspect-square { + aspect-ratio: 1 / 1; +} + +.aspect-video { + aspect-ratio: 16 / 9; +} + +.size-10 { + width: 2.5rem; + height: 2.5rem; +} + +.size-4 { + width: 1rem; + height: 1rem; +} + +.size-5 { + width: 1.25rem; + height: 1.25rem; +} + +.size-8 { + width: 2rem; + height: 2rem; +} + +.h-0 { + height: 0px; +} + +.h-1 { + height: 0.25rem; +} + +.h-10 { + height: 2.5rem; +} + +.h-11 { + height: 2.75rem; +} + +.h-12 { + height: 3rem; +} + +.h-14 { + height: 3.5rem; +} + +.h-2 { + height: 0.5rem; +} + +.h-3 { + height: 0.75rem; +} + +.h-3\.5 { + height: 0.875rem; +} + +.h-32 { + height: 8rem; +} + +.h-4 { + height: 1rem; +} + +.h-5 { + height: 1.25rem; +} + +.h-6 { + height: 1.5rem; +} + +.h-8 { + height: 2rem; +} + +.h-9 { + height: 2.25rem; +} + +.h-\[1\.2rem\] { + height: 1.2rem; +} + +.h-\[100dvh\] { + height: 100dvh; +} + +.h-\[1px\] { + height: 1px; +} + +.h-\[24px\] { + height: 24px; +} + +.h-\[300px\] { + height: 300px; +} + +.h-\[4px\] { + height: 4px; +} + +.h-\[600px\] { + height: 600px; +} + +.h-\[80px\] { + height: 80px; +} + +.h-\[var\(--main-height\)\] { + height: var(--main-height); +} + +.h-\[var\(--title-height\)\] { + height: var(--title-height); +} + +.h-auto { + height: auto; +} + +.h-full { + height: 100%; +} + +.h-px { + height: 1px; +} + +.h-screen { + height: 100vh; +} + +.max-h-24 { + max-height: 6rem; +} + +.max-h-\[300px\] { + max-height: 300px; +} + +.max-h-\[700px\] { + max-height: 700px; +} + +.max-h-\[90vh\] { + max-height: 90vh; +} + +.max-h-\[95vh\] { + max-height: 95vh; +} + +.max-h-\[calc\(90vh-120px\)\] { + max-height: calc(90vh - 120px); +} + +.min-h-\[80px\] { + min-height: 80px; +} + +.min-h-full { + min-height: 100%; +} + +.min-h-screen { + min-height: 100vh; +} + +.w-1 { + width: 0.25rem; +} + +.w-1\/5 { + width: 20%; +} + +.w-10 { + width: 2.5rem; +} + +.w-10\/12 { + width: 83.333333%; +} + +.w-12 { + width: 3rem; +} + +.w-2 { + width: 0.5rem; +} + +.w-3 { + width: 0.75rem; +} + +.w-3\.5 { + width: 0.875rem; +} + +.w-3\/4 { + width: 75%; +} + +.w-4 { + width: 1rem; +} + +.w-44 { + width: 11rem; +} + +.w-48 { + width: 12rem; +} + +.w-5 { + width: 1.25rem; +} + +.w-56 { + width: 14rem; +} + +.w-6 { + width: 1.5rem; +} + +.w-64 { + width: 16rem; +} + +.w-72 { + width: 18rem; +} + +.w-8 { + width: 2rem; +} + +.w-9 { + width: 2.25rem; +} + +.w-9\/12 { + width: 75%; +} + +.w-\[1\.2rem\] { + width: 1.2rem; +} + +.w-\[100px\] { + width: 100px; +} + +.w-\[120px\] { + width: 120px; +} + +.w-\[14px\] { + width: 14px; +} + +.w-\[180px\] { + width: 180px; +} + +.w-\[1px\] { + width: 1px; +} + +.w-\[400px\] { + width: 400px; +} + +.w-\[44px\] { + width: 44px; +} + +.w-\[4px\] { + width: 4px; +} + +.w-\[500px\] { + width: 500px; +} + +.w-\[80px\] { + width: 80px; +} + +.w-\[90\%\] { + width: 90%; +} + +.w-\[95\%\] { + width: 95%; +} + +.w-\[95vw\] { + width: 95vw; +} + +.w-auto { + width: auto; +} + +.w-fit { + width: -moz-fit-content; + width: fit-content; +} + +.w-full { + width: 100%; +} + +.w-max { + width: -moz-max-content; + width: max-content; +} + +.w-screen { + width: 100vw; +} + +.min-w-0 { + min-width: 0px; +} + +.min-w-\[120px\] { + min-width: 120px; +} + +.min-w-\[12rem\] { + min-width: 12rem; +} + +.min-w-\[150px\] { + min-width: 150px; +} + +.min-w-\[200px\] { + min-width: 200px; +} + +.min-w-\[2rem\] { + min-width: 2rem; +} + +.min-w-\[8rem\] { + min-width: 8rem; +} + +.min-w-max { + min-width: -moz-max-content; + min-width: max-content; +} + +.max-w-2xl { + max-width: 42rem; +} + +.max-w-3xl { + max-width: 48rem; +} + +.max-w-4xl { + max-width: 56rem; +} + +.max-w-\[1100px\] { + max-width: 1100px; +} + +.max-w-\[150px\] { + max-width: 150px; +} + +.max-w-\[250px\] { + max-width: 250px; +} + +.max-w-\[300px\] { + max-width: 300px; +} + +.max-w-\[320px\] { + max-width: 320px; +} + +.max-w-\[800px\] { + max-width: 800px; +} + +.max-w-\[80px\] { + max-width: 80px; +} + +.max-w-lg { + max-width: 32rem; +} + +.max-w-md { + max-width: 28rem; +} + +.max-w-none { + max-width: none; +} + +.max-w-screen-lg { + max-width: 1024px; +} + +.max-w-sm { + max-width: 24rem; +} + +.max-w-xl { + max-width: 36rem; +} + +.flex-1 { + flex: 1 1 0%; +} + +.flex-shrink { + flex-shrink: 1; +} + +.flex-shrink-0 { + flex-shrink: 0; +} + +.shrink-0 { + flex-shrink: 0; +} + +.flex-grow { + flex-grow: 1; +} + +.grow { + flex-grow: 1; +} + +.caption-bottom { + caption-side: bottom; +} + +.origin-top-right { + transform-origin: top right; +} + +.-translate-x-1\/2 { + --tw-translate-x: -50%; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.-translate-y-1\/2 { + --tw-translate-y: -50%; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.translate-x-\[-50\%\] { + --tw-translate-x: -50%; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.translate-y-\[-50\%\] { + --tw-translate-y: -50%; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.rotate-0 { + --tw-rotate: 0deg; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.rotate-90 { + --tw-rotate: 90deg; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.scale-0 { + --tw-scale-x: 0; + --tw-scale-y: 0; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.scale-100 { + --tw-scale-x: 1; + --tw-scale-y: 1; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.transform { + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.animate-none { + animation: none; +} + +@keyframes pulse { + 50% { + opacity: .5; + } +} + +.animate-pulse { + animation: pulse 2s cubic-bezier(0.4, 0, 0.6, 1) infinite; +} + +@keyframes spin { + to { + transform: rotate(360deg); + } +} + +.animate-spin { + animation: spin 1s linear infinite; +} + +.cursor-default { + cursor: default; +} + +.cursor-grab { + cursor: grab; +} + +.cursor-move { + cursor: move; +} + +.cursor-pointer { + cursor: pointer; +} + +.cursor-zoom-in { + cursor: zoom-in; +} + +.touch-none { + touch-action: none; +} + +.select-none { + -webkit-user-select: none; + -moz-user-select: none; + user-select: none; +} + +.resize-none { + resize: none; +} + +.resize { + resize: both; +} + +.list-disc { + list-style-type: disc; +} + +.appearance-none { + -webkit-appearance: none; + -moz-appearance: none; + appearance: none; +} + +.grid-cols-1 { + grid-template-columns: repeat(1, minmax(0, 1fr)); +} + +.grid-cols-2 { + grid-template-columns: repeat(2, minmax(0, 1fr)); +} + +.grid-cols-\[var\(--sidebar-width\)_1fr\] { + grid-template-columns: var(--sidebar-width) 1fr; +} + +.grid-cols-\[var\(--sidebar-width\)_auto\] { + grid-template-columns: var(--sidebar-width) auto; +} + +.flex-row { + flex-direction: row; +} + +.flex-col { + flex-direction: column; +} + +.flex-col-reverse { + flex-direction: column-reverse; +} + +.flex-wrap { + flex-wrap: wrap; +} + +.place-items-center { + place-items: center; +} + +.items-start { + align-items: flex-start; +} + +.items-end { + align-items: flex-end; +} + +.items-center { + align-items: center; +} + +.items-stretch { + align-items: stretch; +} + +.justify-start { + justify-content: flex-start; +} + +.justify-end { + justify-content: flex-end; +} + +.justify-center { + justify-content: center; +} + +.justify-between { + justify-content: space-between; +} + +.justify-around { + justify-content: space-around; +} + +.gap-1 { + gap: 0.25rem; +} + +.gap-1\.5 { + gap: 0.375rem; +} + +.gap-2 { + gap: 0.5rem; +} + +.gap-3 { + gap: 0.75rem; +} + +.gap-4 { + gap: 1rem; +} + +.gap-5 { + gap: 1.25rem; +} + +.gap-6 { + gap: 1.5rem; +} + +.gap-\[var\(--gap\)\] { + gap: var(--gap); +} + +.gap-x-2 { + -moz-column-gap: 0.5rem; + column-gap: 0.5rem; +} + +.gap-x-6 { + -moz-column-gap: 1.5rem; + column-gap: 1.5rem; +} + +.gap-y-3 { + row-gap: 0.75rem; +} + +.gap-y-5 { + row-gap: 1.25rem; +} + +.space-x-1 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(0.25rem * var(--tw-space-x-reverse)); + margin-left: calc(0.25rem * calc(1 - var(--tw-space-x-reverse))); +} + +.space-x-2 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(0.5rem * var(--tw-space-x-reverse)); + margin-left: calc(0.5rem * calc(1 - var(--tw-space-x-reverse))); +} + +.space-x-3 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(0.75rem * var(--tw-space-x-reverse)); + margin-left: calc(0.75rem * calc(1 - var(--tw-space-x-reverse))); +} + +.space-x-4 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(1rem * var(--tw-space-x-reverse)); + margin-left: calc(1rem * calc(1 - var(--tw-space-x-reverse))); +} + +.space-x-\[var\(--gap\)\] > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(var(--gap) * var(--tw-space-x-reverse)); + margin-left: calc(var(--gap) * calc(1 - var(--tw-space-x-reverse))); +} + +.space-y-0 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(0px * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(0px * var(--tw-space-y-reverse)); +} + +.space-y-0\.5 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(0.125rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(0.125rem * var(--tw-space-y-reverse)); +} + +.space-y-1 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(0.25rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(0.25rem * var(--tw-space-y-reverse)); +} + +.space-y-1\.5 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(0.375rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(0.375rem * var(--tw-space-y-reverse)); +} + +.space-y-2 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(0.5rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(0.5rem * var(--tw-space-y-reverse)); +} + +.space-y-3 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(0.75rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(0.75rem * var(--tw-space-y-reverse)); +} + +.space-y-4 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(1rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(1rem * var(--tw-space-y-reverse)); +} + +.space-y-6 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(1.5rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(1.5rem * var(--tw-space-y-reverse)); +} + +.space-y-8 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(2rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(2rem * var(--tw-space-y-reverse)); +} + +.space-y-9 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(2.25rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(2.25rem * var(--tw-space-y-reverse)); +} + +.overflow-auto { + overflow: auto; +} + +.overflow-hidden { + overflow: hidden; +} + +.overflow-x-auto { + overflow-x: auto; +} + +.overflow-y-auto { + overflow-y: auto; +} + +.overflow-x-hidden { + overflow-x: hidden; +} + +.truncate { + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.text-ellipsis { + text-overflow: ellipsis; +} + +.whitespace-nowrap { + white-space: nowrap; +} + +.break-words { + overflow-wrap: break-word; +} + +.rounded { + border-radius: 0.25rem; +} + +.rounded-2xl { + border-radius: 1rem; +} + +.rounded-\[var\(--border-radius\)\] { + border-radius: var(--border-radius); +} + +.rounded-full { + border-radius: 9999px; +} + +.rounded-lg { + border-radius: 0.5rem; +} + +.rounded-md { + border-radius: 0.375rem; +} + +.rounded-sm { + border-radius: 0.125rem; +} + +.rounded-xl { + border-radius: 0.75rem; +} + +.rounded-b-lg { + border-bottom-right-radius: 0.5rem; + border-bottom-left-radius: 0.5rem; +} + +.rounded-l-lg { + border-top-left-radius: 0.5rem; + border-bottom-left-radius: 0.5rem; +} + +.rounded-l-md { + border-top-left-radius: 0.375rem; + border-bottom-left-radius: 0.375rem; +} + +.rounded-r-md { + border-top-right-radius: 0.375rem; + border-bottom-right-radius: 0.375rem; +} + +.rounded-t-lg { + border-top-left-radius: 0.5rem; + border-top-right-radius: 0.5rem; +} + +.rounded-br-\[15px\] { + border-bottom-right-radius: 15px; +} + +.rounded-tr-\[15px\] { + border-top-right-radius: 15px; +} + +.border { + border-width: 1px; +} + +.border-2 { + border-width: 2px; +} + +.border-b { + border-bottom-width: 1px; +} + +.border-l { + border-left-width: 1px; +} + +.border-r { + border-right-width: 1px; +} + +.border-t { + border-top-width: 1px; +} + +.border-dashed { + border-style: dashed; +} + +.border-none { + border-style: none; +} + +.border-gray-200 { + --tw-border-opacity: 1; + border-color: rgb(229 231 235 / var(--tw-border-opacity)); +} + +.border-gray-300 { + --tw-border-opacity: 1; + border-color: rgb(209 213 219 / var(--tw-border-opacity)); +} + +.border-slate-300 { + --tw-border-opacity: 1; + border-color: rgb(203 213 225 / var(--tw-border-opacity)); +} + +.border-slate-600 { + --tw-border-opacity: 1; + border-color: rgb(71 85 105 / var(--tw-border-opacity)); +} + +.border-transparent { + border-color: transparent; +} + +.border-white { + --tw-border-opacity: 1; + border-color: rgb(255 255 255 / var(--tw-border-opacity)); +} + +.bg-\[\#24A0ED\] { + --tw-bg-opacity: 1; + background-color: rgb(36 160 237 / var(--tw-bg-opacity)); +} + +.bg-\[\#DED8C4\] { + --tw-bg-opacity: 1; + background-color: rgb(222 216 196 / var(--tw-bg-opacity)); +} + +.bg-\[\#E8E5D8\] { + --tw-bg-opacity: 1; + background-color: rgb(232 229 216 / var(--tw-bg-opacity)); +} + +.bg-\[\#F3F3EE\] { + --tw-bg-opacity: 1; + background-color: rgb(243 243 238 / var(--tw-bg-opacity)); +} + +.bg-\[var\(--background\)\] { + background-color: var(--background); +} + +.bg-black { + --tw-bg-opacity: 1; + background-color: rgb(0 0 0 / var(--tw-bg-opacity)); +} + +.bg-black\/30 { + background-color: rgb(0 0 0 / 0.3); +} + +.bg-black\/50 { + background-color: rgb(0 0 0 / 0.5); +} + +.bg-blue-100 { + --tw-bg-opacity: 1; + background-color: rgb(219 234 254 / var(--tw-bg-opacity)); +} + +.bg-gray-100 { + --tw-bg-opacity: 1; + background-color: rgb(243 244 246 / var(--tw-bg-opacity)); +} + +.bg-gray-200 { + --tw-bg-opacity: 1; + background-color: rgb(229 231 235 / var(--tw-bg-opacity)); +} + +.bg-gray-300 { + --tw-bg-opacity: 1; + background-color: rgb(209 213 219 / var(--tw-bg-opacity)); +} + +.bg-gray-500 { + --tw-bg-opacity: 1; + background-color: rgb(107 114 128 / var(--tw-bg-opacity)); +} + +.bg-gray-700 { + --tw-bg-opacity: 1; + background-color: rgb(55 65 81 / var(--tw-bg-opacity)); +} + +.bg-green-600 { + --tw-bg-opacity: 1; + background-color: rgb(22 163 74 / var(--tw-bg-opacity)); +} + +.bg-orange-600 { + --tw-bg-opacity: 1; + background-color: rgb(234 88 12 / var(--tw-bg-opacity)); +} + +.bg-red-500 { + --tw-bg-opacity: 1; + background-color: rgb(239 68 68 / var(--tw-bg-opacity)); +} + +.bg-red-600 { + --tw-bg-opacity: 1; + background-color: rgb(220 38 38 / var(--tw-bg-opacity)); +} + +.bg-slate-100 { + --tw-bg-opacity: 1; + background-color: rgb(241 245 249 / var(--tw-bg-opacity)); +} + +.bg-slate-200 { + --tw-bg-opacity: 1; + background-color: rgb(226 232 240 / var(--tw-bg-opacity)); +} + +.bg-slate-300 { + --tw-bg-opacity: 1; + background-color: rgb(203 213 225 / var(--tw-bg-opacity)); +} + +.bg-slate-400 { + --tw-bg-opacity: 1; + background-color: rgb(148 163 184 / var(--tw-bg-opacity)); +} + +.bg-slate-700 { + --tw-bg-opacity: 1; + background-color: rgb(51 65 85 / var(--tw-bg-opacity)); +} + +.bg-slate-800 { + --tw-bg-opacity: 1; + background-color: rgb(30 41 59 / var(--tw-bg-opacity)); +} + +.bg-slate-900 { + --tw-bg-opacity: 1; + background-color: rgb(15 23 42 / var(--tw-bg-opacity)); +} + +.bg-transparent { + background-color: transparent; +} + +.bg-white { + --tw-bg-opacity: 1; + background-color: rgb(255 255 255 / var(--tw-bg-opacity)); +} + +.bg-white\/50 { + background-color: rgb(255 255 255 / 0.5); +} + +.bg-white\/70 { + background-color: rgb(255 255 255 / 0.7); +} + +.bg-yellow-600 { + --tw-bg-opacity: 1; + background-color: rgb(202 138 4 / var(--tw-bg-opacity)); +} + +.bg-opacity-30 { + --tw-bg-opacity: 0.3; +} + +.bg-opacity-50 { + --tw-bg-opacity: 0.5; +} + +.bg-gradient-to-r { + background-image: linear-gradient(to right, var(--tw-gradient-stops)); +} + +.from-blue-500 { + --tw-gradient-from: #3b82f6 var(--tw-gradient-from-position); + --tw-gradient-to: rgb(59 130 246 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); +} + +.from-red-400 { + --tw-gradient-from: #f87171 var(--tw-gradient-from-position); + --tw-gradient-to: rgb(248 113 113 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); +} + +.from-red-500 { + --tw-gradient-from: #ef4444 var(--tw-gradient-from-position); + --tw-gradient-to: rgb(239 68 68 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); +} + +.via-blue-500 { + --tw-gradient-to: rgb(59 130 246 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), #3b82f6 var(--tw-gradient-via-position), var(--tw-gradient-to); +} + +.via-green-400 { + --tw-gradient-to: rgb(74 222 128 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), #4ade80 var(--tw-gradient-via-position), var(--tw-gradient-to); +} + +.via-orange-500 { + --tw-gradient-to: rgb(249 115 22 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), #f97316 var(--tw-gradient-via-position), var(--tw-gradient-to); +} + +.via-pink-300 { + --tw-gradient-to: rgb(249 168 212 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), #f9a8d4 var(--tw-gradient-via-position), var(--tw-gradient-to); +} + +.via-purple-500 { + --tw-gradient-to: rgb(168 85 247 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), #a855f7 var(--tw-gradient-via-position), var(--tw-gradient-to); +} + +.via-yellow-400 { + --tw-gradient-to: rgb(250 204 21 / 0) var(--tw-gradient-to-position); + --tw-gradient-stops: var(--tw-gradient-from), #facc15 var(--tw-gradient-via-position), var(--tw-gradient-to); +} + +.to-pink-500 { + --tw-gradient-to: #ec4899 var(--tw-gradient-to-position); +} + +.to-yellow-300 { + --tw-gradient-to: #fde047 var(--tw-gradient-to-position); +} + +.fill-current { + fill: currentColor; +} + +.object-cover { + -o-object-fit: cover; + object-fit: cover; +} + +.p-0 { + padding: 0px; +} + +.p-1 { + padding: 0.25rem; +} + +.p-10 { + padding: 2.5rem; +} + +.p-2 { + padding: 0.5rem; +} + +.p-3 { + padding: 0.75rem; +} + +.p-4 { + padding: 1rem; +} + +.p-6 { + padding: 1.5rem; +} + +.p-\[var\(--main-margin\)\] { + padding: var(--main-margin); +} + +.p-\[var\(--padding\)\] { + padding: var(--padding); +} + +.px-1 { + padding-left: 0.25rem; + padding-right: 0.25rem; +} + +.px-2 { + padding-left: 0.5rem; + padding-right: 0.5rem; +} + +.px-2\.5 { + padding-left: 0.625rem; + padding-right: 0.625rem; +} + +.px-3 { + padding-left: 0.75rem; + padding-right: 0.75rem; +} + +.px-4 { + padding-left: 1rem; + padding-right: 1rem; +} + +.px-6 { + padding-left: 1.5rem; + padding-right: 1.5rem; +} + +.px-8 { + padding-left: 2rem; + padding-right: 2rem; +} + +.px-\[var\(--padding\)\] { + padding-left: var(--padding); + padding-right: var(--padding); +} + +.py-0\.5 { + padding-top: 0.125rem; + padding-bottom: 0.125rem; +} + +.py-1 { + padding-top: 0.25rem; + padding-bottom: 0.25rem; +} + +.py-1\.5 { + padding-top: 0.375rem; + padding-bottom: 0.375rem; +} + +.py-2 { + padding-top: 0.5rem; + padding-bottom: 0.5rem; +} + +.py-3 { + padding-top: 0.75rem; + padding-bottom: 0.75rem; +} + +.py-4 { + padding-top: 1rem; + padding-bottom: 1rem; +} + +.py-6 { + padding-top: 1.5rem; + padding-bottom: 1.5rem; +} + +.py-8 { + padding-top: 2rem; + padding-bottom: 2rem; +} + +.py-\[100px\] { + padding-top: 100px; + padding-bottom: 100px; +} + +.pb-16 { + padding-bottom: 4rem; +} + +.pb-4 { + padding-bottom: 1rem; +} + +.pb-44 { + padding-bottom: 11rem; +} + +.pb-\[50vh\] { + padding-bottom: 50vh; +} + +.pb-\[80px\] { + padding-bottom: 80px; +} + +.pb-\[calc\(var\(--view-height\)\*0\.6\)\] { + padding-bottom: calc(var(--view-height) * 0.6); +} + +.pb-\[var\(--padding\)\] { + padding-bottom: var(--padding); +} + +.pl-10 { + padding-left: 2.5rem; +} + +.pl-2 { + padding-left: 0.5rem; +} + +.pl-2\.5 { + padding-left: 0.625rem; +} + +.pl-4 { + padding-left: 1rem; +} + +.pl-8 { + padding-left: 2rem; +} + +.pr-10 { + padding-right: 2.5rem; +} + +.pr-2 { + padding-right: 0.5rem; +} + +.pr-2\.5 { + padding-right: 0.625rem; +} + +.pr-20 { + padding-right: 5rem; +} + +.pr-8 { + padding-right: 2rem; +} + +.pr-\[20\%\] { + padding-right: 20%; +} + +.pt-0 { + padding-top: 0px; +} + +.pt-16 { + padding-top: 4rem; +} + +.pt-2 { + padding-top: 0.5rem; +} + +.pt-8 { + padding-top: 2rem; +} + +.text-left { + text-align: left; +} + +.text-center { + text-align: center; +} + +.text-right { + text-align: right; +} + +.align-middle { + vertical-align: middle; +} + +.font-serif { + font-family: ui-serif, Georgia, Cambria, "Times New Roman", Times, serif; +} + +.text-2xl { + font-size: 1.5rem; + line-height: 2rem; +} + +.text-3xl { + font-size: 1.875rem; + line-height: 2.25rem; +} + +.text-\[10px\] { + font-size: 10px; +} + +.text-base { + font-size: 1rem; + line-height: 1.5rem; +} + +.text-lg { + font-size: 1.125rem; + line-height: 1.75rem; +} + +.text-sm { + font-size: 0.875rem; + line-height: 1.25rem; +} + +.text-xl { + font-size: 1.25rem; + line-height: 1.75rem; +} + +.text-xs { + font-size: 0.75rem; + line-height: 1rem; +} + +.font-bold { + font-weight: 700; +} + +.font-medium { + font-weight: 500; +} + +.font-normal { + font-weight: 400; +} + +.font-semibold { + font-weight: 600; +} + +.uppercase { + text-transform: uppercase; +} + +.lowercase { + text-transform: lowercase; +} + +.italic { + font-style: italic; +} + +.leading-6 { + line-height: 1.5rem; +} + +.leading-\[0\.5\] { + line-height: 0.5; +} + +.leading-\[1\] { + line-height: 1; +} + +.leading-none { + line-height: 1; +} + +.leading-relaxed { + line-height: 1.625; +} + +.tracking-tight { + letter-spacing: -0.025em; +} + +.tracking-widest { + letter-spacing: 0.1em; +} + +.text-\[\#24A0ED\] { + --tw-text-opacity: 1; + color: rgb(36 160 237 / var(--tw-text-opacity)); +} + +.text-\[\#D2691E\] { + --tw-text-opacity: 1; + color: rgb(210 105 30 / var(--tw-text-opacity)); +} + +.text-black { + --tw-text-opacity: 1; + color: rgb(0 0 0 / var(--tw-text-opacity)); +} + +.text-black\/50 { + color: rgb(0 0 0 / 0.5); +} + +.text-black\/70 { + color: rgb(0 0 0 / 0.7); +} + +.text-blue-500 { + --tw-text-opacity: 1; + color: rgb(59 130 246 / var(--tw-text-opacity)); +} + +.text-current { + color: currentColor; +} + +.text-gray-200 { + --tw-text-opacity: 1; + color: rgb(229 231 235 / var(--tw-text-opacity)); +} + +.text-gray-400 { + --tw-text-opacity: 1; + color: rgb(156 163 175 / var(--tw-text-opacity)); +} + +.text-gray-500 { + --tw-text-opacity: 1; + color: rgb(107 114 128 / var(--tw-text-opacity)); +} + +.text-gray-600 { + --tw-text-opacity: 1; + color: rgb(75 85 99 / var(--tw-text-opacity)); +} + +.text-gray-700 { + --tw-text-opacity: 1; + color: rgb(55 65 81 / var(--tw-text-opacity)); +} + +.text-gray-800 { + --tw-text-opacity: 1; + color: rgb(31 41 55 / var(--tw-text-opacity)); +} + +.text-gray-900 { + --tw-text-opacity: 1; + color: rgb(17 24 39 / var(--tw-text-opacity)); +} + +.text-green-600 { + --tw-text-opacity: 1; + color: rgb(22 163 74 / var(--tw-text-opacity)); +} + +.text-red-400 { + --tw-text-opacity: 1; + color: rgb(248 113 113 / var(--tw-text-opacity)); +} + +.text-red-500 { + --tw-text-opacity: 1; + color: rgb(239 68 68 / var(--tw-text-opacity)); +} + +.text-red-600 { + --tw-text-opacity: 1; + color: rgb(220 38 38 / var(--tw-text-opacity)); +} + +.text-slate-200 { + --tw-text-opacity: 1; + color: rgb(226 232 240 / var(--tw-text-opacity)); +} + +.text-slate-300 { + --tw-text-opacity: 1; + color: rgb(203 213 225 / var(--tw-text-opacity)); +} + +.text-slate-400 { + --tw-text-opacity: 1; + color: rgb(148 163 184 / var(--tw-text-opacity)); +} + +.text-slate-600 { + --tw-text-opacity: 1; + color: rgb(71 85 105 / var(--tw-text-opacity)); +} + +.text-slate-800 { + --tw-text-opacity: 1; + color: rgb(30 41 59 / var(--tw-text-opacity)); +} + +.text-white { + --tw-text-opacity: 1; + color: rgb(255 255 255 / var(--tw-text-opacity)); +} + +.underline { + text-decoration-line: underline; +} + +.no-underline { + text-decoration-line: none; +} + +.underline-offset-4 { + text-underline-offset: 4px; +} + +.opacity-0 { + opacity: 0; +} + +.opacity-100 { + opacity: 1; +} + +.opacity-50 { + opacity: 0.5; +} + +.opacity-60 { + opacity: 0.6; +} + +.opacity-70 { + opacity: 0.7; +} + +.shadow-lg { + --tw-shadow: 0 10px 15px -3px rgb(0 0 0 / 0.1), 0 4px 6px -4px rgb(0 0 0 / 0.1); + --tw-shadow-colored: 0 10px 15px -3px var(--tw-shadow-color), 0 4px 6px -4px var(--tw-shadow-color); + box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow); +} + +.shadow-md { + --tw-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1); + --tw-shadow-colored: 0 4px 6px -1px var(--tw-shadow-color), 0 2px 4px -2px var(--tw-shadow-color); + box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow); +} + +.shadow-sm { + --tw-shadow: 0 1px 2px 0 rgb(0 0 0 / 0.05); + --tw-shadow-colored: 0 1px 2px 0 var(--tw-shadow-color); + box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow); +} + +.shadow-xl { + --tw-shadow: 0 20px 25px -5px rgb(0 0 0 / 0.1), 0 8px 10px -6px rgb(0 0 0 / 0.1); + --tw-shadow-colored: 0 20px 25px -5px var(--tw-shadow-color), 0 8px 10px -6px var(--tw-shadow-color); + box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow); +} + +.shadow-slate-300 { + --tw-shadow-color: #cbd5e1; + --tw-shadow: var(--tw-shadow-colored); +} + +.outline-none { + outline: 2px solid transparent; + outline-offset: 2px; +} + +.outline { + outline-style: solid; +} + +.ring-0 { + --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); + --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(0px + var(--tw-ring-offset-width)) var(--tw-ring-color); + box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); +} + +.ring-1 { + --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); + --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(1px + var(--tw-ring-offset-width)) var(--tw-ring-color); + box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); +} + +.ring-slate-400 { + --tw-ring-opacity: 1; + --tw-ring-color: rgb(148 163 184 / var(--tw-ring-opacity)); +} + +.ring-opacity-5 { + --tw-ring-opacity: 0.05; +} + +.blur { + --tw-blur: blur(8px); + filter: var(--tw-blur) var(--tw-brightness) var(--tw-contrast) var(--tw-grayscale) var(--tw-hue-rotate) var(--tw-invert) var(--tw-saturate) var(--tw-sepia) var(--tw-drop-shadow); +} + +.\!invert { + --tw-invert: invert(100%) !important; + filter: var(--tw-blur) var(--tw-brightness) var(--tw-contrast) var(--tw-grayscale) var(--tw-hue-rotate) var(--tw-invert) var(--tw-saturate) var(--tw-sepia) var(--tw-drop-shadow) !important; +} + +.invert { + --tw-invert: invert(100%); + filter: var(--tw-blur) var(--tw-brightness) var(--tw-contrast) var(--tw-grayscale) var(--tw-hue-rotate) var(--tw-invert) var(--tw-saturate) var(--tw-sepia) var(--tw-drop-shadow); +} + +.\!filter { + filter: var(--tw-blur) var(--tw-brightness) var(--tw-contrast) var(--tw-grayscale) var(--tw-hue-rotate) var(--tw-invert) var(--tw-saturate) var(--tw-sepia) var(--tw-drop-shadow) !important; +} + +.filter { + filter: var(--tw-blur) var(--tw-brightness) var(--tw-contrast) var(--tw-grayscale) var(--tw-hue-rotate) var(--tw-invert) var(--tw-saturate) var(--tw-sepia) var(--tw-drop-shadow); +} + +.backdrop-blur-sm { + --tw-backdrop-blur: blur(4px); + -webkit-backdrop-filter: var(--tw-backdrop-blur) var(--tw-backdrop-brightness) var(--tw-backdrop-contrast) var(--tw-backdrop-grayscale) var(--tw-backdrop-hue-rotate) var(--tw-backdrop-invert) var(--tw-backdrop-opacity) var(--tw-backdrop-saturate) var(--tw-backdrop-sepia); + backdrop-filter: var(--tw-backdrop-blur) var(--tw-backdrop-brightness) var(--tw-backdrop-contrast) var(--tw-backdrop-grayscale) var(--tw-backdrop-hue-rotate) var(--tw-backdrop-invert) var(--tw-backdrop-opacity) var(--tw-backdrop-saturate) var(--tw-backdrop-sepia); +} + +.transition { + transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, -webkit-backdrop-filter; + transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, backdrop-filter; + transition-property: color, background-color, border-color, text-decoration-color, fill, stroke, opacity, box-shadow, transform, filter, backdrop-filter, -webkit-backdrop-filter; + transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); + transition-duration: 150ms; +} + +.transition-all { + transition-property: all; + transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); + transition-duration: 150ms; +} + +.transition-colors { + transition-property: color, background-color, border-color, text-decoration-color, fill, stroke; + transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); + transition-duration: 150ms; +} + +.transition-opacity { + transition-property: opacity; + transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); + transition-duration: 150ms; +} + +.transition-transform { + transition-property: transform; + transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); + transition-duration: 150ms; +} + +.duration-100 { + transition-duration: 100ms; +} + +.duration-150 { + transition-duration: 150ms; +} + +.duration-200 { + transition-duration: 200ms; +} + +.duration-300 { + transition-duration: 300ms; +} + +.ease-in-out { + transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1); +} + +.ease-out { + transition-timing-function: cubic-bezier(0, 0, 0.2, 1); +} + +/* Hide outline on contenteditable elements */ + +[contenteditable] { + outline: none; +} + +/* Optional: Add a custom focus style for accessibility */ + +[contenteditable]:focus { + box-shadow: 0 0 0 0px black; +} + +/* Ensure the text cursor is visible */ + +[contenteditable]:focus::-moz-selection { + background: transparent; +} + +[contenteditable]:focus::selection { + background: transparent; +} + +/* Hide the resize handle in Firefox */ + +[contenteditable] { + resize: none; +} + +/* Remove the default focus ring in Firefox */ + +[contenteditable]::-moz-focus-inner { + border: 0; +} + +/* Optional: Custom styling for placeholder text */ + +[contenteditable]:empty:before { + /* content: attr(placeholder); */ + /* color: #888; */ + /* font-style: italic; */ +} + +.file\:border-0::file-selector-button { + border-width: 0px; +} + +.file\:bg-transparent::file-selector-button { + background-color: transparent; +} + +.file\:text-sm::file-selector-button { + font-size: 0.875rem; + line-height: 1.25rem; +} + +.file\:font-medium::file-selector-button { + font-weight: 500; +} + +.placeholder\:text-sm::-moz-placeholder { + font-size: 0.875rem; + line-height: 1.25rem; +} + +.placeholder\:text-sm::placeholder { + font-size: 0.875rem; + line-height: 1.25rem; +} + +.hover\:-translate-y-1:hover { + --tw-translate-y: -0.25rem; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.hover\:scale-105:hover { + --tw-scale-x: 1.05; + --tw-scale-y: 1.05; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.hover\:scale-\[1\.02\]:hover { + --tw-scale-x: 1.02; + --tw-scale-y: 1.02; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.hover\:bg-\[\#DED8C4\]:hover { + --tw-bg-opacity: 1; + background-color: rgb(222 216 196 / var(--tw-bg-opacity)); +} + +.hover\:bg-black\/10:hover { + background-color: rgb(0 0 0 / 0.1); +} + +.hover\:bg-gray-100:hover { + --tw-bg-opacity: 1; + background-color: rgb(243 244 246 / var(--tw-bg-opacity)); +} + +.hover\:bg-gray-200:hover { + --tw-bg-opacity: 1; + background-color: rgb(229 231 235 / var(--tw-bg-opacity)); +} + +.hover\:bg-gray-400:hover { + --tw-bg-opacity: 1; + background-color: rgb(156 163 175 / var(--tw-bg-opacity)); +} + +.hover\:bg-gray-600:hover { + --tw-bg-opacity: 1; + background-color: rgb(75 85 99 / var(--tw-bg-opacity)); +} + +.hover\:bg-slate-300:hover { + --tw-bg-opacity: 1; + background-color: rgb(203 213 225 / var(--tw-bg-opacity)); +} + +.hover\:bg-slate-500:hover { + --tw-bg-opacity: 1; + background-color: rgb(100 116 139 / var(--tw-bg-opacity)); +} + +.hover\:bg-slate-600:hover { + --tw-bg-opacity: 1; + background-color: rgb(71 85 105 / var(--tw-bg-opacity)); +} + +.hover\:bg-opacity-85:hover { + --tw-bg-opacity: 0.85; +} + +.hover\:text-\[\#24A0ED\]:hover { + --tw-text-opacity: 1; + color: rgb(36 160 237 / var(--tw-text-opacity)); +} + +.hover\:text-\[\#D2691E\]:hover { + --tw-text-opacity: 1; + color: rgb(210 105 30 / var(--tw-text-opacity)); +} + +.hover\:text-black:hover { + --tw-text-opacity: 1; + color: rgb(0 0 0 / var(--tw-text-opacity)); +} + +.hover\:text-black\/70:hover { + color: rgb(0 0 0 / 0.7); +} + +.hover\:text-blue-600:hover { + --tw-text-opacity: 1; + color: rgb(37 99 235 / var(--tw-text-opacity)); +} + +.hover\:text-gray-600:hover { + --tw-text-opacity: 1; + color: rgb(75 85 99 / var(--tw-text-opacity)); +} + +.hover\:text-gray-700:hover { + --tw-text-opacity: 1; + color: rgb(55 65 81 / var(--tw-text-opacity)); +} + +.hover\:text-gray-800:hover { + --tw-text-opacity: 1; + color: rgb(31 41 55 / var(--tw-text-opacity)); +} + +.hover\:text-gray-900:hover { + --tw-text-opacity: 1; + color: rgb(17 24 39 / var(--tw-text-opacity)); +} + +.hover\:text-red-500:hover { + --tw-text-opacity: 1; + color: rgb(239 68 68 / var(--tw-text-opacity)); +} + +.hover\:text-slate-200:hover { + --tw-text-opacity: 1; + color: rgb(226 232 240 / var(--tw-text-opacity)); +} + +.hover\:underline:hover { + text-decoration-line: underline; +} + +.hover\:opacity-100:hover { + opacity: 1; +} + +.hover\:opacity-70:hover { + opacity: 0.7; +} + +.hover\:shadow-xl:hover { + --tw-shadow: 0 20px 25px -5px rgb(0 0 0 / 0.1), 0 8px 10px -6px rgb(0 0 0 / 0.1); + --tw-shadow-colored: 0 20px 25px -5px var(--tw-shadow-color), 0 8px 10px -6px var(--tw-shadow-color); + box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow); +} + +.focus\:outline-none:focus { + outline: 2px solid transparent; + outline-offset: 2px; +} + +.focus\:outline:focus { + outline-style: solid; +} + +.focus\:ring-2:focus { + --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); + --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); + box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); +} + +.focus\:ring-blue-500:focus { + --tw-ring-opacity: 1; + --tw-ring-color: rgb(59 130 246 / var(--tw-ring-opacity)); +} + +.focus\:ring-slate-400:focus { + --tw-ring-opacity: 1; + --tw-ring-color: rgb(148 163 184 / var(--tw-ring-opacity)); +} + +.focus\:ring-opacity-50:focus { + --tw-ring-opacity: 0.5; +} + +.focus\:ring-offset-2:focus { + --tw-ring-offset-width: 2px; +} + +.focus-visible\:outline-none:focus-visible { + outline: 2px solid transparent; + outline-offset: 2px; +} + +.focus-visible\:ring-2:focus-visible { + --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); + --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(2px + var(--tw-ring-offset-width)) var(--tw-ring-color); + box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); +} + +.focus-visible\:ring-offset-2:focus-visible { + --tw-ring-offset-width: 2px; +} + +.active\:scale-95:active { + --tw-scale-x: .95; + --tw-scale-y: .95; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.disabled\:pointer-events-none:disabled { + pointer-events: none; +} + +.disabled\:cursor-not-allowed:disabled { + cursor: not-allowed; +} + +.disabled\:bg-\[\#e0e0dc79\]:disabled { + background-color: #e0e0dc79; +} + +.disabled\:bg-\[\#ececec21\]:disabled { + background-color: #ececec21; +} + +.disabled\:text-black\/50:disabled { + color: rgb(0 0 0 / 0.5); +} + +.disabled\:text-white\/50:disabled { + color: rgb(255 255 255 / 0.5); +} + +.disabled\:opacity-50:disabled { + opacity: 0.5; +} + +.group:hover .group-hover\:opacity-100 { + opacity: 1; +} + +.group.toaster .group-\[\.toaster\]\:shadow-lg { + --tw-shadow: 0 10px 15px -3px rgb(0 0 0 / 0.1), 0 4px 6px -4px rgb(0 0 0 / 0.1); + --tw-shadow-colored: 0 10px 15px -3px var(--tw-shadow-color), 0 4px 6px -4px var(--tw-shadow-color); + box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow); +} + +.peer:disabled ~ .peer-disabled\:cursor-not-allowed { + cursor: not-allowed; +} + +.peer:disabled ~ .peer-disabled\:opacity-70 { + opacity: 0.7; +} + +.data-\[disabled\]\:pointer-events-none[data-disabled] { + pointer-events: none; +} + +.data-\[state\=checked\]\:translate-x-5[data-state=checked] { + --tw-translate-x: 1.25rem; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.data-\[state\=unchecked\]\:translate-x-0[data-state=unchecked] { + --tw-translate-x: 0px; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.data-\[disabled\=true\]\:cursor-not-allowed[data-disabled=true] { + cursor: not-allowed; +} + +.data-\[disabled\=true\]\:opacity-50[data-disabled=true] { + opacity: 0.5; +} + +.data-\[disabled\]\:opacity-50[data-disabled] { + opacity: 0.5; +} + +.data-\[state\=active\]\:shadow-sm[data-state=active] { + --tw-shadow: 0 1px 2px 0 rgb(0 0 0 / 0.05); + --tw-shadow-colored: 0 1px 2px 0 var(--tw-shadow-color); + box-shadow: var(--tw-ring-offset-shadow, 0 0 #0000), var(--tw-ring-shadow, 0 0 #0000), var(--tw-shadow); +} + +.dark\:-rotate-90:is(.dark *) { + --tw-rotate: -90deg; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.dark\:rotate-0:is(.dark *) { + --tw-rotate: 0deg; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.dark\:scale-0:is(.dark *) { + --tw-scale-x: 0; + --tw-scale-y: 0; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.dark\:scale-100:is(.dark *) { + --tw-scale-x: 1; + --tw-scale-y: 1; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.dark\:bg-black\/50:is(.dark *) { + background-color: rgb(0 0 0 / 0.5); +} + +.dark\:bg-black\/70:is(.dark *) { + background-color: rgb(0 0 0 / 0.7); +} + +.dark\:bg-white:is(.dark *) { + --tw-bg-opacity: 1; + background-color: rgb(255 255 255 / var(--tw-bg-opacity)); +} + +.dark\:bg-white\/50:is(.dark *) { + background-color: rgb(255 255 255 / 0.5); +} + +.dark\:fill-\[\#ffffff3b\]:is(.dark *) { + fill: #ffffff3b; +} + +.dark\:text-\[\#202020\]:is(.dark *) { + --tw-text-opacity: 1; + color: rgb(32 32 32 / var(--tw-text-opacity)); +} + +.dark\:text-gray-200:is(.dark *) { + --tw-text-opacity: 1; + color: rgb(229 231 235 / var(--tw-text-opacity)); +} + +.dark\:text-gray-400:is(.dark *) { + --tw-text-opacity: 1; + color: rgb(156 163 175 / var(--tw-text-opacity)); +} + +.dark\:text-white:is(.dark *) { + --tw-text-opacity: 1; + color: rgb(255 255 255 / var(--tw-text-opacity)); +} + +.dark\:text-white\/50:is(.dark *) { + color: rgb(255 255 255 / 0.5); +} + +.dark\:text-white\/70:is(.dark *) { + color: rgb(255 255 255 / 0.7); +} + +.dark\:placeholder\:text-white\/50:is(.dark *)::-moz-placeholder { + color: rgb(255 255 255 / 0.5); +} + +.dark\:placeholder\:text-white\/50:is(.dark *)::placeholder { + color: rgb(255 255 255 / 0.5); +} + +.dark\:hover\:bg-white\/10:hover:is(.dark *) { + background-color: rgb(255 255 255 / 0.1); +} + +.dark\:hover\:text-\[\#24A0ED\]:hover:is(.dark *) { + --tw-text-opacity: 1; + color: rgb(36 160 237 / var(--tw-text-opacity)); +} + +.dark\:hover\:text-white:hover:is(.dark *) { + --tw-text-opacity: 1; + color: rgb(255 255 255 / var(--tw-text-opacity)); +} + +.hover\:dark\:text-white\/70:is(.dark *):hover { + color: rgb(255 255 255 / 0.7); +} + +.dark\:disabled\:bg-\[\#ececec21\]:disabled:is(.dark *) { + background-color: #ececec21; +} + +.dark\:disabled\:text-white\/50:disabled:is(.dark *) { + color: rgb(255 255 255 / 0.5); +} + +@media (min-width: 640px) { + .sm\:right-6 { + right: 1.5rem; + } + + .sm\:mx-4 { + margin-left: 1rem; + margin-right: 1rem; + } + + .sm\:mb-4 { + margin-bottom: 1rem; + } + + .sm\:ml-2 { + margin-left: 0.5rem; + } + + .sm\:mt-0 { + margin-top: 0px; + } + + .sm\:mt-4 { + margin-top: 1rem; + } + + .sm\:w-\[105px\] { + width: 105px; + } + + .sm\:w-auto { + width: auto; + } + + .sm\:max-w-\[500px\] { + max-width: 500px; + } + + .sm\:max-w-sm { + max-width: 24rem; + } + + .sm\:grid-cols-2 { + grid-template-columns: repeat(2, minmax(0, 1fr)); + } + + .sm\:grid-cols-3 { + grid-template-columns: repeat(3, minmax(0, 1fr)); + } + + .sm\:flex-row { + flex-direction: row; + } + + .sm\:justify-start { + justify-content: flex-start; + } + + .sm\:justify-end { + justify-content: flex-end; + } + + .sm\:gap-2\.5 { + gap: 0.625rem; + } + + .sm\:space-x-0 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(0px * var(--tw-space-x-reverse)); + margin-left: calc(0px * calc(1 - var(--tw-space-x-reverse))); + } + + .sm\:space-x-2 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(0.5rem * var(--tw-space-x-reverse)); + margin-left: calc(0.5rem * calc(1 - var(--tw-space-x-reverse))); + } + + .sm\:space-y-2 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(0.5rem * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(0.5rem * var(--tw-space-y-reverse)); + } + + .sm\:rounded-lg { + border-radius: 0.5rem; + } + + .sm\:rounded-l-none { + border-top-left-radius: 0px; + border-bottom-left-radius: 0px; + } + + .sm\:rounded-r-lg { + border-top-right-radius: 0.5rem; + border-bottom-right-radius: 0.5rem; + } + + .sm\:border-l { + border-left-width: 1px; + } + + .sm\:px-3 { + padding-left: 0.75rem; + padding-right: 0.75rem; + } + + .sm\:px-4 { + padding-left: 1rem; + padding-right: 1rem; + } + + .sm\:py-0 { + padding-top: 0px; + padding-bottom: 0px; + } + + .sm\:py-2 { + padding-top: 0.5rem; + padding-bottom: 0.5rem; + } + + .sm\:text-left { + text-align: left; + } + + .sm\:text-sm { + font-size: 0.875rem; + line-height: 1.25rem; + } + + .sm\:text-xl { + font-size: 1.25rem; + line-height: 1.75rem; + } +} + +@media (min-width: 768px) { + .md\:mx-8 { + margin-left: 2rem; + margin-right: 2rem; + } + + .md\:block { + display: block; + } + + .md\:w-1\/2 { + width: 50%; + } + + .md\:w-\[100px\] { + width: 100px; + } + + .md\:w-\[80vw\] { + width: 80vw; + } + + .md\:w-full { + width: 100%; + } + + .md\:max-w-\[600px\] { + max-width: 600px; + } + + .md\:grid-cols-4 { + grid-template-columns: repeat(4, minmax(0, 1fr)); + } + + .md\:text-base { + font-size: 1rem; + line-height: 1.5rem; + } +} + +@media (min-width: 1024px) { + .lg\:fixed { + position: fixed; + } + + .lg\:sticky { + position: sticky; + } + + .lg\:inset-y-0 { + top: 0px; + bottom: 0px; + } + + .lg\:bottom-10 { + bottom: 2.5rem; + } + + .lg\:top-20 { + top: 5rem; + } + + .lg\:z-50 { + z-index: 50; + } + + .lg\:mx-4 { + margin-left: 1rem; + margin-right: 1rem; + } + + .lg\:mx-auto { + margin-left: auto; + margin-right: auto; + } + + .lg\:flex { + display: flex; + } + + .lg\:hidden { + display: none; + } + + .lg\:h-3 { + height: 0.75rem; + } + + .lg\:max-h-36 { + max-height: 9rem; + } + + .lg\:w-20 { + width: 5rem; + } + + .lg\:w-3\/12 { + width: 25%; + } + + .lg\:w-6 { + width: 1.5rem; + } + + .lg\:w-9\/12 { + width: 75%; + } + + .lg\:w-\[300px\] { + width: 300px; + } + + .lg\:w-\[800px\] { + width: 800px; + } + + .lg\:max-w-2xl { + max-width: 42rem; + } + + .lg\:max-w-\[650px\] { + max-width: 650px; + } + + .lg\:grid-cols-3 { + grid-template-columns: repeat(3, minmax(0, 1fr)); + } + + .lg\:grid-cols-4 { + grid-template-columns: repeat(4, minmax(0, 1fr)); + } + + .lg\:flex-row { + flex-direction: row; + } + + .lg\:flex-col { + flex-direction: column; + } + + .lg\:justify-between { + justify-content: space-between; + } + + .lg\:space-x-1\.5 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(0.375rem * var(--tw-space-x-reverse)); + margin-left: calc(0.375rem * calc(1 - var(--tw-space-x-reverse))); + } + + .lg\:space-x-12 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(3rem * var(--tw-space-x-reverse)); + margin-left: calc(3rem * calc(1 - var(--tw-space-x-reverse))); + } + + .lg\:space-x-9 > :not([hidden]) ~ :not([hidden]) { + --tw-space-x-reverse: 0; + margin-right: calc(2.25rem * var(--tw-space-x-reverse)); + margin-left: calc(2.25rem * calc(1 - var(--tw-space-x-reverse))); + } + + .lg\:space-y-0 > :not([hidden]) ~ :not([hidden]) { + --tw-space-y-reverse: 0; + margin-top: calc(0px * calc(1 - var(--tw-space-y-reverse))); + margin-bottom: calc(0px * var(--tw-space-y-reverse)); + } + + .lg\:rounded-sm { + border-radius: 0.125rem; + } + + .lg\:px-8 { + padding-left: 2rem; + padding-right: 2rem; + } + + .lg\:py-0 { + padding-top: 0px; + padding-bottom: 0px; + } + + .lg\:py-6 { + padding-top: 1.5rem; + padding-bottom: 1.5rem; + } + + .lg\:pb-32 { + padding-bottom: 8rem; + } + + .lg\:pl-\[104px\] { + padding-left: 104px; + } + + .lg\:pr-6 { + padding-right: 1.5rem; + } + + .lg\:pt-24 { + padding-top: 6rem; + } + + .lg\:text-3xl { + font-size: 1.875rem; + line-height: 2.25rem; + } + + .lg\:text-xl { + font-size: 1.25rem; + line-height: 1.75rem; + } + + .lg\:font-medium { + font-weight: 500; + } +} + +@media (min-width: 1280px) { + .xl\:max-h-48 { + max-height: 12rem; + } + + .xl\:grid-cols-4 { + grid-template-columns: repeat(4, minmax(0, 1fr)); + } +} + +.\[\&\:has\(\[role\=checkbox\]\)\]\:pr-0:has([role=checkbox]) { + padding-right: 0px; +} + +.\[\&\:has\(svg\)\]\:pl-11:has(svg) { + padding-left: 2.75rem; +} + +.\[\&\>span\]\:line-clamp-1>span { + overflow: hidden; + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-line-clamp: 1; +} + +.\[\&\>svg\+div\]\:translate-y-\[-3px\]>svg+div { + --tw-translate-y: -3px; + transform: translate(var(--tw-translate-x), var(--tw-translate-y)) rotate(var(--tw-rotate)) skewX(var(--tw-skew-x)) skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y)); +} + +.\[\&\>svg\]\:absolute>svg { + position: absolute; +} + +.\[\&\>svg\]\:left-4>svg { + left: 1rem; +} + +.\[\&\>svg\]\:top-4>svg { + top: 1rem; +} + +.\[\&\>svg\]\:size-3\.5>svg { + width: 0.875rem; + height: 0.875rem; +} + +.\[\&_\[data-cmdk-group-heading\]\]\:px-2 [data-cmdk-group-heading] { + padding-left: 0.5rem; + padding-right: 0.5rem; +} + +.\[\&_\[data-cmdk-group-heading\]\]\:py-1\.5 [data-cmdk-group-heading] { + padding-top: 0.375rem; + padding-bottom: 0.375rem; +} + +.\[\&_\[data-cmdk-group-heading\]\]\:text-xs [data-cmdk-group-heading] { + font-size: 0.75rem; + line-height: 1rem; +} + +.\[\&_\[data-cmdk-group-heading\]\]\:font-medium [data-cmdk-group-heading] { + font-weight: 500; +} + +.\[\&_\[data-cmdk-group\]\:not\(\[hidden\]\)_\~\[data-cmdk-group\]\]\:pt-0 [data-cmdk-group]:not([hidden]) ~[data-cmdk-group] { + padding-top: 0px; +} + +.\[\&_\[data-cmdk-group\]\]\:px-2 [data-cmdk-group] { + padding-left: 0.5rem; + padding-right: 0.5rem; +} + +.\[\&_\[data-cmdk-input-wrapper\]_svg\]\:h-5 [data-cmdk-input-wrapper] svg { + height: 1.25rem; +} + +.\[\&_\[data-cmdk-input-wrapper\]_svg\]\:w-5 [data-cmdk-input-wrapper] svg { + width: 1.25rem; +} + +.\[\&_\[data-cmdk-input\]\]\:h-12 [data-cmdk-input] { + height: 3rem; +} + +.\[\&_\[data-cmdk-item\]\]\:px-2 [data-cmdk-item] { + padding-left: 0.5rem; + padding-right: 0.5rem; +} + +.\[\&_\[data-cmdk-item\]\]\:py-3 [data-cmdk-item] { + padding-top: 0.75rem; + padding-bottom: 0.75rem; +} + +.\[\&_\[data-cmdk-item\]_svg\]\:h-5 [data-cmdk-item] svg { + height: 1.25rem; +} + +.\[\&_\[data-cmdk-item\]_svg\]\:w-5 [data-cmdk-item] svg { + width: 1.25rem; +} + +.\[\&_p\]\:leading-relaxed p { + line-height: 1.625; +} + +.\[\&_tr\:last-child\]\:border-0 tr:last-child { + border-width: 0px; +} + +.\[\&_tr\]\:border-b tr { + border-bottom-width: 1px; +} + \ No newline at end of file diff --git a/extension/src/pages/background/allow-cors.js b/extension/src/pages/background/allow-cors.js new file mode 100644 index 0000000..cf44f7c --- /dev/null +++ b/extension/src/pages/background/allow-cors.js @@ -0,0 +1,71 @@ +const initiatorDomains = ["qwksearch.com", "debate-ai.com"]; + +chrome.runtime.onInstalled.addListener(() => { + modifyChromeAPIAllowCORS(initiatorDomains); +}); + + +/** + * Allow CORS - Chrome Extension API. Add (Access-Control-Allow-Origin: *) rule to the response + * header of all requests to enable cross-domain requests to scrape data from other domains. + * CORS is voluntarily enforced by browser frontends, but not by curl or backend + * requests. Allowing CORS on the front-end means no proxy server is needed and web apps + * have Browser App functionality and can turn any data into an API. + * It is suggested to allow only on specific initiator domains to prevent errors + * commonly caused on secure sites like Claude.ai. Needs "permissions":[ "declarativeNetRequest"] + * https://chromewebstore.google.com/detail/allow-cors-access-control/lhobafahddgcelffkeicbaginigeejlf?hl=en + * https://developer.chrome.com/docs/extensions/mv3/declarativeNetRequest/ + * @param {string[]} initiatorDomains - The domains allowed to initiate CORS request + * @param {string} receivingDomains - The domains allowed to receive the request + * @returns {void} + * @category Extension + */ +function modifyChromeAPIAllowCORS( + initiatorDomains = "*://*/*", + receivingDomains = "*" +) { + return chrome?.declarativeNetRequest?.updateDynamicRules({ + removeRuleIds: [1], + addRules: [ + { + id: 1, + priority: 1, + action: { + type: "modifyHeaders", + responseHeaders: [ + { + header: "Access-Control-Allow-Origin", + operation: "set", + value: "*", + }, + { + header: "Access-Control-Allow-Methods", + operation: "set", + value: "GET, POST, OPTIONS, PUT, DELETE, PATCH", + }, + ], + }, + condition: { + urlFilter: receivingDomains, + initiatorDomains, + resourceTypes: [ + "main_frame", + "sub_frame", + "stylesheet", + "script", + "image", + "font", + "object", + "xmlhttprequest", + "ping", + "csp_report", + "media", + "websocket", + "other", + ], + }, + }, + ], + }); +} + diff --git a/extension/src/pages/background/context-menu.js b/extension/src/pages/background/context-menu.js new file mode 100644 index 0000000..95e5626 --- /dev/null +++ b/extension/src/pages/background/context-menu.js @@ -0,0 +1,56 @@ +import { searchEngines } from "$lib/configs/config"; + +// Create the context menu +chrome.contextMenus.removeAll(function () { + chrome.contextMenus.create({ + title: "Search With...", + id: "searchWithParent", + contexts: ["selection"], + }); + + for (const [engine, entry] of Object.entries(searchEngines)) { + chrome.contextMenus.create({ + title: ` ${engine}`, + parentId: "searchWithParent", + id: `searchWith${engine}`, + contexts: ["selection"] + }); + } + + chrome.contextMenus.create({ + title: "Reading Mode", + id: "readingMode", + contexts: [ "page", "frame", "image", "link", "video", "audio"], + }); +}); + +// Listen for clicks on the context menu +chrome.contextMenus.onClicked.addListener((info, tab) => { + const { menuItemId, selectionText } = info; + + if (menuItemId.startsWith("searchWith")) { + const engine = menuItemId.replace("searchWith", ""); + const searchUrl = searchEngines[engine]; + const url = searchUrl + encodeURIComponent(selectionText); + + chrome.tabs.create({ + url, + active: false + }, (newTab) => { + setTimeout(() => { + chrome.tabs.update(newTab.id, { active: true }, (updatedTab) => { + if (chrome.runtime.lastError) { + console.error("Error updating tab:", chrome.runtime.lastError); + } + }); + }, 1500); + }); + } else if (menuItemId === "readingMode") { + chrome.tabs.query({active: true, currentWindow: true}, function(tabs) { + let currentTab = tabs[0]; + if (currentTab) { + chrome.tabs.sendMessage(currentTab.id, {action: "activateReadingMode"}); + } + }); + } +}); diff --git a/extension/src/pages/background/service-worker.js b/extension/src/pages/background/service-worker.js new file mode 100644 index 0000000..f96b250 --- /dev/null +++ b/extension/src/pages/background/service-worker.js @@ -0,0 +1,80 @@ +import * as tabs from "./tabs-api"; +import * as context from "./context-menu"; +import * as cors from "./allow-cors"; + +/** LISTENERS */ + +// https://developer.chrome.com/docs/extensions/develop/concepts/messaging#external-webpage + +// Listen for messages from the background script +chrome.runtime.onMessage.addListener(function (request, sender, callback) { + var { type, url } = request; + + if (type === "openTab") + chrome.tabs.create({ + url, + active: request.bg ? false : true, + selected: request.bg ? false : true, + }); + + if (type === "updateTabOrder") { + updateTabOrder(request.newOrder); + } + + if (type === "openDebateApp") { + chrome.runtime.openOptionsPage(); + + } + + if (type === "requestFullScreen") { + chrome.tabs.query({active: true, currentWindow: true}, function(tabs) { + let currentTab = tabs[0]; + if (currentTab) { + chrome.scripting.executeScript({ + target: { tabId: currentTab.id }, + function: toggleFullScreen + }, () => { + // Open the sidebar after toggling full screen + chrome.sidePanel.open({ windowId: currentTab.windowId }).then(() => { + // Ensure the sidebar is visible + chrome.sidePanel.setOptions({ + enabled: true, + path: 'sidepanel.html', + mobile: true, + active: true + }); + }); + }); + } + }); + } + + return true; +}); + +function toggleFullScreen() { + if (!document.fullscreenElement) { + document.documentElement.requestFullscreen().then(() => { + // Notify that full screen is activated + chrome.runtime.sendMessage({ type: "fullScreenActivated" }); + }).catch(err => { + console.error(`Error attempting to enable full-screen mode: ${err.message} (${err.name})`); + }); + } else { + if (document.exitFullscreen) { + document.exitFullscreen().then(() => { + // Notify that full screen is deactivated + chrome.runtime.sendMessage({ type: "fullScreenDeactivated" }); + }); + } + } +} + +/** SIDEPANEL BEHAVIOR */ + +// allow side panel to open on action click +chrome.sidePanel + .setPanelBehavior({ openPanelOnActionClick: true }) + .catch((error) => console.error(error)); + + \ No newline at end of file diff --git a/extension/src/pages/background/tabs-api.js b/extension/src/pages/background/tabs-api.js new file mode 100644 index 0000000..aafada3 --- /dev/null +++ b/extension/src/pages/background/tabs-api.js @@ -0,0 +1,85 @@ +/** TABS API */ + +// Propagate tab updates to the side panel +function notifyTabUpdate(updateType, tabId, changeInfo) { + chrome.runtime.sendMessage({ + type: "updateTabLists", + updateType: updateType, + tabId: tabId, + changeInfo: changeInfo, + }); + } + // Listen for tab updates + chrome.tabs.onUpdated.addListener((tabId, changeInfo, tab) => { + + if (changeInfo.status === "complete" || changeInfo.title) { + notifyTabUpdate("updated", tabId, { url: tab.url, title: tab.title }); + } + }); + // Listen for tab creation + chrome.tabs.onCreated.addListener((tab) => { + notifyTabUpdate("created", tab.id, { url: tab.url, title: tab.title }); + }); + + // Listen for tab removal + chrome.tabs.onRemoved.addListener((tabId, removeInfo) => { + notifyTabUpdate("removed", tabId, {}); + }); + + // Listen for tab switches + chrome.tabs.onActivated.addListener((activeInfo) => { + chrome.tabs.get(activeInfo.tabId, (tab) => { + notifyTabUpdate("activated", tab.id, { url: tab.url, title: tab.title }); + }); + }); + + + + // Set the side panel options when a tab is updated + chrome.tabs.onUpdated.addListener(async (tabId, info, tab) => { + // console.log("Tab updated (for side panel):", tabId, info, tab.url); + + await chrome.sidePanel.setOptions({ + tabId, + path: "src/pages/sidepanel/index.html", + enabled: true, + }); + + notifyTabUpdate("updated", tabId, { url: tab.url, title: tab.title }); + }); + + // Update the tab order in the background script with the new order + function updateTabOrder(newOrder) { + console.log("Updating tab order:", newOrder); + + chrome.windows.getCurrent({ populate: true }, function (window) { + const currentTabs = window.tabs; + const tabIndexMap = new Map( + currentTabs.map((tab, index) => [tab.id, index]) + ); + + newOrder.forEach((tabId, newIndex) => { + const currentIndex = tabIndexMap.get(parseInt(tabId)); + if (currentIndex !== undefined && currentIndex !== newIndex) { + chrome.tabs.move(parseInt(tabId), { index: newIndex }, (tab) => { + if (chrome.runtime.lastError) { + console.error( + `Error moving tab ${tabId}: ${chrome.runtime.lastError.message}` + ); + } else { + console.log(`Moved tab ${tabId} to index ${newIndex}`); + } + }); + } + }); + + // Use setTimeout to send the completion message after a short delay + setTimeout(() => { + console.log("Tab reordering complete"); + chrome.runtime.sendMessage({ + type: "tabReorderComplete", + }); + }, 500); // 500ms delay + }); + } + \ No newline at end of file diff --git a/extension/src/pages/content/index.js b/extension/src/pages/content/index.js new file mode 100644 index 0000000..9a5cc95 --- /dev/null +++ b/extension/src/pages/content/index.js @@ -0,0 +1,4 @@ +import * as tabToSearch from '$lib/components/ShortcutSearch/shortcut-search' +import * as readMode from '$lib/components/ReadMode/read-mode-view' + + diff --git a/extension/src/pages/sidepanel/app.pcss b/extension/src/pages/sidepanel/app.pcss new file mode 100644 index 0000000..5bd6fb7 --- /dev/null +++ b/extension/src/pages/sidepanel/app.pcss @@ -0,0 +1,70 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +@layer base { + :root { + --background: 0 0% 100%; + --foreground: 222.2 84% 4.9%; + + --muted: 210 40% 96.1%; + --muted-foreground: 215.4 16.3% 46.9%; + + --popover: 0 0% 100%; + --popover-foreground: 222.2 84% 4.9%; + + --card: 0 0% 100%; + --card-foreground: 222.2 84% 4.9%; + + --border: 214.3 31.8% 91.4%; + --input: 214.3 31.8% 91.4%; + + --primary: 222.2 47.4% 11.2%; + --primary-foreground: 210 40% 98%; + + --secondary: 210 40% 96.1%; + --secondary-foreground: 222.2 47.4% 11.2%; + + --accent: 210 40% 96.1%; + --accent-foreground: 222.2 47.4% 11.2%; + + --destructive: 0 72.2% 50.6%; + --destructive-foreground: 210 40% 98%; + + --ring: 222.2 84% 4.9%; + + --radius: 0.5rem; + } + + .dark { + --background: 222.2 84% 4.9%; + --foreground: 210 40% 98%; + + --muted: 217.2 32.6% 17.5%; + --muted-foreground: 215 20.2% 65.1%; + + --popover: 222.2 84% 4.9%; + --popover-foreground: 210 40% 98%; + + --card: 222.2 84% 4.9%; + --card-foreground: 210 40% 98%; + + --border: 217.2 32.6% 17.5%; + --input: 217.2 32.6% 17.5%; + + --primary: 210 40% 98%; + --primary-foreground: 222.2 47.4% 11.2%; + + --secondary: 217.2 32.6% 17.5%; + --secondary-foreground: 210 40% 98%; + + --accent: 217.2 32.6% 17.5%; + --accent-foreground: 210 40% 98%; + + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 210 40% 98%; + + --ring: hsl(212.7,26.8%,83.9); + } +} + \ No newline at end of file diff --git a/extension/src/pages/sidepanel/index.html b/extension/src/pages/sidepanel/index.html new file mode 100644 index 0000000..7ce4ab4 --- /dev/null +++ b/extension/src/pages/sidepanel/index.html @@ -0,0 +1,11 @@ + + + + + Sidepanel + + +
    + + + diff --git a/extension/src/pages/sidepanel/index.ts b/extension/src/pages/sidepanel/index.ts new file mode 100644 index 0000000..9e8292b --- /dev/null +++ b/extension/src/pages/sidepanel/index.ts @@ -0,0 +1,10 @@ +import Main from '$lib/components/TabManager/TabManager.svelte'; + +import "./app.pcss"; + + +document.addEventListener('DOMContentLoaded', function() { + new Main({ + target: document.body, + }); +}); diff --git a/extension/svelte.config.js b/extension/svelte.config.js new file mode 100644 index 0000000..b22dc94 --- /dev/null +++ b/extension/svelte.config.js @@ -0,0 +1,14 @@ +import { vitePreprocess } from "@sveltejs/vite-plugin-svelte"; +import adapter from '@sveltejs/adapter-auto'; + +export default { + preprocess: [vitePreprocess({})], + kit: { + adapter: adapter(), + alias: { + $lib: '../web-app/src/lib', + $assets: "../web-app/src/assets", + $components: '../web-app/src/lib/components' + } + } +}; diff --git a/extension/tailwind.config.ts b/extension/tailwind.config.ts new file mode 100644 index 0000000..2b8fb49 --- /dev/null +++ b/extension/tailwind.config.ts @@ -0,0 +1,22 @@ +import { fontFamily } from "tailwindcss/defaultTheme"; +import type { Config } from "tailwindcss"; + +const config: Config = { + darkMode: ["class"], + content: ["../web-app/src/**/*.{html,js,svelte,ts}"], + safelist: ["dark"], + theme: { + + extend: { + + fontFamily: { + sans: [...fontFamily.sans] + } + } + }, + corePlugins: { + preflight: true, + } +}; + +export default config; diff --git a/extension/tsconfig.json b/extension/tsconfig.json new file mode 100644 index 0000000..c6e4b86 --- /dev/null +++ b/extension/tsconfig.json @@ -0,0 +1,19 @@ +{ + "extends": "./.svelte-kit/tsconfig.json", + "compilerOptions": { + "baseUrl": ".", + "paths": { + "$lib": ["../web-app/src/lib"], + "$lib/*": ["../web-app/src/lib/*"] + }, + "target": "ESNext", + "useDefineForClassFields": true, + "module": "ESNext", + "resolveJsonModule": true, + "allowJs": true, + "checkJs": true, + "isolatedModules": true + }, + "include": ["src/**/*.d.ts", "src/**/*.ts", "src/**/*.js", "src/**/*.svelte", "src/pages/app/index.ts"], + "references": [{ "path": "./tsconfig.node.json" }], +} diff --git a/extension/tsconfig.node.json b/extension/tsconfig.node.json new file mode 100644 index 0000000..d2acd88 --- /dev/null +++ b/extension/tsconfig.node.json @@ -0,0 +1,10 @@ +// vite tsconfig +{ + "compilerOptions": { + "composite": true, + "skipLibCheck": true, + "module": "ESNext", + "moduleResolution": "bundler" + }, + "include": ["vite.config.ts", "src/manifest.config.ts", "package.json"] +} diff --git a/extension/vite.config.ts b/extension/vite.config.ts new file mode 100644 index 0000000..3aeea48 --- /dev/null +++ b/extension/vite.config.ts @@ -0,0 +1,34 @@ +import { crx } from "@crxjs/vite-plugin"; +import { svelte } from '@sveltejs/vite-plugin-svelte' +import { defineConfig } from "vite"; +import manifest from "./src/manifest.config"; +import path from "path"; +import topLevelAwait from "vite-plugin-top-level-await"; + +export default defineConfig({ + plugins: [ + topLevelAwait(), + svelte(), crx({ manifest })], + server: { + port: 5173, + strictPort: true, + }, + build: { + rollupOptions: { + input: { + sidepanel: 'src/pages/sidepanel/index.html', + options: 'index.html' + }, + + + } + + }, + resolve: { + alias: { + $lib: path.resolve("../web-app/src/lib"), + $assets: path.resolve("../web-app/src/assets"), + $components: path.resolve("../web-app/src/lib/components"), + }, + }, +}); diff --git a/index.js b/index.js index 97fb1d3..4e375dc 100644 --- a/index.js +++ b/index.js @@ -1,14 +1,34 @@ +/** + * #TODO + * seek topic + * login + * follow ups + * browser sidebar results + * QwkSearch + * QwkDocs + * QwkTabs + * QwkGraph + * QwkAds + * if select autocompelte in red.com go direct + * share button. email to friends. login to social media and like follow + * for each topic, next word prediction + * auto search for topics in sidebar + * + * prioritize sidebar - ai tips about this page + */ + import { searchWeb } from "./src/search/search-web.js"; import { searchSTREAM } from "./src/search/search-stream.js"; -import { extract } from "./src/extractor/url-to-content/url-to-content.js"; +import { extractContent } from "./src/extractor/url-to-content/url-to-content.js"; import { scrapeURL } from "./src/extractor/url-to-content/scrape-url.js"; import { extractSEEKTOPIC } from "./src/topics/seektopic-keyphrases.js"; import { weighRelevanceConceptVectorAPI } from "./src/similarity/similarity-remote-api.js"; +import {convertLanguageReplyToJSON} from "./src/generate/languagereply-to-json.js" import { convertHTMLSpecialChars, @@ -52,10 +72,10 @@ import { importVectorIndexFromString, } from "./src/similarity/similarity-vector.js"; -import { torch } from "./src/train/neural-net.js"; +// import { torch } from "./src/train/neural-net.js"; -import { extractContentHTML } from "./src/extractor/html-to-content/extract-content/extractor1-content.js"; -import { extractContentHTML2 } from "./src/extractor/html-to-content/extract-content/extractor2-content.js"; +import { extractMainContentFromHTML } from "./src/extractor/html-to-content/extract-content/extractor1-content.js"; +import { extractMainContentFromHTML2 } from "./src/extractor/html-to-content/extract-content/extractor2-content.js"; import { convertHTMLToBasicHTML } from "./src/extractor/html-to-content/html-to-basic-html.js"; import { extractCite } from "./src/extractor/html-to-cite/extract-cite.js"; // import {compileTopicModel} from "./src/dataset-import/compile-topic-model.js" @@ -72,15 +92,16 @@ export { importVectorIndexFromString, convertHTMLSpecialChars, convertPDFToHTML, - extract, + extractContent, extractFavicon, embedYoutubePlayer, stemWordToRoot, copyHTMLToClipboard, convertMathLaTexToImage, extractCite, - extractContentHTML, - extractContentHTML2, + convertLanguageReplyToJSON, + extractMainContentFromHTML, + extractMainContentFromHTML2, convertHTMLToBasicHTML, exportEmbeddingsIndex, addEmbeddingVectorsToIndex, @@ -99,7 +120,7 @@ export { searchVectorIndex, splitSentences, splitTextSemanticChars, - torch, + // torch, suggestNextWordCompletions, weighSimilarityByCharacter, weighRelevanceConceptVectorAPI, diff --git a/package.json b/package.json index 9482204..fda2254 100644 --- a/package.json +++ b/package.json @@ -8,7 +8,7 @@ "description": "Search and outline a topic base with AI Research Agent", "scripts": { "dev": "cd web-app; vite dev", - "prod": "cd web-app; vite build; wrangler deploy; wrangler tail", + "prod": "cd web-app; bunx --max-old-space-size=8192 vite build; wrangler deploy; wrangler tail", "docs": "typedoc; open docs/index.html", "docs-win": "typedoc; cmd.exe /c start \"\" docs/index.html", "test": "vitest", @@ -43,4 +43,4 @@ "pdfjs-serverless": "^0.5.1", "umap-js": "^1.4.0" } -} +} \ No newline at end of file diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml deleted file mode 100644 index ca20f9f..0000000 --- a/pnpm-lock.yaml +++ /dev/null @@ -1,3793 +0,0 @@ -lockfileVersion: '9.0' - 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    +

    + Being is Becoming
    + Whatever Research Can Be,
    + That is What It Must Become.
    + If AI is Humanity's Last Invention,
    + Then Vector Space is the Final Frontier.
    +


    @@ -13,6 +20,7 @@ Join Discord + GitHub Stars GitHub Discussions @@ -39,14 +47,10 @@

    Javascript Docs (airesearch.js.org) Live Demo (qwksearch.com)

    -### 🧠💻 Reimagine the Internet as 3D Mind Map +### 🧠💻 Reimagine the Internet as Self-Organizing Mind Map

    - Being is Becoming
    - Whatever Research Can Be,
    - That is What It Must Become.
    - If AI is Humanity's Last Invention,
    - Then Vector Space is the Final Frontier.
    +

    Critical times call for critical thinkers to create a crowdsourced argument reasoning dataset, for AI models to recommend research quotes, evolve crowdsourced chain-of-thought reasoning, unlock faster ways to read long articles, monitor developments by topic modeling a knowledge base graph, and provide a public service of answers to research. @@ -70,13 +74,10 @@ Language Models can distill the essence of collective thought into a vector spac 5. Research Agent prompt with key sentences from relevant sources to answer via Groq Llama, OpenAI, or Anthropic API Key and suggest follow-ups #### 🔤📊 SEEKTOPIC: Summarization by Extracting Entities, Keyword Tokens, and Outline Phrases Important to Context - -

    -

    - [extractSEEKTOPIC Docs](https://airesearch.js.org/functions/extractSEEKTOPIC.html) [SEEKTOPIC Sample Output](https://github.com/vtempest/ai-research-agent/blob/master/test/data/) @@ -129,18 +130,23 @@ SEEKTOPIC extracts unique, domain-specific key phrases from a document using nou [extract Docs](https://airesearch.js.org/functions/extract.html) -1. Extract URL or HTML to main content, improved version combining Mozilla Readability and Postlight Mercury using 100+ custom adapters for major websites. -2. Strips to basic HTML for reading mode or saving research notes -3. Youtube - get full transcript for video if detected a youtube video -4. PDF - Extracts formatted text from PDF with parsing of linebreaks, page headers, footnotes, and adding infer headings based on standard deviation from average text height -5. Cite - Extract author, date, source, and title from HTML using meta tags and common class names. Validates human name from author string to check against common list of 90k first names, last names, and organizations to infer if last name should be reversed starting by author last name (accounting for affixes/titles), since organizations are not reversed. +1. Main Content Detection: Extract the main content from a URL by combining Mozilla Readability and Postlight Mercury algorithms, utilizing over 100 custom adapters for major sites for article, author, date HTML classes. +2. Basic HTML Standardization: Transform complex HTML into a simplified reading-mode format of basic HTML, making it ideal for research note archival and focused reading, with headings, images and links. +3. YouTube Transcript Processing: When a YouTube video URL is detected, retrieve the complete video transcript including both manual captions and auto-generated subtitles, maintaining proper timestamp synchronization and speaker identification where available. +4. PDF Text Extraction and Structure: Process PDF documents by extracting formatted text while intelligently handling line breaks, page headers, and footnotes. The system analyzes text height statistics to automatically infer heading levels, creating a properly structured document hierarchy based on standard deviation from mean text size. +5. Citation Information Extraction: Identify and extract citation metadata including author names, publication dates, sources, and titles using HTML meta tags and common class name patterns. The system validates author names against a comprehensive database of 90,000 first and last names, distinguishing between personal and organizational authors to properly format citations. +6. Author Name Formatting: Process author names by checking against known name databases, handling affixes and titles correctly, and determining whether to reverse the name order based on whether it's a personal or organizational author, ensuring proper citation formatting. +7. Content Validation: Verify the extracted content's completeness and accuracy by comparing results from multiple extraction methods, ensuring all essential elements are preserved and properly formatted for the intended use case. -### Tardigrade the Web Crawler - +#### 🕸️🖥️ Tardigrade the Web Crawler +

    +

    + +[scrapeURL Docs](https://airesearch.js.org/functions/scrapeURL.html) -1. *Use Fetch API, check for bot detection.* Scrape any domain's URL to get its HTML, JSON, or arraybuffer. +1. First Use Fetch API, check for bot detection. Scrape any domain's URL to get its HTML, JSON, or Binary Buffer. Scraping internet pages is a [free speech right globally](https://blog.apify.com/is-web-scraping-legal/). 2. Features: timeout, redirects, default UA, referer as google, and bot @@ -178,28 +184,16 @@ to bypass Cloudflare anti bot using cookie id javascript method. [weighRelevanceTermFrequency Docs](https://airesearch.js.org/functions/weighRelevanceTermFrequency.html) +$$\text{score}(D,Q) = \sum_{i=1}^{N} \text{Wiki-IDF}(q_i) \times \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \frac{|D|}{\text{avgdl}}\right)}$$ Calculate term specificity for a single doc with BM25 formula by using Wikipedia term frequencies as the baseline Inverse Frequency across Documents. WikiBM25 solves the need to pass in all docs to compute against all documents in a database. The problem with BM25 and TF-IDF is that a large set of documents is needed to find the words that are repeated often across all. These overused words are often the same list of words, so using Wikipedia's term frequencies ensures a common sense baseline against a neutral corpus. -Use this list to Replace or Combine with All Documents IDF - Many websites may have less than a hundred pages to search through and that is not enough to find which terms are domain-specific. They can score a single doc at a time to find the weight each word in query gets. Wikipedia IDf can be a baseline IDF to average with the All Docs IDF for uniqueness across the average public and the specific domain. +All words in English Wikipedia are sorted by number of pages they are in for 325K words with frequencies of at least 32 wikipages, between 3 to 23 characters of Latin alphanumerics like az09, punctuation like .-, and diacritics like éï, but filtering out numbers and foreign language. + Use this list to Replace or Combine with All Documents IDF - Many websites may have less than a hundred pages to search through and that is not enough to find which terms are domain-specific. They can score a single doc at a time to find the weight each word in query gets. Wikipedia IDf can be a baseline IDF to average with the All Docs IDF for uniqueness across the average public and the specific domain. Example: Given a query "Superbowl wins by year" we do not want to simply return docs filled with common words like year, but rather recognize Superbowl is more domains-specific. This requires precomputing IDF values across all docs, and for websites that may not have that many docs to start with may consider averaging their precomputed score with wikiIDF values to ensure most unique words get a score. -**LLM RAG Chunk to Query Similarity** - When we chunk a document into parts to find which to pass into a LLM prompt, they need to be weighted to relevance to the query. Semantic Embedding with a LLM not only takes resources to compute & store the vectors, it also [performs worse](https://youtu.be/9QJXvNiJIG8?si=ey4GbqtV8tD5WV2P&t=725) than BM25 on its own. Hybrid BM25 & Embeddings RAG is best, but there may not be time to compute BM25 idf scores across all doc chunks. We need a fast way to distinguish more unique words to give them more weight rather than common short words that get repeated a lot in an edge case paragraph. WikiBM25 is the best in use cases like realtime web search where chunking the text cannot be done beforehand. - -$$\text{score}(D,Q) = \sum_{i=1}^{N} \text{Wiki-IDF}(q_i) \times \frac{f(q_i, D) \cdot (k_1 + 1)}{f(q_i, D) + k_1 \cdot \left(1 - b + b \cdot \frac{|D|}{\text{avgdl}}\right)}$$ - -**WikiIDF Term Specificity Statistics Metadata** - -- Phrase Starter Words and Single Words: 104556 - -- Total Terms: 204169 - -- Dict single words: 84493 - -- Dict phrases: 67444 - -All words in English Wikipedia are sorted by number of pages they are in for 325K words with frequencies of at least 32 wikipages, between 3 to 23 characters of Latin alphanumerics like az09, punctuation like .-, and diacritics like éï, but filtering out numbers and foreign language. +Use Case: LLM RAG Chunk to Query Similarity - When we chunk a document into parts to find which to pass into a LLM prompt, they need to be weighted to relevance to the query. Semantic Embedding with a LLM not only takes resources to compute & store the vectors, it also [performs worse](https://youtu.be/9QJXvNiJIG8?si=ey4GbqtV8tD5WV2P&t=725) than BM25 on its own. Hybrid BM25 & Embeddings RAG is best, but there may not be time to compute BM25 idf scores across all doc chunks. We need a fast way to distinguish more unique words to give them more weight rather than common short words that get repeated a lot in an edge case paragraph. WikiBM25 is the best in use cases like realtime web search where chunking the text cannot be done beforehand. @@ -232,5 +226,4 @@ Search-on-keystroke and load this JSON index for word and phrase completion, sor * [Transformers Explained Visually (Part 3)](https://towardsdatascience.com/transformers-explained-visually-part-3-multi-head-attention-deep-dive-1c1ff1024853) * [Can LLMs Generate Novel Research Ideas?](https://arxiv.org/html/2409.04109v1) * [Graph Algorithms Playground](https://playground.memgraph.com) - -[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) +* [CommonCrawl C4 Download](https://huggingface.co/datasets/allenai/c4) \ No newline at end of file diff --git a/src/crawler/compose.yml b/src/crawler/compose.yml index 122e287..674a5dc 100644 --- a/src/crawler/compose.yml +++ b/src/crawler/compose.yml @@ -12,12 +12,13 @@ services: # - FRONTEND_JWT_SECRET=secret2 crawler: + container_name: crawler build: . ports: - 3000:3000 - # environment: - # - DEBUG=true - + environment: + - DEBUG=true + - PROXY_URL=51.254.121.123:8088 networks: - caddy labels: @@ -26,23 +27,77 @@ services: - # Caddy - proxy server router for containers - caddy: - image: lucaslorentz/caddy-docker-proxy:ci-alpine - container_name: caddy - ports: - - 80:80 - - 443:443 + # # Caddy - proxy server router for containers + # caddy: + # image: lucaslorentz/caddy-docker-proxy:ci-alpine + # ports: + # - 80:80 + # - 443:443 + # volumes: + # - /var/run/docker.sock:/var/run/docker.sock + # - /var/lib/caddy/data:/data + # restart: unless-stopped + # environment: + # - CADDY_INGRESS_NETWORKS=caddy + # networks: + # - caddy + + + redis: + container_name: redis + image: docker.io/valkey/valkey:8-alpine + command: valkey-server --save 30 1 --loglevel warning + restart: unless-stopped + networks: + - searxng volumes: - - /var/run/docker.sock:/var/run/docker.sock - - /var/lib/caddy/data:/data + - valkey-data2:/data + cap_drop: + - ALL + cap_add: + - SETGID + - SETUID + - DAC_OVERRIDE + logging: + driver: "json-file" + options: + max-size: "1m" + max-file: "1" + + searxng: + container_name: searxng + image: docker.io/searxng/searxng:latest restart: unless-stopped - environment: - - CADDY_INGRESS_NETWORKS=caddy networks: - - caddy + - searxng + ports: + - "127.0.0.1:8080:8080" + volumes: + - ./searxng:/etc/searxng:rw + environment: + - SEARXNG_BASE_URL=https://${SEARXNG_HOSTNAME:-localhost}/ + - UWSGI_WORKERS=${SEARXNG_UWSGI_WORKERS:-4} + - UWSGI_THREADS=${SEARXNG_UWSGI_THREADS:-4} + cap_drop: + - ALL + cap_add: + - CHOWN + - SETGID + - SETUID + logging: + driver: "json-file" + options: + max-size: "1m" + max-file: "1" +networks: + searxng: +volumes: + caddy-data: + caddy-config: + valkey-data2: + networks: caddy: external: true \ No newline at end of file diff --git a/src/crawler/crawler.js b/src/crawler/crawler.js index 8db567d..7ca4414 100644 --- a/src/crawler/crawler.js +++ b/src/crawler/crawler.js @@ -1,4 +1,8 @@ /** + * + * https://www.webshare.io/academy-article/puppeteer-login + * + * * 1. Server API takes url and renders with puppeteer DOM to get all HTML. * 2. Bypass Cloudflare bot check * A webpage proxy that request through Chromium (puppeteer) - can be used @@ -9,6 +13,10 @@ import puppeteer from "puppeteer-extra"; import StealthPlugin from "puppeteer-extra-plugin-stealth"; puppeteer.use(StealthPlugin()); // Use stealth plugin to make puppeteer harder to detect + +// Add adblocker plugin to block all ads and trackers (saves bandwidth) +// const AdblockerPlugin = require("puppeteer-extra-plugin-adblocker"); +// puppeteer.use(AdblockerPlugin({ })); // Import Koa and its middleware import Koa from "koa"; @@ -46,14 +54,10 @@ let options = { args: ["--no-sandbox", "--disable-setuid-sandbox"], // Security-related arguments }; -// Configure Puppeteer based on environment variables -if (process.env.PUPPETEER_SKIP_CHROMIUM_DOWNLOAD) options.executablePath = "/usr/bin/chromium-browser"; -if (process.env.PUPPETEER_HEADFUL) options.headless = false; -if (process.env.PUPPETEER_USERDATADIR) - options.userDataDir = process.env.PUPPETEER_USERDATADIR; -if (process.env.PUPPETEER_PROXY) - options.args.push(`--proxy-server=${process.env.PUPPETEER_PROXY}`); +if (process.env.PROXY_URL) + options.args.push(`--proxy-server=`+process.env.PROXY_URL); + // Launch the browser const browser = await puppeteer.launch(options); @@ -70,9 +74,26 @@ app.use(async (ctx) => { let responseData; let responseHeaders; - // Create a new page + + + // Create a new page const page = await browser.newPage(); + + //login to proxy + if (process.env.PROXY_USER) + await page.authenticate({ + username: process.env.PROXY_USER, + password: process.env.PROXY_PASS, + }); + + // await page.goto('https://fingerprint.scrapoxy.io'); + + // const content = await page.content(); + // console.log(content); + + + // Set up request interception await page.removeAllListeners("request"); await page.setRequestInterception(true); @@ -84,22 +105,21 @@ app.use(async (ctx) => { // Handle each intercepted request page.on("request", (request) => { - //no downloading images/css in the virtual renderer saves time - if (["image", "stylesheet", "font"].includes(request.resourceType())) - request.abort(); - - requestHeaders = Object.assign({}, request.headers(), requestHeaders); - - if (ctx.method == "POST") { - request.continue({ - headers: requestHeaders, - method: "POST", - postData: ctx.request.rawBody, - }); - } else { - request.continue({ headers: requestHeaders }); - } + // if (["image", "stylesheet", "font"].includes(request.resourceType())) + // return request.abort(); + + requestHeaders = Object.assign({}, request.headers(), requestHeaders); + + if (ctx.method == "POST") { + request.continue({ + headers: requestHeaders, + method: "POST", + postData: ctx.request.rawBody, + }); + } else { + request.continue({ headers: requestHeaders }); + } }); // Set up CDP session for more control over the browser diff --git a/src/crawler/package.json b/src/crawler/package.json index 098d494..0462688 100644 --- a/src/crawler/package.json +++ b/src/crawler/package.json @@ -16,9 +16,9 @@ "koa-static": "^5.0.0", "puppeteer": "^21.7.0", "puppeteer-extra": "^3.3.6", + "puppeteer-extra-plugin-adblocker": "^2.13.6", "puppeteer-extra-plugin-stealth": "^2.11.2", "swagger-jsdoc": "^6.2.8", "swagger-ui-koa": "^0.0.1" } - } diff --git a/src/datasets/compile-topic-model.js b/src/datasets/compile-topic-model.js index 52a7f41..a377ef2 100644 --- a/src/datasets/compile-topic-model.js +++ b/src/datasets/compile-topic-model.js @@ -1,5 +1,5 @@ import wikiWordFrequency from "../../data/wiki-word-freq-325k.json" //with { type: "json" }; -; +import fs from "fs"; /** * Compile a topic phrases model from a dictionary and Wikipedia page titles.
    @@ -19,7 +19,6 @@ import wikiWordFrequency from "../../data/wiki-word-freq-325k.json" //with { typ * @param {Object} [options] * @param {number} options.addJSONLineBreaks - include line breaks in JSON output for debugging * @param {number} options.maxSynonymsPerTerm - max synonyms per term - * @param {boolean} options.outputJSExport - filetype js instead of json with "export" * @param {boolean} options.addWikiPageTitles - true to add wiki page titles, false for dictionary only * @param {boolean} options.sortInFirstTwoLettersTrie - sort the first words by first two letters Trie, needd for autocomplete after 2 letters typed * @param {number} options.minTermCharCount - min length of term to include @@ -30,7 +29,6 @@ export async function compileTopicModel(options = {}) { const { minTermCharCount = 3, maxSynonymsPerTerm = 0, - outputJSExport = false, addWikiPageTitles = true, sortInFirstTwoLettersTrie = true, addJSONLineBreaks = 0, @@ -39,8 +37,6 @@ export async function compileTopicModel(options = {}) { //array key: var [nextWords, wikiTitle, category, // uniqueness, capsIndexes] = dict[key.slice(0,2)][key]; - const pos_categories = ["n", "v", "r", "a", "s"]; //a and s is for adjectives - var dict = JSON.parse(fs.readFileSync("./data/dictionary-index-152k.json", "utf8")); var wikiTopPages = JSON.parse( @@ -109,27 +105,23 @@ export async function compileTopicModel(options = {}) { var words = key.split(" "); var firstWord = words[0]; var phraseSize = words.length; - - var { syns, cat, pos, caps } = dict[key]; - - pos = pos_categories.indexOf(pos); - if (caps) { - //compare caps to key and see which letters have capitaication, return their idnexes - var capsIndexes = []; - for (var i = 0; i < key.length; i++) { - if (caps[i] === key[i].toUpperCase()) { - capsIndexes.push(i); - } - } - caps = capsIndexes; //.join(","); - } - - syns = syns?.split(",").slice(0, maxSynonymsPerTerm).join(","); //max - - var phraseObj = { cat, pos }; + // if (caps) { + // //compare caps to key and see which letters have capitaication, return their idnexes + // var capsIndexes = []; + // for (var i = 0; i < key.length; i++) { + // if (caps[i] === key[i].toUpperCase()) { + // capsIndexes.push(i); + // } + // } + // caps = capsIndexes; //.join(","); + // } + + // syns = syns?.split(",").slice(0, maxSynonymsPerTerm).join(","); //max + + var phraseObj = { cat: dict[key] }; if (phraseSize > 1) phraseObj.n = words.slice(1)?.join(" "); - if (syns?.length) phraseObj.s = syns; - if (caps) phraseObj.caps = caps; + // if (syns?.length) phraseObj.s = syns; + // if (caps) phraseObj.caps = caps; if (!wordsPhrasesTree[firstWord] || !wordsPhrasesTree[firstWord].push) wordsPhrasesTree[firstWord] = []; @@ -210,16 +202,6 @@ export async function compileTopicModel(options = {}) { return acc; }, {}); - if (outputJSExport) - fs.writeFileSync( - "./data/wiki-phrases-model-240k.js", - "export default " + - (addJSONLineBreaks - ? JSON.stringify(firstTwoLettersTree, null, 2) - : JSON.stringify(firstTwoLettersTree)), - "utf8" - ); - else fs.writeFileSync( "./data/wiki-phrases-model-240k.json", addJSONLineBreaks @@ -274,4 +256,4 @@ export function weightWikiWordSpecificity(query) { return phraseScore; } -// await compileTopicModel(); +await compileTopicModel(); diff --git a/src/datasets/metadata-stats.js b/src/datasets/metadata-stats.js index fbd1a08..b75a116 100644 --- a/src/datasets/metadata-stats.js +++ b/src/datasets/metadata-stats.js @@ -1,4 +1,6 @@ import fs from "fs"; +import {compileTopicModel} from "./compile-topic-model.js"; + var dictindex = fs.readFileSync("./data/dictionary-152k.json", "utf8"); // var dictDefs = JSON.parse(fs.readFileSync("./data/dictionary-defs.json", "utf8")); diff --git a/src/extractor/html-to-cite/extract-author.js b/src/extractor/html-to-cite/extract-author.js index 6a194c0..4e6d333 100644 --- a/src/extractor/html-to-cite/extract-author.js +++ b/src/extractor/html-to-cite/extract-author.js @@ -1,4 +1,4 @@ -import { extractNamedEntity } from "./human-names-recognize.js"; +import { extractHumanName } from "./human-names-recognize.js"; import { parseDate } from "chrono-node"; // https://www.scribbr.com/citation/generator/folders/2rx21jyIjZIKRrcwLk2oXE/lists/4OeJQ4euxzyTk9BiPTRjwn/ @@ -100,14 +100,6 @@ export function extractAuthor(document) { } } - // 5. As a last resort, look for "By" pattern in any paragraph - const paragraphs = document.getElementsByTagName("p"); - for (const p of paragraphs) { - if (BYLINE_REGEX.test(p.textContent)) { - const author = extractAndValidateHumanName(p.textContent); - if (author) return author; - } - } return null; } @@ -125,7 +117,7 @@ const validateAuthor = (author) => { const extractAndValidateHumanName = (author) => { const validatedAuthor = validateAuthor(author); if (validatedAuthor) { - return extractNamedEntity(validatedAuthor); + return extractHumanName(validatedAuthor); } return null; }; diff --git a/src/extractor/html-to-cite/extract-cite.js b/src/extractor/html-to-cite/extract-cite.js index 367be75..7857ddb 100644 --- a/src/extractor/html-to-cite/extract-cite.js +++ b/src/extractor/html-to-cite/extract-cite.js @@ -5,7 +5,7 @@ import { extractDate } from "./extract-date.js"; import { extractSource } from "./extract-source.js"; import { extractTitle } from "./extract-title.js"; import { extractCiteFromMetadata } from "./metadata-to-cite.js"; -import { extractNamedEntity } from "./human-names-recognize.js"; +import { extractHumanName } from "./human-names-recognize.js"; /** *

    📚💎 Extract Expert Excerpt

    @@ -33,7 +33,7 @@ export function extractCite(document) { if (author?.length < 3 || author?.length > 50) author = null; if (author) - var { author_cite, author_short, author_type } = extractNamedEntity(author); + var { author_cite, author_short, author_type } = extractHumanName(author); else var { author_cite, author_short, author_type } = extractAuthor(document) || {}; @@ -44,5 +44,9 @@ export function extractCite(document) { title = extractTitle(document) || title; source = extractSource(document) || source; - return { author, author_cite, author_short, date, title, source }; + var cite = `${author_cite} ${date ? `(${new Date(date).getFullYear()}, ${new Date( + date).toLocaleDateString('en-US', {month: 'long', day: 'numeric'})}).` : + ''} ${title || ''}. ${source || ''}. ${document.location.href}`; + + return { author, author_cite, author_short, date, title, source , cite}; } diff --git a/src/extractor/html-to-cite/human-names-recognize.js b/src/extractor/html-to-cite/human-names-recognize.js index bdd102d..408f82a 100644 --- a/src/extractor/html-to-cite/human-names-recognize.js +++ b/src/extractor/html-to-cite/human-names-recognize.js @@ -1,23 +1,24 @@ import dataHumanNames from "../../../data/human-names-92k.json" // with { type: "json" }; /** - * @typedef {Object} AuthorObject - * @property {string} author_cite - Author name in Last, First Middle format - * @property {string} author_short - Author name in Last format - * @property {number} author_type - Author type ["single", "two-author", "more-than-two", "organization"] -*/ /** - * Validates human name from author string to check against common list of first - * names, last names, name affixes, and organizations to infer if it should be - * reversed starting by author last name in cite, since organizations are not reversed. - - * Checks against common salutations, middle parts, and titles to properly - * format for citation in Last, First Middle format. - * Author type is ["single", "two-author", "more-than-two", "organization"] - * where organization is a non-human name that is not reversed. - * @param {string} author - * @returns {Object} {author_cite, author_short, author_type} + * Validates and formats an author name string by comparing it against common lists of + * first names, last names, name affixes, and organizations. + * + * This function determines whether the name should be reversed (starting with the last name) + * for citation purposes, as organizations are not reversed. It also checks against common + * salutations, middle parts, and titles to properly format the citation in "Last, First Middle" format. + * + * @param {string} author - The author name string to be processed. + * @returns {Object} An object containing the following properties: + * - author_cite {string}: The formatted author name for citation (e.g., "Last, First Middle"). + * - author_short {string}: A shortened version of the author name. + * - author_type {string}: The type of author, which can be one of: + * - "single": A single author or a two-word name. + * - "two-author": Two authors. + * - "more-than-two": More than two authors. + * - "organization": A non-human name (organization) that should not be reversed. */ -export function extractNamedEntity(author) { +export function extractHumanName(author) { var authorType = 4; @@ -34,13 +35,14 @@ export function extractNamedEntity(author) { if (TERMS_ORG.includes(name?.toLowerCase().replace(/[^a-z]/g, ""))) return { name, type: "org" }; + //recognize human names from list of common first and last names return dataHumanNames[nameTitle] != null ? { name, type: enumTypes[dataHumanNames[nameTitle]] } - : { name, type: "none" }; + : { name, type: false }; }); var foundNameParts = names.filter( - (name) => name.type != "none" && name.type != "org" + (name) => name.type != false && name.type != "org" ).length; var foundOrgParts = names.filter((name) => name.type == "org").length; @@ -57,26 +59,26 @@ export function extractNamedEntity(author) { if (foundOrgParts > 0 || (foundNameParts == 0 && !isTwoWord)) authorType = 3; //cut off non-name intro parts like By: //TODO && names.length > 4 - if (foundNameParts > 0) { - var partFound; - names = names - .map((name) => { - if (name.type != null) partFound = 1; - if (partFound) return name; - else return false; - }) - .filter(Boolean); - } - - author = names.map((name) => name.name).join(" ") .replace(/(undefined|by\:|by )/gi, "") + // if (foundNameParts > 0 && names.length >=4) { + // var candidateName = names.filter((name) => name.type); + // if (candidateName.length >=2) + // names = candidateName + // } + + author = names.map((name) => name.name).join(" ") + .replace(/(undefined|by\:|by )/gi, "") .replace(/(undefined|by:|by )/gi, "") ; + const maxOrgNameLength = 30; + //split author into parts and reverse Last, First if not Organization if (authorType == 3) { + if (author.length > maxOrgNameLength) + author = author.substring(0,author.slice(0, maxOrgNameLength).lastIndexOf(" ")) ; author_short = author; } else { - var authorObj = extractNamedEntityParts(author); + var authorObj = extractHumanNameParts(author); if (authorObj) { author = authorObj.lastname + @@ -108,7 +110,7 @@ export function extractNamedEntity(author) { * @returns {Object} */ -const extractNamedEntityParts = (input) => { +const extractHumanNameParts = (input) => { // Initialize the result object const result = { prefix: "", //van der von de diff --git a/src/extractor/html-to-content/extract-content/extractor1-content.js b/src/extractor/html-to-content/extract-content/extractor1-content.js index 7801fdb..92e59bc 100644 --- a/src/extractor/html-to-content/extract-content/extractor1-content.js +++ b/src/extractor/html-to-content/extract-content/extractor1-content.js @@ -46,7 +46,7 @@ import { parseHTML } from 'linkedom'; * @example * var url = "https://www.nytimes.com/2024/08/28/business/telegram-ceo-pavel-durov-charged.html" * const html = await (await fetch(url)).text(); - * var articleContent = extractContentHTML(html); + * var articleContent = extractMainContentFromHTML(html); * @param {Object} [options] * @param {number} options.minContentLength default=140 - Minimum length of content to be considered valid * @param {number} options.minScore default=20 - Minimum score for content to be considered valid @@ -56,7 +56,7 @@ import { parseHTML } from 'linkedom'; * @author Based on [Mozilla Readability (2015), Arc90 (2010)](https://github.com/mozilla/readability) * @category Extract */ -export function extractContentHTML(html, options = {}) { +export function extractMainContentFromHTML(html, options = {}) { const { minContentLength = 140, minScore = 20, diff --git a/src/extractor/html-to-content/extract-content/extractor2-content-utils.js b/src/extractor/html-to-content/extract-content/extractor2-content-utils.js index 92a70c5..55d0144 100644 --- a/src/extractor/html-to-content/extract-content/extractor2-content-utils.js +++ b/src/extractor/html-to-content/extract-content/extractor2-content-utils.js @@ -284,16 +284,12 @@ const CANDIDATES_BLACKLIST = new RegExp(candidatesBlacklist, "i"); const candidatesWhitelist = UNLIKELY_CANDIDATES_WHITELIST.join("|"); const CANDIDATES_WHITELIST = new RegExp(candidatesWhitelist, "i"); -/** - * @private - */ + function normalizeSpaces(text) { return text.replace(/\s{2,}(?![^<>]*<\/(pre|code|textarea)>)/g, " ").trim(); } -/** - * @private - */ + function paragraphize(node, document, br = false) { if (br) { let sibling = node.nextSibling; @@ -316,9 +312,7 @@ function paragraphize(node, document, br = false) { return document; } -/** - * @private - */ + function getAttrs(node) { return node.attributes ? Array.from(node.attributes).reduce((acc, attr) => { @@ -328,9 +322,7 @@ function getAttrs(node) { : {}; } -/** - * @private - */ + function convertNodeTo(node, document, tag = "p") { if (!node) { return document; @@ -353,9 +345,7 @@ function convertNodeTo(node, document, tag = "p") { return document; } -/** - * @private - */ + function brsToPs(document) { let collapsing = false; document.querySelectorAll("br").forEach((element) => { @@ -373,9 +363,7 @@ function brsToPs(document) { return document; } -/** - * @private - */ + function convertDivs(document) { document.querySelectorAll("div").forEach((div) => { const convertible = div.querySelectorAll(DIV_TO_P_BLOCK_TAGS).length === 0; @@ -388,9 +376,7 @@ function convertDivs(document) { return document; } -/** - * @private - */ + function convertSpans(document) { document.querySelectorAll("span").forEach((span) => { const convertible = !span.closest("p, div, li, figcaption"); @@ -402,9 +388,7 @@ function convertSpans(document) { return document; } -/** - * @private - */ + function convertToParagraphs(document) { document = brsToPs(document); document = convertDivs(document); @@ -413,9 +397,7 @@ function convertToParagraphs(document) { return document; } -/** - * @private - */ + function cleanForHeight(img, document) { const height = parseInt(img.getAttribute("height"), 10); const width = parseInt(img.getAttribute("width"), 10) || 20; @@ -429,9 +411,7 @@ function cleanForHeight(img, document) { return document; } -/** - * @private - */ + function removeSpacers(img, document) { if (SPACER_RE.test(img.getAttribute("src"))) { img.remove(); @@ -440,9 +420,7 @@ function removeSpacers(img, document) { return document; } -/** - * @private - */ + function cleanImages(article, document) { article.querySelectorAll("img").forEach((img) => { cleanForHeight(img, document); @@ -452,9 +430,7 @@ function cleanImages(article, document) { return document; } -/** - * @private - */ + function stripJunkTags(article, document, tags = []) { if (tags.length === 0) { tags = STRIP_OUTPUT_TAGS; @@ -469,9 +445,7 @@ function stripJunkTags(article, document, tags = []) { return document; } -/** - * @private - */ + function cleanHOnes(article, document) { const hOnes = article.querySelectorAll("h1"); @@ -486,9 +460,7 @@ function cleanHOnes(article, document) { return document; } -/** - * @private - */ + function cleanAttributes(article, document) { return removeAllButWhitelist( article.parentNode ? article.parentNode : article, @@ -519,9 +491,7 @@ function removeAllButWhitelist(article) { return article; } -/** - * @private - */ + function removeEmpty(article) { article.querySelectorAll("p").forEach((p) => { if ( @@ -534,9 +504,7 @@ function removeEmpty(article) { return article; } -/** - * @private - */ + function removeUnlessContent(node, weight) { if (node.classList.contains("entry-content-asset")) { return; @@ -593,9 +561,7 @@ function removeUnlessContent(node, weight) { } } -/** - * @private - */ + function rewriteTopLevel(article, document) { document.documentElement.outerHTML = `
    ${document.documentElement.innerHTML}
    `; document.body.outerHTML = `
    ${document.body.innerHTML}
    `; @@ -603,16 +569,12 @@ function rewriteTopLevel(article, document) { return document; } -/** - * @private - */ + function textLength(text) { return text.trim().replace(/\s+/g, " ").length; } -/** - * @private - */ + function linkDensity(node) { const totalTextLength = textLength(node.textContent); @@ -631,9 +593,7 @@ function linkDensity(node) { return 0; } -/** - * @private - */ + function stripTags(text, document) { const span = document.createElement("span"); span.innerHTML = text; @@ -641,9 +601,7 @@ function stripTags(text, document) { return cleanText === "" ? text : cleanText; } -/** - * @private - */ + function stripUnlikelyCandidates(document) { document.querySelectorAll("*:not(a)").forEach((node) => { const classes = node.getAttribute("class"); @@ -662,9 +620,7 @@ function stripUnlikelyCandidates(document) { return document; } -/** - * @private - */ + function withinComment(node) { let parent = node.parentNode; while (parent) { @@ -680,31 +636,23 @@ function withinComment(node) { return false; } -/** - * @private - */ + function nodeIsSufficient(node) { return node.textContent.trim().length >= 100; } -/** - * @private - */ + function isWordpress(document) { return document.querySelectorAll(IS_WP_SELECTOR).length > 0; } -/** - * @private - */ + function setAttr(node, attr, val) { node.setAttribute(attr, val); return node; } -/** - * @private - */ + function setAttrs(node, attrs) { while (node.attributes.length > 0) { node.removeAttribute(node.attributes[0].name); diff --git a/src/extractor/html-to-content/extract-content/extractor2-content.js b/src/extractor/html-to-content/extract-content/extractor2-content.js index 687ad8f..cd12191 100644 --- a/src/extractor/html-to-content/extract-content/extractor2-content.js +++ b/src/extractor/html-to-content/extract-content/extractor2-content.js @@ -54,11 +54,11 @@ import { * @author Based on [Postlight Mercury Parser (2017-)](https://github.com/postlight/parser/tree/main/src) * @example var url = "https://en.wikipedia.org/wiki/David_Hilbert" * var html = await (await fetch(url)).text(); - * var content = extractContentHTML(html); + * var content = extractMainContentFromHTML(html); * console.log(content); // HTML content of main article body * @category Extract */ -export function extractContentHTML2(html, opts) { +export function extractMainContentFromHTML2(html, opts) { opts = { stripUnlikelyCandidates: true, weightNodes: true, diff --git a/src/extractor/html-to-content/html-to-content.js b/src/extractor/html-to-content/html-to-content.js index e5d6dd2..a9ab0aa 100644 --- a/src/extractor/html-to-content/html-to-content.js +++ b/src/extractor/html-to-content/html-to-content.js @@ -2,9 +2,9 @@ import { parseDate } from "chrono-node"; import { extractCite } from "../html-to-cite/extract-cite.js"; import { convertHTMLToBasicHTML } from "./html-to-basic-html.js"; import { parseHTML } from "linkedom"; -import { extractNamedEntity } from "../html-to-cite/human-names-recognize.js"; -import { extractContentHTML } from "./extract-content/extractor1-content.js"; -import { extractContentHTML2 } from "./extract-content/extractor2-content.js"; +import { extractHumanName } from "../html-to-cite/human-names-recognize.js"; +import { extractMainContentFromHTML } from "./extract-content/extractor1-content.js"; +import { extractMainContentFromHTML2 } from "./extract-content/extractor2-content.js"; /** * Extracts the main content and citation information from a document or HTML string @@ -49,19 +49,19 @@ export function extractContentAndCite(documentOrHTML, options = {}) { var content = useExtractor2 - ? extractContentHTML2(html, options) + ? extractMainContentFromHTML2(html, options) : - extractContentHTML(html, options); + extractMainContentFromHTML(html, options); // if Postlight Mercury returns less than 200 characters, try Mozilla Readability if (content.length < 200) { - var content2 = extractContentHTML(html, options); + var content2 = extractMainContentFromHTML(html, options); if (content2.length > content.length) content = content2; } - var { author, author_cite, author_short, date, title, source } = + var { author, author_cite, author_short, date, title, source, cite } = extractCite(html); @@ -77,6 +77,7 @@ export function extractContentAndCite(documentOrHTML, options = {}) { author, date, source, + cite, html, }; } diff --git a/src/extractor/url-to-content/scrape-url.js b/src/extractor/url-to-content/scrape-url.js index fbb3008..47785c6 100644 --- a/src/extractor/url-to-content/scrape-url.js +++ b/src/extractor/url-to-content/scrape-url.js @@ -59,7 +59,7 @@ export async function scrapeURL(url, options = {}) { if(proxy) url = proxy + url; - console.log(url); + // console.log(url); var userAgentStrings = ['Chrome/41.0.2272.96 Mobile Safari/537.36 (compatible ; Googlebot/2.1 ; +http://www.google.com/bot.html)', @@ -144,6 +144,7 @@ function isHTMLBotDetection(html) { "403 Forbidden", "504 Gateway Timeout", "Agree & Join LinkedIn", + "Verifying you are human. This may take a few seconds", "500 Internal Server Error", "By clicking Continue to join or sign in, you agree to LinkedIn", ]; diff --git a/src/extractor/url-to-content/url-to-content.js b/src/extractor/url-to-content/url-to-content.js index bca645b..f3305aa 100644 --- a/src/extractor/url-to-content/url-to-content.js +++ b/src/extractor/url-to-content/url-to-content.js @@ -20,15 +20,36 @@ import { scrapeURL } from "./scrape-url.js"; /** * ### 🚜📜 Tractor the Text Extractor + * * - * 1. Extract URL or HTML to main content, based on Readability with improved version - * using 100+ custom adapters for major websites.
    - * 2. Strips to basic HTML for reading mode or saving research notes.
    - * 3. Youtube - get full transcript for video if detected a youtube video.
    - * 4. PDF - Extracts formatted text from PDF with page headers, - * footnotes, linebreaks, and adding headings based on standard deviation of range text height.
    - * - *
    + * 1. Main Content Detection: Extract the main content from a URL by combining + * Mozilla Readability and Postlight Mercury algorithms, utilizing over 100 + * custom adapters for major sites for article, author, date HTML classes. + * 2. Basic HTML Standardization: Transform complex HTML into a simplified + * reading-mode format of basic HTML, making it ideal for research note archival + * and focused reading, with headings, images and links. + * 3. YouTube Transcript Processing: When a YouTube video URL is detected, + * retrieve the complete video transcript including both manual captions and + * auto-generated subtitles, maintaining proper timestamp synchronization and + * speaker identification where available. + * 4. PDF Text Extraction and Structure: Process PDF documents by extracting + * formatted text while intelligently handling line breaks, page headers, + * footnotes. The system analyzes text height statistics to automatically + * infer heading levels, creating a properly structured document hierarchy + * based on standard deviation from mean text size. + * 5. Citation Information Extraction: Identify and extract citation metadata + * including author names, publication dates, sources, and titles using HTML + * meta tags and common class name patterns. The system validates author names + * against a comprehensive database of 90,000 first and last names, + * distinguishing between personal and organizational authors to properly + * format citations. + * 6. Author Name Formatting: Process author names by checking against + * known name databases, handling affixes and titles correctly, and determining + * whether to reverse the name order based on whether it's a personal or + * organizational author, ensuring proper citation formatting. + * 7. Content Validation: Verify the extracted content's accuracy by comparing + * results from multiple extraction methods, ensuring all essential elements + * are preserved and properly formatted for the intended use case. * @param {document|string} urlOrDoc - url or dom object with article content * @param {Object} [options] * @param {boolean} options.images default=true - include images @@ -60,7 +81,7 @@ import { scrapeURL } from "./scrape-url.js"; * @category Extract * @author [ai-research-agent (2024)](https://airesearch.js.org) */ -export async function extract(urlOrDoc, options = {}) { +export async function extractContent(urlOrDoc, options = {}) { var { images = true, links = true, diff --git a/src/generate/languagereply-to-json.js b/src/generate/languagereply-to-json.js new file mode 100644 index 0000000..084ef58 --- /dev/null +++ b/src/generate/languagereply-to-json.js @@ -0,0 +1,38 @@ +/** + * This function extracts and cleans content between XML-style tags and returns a JSON object. + * @param {string} text - Input text to parse + * @param {string} [key='questions'] - Tag name to look for + * @returns {Object[]} Array of objects containing cleaned content items + */ +export function convertLanguageReplyToJSON(text, key = null) { + // Return empty string for invalid input + if (!text || typeof text !== 'string') { + return ''; + } + + // Find content between tags + let startTag = `<${key}>`; + if (!key) { + const lines = text.split('\n').filter(line => /^\d+[.)]\s*|\s*[-*•]\s/.test(line)).map(line => line.replace(/^\s*[-*•]|\d+[.)]\s*|[\u2022]\s*/g, '')); + return lines + } + let endTag = ``; + const startIndex = text.indexOf(startTag); + const endIndex = text.indexOf(endTag); + + // Return empty string if tags aren't found + if (startIndex === -1 || endIndex === -1) { + return ''; + } + + // Extract content between tags + const contentStartIndex = startIndex + startTag.length; + const content = text.slice(contentStartIndex, endIndex).trim(); + + // Remove list markers and clean whitespace + return content + .replace(/^\s*[-*•]|\d+[.)]\s*|[\u2022]\s*/gm, '') + .split('\n') + .map(line => line.trim()) + .filter(line => line) +} diff --git a/src/search/ip-to-lang.js b/src/search/ip-to-lang.js index 768dd56..d6184c4 100644 --- a/src/search/ip-to-lang.js +++ b/src/search/ip-to-lang.js @@ -16,7 +16,7 @@ * @returns {string} returns.greeting - A greeting in the detected language. * @author [ai-research-agent (2024)](https://airesearch.js.org) */ -export async function getLanguageFromIPRemoteAPI(ip) { +export async function getLanguageFromIP(ip) { var url = `http://ip-api.com/line/${ip}?fields=countryCode`; var countryCode = (await (await fetch(url)).text())?.toLowerCase().trim(); diff --git a/src/search/search-stream.js b/src/search/search-stream.js index 410ab71..ad74f9f 100644 --- a/src/search/search-stream.js +++ b/src/search/search-stream.js @@ -1,5 +1,5 @@ import {searchWeb} from "../../index.js"; -import {extract} from "../../index.js"; +import {extractContent} from "../../index.js"; /** * ### 🤖🔎 STREAM: Search with Top Result Extraction & Answer Model @@ -61,10 +61,10 @@ export async function searchSTREAM(query, options = {}) { let {url, cached} = results[i]; - let extraction = await extract(url); + let extraction = await extractContent(url); if (optionUseCacheBackup && extraction.error) - extraction = await extract(cached); + extraction = await extractContent(cached); if (extraction.error) console.error("error", extraction); else{ diff --git a/src/search/search-web.js b/src/search/search-web.js index 7423f3d..86d32fb 100644 --- a/src/search/search-web.js +++ b/src/search/search-web.js @@ -6,12 +6,14 @@ import { scrapeURL } from "../../index.js"; * times to retry other domains if first time fails. * SearXNG is a free internet metasearch engine which aggregates results from * more than [180+ search sources](https://docs.searxng.org/user/configured_engines.html). + * + * [Searxng Installation Guide](https://github.com/searxng/searxng-docker/tree/master) * @param {string} query - The search query string. * @param {Object} [options] * @param {number} options.category default=0 - ["general", "news", "videos", "images", * "science", "map", "music", "it", "files", "social+media"] * @param {number} options.recency default=0 - ["", "day", "week", "month", "year"] - * @param {string|boolean} options.customSearxngDomain default=null - Use your custom domain SearXNG + * @param {string|boolean} options.privateSearxng default=null - Use your custom domain SearXNG * @param {number} options.maxRetries default=3 - Maximum number of retry attempts if the initial search fails. * @param {number} options.page default=1 - The page number to retrieve. * @param {string} options.proxy default=false - Use corsproxy.io to access in frontend JS @@ -29,7 +31,7 @@ export async function searchWeb(query, options = {}) { const { category = 0, recency = 0, - customSearxngDomain = null, + privateSearxng = null, maxRetries = 3, page = 1, language = "en-US", @@ -117,29 +119,57 @@ export async function searchWeb(query, options = {}) { ]; //select a random domain if none is provided - const searchDomain = customSearxngDomain || "https://" + + const searchDomain = privateSearxng || "https://" + SEARX_DOMAINS[Math.floor(Math.random() * SEARX_DOMAINS.length)]; const categoryName = CATEGORY_LIST[category]; // Using the first category as default const timeRangeName = RECENCY_LIST[recency]; // Using the first time range as default var url = `${searchDomain}/search?q=${encodeURIComponent(query)}` + - `&category_${categoryName}=1&language=${language}&time_range=${timeRangeName}` + - `&safesearch=0&pageno=${page}`; + `&category_${categoryName}=1&language=${language}&time_range=` + + `${timeRangeName}&safesearch=0&pageno=${page}`; + + // if(privateSearxng) + // url+="&format=json" //on cloudflare to avoid "Too many redirects" change SSL mode to Full - if (proxy) + + if (proxy && !privateSearxng) url = proxy + url; + + console.log(url) const resultHTML = await (await fetch(url, { headers: { "accept-language": language+",en;q=0.9", } })).text(); - console.log(resultHTML); + if (privateSearxng&&0){ + + console.log(resultHTML); + if (!resultHTML.startsWith("{")) + return {error:1} + + var {results, suggestions, infoboxes} = JSON.parse(resultHTML); + + results.forEach((result) => { + result.url = result.url.replace(/&/g, "&"); + }); + + results = results.map(({title, url, content, score}) => { + return {title, url, snippet: content, score}; + }); + + + console.log(resultHTML); + return {results, suggestions}; + + + } + - let results = []; + results = []; const resultRegex = /
    ]*>(.*?)<\/a><\/h3>/; const snippetRegex = /

    \s*(.*?)\s*<\/p>/; @@ -174,12 +204,12 @@ export async function searchWeb(query, options = {}) { title = convertHTMLSpecialChars(title); snippet = convertHTMLSpecialChars(snippet); - if (!url.includes(".de/")) results.push({ title, url, snippet }); + // if (!url.includes(".de/")) + results.push({ title, url, snippet }); } } if (results.length === 0 && maxRetries > 0) { - console.log(url); results = await searchWeb(query, {...options, maxRetries: maxRetries - 1, @@ -188,7 +218,7 @@ export async function searchWeb(query, options = {}) { } //filter out url that end with .de - results = results.filter((result) => !result.url.includes(".de/")); + // results = results.filter((result) => !result.url.includes(".de/")); return results; // } catch (error) { @@ -233,9 +263,6 @@ var sources = [ ["wikidata", "wd", "Wikimedia's collaborative knowledge base."], ["wikipedia", "wp", "Free online encyclopedia."], ["wolframalpha", "wa", "Computational knowledge engine."], - ["yacy", "ya", "Decentralized, peer-to-peer search engine."], - ["yep", "yep", "Likely a specialized or alternative search engine."], - ["bpb (DE)", "bpb", "German Federal Agency for Civic Education search."], ["tagesschau (DE)", "ts", "Search for German news from Tagesschau."], ["wikimini (FR)", "wkmn", "French-language wiki encyclopedia for children."], ["bing images", "bii", "Image search by Microsoft's Bing."], @@ -362,12 +389,8 @@ var sources = [ ["fdroid", "fd", "App store for Free and Open Source Software on Android."], ["google play apps", "gpa", "Official app store for Android devices."], ["9gag", "9g", "Social media platform for sharing humor content."], - ["lemmy comments", "lecom", "Comment search for Lemmy, a link aggregator."], - ["lemmy communities", "leco", "Community search for Lemmy."], ["lemmy posts", "lepo", "Post search for Lemmy."], - ["lemmy users", "leus", "User search for Lemmy."], ["mastodon hashtags", "mah", "Hashtag search for the Mastodon social network."], - ["mastodon users", "mau", "User search for Mastodon."], ["reddit", "re", "Social news and discussion website."], ["tootfinder", "toot", "Search engine for Mastodon and other federated networks."] ] diff --git a/src/search/wiki-news.js b/src/search/wiki-news.js new file mode 100644 index 0000000..259d86b --- /dev/null +++ b/src/search/wiki-news.js @@ -0,0 +1,50 @@ +async function fetchCurrentEventsHTML() { + const response = await fetch('https://en.wikipedia.org/wiki/Portal:Current_events'); + const htmlString = await response.text(); // Get HTML as string + + const parser = new DOMParser(); + const doc = parser.parseFromString(htmlString, 'text/html'); + const container = doc.querySelector('.p-current-events-headlines'); + + if (!container) return null; + const extractLinks = (element) => { + return Array.from(element.querySelectorAll('a')).map(link => ({ + title: link.textContent.trim(), + url: link.getAttribute('href') + })); + }; + const mainNews = Array.from(container.querySelectorAll('ul > li')).map(item => { + const links = extractLinks(item); + return { + headline: item.textContent.trim(), + relatedTopics: links + }; + }); + const ongoingEvents = Array.from(container.querySelectorAll('.itn-footer > div:nth-child(1) .hlist.inline li')).map(item => { + const link = item.querySelector('a'); + const subItems = Array.from(item.querySelectorAll('ul li a')).map(subLink => ({ + title: subLink.textContent.trim(), + url: subLink.getAttribute('href') + })); + return { + title: link.textContent.trim(), + url: link.getAttribute('href'), + subItems: subItems.length > 0 ? subItems : undefined + }; + }); + const recentDeaths = extractLinks(container.querySelector('.itn-footer > div:nth-child(2) .hlist.inline')); + const featuredImage = container.querySelector('.itn-img img'); + const imageInfo = featuredImage ? { + src: featuredImage.getAttribute('src'), + alt: featuredImage.getAttribute('alt'), + caption: container.querySelector('.itn-img .thumbcaption').textContent.trim() + } : null; + return { + mainNews, + ongoingEvents, + recentDeaths, + featuredImage: imageInfo + }; + }; + // Example usage: + \ No newline at end of file diff --git a/src/similarity/embeddings-to-graph.js b/src/similarity/embeddings-to-graph.js index 13ef2c6..c0da9d1 100644 --- a/src/similarity/embeddings-to-graph.js +++ b/src/similarity/embeddings-to-graph.js @@ -30,8 +30,10 @@ import { UMAP } from "umap-js/lib/umap-js.js"; * * [Understanding UMAP](https://pair-code.github.io/understanding-umap/)
    * [UMAP Algorithm Overview](https://www.youtube.com/watch?v=VPq4Ktf2zJ4)
    - * + * * + * + * * * @param {Object.} embeddingsDict - The dictionary of embeddings. * @param {Object} [options] diff --git a/src/topics/ngrams.js b/src/topics/ngrams.js index 975a713..771b168 100644 --- a/src/topics/ngrams.js +++ b/src/topics/ngrams.js @@ -70,5 +70,5 @@ export function extractNounEdgeGrams( export function isNoun(token) { // POS tags 3-28 and 50 are considered nouns // This may need to be adjusted based on the specific POS tagging system used - return (token[1] >= 3 && token[1] <= 28) || token[1] == 50; + return token[1] == 10 || token[1] == 5; } \ No newline at end of file diff --git a/src/topics/seektopic-keyphrases.js b/src/topics/seektopic-keyphrases.js index 38a2511..141ee25 100644 --- a/src/topics/seektopic-keyphrases.js +++ b/src/topics/seektopic-keyphrases.js @@ -48,7 +48,7 @@ import { extractNounEdgeGrams } from "./ngrams.js"; * width="550px" controls /> * @param {string} docText - input text to analyze * @param {Object} [options] - * @param {Object} options.phrasesModel - phrases model + * @param {any} options.phrasesModel - phrases model * @param {number} options.maxWords default=5 - maximum words in a keyphrase * @param {number} options.minWords default=1 - minimum words in a keyphrase * @param {number} options.minWordLength default=3 - minimum length of a word @@ -73,8 +73,8 @@ import { extractNounEdgeGrams } from "./ngrams.js"; export function extractSEEKTOPIC(docText, options = {}) { var { phrasesModel, - maxWords = 5, - minWords = 2, + maxWords = 3, + minWords = 1, minWordLength = 3, topKeyphrasesPercent = 0.5, limitTopSentences = 5, diff --git a/test/data/___airesearch_js_org.json b/test/data/___airesearch_js_org.json new file mode 100644 index 0000000..f5c7eca --- /dev/null +++ b/test/data/___airesearch_js_org.json @@ -0,0 +1,211 @@ +{ + "url": "https://airesearch.js.org", + "title": "ai-research-agent", + "word_count": 2120, + "topSentences": [ + { + "text": "Assign weights to links based on concept usage probability. Sentence Weighting: Apply TextRank algorithm to weight sentences, identifying those that centralize and connect key phrase concepts most referenced by other sentences.", + "index": 17, + "keyphrases": [ + "based combination frequency word count", + "concept vector cosine similarity", + "concepts enabling efficient", + "frequently referenced" + ], + "weight": 113 + }, + { + "text": "This process, based on TextRank and PageRank, includes random surfing and jumping to avoid loops. Top Results Selection: Select top sentences and key phrases based on overall weight and graph centrality, using either a fixed number or percentage for larger documents. Output Generation: Return top sentences (with associated key phrases) and top key phrases (with associated sentences).", + "index": 18, + "keyphrases": [ + "english wikipedia sorted number", + "random surfing" + ], + "weight": 72 + }, + { + "text": "240K total words & phrases, first 117K first-word or single words to check every token against. 100K Wikipedia Page Titles and links - Wikipedia most popular pages titles.", + "index": 34, + "keyphrases": [ + "english wikipedia sorted number" + ], + "weight": 65 + }, + { + "text": "$$\\text{score}(D,Q) = \\sum_{i=1}^{N} \\text{Wiki-IDF}(q_i) \\times \\frac{f(q_i, D) \\cdot (k_1 + 1)}{f(q_i, D) + k_1 \\cdot \\left(1 - b + b \\cdot \\frac{|D|}{\\text{avgdl}}\\right)}$$ WikiIDF Term Specificity Statistics Metadata All words in English Wikipedia are sorted by number of pages they are in for 325K words with frequencies of at least 32 wikipages, between 3 to 23 characters of Latin alphanumerics like az09, punctuation like .", + "index": 50, + "keyphrases": [ + "english wikipedia sorted number", + "statistics metadata" + ], + "weight": 63 + }, + { + "text": "Research Agent prompt with key sentences from relevant sources to answer via Groq Llama, OpenAI, or Anthropic API Key and suggest follow-ups extractSEEKTOPIC Docs SEEKTOPIC Sample Output SEEKTOPIC can be used to find unique, domain-specific keyphrases using noun Ngrams.", + "index": 8, + "keyphrases": [ + "groq llama openai anthropic api", + "similar anthropic obsidian" + ], + "weight": 51 + }, + { + "text": "People will make personal knowledge bases of what influences them to create AI assistants cloning their mind-uploaded perspective. Similar apps are Anthropic Claude, Obsidian, SciSpace and Perplexity, showing that people need this new way of self-organizing information. searchSTREAM Docs Search Web for query via metasearch of major engines or your custom data Extract text of top results with Tractor the Text Extractor.", + "index": 6, + "keyphrases": [ + "groq llama openai anthropic api", + "similar anthropic obsidian", + "new way self", + "cloning mind" + ], + "weight": 50 + }, + { + "text": "The user can click on keyphrases or LLM can suggest questions based on them.", + "index": 9, + "keyphrases": [ + "based combination frequency word count" + ], + "weight": 50 + }, + { + "text": "Tokenization and Phrase Extraction: Employ a Wiki Phrases tokenizer to identify wiki topics, phrases, and nouns.", + "index": 14, + "keyphrases": [ + "mappings dictionary wiki" + ], + "weight": 50 + }, + { + "text": "If all the available docs are nodes, the links in the graph can be extracted Wiki page entities and mappings of dictionary phrases to their wiki page.", + "index": 30, + "keyphrases": [ + "mappings dictionary wiki" + ], + "weight": 50 + }, + { + "text": "There is \"unanimous consensus\" that Prefix Trie O(1) lookups (instead of thaving to loop through the index for each lookup) makes it the best data type for this task.", + "index": 37, + "keyphrases": [ + "best data type" + ], + "weight": 50 + } + ], + "keyphrases": [ + { + "sentences": "19,22,20,25,26", + "keyphrase": "url html main", + "words": 3, + "weight": 56 + }, + { + "sentences": "8,6", + "keyphrase": "groq llama openai anthropic api", + "words": 5, + "weight": 54 + }, + { + "sentences": "26,19,22", + "keyphrase": "url url query string http localhost", + "words": 5, + "weight": 50 + }, + { + "sentences": "16,9,17", + "keyphrase": "based combination frequency word count", + "words": 5, + "weight": 49 + }, + { + "sentences": "20", + "keyphrase": "video detected youtube video pdf", + "words": 5, + "weight": 49 + }, + { + "sentences": "20,19,22,25", + "keyphrase": "basic html reading", + "words": 3, + "weight": 45 + }, + { + "sentences": "3,5,20,32,33", + "keyphrase": "research people aligned interests", + "words": 4, + "weight": 40 + }, + { + "sentences": "33,3,5,20,32", + "keyphrase": "research fully grok", + "words": 3, + "weight": 32 + }, + { + "sentences": "23", + "keyphrase": "free speech right globally", + "words": 4, + "weight": 31 + }, + { + "sentences": "30,14", + "keyphrase": "mappings dictionary wiki", + "words": 3, + "weight": 25 + } + ], + "sentences": [ + "Being is Becoming Whatever Research Can Be, That is What It Must Become. If AI is Humanity's Last Invention, Then Vector Space is the Final Frontier.", + "Critical times call for critical thinkers to create a crowdsourced argument reasoning dataset, for AI models to recommend research quotes, evolve crowdsourced chain-of-thought reasoning, unlock faster ways to read long articles, monitor developments by topic modeling a knowledge base graph, and provide a public service of answers to research.", + "Language Models can distill the essence of collective thought into a vector space where every point has a weighted value representing its contribution to the overall decision-making process, leading to direct democratic AI economy where public votes reward influence.", + "AI will show its reasoning based on what sentences and cites it used from the collective research, so that people can see it is aligned with our interests.", + "Research Agents recommend articles for human researchers working alongside AI to develop a summarized topic outline as a public service.", + "The agents monitor for any related articles via web searches for keywords associated with that Topic Model. Users can upload research PDFs, then the app finds the citations full text and creates topic model and keyword summaries, then monitors that literature base and stores highlights.", + "People will make personal knowledge bases of what influences them to create AI assistants cloning their mind-uploaded perspective. Similar apps are Anthropic Claude, Obsidian, SciSpace and Perplexity, showing that people need this new way of self-organizing information. searchSTREAM Docs Search Web for query via metasearch of major engines or your custom data Extract text of top results with Tractor the Text Extractor.", + "SEEKTOPIC: Extract Keyphrase Topics and Top Sentences that centralize those topics Rerank documents's chunks based on relevance to query, using embeddings by convert text to concept vector, get cosine similarity of query to topic, returning the sentences central to key relevant parts of the article.", + "Research Agent prompt with key sentences from relevant sources to answer via Groq Llama, OpenAI, or Anthropic API Key and suggest follow-ups extractSEEKTOPIC Docs SEEKTOPIC Sample Output SEEKTOPIC can be used to find unique, domain-specific keyphrases using noun Ngrams.", + "The user can click on keyphrases or LLM can suggest questions based on them.", + "The user can see highlighted just the most important sentences that centralize and tie in the core topics.", + "It is possible to vectorize and compare the dot product similarity of query to keyphrases which are then mapped to parts of the document like section labels.", + "This is more in line with how humans think of article organization into section headings and lead sentences which tie in concepts from others.", + "SEEKTOPIC extracts unique, domain-specific key phrases from a document using noun n-grams and ranks sentences based on their centrality to the most frequently referenced key phrase concepts, enabling efficient extraction of domain-specific content and provides a flexible framework for summarization. Sentence Segmentation: Split the text into sentences, accounting for common abbreviations, numbers, URLs, and other exceptions.", + "Tokenization and Phrase Extraction: Employ a Wiki Phrases tokenizer to identify wiki topics, phrases, and nouns.", + "This includes spell-checking and root word verification using Porter Stemmer. Noun N-gram Extraction: Generate noun edge-grams, allowing for stop words in the middle (e.g., \"state of the art\"). Key Phrase Consolidation: Merge smaller n-grams that are subsets of larger ones by comparing weights. Domain Specificity Calculation: Determine named entities and phrase domain specificity using WikiIDF.", + "This rewards unique key phrases specific to the document's field (e.g., \"endocrinology\" in medical texts or \"thou shall\" in religious texts). Key Phrase Filtering: Select top key phrases based on a combination of frequency and word count. Graph Construction: Create a double-ring weighted graph with key phrases in the central ring and sentences in the outer ring.", + "Assign weights to links based on concept usage probability. Sentence Weighting: Apply TextRank algorithm to weight sentences, identifying those that centralize and connect key phrase concepts most referenced by other sentences.", + "This process, based on TextRank and PageRank, includes random surfing and jumping to avoid loops. Top Results Selection: Select top sentences and key phrases based on overall weight and graph centrality, using either a fixed number or percentage for larger documents. Output Generation: Return top sentences (with associated key phrases) and top key phrases (with associated sentences).", + "Dynamic Reranking: If a user interacts with a key phrase or if there's a search query leading to the document, compare query similarity to key phrases, heavily weight the most similar key phrase, and reapply TextRank from step 8. extract Docs Extract URL or HTML to main content, improved version combining Mozilla Readability and Postlight Mercury using 100+ custom adapters for major websites.", + "Strips to basic HTML for reading mode or saving research notes Youtube - get full transcript for video if detected a youtube video PDF - Extracts formatted text from PDF with parsing of linebreaks, page headers, footnotes, and adding infer headings based on standard deviation from average text height Cite - Extract author, date, source, and title from HTML using meta tags and common class names.", + "Validates human name from author string to check against common list of 90k first names, last names, and organizations to infer if last name should be reversed starting by author last name (accounting for affixes/titles), since organizations are not reversed. Use Fetch API, check for bot detection.", + "Scrape any domain's URL to get its HTML, JSON, or arraybuffer.", + "Scraping internet pages is a free speech right globally .", + "Features: timeout, redirects, default UA, referer as google, and bot detection checking.", + "If fetch method does not get needed HTML, use Docker container wih proxy as backup. Setup Docker container with NodeJS server API renders with puppeteer DOM to get all HTML loaded by secondary in-page API requests after the initial page request, including user login and cookie storage. Bypass Cloudflare bot check: A webpage proxy that request through Chromium (puppeteer) - can be used to bypass Cloudflare anti bot using cookie id javascript method.", + "Send your request to the server with the port 3000 and add your URL to the \"url\" query string like this: http://localhost:3000/?url=https://example.org .", + "Pass in this proxy url in the fetch request.", + "compileTopicModel Docs Search and outline a research base using Wikipedia's 100k popular pages as the core topic phrases graph for LLM Research Agents.", + "Most of the documents online (and by extension thinking in the collective conciousness) can revolve around core topic phrases linked as a graph.", + "If all the available docs are nodes, the links in the graph can be extracted Wiki page entities and mappings of dictionary phrases to their wiki page.", + "These can serve as topic labels, keywords, and suggestions for LLM followup questions.", + "Documents can be linked in a graph with: 1. wiki page entity recognition 2. frequent keyphrases 3. html links 4. research paper references 5. keyphrases to query in global web search 6. site-specific recommendations.", + "These can lay the foundation for LLM Research Agents to fully grok, summarize, and outline a research base.", + "240K total words & phrases, first 117K first-word or single words to check every token against. 100K Wikipedia Page Titles and links - Wikipedia most popular pages titles.", + "Also includes domain specificity score and what letters should be capital.", + "84K words and 67K phrases in dictionary lexicon OpenEnglishWordNet, a better updated version of Wordnet - multiple definitions per term, 120k definitions, 45 concept categories JSON Prefix Trie - arranged by sorting words and phrases for lookup by first word to tokenize by word, then find if it starts a phrase based on entries, for Phrase Extraction from a text.", + "There is \"unanimous consensus\" that Prefix Trie O(1) lookups (instead of thaving to loop through the index for each lookup) makes it the best data type for this task.", + "weighRelevanceTermFrequency Docs Calculate term specificity for a single doc with BM25 formula by using Wikipedia term frequencies as the baseline Inverse Frequency across Documents.", + "WikiBM25 solves the need to pass in all docs to compute against all documents in a database.", + "The problem with BM25 and TF-IDF is that a large set of documents is needed to find the words that are repeated often across all.", + "These overused words are often the same list of words, so using Wikipedia's term frequencies ensures a common sense baseline against a neutral corpus.", + "Use this list to Replace or Combine with All Documents IDF - Many websites may have less than a hundred pages to search through and that is not enough to find which terms are domain-specific.", + "They can score a single doc at a time to find the weight each word in query gets.", + "Wikipedia IDf can be a baseline IDF to average with the All Docs IDF for uniqueness across the average public and the specific domain.", + "Example: Given a query \"Superbowl wins by year\" we do not want to simply return docs filled with common words like year, but rather recognize Superbowl is more domains-specific.", + "This requires precomputing IDF values across all docs, and for websites that may not have that many docs to start with may consider averaging their precomputed score with wikiIDF values to ensure most unique words get a score. LLM RAG Chunk to Query Similarity - When we chunk a document into parts to find which to pass into a LLM prompt, they need to be weighted to relevance to the query.", + "Semantic Embedding with a LLM not only takes resources to compute & store the vectors, it also performs worse than BM25 on its own. Hybrid BM25 & Embeddings RAG is best, but there may not be time to compute BM25 idf scores across all doc chunks.", + "We need a fast way to distinguish more unique words to give them more weight rather than common short words that get repeated a lot in an edge case paragraph.", + "WikiBM25 is the best in use cases like realtime web search where chunking the text cannot be done beforehand.", + "$$\\text{score}(D,Q) = \\sum_{i=1}^{N} \\text{Wiki-IDF}(q_i) \\times \\frac{f(q_i, D) \\cdot (k_1 + 1)}{f(q_i, D) + k_1 \\cdot \\left(1 - b + b \\cdot \\frac{|D|}{\\text{avgdl}}\\right)}$$ WikiIDF Term Specificity Statistics Metadata All words in English Wikipedia are sorted by number of pages they are in for 325K words with frequencies of at least 32 wikipages, between 3 to 23 characters of Latin alphanumerics like az09, punctuation like ." + ] +} \ No newline at end of file diff --git a/test/data/hat_you_can_talk_to_.json b/test/data/hat_you_can_talk_to_.json deleted file mode 100644 index 4458194..0000000 --- a/test/data/hat_you_can_talk_to_.json +++ /dev/null @@ -1,118 +0,0 @@ -{ - "url": "https://www.technologyreview.com/2024/07/30/1095489/openai-has-released-a-new-chatgpt-bot-that-you-can-talk-to/", - "title": "OpenAI has released a new ChatGPT bot that you can talk to", - "author_cite": "Melissa Heikkiläarchive page", - "author_short": "Melissa Heikkiläarchive page", - "source": "MIT Technology Review", - "word_count": 485, - "topSentences": [ - { - "text": "To gather feedback, the company is initially launching the chatbot to a “small group of users” paying for ChatGPT Plus, but it says it will make the bot available to all ChatGPT Plus subscribers this fall. A ChatGPT Plus subscription costs $20 a month.", - "index": 1, - "keyphrases": [ - "feedback company" - ], - "weight": 500 - }, - { - "text": "The company says it has put several safety mechanisms in place.", - "index": 7, - "keyphrases": [ - "feedback company" - ], - "weight": 500 - }, - { - "text": "The voice mode is powered by OpenAI’s new GPT-4o model, which combines voice, text, and vision capabilities.", - "index": 0, - "keyphrases": [], - "weight": 0 - }, - { - "text": "OpenAI says it will notify customers who are part of the first rollout wave in the ChatGPT app and provide instructions on how to use the new model.", - "index": 2, - "keyphrases": [], - "weight": 0 - }, - { - "text": "The new voice feature, which was announced in May , is being launched a month later than originally planned because the company said it needed more time to improve safety features, such as the model’s ability to detect and refuse unwanted content.", - "index": 3, - "keyphrases": [], - "weight": 0 - }, - { - "text": "The company also said it was preparing its infrastructure to offer real-time responses to millions of users.", - "index": 4, - "keyphrases": [], - "weight": 0 - }, - { - "text": "OpenAI says it has tested the model’s voice capabilities with more than 100 external red-teamers, who were tasked with probing the model for flaws.", - "index": 5, - "keyphrases": [], - "weight": 0 - }, - { - "text": "These testers spoke a total of 45 languages and represented 29 countries, according to OpenAI.", - "index": 6, - "keyphrases": [], - "weight": 0 - }, - { - "text": "In a move that aims to prevent the model from being used to create audio deepfakes, for example, it has created four preset voices in collaboration with voice actors.", - "index": 8, - "keyphrases": [], - "weight": 0 - }, - { - "text": "GPT-4o will not impersonate or generate other people’s voices. When OpenAI first introduced GPT-4o, the company faced a backlash over its use of a voice called “Sky,” which sounded a lot like the actress Scarlett Johansson.", - "index": 9, - "keyphrases": [ - "sky sounded" - ], - "weight": 0 - } - ], - "keyphrases": [ - { - "sentences": "1,7", - "keyphrase": "feedback company", - "words": 2, - "weight": 150 - }, - { - "sentences": "9", - "keyphrase": "sky sounded", - "words": 2, - "weight": 100 - }, - { - "sentences": "16", - "keyphrase": "line openai", - "words": 2, - "weight": 100 - } - ], - "sentences": [ - "The voice mode is powered by OpenAI’s new GPT-4o model, which combines voice, text, and vision capabilities.", - "To gather feedback, the company is initially launching the chatbot to a “small group of users” paying for ChatGPT Plus, but it says it will make the bot available to all ChatGPT Plus subscribers this fall. A ChatGPT Plus subscription costs $20 a month.", - "OpenAI says it will notify customers who are part of the first rollout wave in the ChatGPT app and provide instructions on how to use the new model.", - "The new voice feature, which was announced in May , is being launched a month later than originally planned because the company said it needed more time to improve safety features, such as the model’s ability to detect and refuse unwanted content.", - "The company also said it was preparing its infrastructure to offer real-time responses to millions of users.", - "OpenAI says it has tested the model’s voice capabilities with more than 100 external red-teamers, who were tasked with probing the model for flaws.", - "These testers spoke a total of 45 languages and represented 29 countries, according to OpenAI.", - "The company says it has put several safety mechanisms in place.", - "In a move that aims to prevent the model from being used to create audio deepfakes, for example, it has created four preset voices in collaboration with voice actors.", - "GPT-4o will not impersonate or generate other people’s voices. When OpenAI first introduced GPT-4o, the company faced a backlash over its use of a voice called “Sky,” which sounded a lot like the actress Scarlett Johansson.", - "Johansson released a statement saying the company had reached out to her for permission to use her voice for the model, which she declined.", - "She said she was shocked to hear a voice “eerily similar” to hers in the model’s demo.", - "OpenAI has denied that the voice is Johansson’s but has paused the use of Sky.", - "The company is also embroiled in several lawsuits over alleged copyright infringement.", - "OpenAI says it has adopted filters that recognize and block requests to generate music or other copyrighted audio.", - "OpenAI also says it has applied the same safety mechanisms it uses in its text-based model to GPT-4o to prevent it from breaking laws and generating harmful content.", - "Down the line, OpenAI plans to include more advanced features, such as video and screen sharing, which could make the assistant more useful.", - "In its May demo, employees pointed their phone cameras at a piece of paper and asked the AI model to help them solve math equations.", - "They also shared their computer screens and asked the model to help them solve coding problems.", - "OpenAI says these features will not be available now but at an unspecified later date." - ] -} \ No newline at end of file diff --git a/test/data/v_org_pdf_1706_03762.json b/test/data/v_org_pdf_1706_03762.json index 1b566cb..10a2436 100644 --- a/test/data/v_org_pdf_1706_03762.json +++ b/test/data/v_org_pdf_1706_03762.json @@ -3,191 +3,233 @@ "author": "", "title": " Attention Is All You Need ", "format": "pdf", - "word_count": 8065, + "word_count": 8050, "topSentences": [ { "text": "Advances in Neural Information Processing Systems 28 , pages 2440–2448. Curran Associates, Inc., 2015. [35] Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems , pages 3104–3112, 2014. [36] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna.", - "index": 286, + "index": 299, "keyphrases": [ + "sequence sequence learning", "sequence sequence", - "sequence learning", - "convolutional neural network basic" + "jian sun deep residual learning", + "jeff dean large neural network sparsely mixture of experts", + "recurrent convolutional neural network", + "geoffrey hinton jeff dean large neural network sparsely" ], - "weight": 75 + "weight": 32 }, { - "text": "This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as [38, 2, 9]. • The encoder contains self-attention layers.", - "index": 99, + "text": "Most competitive neural sequence transduction models have an encoder-decoder structure [ 5 , 2 , 35 ]. Here, the encoder maps an input sequence of symbol representations ( x 1 , ..., x n ) to a sequence of continuous representations z = ( z 1 , ..., z n ) .", + "index": 51, "keyphrases": [ - "sequence sequence" + "sequence sequence learning", + "sequence sequence", + "jeff dean large neural network sparsely mixture of experts", + "recurrent convolutional neural network", + "geoffrey hinton jeff dean large neural network sparsely" ], - "weight": 66 + "weight": 28 }, { - "text": "In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations", - "index": 135, + "text": "On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2) outperforms the best previously reported models (including ensembles) by more than 2 .", + "index": 195, "keyphrases": [ - "sequence sequence" + "english french translation", + "english french", + "best knowledge transformer", + "transformer trained significantly faster", + "german translation", + "designed implemented transformer crucially involved", + "transformer based solely", + "best previously reported", + "bleu worse best", + "big described line", + "big trained 300", + "improving existing best" ], - "weight": 62 + "weight": 28 }, { - "text": "Multi-task sequence to sequence learning.", - "index": 266, + "text": "Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [ 2 , 19 ]. In all but a few cases [ 27 ], however, such attention mechanisms are used in conjunction with a recurrent network.", + "index": 40, "keyphrases": [ + "sequence sequence learning", "sequence sequence", - "sequence learning" + "integral compelling" ], - "weight": 60 + "weight": 27 }, { - "text": "Convolutional sequence to sequence learning.", - "index": 249, + "text": "For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles.", + "index": 242, "keyphrases": [ - "sequence sequence", - "sequence learning" + "english french translation", + "english french", + "best knowledge transformer", + "transformer trained significantly faster", + "german translation", + "designed implemented transformer crucially involved", + "transformer based solely", + "best previously reported", + "gradient flow recurrent nets", + "significantly time train", + "recurrent convolutional neural network", + "big trained 300" ], - "weight": 58 + "weight": 22 }, { - "text": "7 Conclusion In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.", - "index": 228, + "text": "Convolutional sequence to sequence learning.", + "index": 262, "keyphrases": [ + "sequence sequence learning", "sequence sequence", - "best knowledge transformer" + "jian sun deep residual learning" ], - "weight": 56 + "weight": 22 }, { - "text": "Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37].", - "index": 216, + "text": "Google Brain noam@google.com", + "index": 4, "keyphrases": [ - "sequence sequence" + "google com" ], - "weight": 55 + "weight": 20 }, { - "text": "In contrast to RNN sequence-to-sequence models [ 37 ], the Transformer outperforms the BerkeleyParser [29] even when training only on the WSJ training set of 40K sentences.", - "index": 227, + "text": "lukaszkaiser@google.com", + "index": 13, "keyphrases": [ - "sequence sequence" + "google com" ], - "weight": 55 + "weight": 20 }, { - "text": "Most competitive neural sequence transduction models have an encoder-decoder structure [ 5 , 2 , 35 ]. Here, the encoder maps an input sequence of symbol representations ( x , ..., x ) to a sequence of continuous representations z = ( z , ..., z ) .", - "index": 48, + "text": "7 Conclusion In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.", + "index": 241, "keyphrases": [ - "sequence sequence" + "sequence sequence learning", + "sequence sequence", + "best knowledge transformer", + "transformer trained significantly faster", + "designed implemented transformer crucially involved", + "transformer based solely", + "architecture stacked self", + "structured self attentive" ], - "weight": 51 + "weight": 20 }, { - "text": "Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.", - "index": 18, + "text": "Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t − 1 and the input for position t .", + "index": 36, "keyphrases": [ - "train ppl bleu" + "sequence sequence learning", + "sequence sequence", + "previous hidden state" ], - "weight": 50 + "weight": 19 } ], "keyphrases": [ { - "sentences": "4,12,2,28,3,6,7,9,13,5,8,14,233", + "sentences": "160,175,20,199,242,195,211,214,201,21,158,232", + "keyphrase": "english french translation", + "words": 3, + "weight": 129 + }, + { + "sentences": "299,51,229,262,279,36,37,40,141,105,240,118,142,152,170,173,241,251,265,290", + "keyphrase": "sequence sequence learning", + "words": 3, + "weight": 125 + }, + { + "sentences": "31,2,4,12,30,3,6,7,9,13,5,8,15,246", "keyphrase": "google com", "words": 2, - "weight": 1012 + "weight": 103 }, { - "sentences": "286,249,266,48,135,99,216,227,228", + "sentences": "299,36,37,40,43,51,141,105,229,240,262,279,118,142,152,241", "keyphrase": "sequence sequence", "words": 2, - "weight": 756 + "weight": 92 }, { - "sentences": "46,19,199,228", - "keyphrase": "best knowledge transformer", - "words": 3, - "weight": 316 - }, - { - "sentences": "19,186,229", - "keyphrase": "french translation", + "sentences": "160,19,20,175,199,201,242,21,158,195,211,214,232", + "keyphrase": "english french", "words": 2, - "weight": 300 + "weight": 89 }, { - "sentences": "203,18", - "keyphrase": "train ppl bleu", - "words": 3, - "weight": 250 + "sentences": "265,170,173,175,182,251,262,279,299,288,290,304", + "keyphrase": "jian sun deep residual learning", + "words": 5, + "weight": 75 }, { - "sentences": "287,199", - "keyphrase": "inception architecture computer vision", + "sentences": "49,18,20,25,45,188,201,195,211,212,221,241,242", + "keyphrase": "best knowledge transformer", "words": 3, - "weight": 225 + "weight": 71 }, { - "sentences": "246,249,266,286,252", - "keyphrase": "sequence learning", - "words": 2, - "weight": 225 + "sentences": "242,18,19,25,42,45,49,82,151,160,163,167,188,201,195,211,212,228,241", + "keyphrase": "transformer trained significantly faster", + "words": 4, + "weight": 70 }, { - "sentences": "222,222", - "keyphrase": "kaiser el al", + "sentences": "232,20,195,211,214,199,242,158,175", + "keyphrase": "german translation", "words": 2, - "weight": 200 - }, - { - "sentences": "103,253", - "keyphrase": "information flow", - "words": 2, - "weight": 150 + "weight": 66 }, { - "sentences": "233", - "keyphrase": "https github", - "words": 2, - "weight": 150 + "sentences": "25,18,27,26,45,49,82,188,201,195,211,212,241,242,345", + "keyphrase": "designed implemented transformer crucially involved", + "words": 5, + "weight": 57 } ], "sentences": [ "Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works.", "Attention Is All You Need", - "Ashish Vaswani Google Brain", - "avaswani@google.com Noam Shazeer", + "Ashish Vaswani ∗ Google Brain", + "avaswani@google.com Noam Shazeer ∗", "Google Brain noam@google.com", - "Niki Parmar Google Research", - "nikip@google.com Jakob Uszkoreit", + "Niki Parmar ∗ Google Research", + "nikip@google.com Jakob Uszkoreit ∗", "Google Research usz@google.com", - "Llion Jones Google Research", - "llion@google.com Aidan N. Gomez", + "Llion Jones ∗ Google Research", + "llion@google.com Aidan N. Gomez ∗ †", "University of Toronto", "aidan@cs.toronto.edu", - "Łukasz Kaiser Google Brain", + "Łukasz Kaiser ∗ Google Brain", "lukaszkaiser@google.com", - "Illia Polosukhin illia.polosukhin@gmail.com Abstract", + "Illia Polosukhin ∗ ‡", + "illia.polosukhin@gmail.com Abstract", "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder.", "The best performing models also connect the encoder and decoder through an attention mechanism.", "We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.", "Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.", "Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.", "We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.", - "Equal contribution. Listing order is random.", + "∗ Equal contribution.", + "Listing order is random.", "Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea.", "Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work.", "Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail.", "Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor.", "Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations.", "Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.", - "Work performed while at Google Brain. Work performed while at Google Research. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.", + "† Work performed while at Google Brain.", + "‡ Work performed while at Google Research. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.", "arXiv:1706.03762v7 [cs.", "CL] 2 Aug 2023 [ 1 ] 1 Introduction", "Recurrent neural networks, long short-term memory [ 13 ] and gated recurrent [ 7 ] neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation [ 35 , 2 , 5 ]. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures [38, 24, 15].", "Recurrent models typically factor computation along the symbol positions of the input and output sequences.", - "Aligning the positions to steps in computation time, they generate a sequence of hidden states h , as a function of the previous hidden state h and the input for position t .", + "Aligning the positions to steps in computation time, they generate a sequence of hidden states h t , as a function of the previous hidden state h t − 1 and the input for position t .", "This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.", "Recent work has achieved significant improvements in computational efficiency through factorization tricks [ 21 ] and conditional computation [ 32 ], while also improving model performance in case of the latter.", "The fundamental constraint of sequential computation, however, remains.", @@ -202,8 +244,8 @@ "End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks [34].", "To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequencealigned RNNs or convolution.", "In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as [17, 18] and [9]. 3 Model Architecture", - "Most competitive neural sequence transduction models have an encoder-decoder structure [ 5 , 2 , 35 ]. Here, the encoder maps an input sequence of symbol representations ( x , ..., x ) to a sequence of continuous representations z = ( z , ..., z ) .", - "Given z , the decoder then generates an output sequence ( y , ..., y ) of symbols one element at a time.", + "Most competitive neural sequence transduction models have an encoder-decoder structure [ 5 , 2 , 35 ]. Here, the encoder maps an input sequence of symbol representations ( x 1 , ..., x n ) to a sequence of continuous representations z = ( z 1 , ..., z n ) .", + "Given z , the decoder then generates an output sequence ( y 1 , ..., y m ) of symbols one element at a time.", "At each step the model is auto-regressive [10], consuming the previously generated symbols as additional input when generating the next.", "2 [ 2 ] Figure 1: The Transformer - model architecture.", "The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively.", @@ -212,7 +254,7 @@ "The first is a multi-head self-attention mechanism, and the second is a simple, positionwise fully connected feed-forward network.", "We employ a residual connection [ 11 ] around each of the two sub-layers, followed by layer normalization [ 1 ]. That is, the output of each sub-layer is", "LayerNorm( x + Sublayer( x )) , where Sublayer( x ) is the function implemented by the sub-layer itself.", - "To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d = 512 .", + "To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension d model = 512 .", "Decoder: The decoder is also composed of a stack of N = 6 identical layers.", "In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack.", "Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.", @@ -224,32 +266,35 @@ "of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.", "3.2.1 Scaled Dot-Product Attention", "We call our particular attention \"Scaled Dot-Product Attention\" (Figure 2).", - "The input consists of queries and keys of dimension d , and values of dimension d .", - "We compute the dot products of the query with all keys, divide each by √ d , and apply a softmax function to obtain the weights on the values.", + "The input consists of queries and keys of dimension d k , and values of dimension d v .", + "We compute the dot products of the query with all keys, divide each by √ d k , and apply a softmax function to obtain the weights on the values.", "In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix Q .", "The keys and values are also packed together into matrices K and V .", "We compute the matrix of outputs as:", - "Attention( Q, K, V ) = softmax( QK", - "√ d ) V (1) The two most commonly used attention functions are additive attention [ 2 ], and dot-product (multiplicative) attention.", - "Dot-product attention is identical to our algorithm, except for the scaling factor of", + "Attention( Q, K, V ) = softmax( QK T", + "√ d k ) V (1) The two most commonly used attention functions are additive attention [ 2 ], and dot-product (multiplicative) attention.", + "Dot-product attention is identical to our algorithm, except for the scaling factor of 1 √ d k", ". Additive attention computes the compatibility function using a feed-forward network with a single hidden layer.", "While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.", - "While for small values of d the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d [ 3 ]. We suspect that for large values of", - "d , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients .", - "To counteract this effect, we scale the dot products by", + "While for small values of d k the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of d k [ 3 ]. We suspect that for large values of", + "d k , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients 4 .", + "To counteract this effect, we scale the dot products by 1 √ d k", ". 3.2.2 Multi-Head Attention", - "Instead of performing a single attention function with d -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d , d and d dimensions, respectively.", - "On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d -dimensional", + "Instead of performing a single attention function with d model -dimensional keys, values and queries, we found it beneficial to linearly project the queries, keys and values h times with different, learned linear projections to d k , d k and d v dimensions, respectively.", + "On each of these projected versions of queries, keys and values we then perform the attention function in parallel, yielding d v -dimensional 4 To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1 .", + "Then their dot product, q · k = P d k i =1 q i k i , has mean 0 and variance d k .", "4 [ 4 ] output values.", "These are concatenated and once again projected, resulting in the final values, as depicted in Figure 2.", "Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions.", "With a single attention head, averaging inhibits this.", - "MultiHead( Q, K, V ) = Concat(head , ..., head ) W", - "where head = Attention( QW , KW , V W )", - "Where the projections are parameter matrices W ∈ R , W ∈ R , W ∈ R", - "and W ∈ R . In this work we employ h = 8 parallel attention layers, or heads.", + "MultiHead( Q, K, V ) = Concat(head 1 , ..., head h ) W O", + "where head i = Attention( QW Q i , KW K i , V W V i )", + "Where the projections are parameter matrices W Q i ∈ R d model × d k , W K i ∈ R d model × d k , W V i ∈ R d model × d v", + "and W O ∈ R hd v × d model .", + "In this work we employ h = 8 parallel attention layers, or heads.", "For each of these we use", - "d = d = d /h = 64 . Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.", + "d k = d v = d model /h = 64 .", + "Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.", "3.2.3 Applications of Attention in our Model", "The Transformer uses multi-head attention in three different ways: • In \"encoder-decoder attention\" layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder.", "This allows every position in the decoder to attend over all positions in the input sequence.", @@ -262,35 +307,35 @@ "See Figure 2. 3.3 Position-wise Feed-Forward Networks", "In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically.", "This consists of two linear transformations with a ReLU activation in between.", - "FFN( x ) = max(0 , xW + b ) W + b (2) While the linear transformations are the same across different positions, they use different parameters from layer to layer.", + "FFN( x ) = max(0 , xW 1 + b 1 ) W 2 + b 2 (2) While the linear transformations are the same across different positions, they use different parameters from layer to layer.", "Another way of describing this is as two convolutions with kernel size 1.", - "The dimensionality of input and output is d = 512 , and the inner-layer has dimensionality", - "d = 2048 . 3.4 Embeddings and Softmax", - "Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d .", + "The dimensionality of input and output is d model = 512 , and the inner-layer has dimensionality", + "d f f = 2048 . 3.4 Embeddings and Softmax", + "Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension d model .", "We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities.", - "In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [ 30 ]. In the embedding layers, we multiply those weights by √ d .", + "In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to [ 30 ]. In the embedding layers, we multiply those weights by √ d model .", "5 [ 5 ] Table 1: Maximum path lengths, per-layer complexity and minimum number of sequential operations for different layer types.", - "n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention. Layer Type Complexity per Layer Sequential Maximum Path Length Operations Self-Attention O ( n · d ) O (1) O (1)", - "Recurrent O ( n · d ) O ( n ) O ( n )", - "Convolutional O ( k · n · d ) O (1) O ( log ( n ))", + "n is the sequence length, d is the representation dimension, k is the kernel size of convolutions and r the size of the neighborhood in restricted self-attention. Layer Type Complexity per Layer Sequential Maximum Path Length Operations Self-Attention O ( n 2 · d ) O (1) O (1)", + "Recurrent O ( n · d 2 ) O ( n ) O ( n )", + "Convolutional O ( k · n · d 2 ) O (1) O ( log k ( n ))", "Self-Attention (restricted) O ( r · n · d ) O (1) O ( n/r )", "3.5 Positional Encoding", "Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence.", "To this end, we add \"positional encodings\" to the input embeddings at the bottoms of the encoder and decoder stacks.", - "The positional encodings have the same dimension d", + "The positional encodings have the same dimension d model", "as the embeddings, so that the two can be summed.", "There are many choices of positional encodings, learned and fixed [9].", "In this work, we use sine and cosine functions of different frequencies:", - "P E = sin ( pos/ 10000 )", - "P E = cos ( pos/ 10000 )", + "P E ( pos, 2 i ) = sin ( pos/ 10000 2 i/d model )", + "P E ( pos, 2 i +1) = cos ( pos/ 10000 2 i/d model )", "where pos is the position and i is the dimension.", "That is, each dimension of the positional encoding corresponds to a sinusoid.", "The wavelengths form a geometric progression from 2 π to 10000 · 2 π .", - "We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k , P E can be represented as a linear function of", - "P E . We also experimented with using learned positional embeddings [ 9 ] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)).", + "We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k , P E pos + k can be represented as a linear function of", + "P E pos . We also experimented with using learned positional embeddings [ 9 ] instead, and found that the two versions produced nearly identical results (see Table 3 row (E)).", "We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training. 4 Why Self-Attention", "In this section we compare various aspects of self-attention layers to the recurrent and convolutional layers commonly used for mapping one variable-length sequence of symbol representations", - "( x , ..., x ) to another sequence of equal length ( z , ..., z ) , with x , z ∈ R , such as a hidden layer in a typical sequence transduction encoder or decoder.", + "( x 1 , ..., x n ) to another sequence of equal length ( z 1 , ..., z n ) , with x i , z i ∈ R d , such as a hidden layer in a typical sequence transduction encoder or decoder.", "Motivating our use of self-attention we consider three desiderata.", "One is the total computational complexity per layer.", "Another is the amount of computation that can be parallelized, as measured by the minimum number of sequential operations required.", @@ -316,24 +361,31 @@ "We trained the base models for a total of 100,000 steps or 12 hours.", "For our big models,(described on the bottom line of table 3), step time was 1.0 seconds.", "The big models were trained for 300,000 steps (3.5 days).", - "5.3 Optimizer We used the Adam optimizer [ 20 ] with β = 0 .", - "9 , β = 0 . 98 and ϵ = 10 .", + "5.3 Optimizer We used the Adam optimizer [ 20 ] with β 1 = 0 .", + "9 , β 2 = 0 . 98 and ϵ = 10 − 9 .", "We varied the learning rate over the course of training, according to the formula:", - "lrate = d · min( step _ num , step _ num · warmup _ steps ) (3) This corresponds to increasing the learning rate linearly for the first warmup _ steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number.", + "lrate = d − 0 . 5 model · min( step _ num − 0 .", + "5 , step _ num · warmup _ steps − 1 .", + "5 ) (3) This corresponds to increasing the learning rate linearly for the first warmup _ steps training steps, and decreasing it thereafter proportionally to the inverse square root of the step number.", "We used warmup _ steps = 4000 .", "5.4 Regularization We employ three types of regularization during training: 7 [ 7 ] Table 2: The Transformer achieves better BLEU scores than previous state-of-the-art models on the English-to-German and English-to-French newstest2014 tests at a fraction of the training cost. Model BLEU Training Cost (FLOPs) EN-DE EN-FR EN-DE EN-FR ByteNet [18] 23.75 Deep-Att + PosUnk [39] 39.2 1 .", - "0 · 10 GNMT + RL [38] 24.6 39.92 2 .", - "3 · 10 1 . 4 · 10 ConvS2S [9] 25.16 40.46 9 .", - "6 · 10 1 . 5 · 10 MoE [32] 26.03 40.56 2 .", - "0 · 10 1 . 2 · 10 Deep-Att + PosUnk Ensemble [39] 40.4 8 .", - "0 · 10 GNMT + RL Ensemble [38] 26.30 41.16 1 .", - "8 · 10 1 . 1 · 10 ConvS2S Ensemble [9] 26.36 41.29 7 .", - "7 · 10 1 . 2 · 10 Transformer (base model) 27.3 38.1 3 .", - "3 · 10 Transformer (big) 28.4 41.8 2 .", - "3 · 10 Residual Dropout We apply dropout [ 33 ] to the output of each sub-layer, before it is added to the sub-layer input and normalized.", + "0 · 10 20 GNMT + RL [38] 24.6 39.92 2 .", + "3 · 10 19 1 . 4 · 10 20", + "ConvS2S [9] 25.16 40.46 9 .", + "6 · 10 18 1 . 5 · 10 20", + "MoE [32] 26.03 40.56 2 .", + "0 · 10 19 1 . 2 · 10 20", + "Deep-Att + PosUnk Ensemble [39] 40.4 8 .", + "0 · 10 20 GNMT + RL Ensemble [38] 26.30 41.16 1 .", + "8 · 10 20 1 . 1 · 10 21", + "ConvS2S Ensemble [9] 26.36 41.29 7 .", + "7 · 10 19 1 . 2 · 10 21", + "Transformer (base model) 27.3 38.1 3 .", + "3 · 10 18 Transformer (big) 28.4 41.8 2 .", + "3 · 10 19 Residual Dropout We apply dropout [ 33 ] to the output of each sub-layer, before it is added to the sub-layer input and normalized.", "In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks.", "For the base model, we use a rate of", - "P = 0 . 1 . Label Smoothing During training, we employed label smoothing of value ϵ = 0 .", + "P drop = 0 . 1 . Label Smoothing During training, we employed label smoothing of value ϵ ls = 0 .", "1 [ 36 ]. This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.", "6 Results 6.1 Machine Translation", "On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2) outperforms the best previously reported models (including ensembles) by more than 2 .", @@ -342,7 +394,7 @@ "Training took 3 . 5 days on 8 P100 GPUs.", "Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models. On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41 .", "0 , outperforming all of the previously published single models, at less than 1 / 4 the training cost of the previous state-of-the-art model.", - "The Transformer (big) model trained for English-to-French used dropout rate P = 0 .", + "The Transformer (big) model trained for English-to-French used dropout rate P drop = 0 .", "1 , instead of 0 . 3 .", "For the base models, we used a single model obtained by averaging the last 5 checkpoints, which were written at 10-minute intervals.", "For the big models, we averaged the last 20 checkpoints.", @@ -350,20 +402,20 @@ "6 [ 38 ]. These hyperparameters were chosen after experimentation on the development set.", "We set the maximum output length during inference to input length + 50 , but terminate early when possible [38].", "Table 2 summarizes our results and compares our translation quality and training costs to other model architectures from the literature.", - "We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU .", + "We estimate the number of floating point operations used to train a model by multiplying the training time, the number of GPUs used, and an estimate of the sustained single-precision floating-point capacity of each GPU 5 .", "6.2 Model Variations", - "To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the", + "To evaluate the importance of different components of the Transformer, we varied our base model in different ways, measuring the change in performance on English-to-German translation on the 5 We used values of 2.8, 3.7, 6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively.", "8 [ 8 ] Table 3: Variations on the Transformer architecture.", "Unlisted values are identical to those of the base model.", "All metrics are on the English-to-German translation development set, newstest2013.", "Listed perplexities are per-wordpiece, according to our byte-pair encoding, and should not be compared to per-word perplexities.", - "N d d h d d P ϵ train PPL BLEU params steps (dev) (dev) × 10", + "N d model d ff h d k d v P drop ϵ ls train PPL BLEU params steps (dev) (dev) × 10 6", "base 6 512 2048 8 64 64 0.1 0.1 100K 4.92 25.8 65 (A) 1 512 512 5.29 24.9 4 128 128 5.00 25.5 16 32 32 4.91 25.8 32 16 16 5.01 25.4 (B) 16 5.16 25.1 58 32 5.01 25.4 60 (C) 2 6.11 23.7 36 4 5.19 25.3 50 8 4.88 25.5 80 256 32 32 5.75 24.5 28 1024 128 128 4.66 26.0 168 1024 5.12 25.4 53 4096 4.75 26.2 90 (D) 0.0 5.77 24.6 0.2 4.95 25.5 0.0 4.67 25.3 0.2 5.47 25.7 (E) positional embedding instead of sinusoids 4.92 25.7 big 6 1024 4096 16 0.3 300K 4.33 26.4 213 development set, newstest2013.", "We used beam search as described in the previous section, but no checkpoint averaging.", "We present these results in Table 3.", "In Table 3 rows (A), we vary the number of attention heads and the attention key and value dimensions, keeping the amount of computation constant, as described in Section 3.2.2.", "While single-head attention is 0.9 BLEU worse than the best setting, quality also drops off with too many heads.", - "In Table 3 rows (B), we observe that reducing the attention key size d hurts model quality.", + "In Table 3 rows (B), we observe that reducing the attention key size d k hurts model quality.", "This suggests that determining compatibility is not easy and that a more sophisticated compatibility function than dot product may be beneficial.", "We further observe in rows (C) and (D) that, as expected, bigger models are better, and dropout is very helpful in avoiding over-fitting.", "In row (E) we replace our sinusoidal positional encoding with learned positional embeddings [ 9 ], and observe nearly identical results to the base model.", @@ -371,7 +423,7 @@ "To evaluate if the Transformer can generalize to other tasks we performed experiments on English constituency parsing.", "This task presents specific challenges: the output is subject to strong structural constraints and is significantly longer than the input.", "Furthermore, RNN sequence-to-sequence models have not been able to attain state-of-the-art results in small-data regimes [37].", - "We trained a 4-layer transformer with d = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [ 25 ], about 40K training sentences.", + "We trained a 4-layer transformer with d model = 1024 on the Wall Street Journal (WSJ) portion of the Penn Treebank [ 25 ], about 40K training sentences.", "We also trained it in a semi-supervised setting, using the larger high-confidence and BerkleyParser corpora from with approximately 17M sentences [ 37 ]. We used a vocabulary of 16K tokens for the WSJ only setting and a vocabulary of 32K tokens for the semi-supervised setting.", "We performed only a small number of experiments to select the dropout, both attention and residual (section 5.4), learning rates and beam size on the Section 22 development set, all other parameters remained unchanged from the English-to-German base translation model.", "During inference, we 9 [ 9 ] Table 4: The Transformer generalizes well to English constituency parsing (Results are on Section 23 of WSJ)", @@ -512,11 +564,6 @@ "what we are missing ,", "in my opinion . Figure 5: Many of the attention heads exhibit behaviour that seems related to the structure of the sentence.", "We give two such examples above, from two different heads from the encoder self-attention at layer 5 of 6.", - "The heads clearly learned to perform different tasks.", - "15 [ 15 ] Footnotes ∗ ∗ ∗ ∗ ∗ ∗ † ∗ ∗ ‡ ∗ † ‡ t t − 1 1 n 1 n 1 m model k v k T k 1 √ d k k k k 4 1 √ d k model k k v v 4 To illustrate why the dot products get large, assume that the components of q and k are independent random variables with mean 0 and variance 1 .", - "Then their dot product, q · k = P d k i =1 q i k i , has mean 0 and variance d k .", - "1 h O i Q i K i V i Q i d model × d k K i d model × d k V i d model × d v O hd v × d model k v model 1 1 2 2 model f f model model 2 2 2 k model ( pos, 2 i ) 2 i/d model ( pos, 2 i +1) 2 i/d model pos + k pos 1 n 1 n i i d k 2 1 2 − 9 − 0 .", - "5 model − 0 . 5 − 1 .", - "5 20 19 20 18 20 19 20 20 20 21 19 21 18 19 drop ls drop 5 5 We used values of 2.8, 3.7, 6.0 and 9.5 TFLOPS for K80, K40, M40 and P100, respectively." + "The heads clearly learned to perform different tasks." ] } \ No newline at end of file diff --git a/test/reply-to-json.test.js b/test/reply-to-json.test.js new file mode 100644 index 0000000..434599d --- /dev/null +++ b/test/reply-to-json.test.js @@ -0,0 +1,37 @@ +import { test, expect } from "vitest"; +import {convertLanguageReplyToJSON} from "../index.js" + +test("convertLanguageReplyToJSON", async () => { + +// Example usage: +const sampleText = ` +Some header text + +1. First question +2. Second question +- Bullet point +* Star point +• Special bullet + +Some footer text +`; + +const result = convertLanguageReplyToJSON(sampleText); +console.log(result); + +// Example with custom key +const customText = ` + +1) Item one +2) Item two +- Item three + +`; + +const customResult = convertLanguageReplyToJSON(customText, 'items'); +console.log(customResult); + + expect(customResult.length==3).toBeTruthy(); +}); + + diff --git a/test/seektopic-keyphrase.test.js b/test/seektopic-keyphrase.test.js index e1270c1..492e29b 100644 --- a/test/seektopic-keyphrase.test.js +++ b/test/seektopic-keyphrase.test.js @@ -1,5 +1,5 @@ import { test, expect } from "vitest"; -import { extractSEEKTOPIC, extract } from "../index.js"; +import { extractSEEKTOPIC, extractContent } from "../index.js"; import fs from "fs"; //load models or pass them in @@ -15,15 +15,15 @@ var typosModel = JSON.parse( test("get top sentences specific to a query", async () => { let urls = [ "https://www.youtube.com/watch?v=T_IdLTofTUU", - "https://www.technologyreview.com/2024/07/30/1095489/openai-has-released-a-new-chatgpt-bot-that-you-can-talk-to/", "https://www.youtube.com/watch?v=OsW_kdOV6c8", "https://arxiv.org/pdf/1706.03762", - "https://ai-research-agent.github.io/ai-research-agent/docs", + "https://airesearch.js.org", + ]; for (var url of urls){ - let extraction = await extract(url); + let extraction = await extractContent(url); if(!extraction || !extraction.html ) continue; @@ -34,7 +34,6 @@ test("get top sentences specific to a query", async () => { typosModel, heavyWeightQuery: "extract word pairings", limitTopSentences: 10, - removeHTML: true, }) ); diff --git a/test/topic-distribution.test.js b/test/topic-distribution.test.js index 2ad4cad..6ee1e3a 100644 --- a/test/topic-distribution.test.js +++ b/test/topic-distribution.test.js @@ -191,7 +191,7 @@ test('topic distribution Test', () => { {author, date, title, source} Type: object # - extractNamedEntity(author) → {object} + extractHumanName(author) → {object} Validates human name from author string to check against common list of first names, last names, name affixes, and organizations to infer if it should be reversed starting by author last name in cite, since organizations are not reversed. Checks against common salutations, middle parts, and titles to properly format for citation in Last, First Middle format. Author type is ["single", "two-author", "more-than-two", "organization"] where organization is a non-human name that is not reversed. Parameters: Name Type Description @@ -202,7 +202,7 @@ test('topic distribution Test', () => { {author_cite, author_short, author_type} Type: object # - extractNamedEntityParts(input) → {Object} + extractHumanNameParts(input) → {Object} Parses a full name into its component parts: Title, Firstname, Prefix, Middle, Lastname, Honorific, Alias https://en.wikipedia.org/wiki/List_of_family_name_affixes Parameters: Name Type Description diff --git a/test/trafilatura-extractor/readability.js b/test/trafilatura-extractor/readability.js index dde47fb..02d023f 100644 --- a/test/trafilatura-extractor/readability.js +++ b/test/trafilatura-extractor/readability.js @@ -19,7 +19,7 @@ * @example * var url = "https://en.wikipedia.org/wiki/David_Hilbert"; * var html = await (await fetch(url)).text(); - * var content = extractContentHTML(html); + * var content = extractMainContentFromHTML(html); */ export function Readability(doc, options) { // In some older versions, people passed a URI as the first argument. Cope: diff --git a/test/trafilatura-extractor/readability_lxml.js b/test/trafilatura-extractor/readability_lxml.js index 885f880..1b21fb8 100644 --- a/test/trafilatura-extractor/readability_lxml.js +++ b/test/trafilatura-extractor/readability_lxml.js @@ -18,7 +18,7 @@ import { JSDOM } from 'jsdom'; * Based on Mozilla (2015), Arc90 (2010) [github](https://github.com/mozilla/readability) * @see {@link https://github.com/scrapinghub/article-extraction-benchmark?tab=readme-ov-file#results|Article extraction benchmark} */ -export function extractContentHTML(html, options = {}) { +export function extractMainContentFromHTML(html, options = {}) { // Initialize configuration options const minContentLength = options.minContentLength || 140; const minScore = options.minScore || 20; diff --git a/web-app/.env.example b/web-app/.env.example new file mode 100644 index 0000000..963256d --- /dev/null +++ b/web-app/.env.example @@ -0,0 +1,3 @@ +PUBLIC_PROXY = +PUBLIC_SEARCH_URL = +PUBLIC_GROQ_API_KEY= \ No newline at end of file diff --git a/web-app/package.json b/web-app/package.json index 9ebcc2c..87851df 100644 --- a/web-app/package.json +++ b/web-app/package.json @@ -5,15 +5,8 @@ "dev": "vite dev --host --port 5173", "build": "vite build", "preview": "vite preview", - "prod": "vite build; wrangler deploy; wrangler tail", - "package": "svelte-kit sync && svelte-package && publint", - "prepublishOnly": "npm run package", - "check": "svelte-kit sync && svelte-check --tsconfig ./jsconfig.json", - "check:watch": "svelte-kit sync && svelte-check --tsconfig ./jsconfig.json --watch", - "docs": " jsdoc -c jsdoc.json src/keyphrases/* src/topic-distribution/* src/autocomplete/* src/match/* src/autocomplete/* src/extractor/**/* src/tokenize/* src/wiki-api/* src/extractor/url-to-content/* src/similarity/* src/search-web/* -t node_modules/better-docs/ ; bun run open" - }, - "peerDependencies": { - "svelte": "^4.0.0" + "prod": "bunx --max-old-space-size=8192 vite build; wrangler deploy; wrangler tail", + "package": "svelte-kit sync && svelte-package && publint" }, "devDependencies": { "@sveltejs/adapter-auto": "^3.2.4", @@ -28,7 +21,7 @@ "tslib": "^2.6.3", "typescript": "^5.5.4", "vite": "^5.4.1", - "wrangler": "^3.81.0" + "wrangler": "3.57" }, "type": "module", "dependencies": { @@ -42,4 +35,4 @@ "tailwindcss": "^3.4.10", "three": "^0.168.0" } -} +} \ No newline at end of file diff --git a/web-app/pnpm-lock.yaml b/web-app/pnpm-lock.yaml new file mode 100644 index 0000000..048075b --- /dev/null +++ b/web-app/pnpm-lock.yaml @@ -0,0 +1,4599 @@ +lockfileVersion: '9.0' + +settings: + autoInstallPeers: true + excludeLinksFromLockfile: false + +importers: + + .: + dependencies: + autoprefixer: + specifier: ^10.4.20 + version: 10.4.20(postcss@8.4.47) + lucide-svelte: + specifier: ^0.454.0 + version: 0.454.0(svelte@5.1.4) + postcss: + specifier: ^8.4.47 + version: 8.4.47 + svelte: + specifier: ^5.1.4 + version: 5.1.4 + svelte-splitpanes: + specifier: ^8.0.8 + version: 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wrap-ansi@7.0.0: + dependencies: + ansi-styles: 4.3.0 + string-width: 4.2.3 + strip-ansi: 6.0.1 + + wrap-ansi@8.1.0: + dependencies: + ansi-styles: 6.2.1 + string-width: 5.1.2 + strip-ansi: 7.1.0 + + ws@6.2.0: + dependencies: + async-limiter: 1.0.1 + + ws@8.18.0: {} + + xxhash-wasm@1.0.2: {} + + yaml@2.6.0: {} + + youch@3.3.4: + dependencies: + cookie: 0.7.2 + mustache: 4.2.0 + stacktracey: 2.1.8 + + zimmerframe@1.1.2: {} + + zod@3.23.8: {} diff --git a/web-app/src/app.html b/web-app/src/app.html index 9e75329..eb7ca37 100644 --- a/web-app/src/app.html +++ b/web-app/src/app.html @@ -15,6 +15,22 @@ %sveltekit.head% + + + + + + + + + +

    %sveltekit.body%
    + + diff --git a/web-app/src/components/Home/ActionsPanel.svelte b/web-app/src/components/Home/ActionsPanel.svelte index bc11e86..ac77813 100644 --- a/web-app/src/components/Home/ActionsPanel.svelte +++ b/web-app/src/components/Home/ActionsPanel.svelte @@ -12,7 +12,7 @@ } from "lucide-svelte"; import { apiKey, defaultPrompt, enumLLMs } from "$lib/config/config"; - import { generateLanguageModelReply } from "$airesearchagent"; + import { generateLanguageModelReply, copyHTMLToClipboard } from "$airesearchagent"; export let selectedArticle: any; @@ -61,18 +61,13 @@ isExpanded = !isExpanded; } - function copyHTMLToClipboard() { + function handleCopyHTMLToClipboard() { if (!selectedArticle) return; - if (typeof window == "undefined") return; - - const htmlBlob = new Blob([selectedArticle.html], { type: "text/html" }); - const textBlob = new Blob([selectedArticle.html], { type: "text/plain" }); - const clipboardItem = new window.ClipboardItem({ - "text/html": htmlBlob, - "text/plain": textBlob, - }); - navigator.clipboard.write([clipboardItem]).then(() => { + var textToCopy = aiResponse + + (selectedArticle.cite || "" ) + "\n"+ + selectedArticle.html; + copyHTMLToClipboard(textToCopy).then(() => { showCopiedMessage = true; setTimeout(() => { showCopiedMessage = false; @@ -118,7 +113,7 @@