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GitHub - enjalot/latent-scope: A scientific instrument for investigating latent spaces
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class="flex-1"> <button aria-label="Toggle navigation" aria-expanded="false" type="button" data-view-component="true" class="js-details-target js-nav-padding-recalculate js-header-menu-toggle Button--link Button--medium Button d-lg-none color-fg-inherit p-1"> <span class="Button-content"> <span class="Button-label"><div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div></span> </span> </button> </div> <a class="mr-lg-3 color-fg-inherit flex-order-2 js-prevent-focus-on-mobile-nav" href="/" aria-label="Homepage" data-analytics-event="{"category":"Marketing nav","action":"click to go to homepage","label":"ref_page:Marketing;ref_cta:Logomark;ref_loc:Header"}"> <svg height="32" aria-hidden="true" viewBox="0 0 24 24" version="1.1" width="32" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12.5.75C6.146.75 1 5.896 1 12.25c0 5.089 3.292 9.387 7.863 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js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 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0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" 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6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="product-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"all_features","context":"product","tag":"link","label":"all_features_link_product_navbar"}" href="https://github.com/features"> All features </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative 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6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="zWbNyFL6K877GQHTIcj9bbhVn3n5m6RzimEL4mgpBUl2zT41FVjeQrGy8M+YRg5LBjKHvTuyxm1W7oCv1sjqjw==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" 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Scope\u003c/h1\u003e\u003ca id=\"user-content-latent-scope\" class=\"anchor\" aria-label=\"Permalink: Latent Scope\" href=\"#latent-scope\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://discord.gg/x7NvpnM4pY\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/7d9d521bb23c7b7a975f016460fac0be22e40b08acd8ef07e397b41d28b77549/68747470733a2f2f646362616467652e76657263656c2e6170702f6170692f7365727665722f78374e76706e4d3470593f7374796c653d666c6174\" alt=\"\" data-canonical-src=\"https://dcbadge.vercel.app/api/server/x7NvpnM4pY?style=flat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pypi.org/project/latentscope/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/dc381be35e6d71f14f7fd01a392121d5a0d426f8a797e990559755573314a989/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6174656e7473636f70652e737667\" alt=\"PyPI version\" data-canonical-src=\"https://img.shields.io/pypi/v/latentscope.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eQuickly embed, project, cluster and explore a dataset with open models locally or via API. This project is a new kind of workflow + tool for visualizing and exploring datasets through the lens of latent spaces.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://enjalot.github.io/latent-scope/\" rel=\"nofollow\"\u003eDocs\u003c/a\u003e · \u003ca href=\"https://latent.estate\" rel=\"nofollow\"\u003eDemos\u003c/a\u003e\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"http://latent.estate/scope/enjalot/ls-fineweb-edu-100k/scopes-001\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/71a5523063e9574a2987fcf61cd81fe4a7b93e9282be0bcbcfab5c026f3eea75/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f66756e2d646174612f6c6174656e742d73636f70652f64656d6f732f656e6a616c6f742f6c732d66696e657765622d6564752d3130306b2f73636f7065732d3030312e706e67\" alt=\"\" data-canonical-src=\"https://storage.googleapis.com/fun-data/latent-scope/demos/enjalot/ls-fineweb-edu-100k/scopes-001.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"https://latent.estate/scope/enjalot/ls-dadabase/scopes-001\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/8e8b5e5946614e423623e69961dfcc0485647db79fdf1b38184878bf6bd9ceac/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f66756e2d646174612f6c6174656e742d73636f70652f64656d6f732f656e6a616c6f742f6c732d64616461626173652f73636f7065732d3030312e706e67\" alt=\"\" data-canonical-src=\"https://storage.googleapis.com/fun-data/latent-scope/demos/enjalot/ls-dadabase/scopes-001.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"https://latent.estate/scope/enjalot/ls-common-corpus-100k/scopes-001\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/746a89e61a1c0b523cbff6d270c3df71f98b0034d2cbd733a28868a43fd5aeac/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f66756e2d646174612f6c6174656e742d73636f70652f64656d6f732f656e6a616c6f742f6c732d636f6d6d6f6e2d636f727075732d3130306b2f73636f7065732d3030312e706e67\" alt=\"\" data-canonical-src=\"https://storage.googleapis.com/fun-data/latent-scope/demos/enjalot/ls-common-corpus-100k/scopes-001.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003cth align=\"center\"\u003e\u003ca href=\"https://latent.estate/scope/enjalot/ls-dataisplural/scopes-001\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2df59eae90eff263f33a42a8f84a5bb3260e2cd06ef2e3165d1c70e55c08dc07/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f66756e2d646174612f6c6174656e742d73636f70652f64656d6f732f656e6a616c6f742f6c732d646174616973706c7572616c2f73636f7065732d3030312e706e67\" alt=\"\" data-canonical-src=\"https://storage.googleapis.com/fun-data/latent-scope/demos/enjalot/ls-dataisplural/scopes-001.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"http://latent.estate/scope/enjalot/ls-fineweb-edu-100k/scopes-001\" rel=\"nofollow\"\u003eFineweb EDU\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://latent.estate/scope/enjalot/ls-dadabase/scopes-001\" rel=\"nofollow\"\u003eDad Jokes\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://latent.estate/scope/enjalot/ls-common-corpus-100k/scopes-001\" rel=\"nofollow\"\u003eCommon Corpus\u003c/a\u003e\u003c/td\u003e\n\u003ctd align=\"center\"\u003e\u003ca href=\"https://latent.estate/scope/enjalot/ls-dataisplural/scopes-001\" rel=\"nofollow\"\u003eData is Plural\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cp dir=\"auto\"\u003eLatent Scope encodes a process that is increasingly common in AI and data science workflows: Embed unstructured data into high-dimensional vectors, reduce the dimensionality of those vectors, cluster the resulting points, label the clusters with an LLM and then explore the annotated data.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/enjalot/latent-scope/blob/main/documentation/process-crop.png?raw=true\"\u003e\u003cimg src=\"https://github.com/enjalot/latent-scope/raw/main/documentation/process-crop.png?raw=true\" alt=\"Setup your scope\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn addition to making this process easier by providing a web interface for each step, Latent Scope provides an intuitive way to explore the resulting annotated data via an interactive visualization tightly coupled with the input data.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/enjalot/latent-scope/blob/main/documentation/explore.png?raw=true\"\u003e\u003cimg src=\"https://github.com/enjalot/latent-scope/raw/main/documentation/explore.png?raw=true\" alt=\"Explore and your data\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting started\" href=\"#getting-started\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFollow the documentation guides to get started:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://enjalot.github.io/latent-scope/install-and-config\" rel=\"nofollow\"\u003eInstall and Configure\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://enjalot.github.io/latent-scope/your-first-scope\" rel=\"nofollow\"\u003eYour First Scope\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://enjalot.github.io/latent-scope/explore-and-curate\" rel=\"nofollow\"\u003eExplore and Curate\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://enjalot.github.io/latent-scope/exporting-data\" rel=\"nofollow\"\u003eExporting Data\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample Analysis\u003c/h2\u003e\u003ca id=\"user-content-example-analysis\" class=\"anchor\" aria-label=\"Permalink: Example Analysis\" href=\"#example-analysis\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhat can you do with Latent Scope? The following examples demonstrate the kinds of perspective and insights you can gain from your unstructured text data.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eExplore free-responses from surveys in this \u003ca href=\"https://enjalot.github.io/latent-scope/datavis-survey\" rel=\"nofollow\"\u003edatavis survey analysis\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eCluster thousands of \u003ca href=\"https://enjalot.github.io/latent-scope/plot-issues\" rel=\"nofollow\"\u003eGitHub issues and PRs\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eExplore 50,000 \u003ca href=\"https://enjalot.github.io/latent-scope/us-federal-laws\" rel=\"nofollow\"\u003eUS Federal laws\u003c/a\u003e spanning two hundred years.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick Start\u003c/h3\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eLatent Scope works on Mac, Linux and Windows. Python 3.12 is the recommended python version.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo get started, install the \u003ca href=\"https://pypi.org/project/latentscope/\" rel=\"nofollow\"\u003elatent-scope python module\u003c/a\u003e and run the server via the Command Line:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python -m venv venv\nsource venv/bin/activate\npip install latentscope\nls-init ~/latent-scope-data --openai_key=XXX --mistral_key=YYY # optional api keys to enable API models\nls-serve\"\u003e\u003cpre\u003epython -m venv venv\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e venv/bin/activate\npip install latentscope\nls-init \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/latent-scope-data --openai_key=XXX --mistral_key=YYY \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e optional api keys to enable API models\u003c/span\u003e\nls-serve\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen open your browser to \u003ca href=\"http://localhost:5001\" rel=\"nofollow\"\u003ehttp://localhost:5001\u003c/a\u003e and start processing your first dataset!\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSee the \u003ca href=\"https://enjalot.github.io/latent-scope/your-first-scope\" rel=\"nofollow\"\u003eYour First Scope\u003c/a\u003e guide for a detailed walk-through of the process.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePython interface\u003c/h3\u003e\u003ca id=\"user-content-python-interface\" class=\"anchor\" aria-label=\"Permalink: Python interface\" href=\"#python-interface\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can also ingest data from a Pandas dataframe using the Python interface:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import latentscope as ls\ndf = pd.read_parquet(\u0026quot;...\u0026quot;)\nls.init(\u0026quot;~/latent-scope-data\u0026quot;) # you can also pass in openai_key=\u0026quot;XXX\u0026quot;, mistral_key=\u0026quot;XXX\u0026quot; etc.)\nls.ingest(\u0026quot;dadabase\u0026quot;, df, text_column=\u0026quot;joke\u0026quot;)\nls.serve()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003elatentscope\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003els\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epd\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread_parquet\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"...\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003els\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003einit\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"~/latent-scope-data\"\u003c/span\u003e) \u003cspan class=\"pl-c\"\u003e# you can also pass in openai_key=\"XXX\", mistral_key=\"XXX\" etc.)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003els\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eingest\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"dadabase\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etext_column\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"joke\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003els\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eserve\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSee these notebooks for detailed examples of using the Python interface to prepare and load data.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/enjalot/latent-scope/blob/main/notebooks/dvs-survey.ipynb\"\u003edvs-survey\u003c/a\u003e - A small test dataset of 700 rows to quickly illustrate the process. This notebook shows how you can do every step of the process with the Python interface.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/enjalot/latent-scope/blob/main/notebooks/dadabase.ipynb\"\u003edadabase\u003c/a\u003e - A more interesting (and funny) dataset of 50k rows. This notebook shows how you can preprocess a dataset, ingest it into latentscope and then use the web interface to complete the process.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/enjalot/latent-scope/blob/main/notebooks/dolly15k.ipynb\"\u003edolly15k\u003c/a\u003e - Grab data from HuggingFace datasets and ingest into the process.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/enjalot/latent-scope/blob/main/notebooks/emotion.ipynb\"\u003eemotion\u003c/a\u003e - 400k rows of emotional tweets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCommand line quick start\u003c/h3\u003e\u003ca id=\"user-content-command-line-quick-start\" class=\"anchor\" aria-label=\"Permalink: Command line quick start\" href=\"#command-line-quick-start\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhen latent-scope is installed, it creates a suite of command line scripts that can be used to setup the scopes for exploring in the web application. The output of each step in the process is flat files stored in the data directory specified at init. These files are in standard formats that were designed to be ported into other pipelines or interfaces.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# like above, we make sure to install latent-scope\npython -m venv venv\nsource venv/bin/activate\npip install latent-scope\n\n# prepare some data\nwget \u0026quot;https://storage.googleapis.com/fun-data/latent-scope/examples/dvs-survey/datavis-misunderstood.csv\u0026quot; \u0026gt; ~/Downloads/datavis-misunderstood.csv\n\nls-init \u0026quot;~/latent-scope-data\u0026quot;\n# ls-ingest dataset_id csv_path\nls-ingest-csv \u0026quot;datavis-misunderstood\u0026quot; \u0026quot;~/Downloads/datavis-misunderstood.csv\u0026quot;\n# get a list of model ids available (lists both embedding and chat models available)\nls-list-models\n# ls-embed dataset_id text_column model_id prefix\nls-embed datavis-misunderstood \u0026quot;answer\u0026quot; transformers-intfloat___e5-small-v2 \u0026quot;\u0026quot;\n# ls-umap dataset_id embedding_id n_neighbors min_dist\nls-umap datavis-misunderstood embedding-001 25 .1\n# ls-cluster dataset_id umap_id samples min_samples\nls-cluster datavis-misunderstood umap-001 5 5\n# ls-label dataset_id text_column cluster_id model_id context\nls-label datavis-misunderstood \u0026quot;answer\u0026quot; cluster-001 transformers-HuggingFaceH4___zephyr-7b-beta \u0026quot;\u0026quot;\n# ls-scope dataset_id embedding_id umap_id cluster_id cluster_labels_id label description\nls-scope datavis-misunderstood cluster-001-labels-001 \u0026quot;E5 demo\u0026quot; \u0026quot;E5 embeddings summarized by Zephyr 7B\u0026quot;\n# start the server to explore your scope\nls-serve\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e like above, we make sure to install latent-scope\u003c/span\u003e\npython -m venv venv\n\u003cspan class=\"pl-c1\"\u003esource\u003c/span\u003e venv/bin/activate\npip install latent-scope\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e prepare some data\u003c/span\u003e\nwget \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ehttps://storage.googleapis.com/fun-data/latent-scope/examples/dvs-survey/datavis-misunderstood.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/Downloads/datavis-misunderstood.csv\n\nls-init \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e~/latent-scope-data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-ingest dataset_id csv_path\u003c/span\u003e\nls-ingest-csv \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003edatavis-misunderstood\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e~/Downloads/datavis-misunderstood.csv\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e get a list of model ids available (lists both embedding and chat models available)\u003c/span\u003e\nls-list-models\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-embed dataset_id text_column model_id prefix\u003c/span\u003e\nls-embed datavis-misunderstood \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eanswer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e transformers-intfloat___e5-small-v2 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-umap dataset_id embedding_id n_neighbors min_dist\u003c/span\u003e\nls-umap datavis-misunderstood embedding-001 25 .1\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-cluster dataset_id umap_id samples min_samples\u003c/span\u003e\nls-cluster datavis-misunderstood umap-001 5 5\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-label dataset_id text_column cluster_id model_id context\u003c/span\u003e\nls-label datavis-misunderstood \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eanswer\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e cluster-001 transformers-HuggingFaceH4___zephyr-7b-beta \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-scope dataset_id embedding_id umap_id cluster_id cluster_labels_id label description\u003c/span\u003e\nls-scope datavis-misunderstood cluster-001-labels-001 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eE5 demo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eE5 embeddings summarized by Zephyr 7B\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e start the server to explore your scope\u003c/span\u003e\nls-serve\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRepository overview\u003c/h3\u003e\u003ca id=\"user-content-repository-overview\" class=\"anchor\" aria-label=\"Permalink: Repository overview\" href=\"#repository-overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis repository is currently meant to run locally, with a React frontend that communicates with a python server backend. We support several popular open source embedding models that can run locally as well as proprietary API embedding services. Adding new models and services should be quick and easy.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo learn more about contributing and the project roadmap see \u003ca href=\"/enjalot/latent-scope/blob/main/CONTRIBUTION.md\"\u003eCONTRIBUTION.md\u003c/a\u003e, for technical details see \u003ca href=\"/enjalot/latent-scope/blob/main/DEVELOPMENT.md\"\u003eDEVELOPMENT.md\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDesign principles\u003c/h3\u003e\u003ca id=\"user-content-design-principles\" class=\"anchor\" aria-label=\"Permalink: Design principles\" href=\"#design-principles\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis tool is meant to be a part of a larger process. Something that hopefully helps you see things in your data that you wouldn't otherwise have. That means it needs to be easy to get data in, and easily get useful data out.\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eFlat files\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAll of the data that drives the app is stored in flat files. This is so that both final and intermediate outputs can easily be exported for other uses. It also makes it easy to see the status of any part of the process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"2\" dir=\"auto\"\u003e\n\u003cli\u003eRemember everything\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThis tool is intended to aid in research, the purpose is experimentation and exploration. I developed it because far too often I try a lot of things and then I forget what parameters lead me down a promising path in the first place. All choices you make in the process are recorded in metadata files along with the output of the process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003col start=\"3\" dir=\"auto\"\u003e\n\u003cli\u003eIt's all about the indices\u003c/li\u003e\n\u003c/ol\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWe consider an input dataset the source of truth, a list of rows that can be indexed into. So all downstream operations, whether its embeddings, pointing to nearest neighbors or assigning data points to clusters, all use indices into the input dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCommand Line Scripts: Detailed description\u003c/h2\u003e\u003ca id=\"user-content-command-line-scripts-detailed-description\" class=\"anchor\" aria-label=\"Permalink: Command Line Scripts: Detailed description\" href=\"#command-line-scripts-detailed-description\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you want to use the CLI instead of the web UI you can use the following scripts.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe scripts should be run in order once you have an \u003ccode\u003einput.csv\u003c/code\u003e file in your folder. Alternatively the Setup page in the web UI will run these scripts via API calls to the server for you.\u003cbr\u003e\nThese scripts expect at the least a \u003ccode\u003eLATENT_SCOPE_DATA\u003c/code\u003e environment variable with a path to where you want to store your data. If you run \u003ccode\u003els-serve\u003c/code\u003e it will set the variable and put it in a \u003ccode\u003e.env\u003c/code\u003e file. You can add API keys to the .env file to enable usage of the various API services, see \u003ca href=\"/enjalot/latent-scope/blob/main/.env.example\"\u003e.env.example\u003c/a\u003e for the structure.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e0. ingest\u003c/h3\u003e\u003ca id=\"user-content-0-ingest\" class=\"anchor\" aria-label=\"Permalink: 0. ingest\" href=\"#0-ingest\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis script turns the \u003ccode\u003einput.csv\u003c/code\u003e into \u003ccode\u003einput.parquet\u003c/code\u003e and sets up the directories and \u003ccode\u003emeta.json\u003c/code\u003e which run the app.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# ls-ingest \u0026lt;dataset_name\u0026gt;\nls-ingest database-curated\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-ingest \u0026lt;dataset_name\u0026gt;\u003c/span\u003e\nls-ingest database-curated\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e1. embed\u003c/h3\u003e\u003ca id=\"user-content-1-embed\" class=\"anchor\" aria-label=\"Permalink: 1. embed\" href=\"#1-embed\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTake the text from the input and embed it. Default is to use \u003ccode\u003eBAAI/bge-small-en-v1.5\u003c/code\u003e locally via HuggingFace transformers. API services are supported as well, see \u003ca href=\"/enjalot/latent-scope/blob/main/latentscope/models/embedding_models.json\"\u003elatentscope/models/embedding_models.json\u003c/a\u003e for model ids.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# you can get a list of models available with:\nls-list-models\n# ls-embed \u0026lt;dataset_name\u0026gt; \u0026lt;text_column\u0026gt; \u0026lt;model_id\u0026gt;\nls-embed dadabase joke transformers-intfloat___e5-small-v2\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e you can get a list of models available with:\u003c/span\u003e\nls-list-models\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-embed \u0026lt;dataset_name\u0026gt; \u0026lt;text_column\u0026gt; \u0026lt;model_id\u0026gt;\u003c/span\u003e\nls-embed dadabase joke transformers-intfloat___e5-small-v2\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2. umap\u003c/h3\u003e\u003ca id=\"user-content-2-umap\" class=\"anchor\" aria-label=\"Permalink: 2. umap\" href=\"#2-umap\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eMap the embeddings from high-dimensional space to 2D with UMAP. Will generate a thumbnail of the scatterplot.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# ls-umap \u0026lt;dataset_name\u0026gt; \u0026lt;embedding_id\u0026gt; \u0026lt;neighbors\u0026gt; \u0026lt;min_dist\u0026gt;\nls-umap dadabase embedding-001 50 0.1\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-umap \u0026lt;dataset_name\u0026gt; \u0026lt;embedding_id\u0026gt; \u0026lt;neighbors\u0026gt; \u0026lt;min_dist\u0026gt;\u003c/span\u003e\nls-umap dadabase embedding-001 50 0.1\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e3. cluster\u003c/h3\u003e\u003ca id=\"user-content-3-cluster\" class=\"anchor\" aria-label=\"Permalink: 3. cluster\" href=\"#3-cluster\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCluster the UMAP points using HDBSCAN. This will label each point with a cluster label\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# ls-cluster \u0026lt;dataset_name\u0026gt; \u0026lt;umap_id\u0026gt; \u0026lt;samples\u0026gt; \u0026lt;min-samples\u0026gt;\nls-cluster dadabase umap-001 5 3\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-cluster \u0026lt;dataset_name\u0026gt; \u0026lt;umap_id\u0026gt; \u0026lt;samples\u0026gt; \u0026lt;min-samples\u0026gt;\u003c/span\u003e\nls-cluster dadabase umap-001 5 3\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e4. label\u003c/h3\u003e\u003ca id=\"user-content-4-label\" class=\"anchor\" aria-label=\"Permalink: 4. label\" href=\"#4-label\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe support auto-labeling clusters by summarizing them with an LLM. Supported models and APIs are listed in \u003ca href=\"/enjalot/latent-scope/blob/main/latentscope/models/chat_models.json\"\u003elatentscope/models/chat_models.json\u003c/a\u003e.\nYou can pass context that will be injected into the system prompt for your dataset.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# ls-label \u0026lt;dataset_id\u0026gt; \u0026lt;cluster_id\u0026gt; \u0026lt;chat_model_id\u0026gt; \u0026lt;context\u0026gt;\nls-label dadabase \u0026quot;joke\u0026quot; cluster-001 openai-gpt-3.5-turbo \u0026quot;\u0026quot;\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-label \u0026lt;dataset_id\u0026gt; \u0026lt;cluster_id\u0026gt; \u0026lt;chat_model_id\u0026gt; \u0026lt;context\u0026gt;\u003c/span\u003e\nls-label dadabase \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ejoke\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e cluster-001 openai-gpt-3.5-turbo \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e5. scope\u003c/h3\u003e\u003ca id=\"user-content-5-scope\" class=\"anchor\" aria-label=\"Permalink: 5. scope\" href=\"#5-scope\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe scope command ties together each step of the process to create an explorable configuration. You can have several scopes to view different choices, for example using different embeddings or even different parameters for UMAP and clustering. Switching between scopes in the UI is instant.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# ls-scope \u0026lt;dataset_id\u0026gt; \u0026lt;embedding_id\u0026gt; \u0026lt;umap_id\u0026gt; \u0026lt;cluster_id\u0026gt; \u0026lt;cluster_labels_id\u0026gt; \u0026lt;label\u0026gt; \u0026lt;description\u0026gt;\nls-scope datavis-misunderstood cluster-001-labels-001 \u0026quot;E5 demo\u0026quot; \u0026quot;E5 embeddings summarized by GPT3.5-Turbo\u0026quot;\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e ls-scope \u0026lt;dataset_id\u0026gt; \u0026lt;embedding_id\u0026gt; \u0026lt;umap_id\u0026gt; \u0026lt;cluster_id\u0026gt; \u0026lt;cluster_labels_id\u0026gt; \u0026lt;label\u0026gt; \u0026lt;description\u0026gt;\u003c/span\u003e\nls-scope datavis-misunderstood cluster-001-labels-001 \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eE5 demo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eE5 embeddings summarized by GPT3.5-Turbo\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e6. serve\u003c/h3\u003e\u003ca id=\"user-content-6-serve\" class=\"anchor\" aria-label=\"Permalink: 6. serve\" href=\"#6-serve\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo start the web UI we run a small server. This also enables nearest neighbor similarity search and interactively querying subsets of the input data while exploring the scopes.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"ls-serve ~/latent-scope-data\"\u003e\u003cpre\u003els-serve \u003cspan class=\"pl-k\"\u003e~\u003c/span\u003e/latent-scope-data\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDataset directory structure\u003c/h2\u003e\u003ca id=\"user-content-dataset-directory-structure\" class=\"anchor\" aria-label=\"Permalink: Dataset directory structure\" href=\"#dataset-directory-structure\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eEach dataset will have its own directory in data/ created when you ingest your CSV. All subsequent steps of setting up a dataset write their data and metadata to this directory.\nThere are no databases in this tool, just flat files that are easy to copy and edit.\u003c/p\u003e\n\u003cpre\u003e├── data/\n| ├── dataset1/\n| | ├── input.parquet # from ingest.py, the dataset\n| | ├── meta.json # from ingest.py, metadata for dataset, #rows, columns, text_column\n| | ├── embeddings/\n| | | ├── embedding-001.h5 # from embed.py, embedding vectors\n| | | ├── embedding-001.json # from embed.py, parameters used to embed\n| | | ├── embedding-002... \n| | ├── umaps/\n| | | ├── umap-001.parquet # from umap.py, x,y coordinates\n| | | ├── umap-001.json # from umap.py, params used\n| | | ├── umap-001.png # from umap.py, thumbnail of plot\n| | | ├── umap-002.... \n| | ├── clusters/\n| | | ├── clusters-001.parquet # from cluster.py, cluster indices\n| | | ├── clusters-001-labels-default.parquet # from cluster.py, default labels\n| | | ├── clusters-001-labels-001.parquet # from label_clusters.py, LLM generated labels\n| | | ├── clusters-001.json # from cluster.py, params used\n| | | ├── clusters-001.png # from cluster.py, thumbnail of plot\n| | | ├── clusters-002... \n| | ├── scopes/\n| | | ├── scopes-001.json # from scope.py, combination of embed, umap, clusters and label choice\n| | | ├── scopes-... \n| | ├── tags/\n| | | ├── ❤️.indices # tagged by UI, powered by tags.py\n| | | ├── ... # can have arbitrary named tags\n| | ├── jobs/\n| | | ├── 8980️-12345...json # created when job is run via web UI\n\u003c/pre\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Latent Scope","anchor":"latent-scope","htmlText":"Latent Scope"},{"level":2,"text":"Getting started","anchor":"getting-started","htmlText":"Getting started"},{"level":2,"text":"Example Analysis","anchor":"example-analysis","htmlText":"Example Analysis"},{"level":3,"text":"Quick Start","anchor":"quick-start","htmlText":"Quick Start"},{"level":3,"text":"Python interface","anchor":"python-interface","htmlText":"Python interface"},{"level":3,"text":"Command line quick start","anchor":"command-line-quick-start","htmlText":"Command line quick start"},{"level":3,"text":"Repository overview","anchor":"repository-overview","htmlText":"Repository overview"},{"level":3,"text":"Design principles","anchor":"design-principles","htmlText":"Design principles"},{"level":2,"text":"Command Line Scripts: Detailed description","anchor":"command-line-scripts-detailed-description","htmlText":"Command Line Scripts: Detailed description"},{"level":3,"text":"0. ingest","anchor":"0-ingest","htmlText":"0. ingest"},{"level":3,"text":"1. embed","anchor":"1-embed","htmlText":"1. embed"},{"level":3,"text":"2. umap","anchor":"2-umap","htmlText":"2. umap"},{"level":3,"text":"3. cluster","anchor":"3-cluster","htmlText":"3. cluster"},{"level":3,"text":"4. label","anchor":"4-label","htmlText":"4. label"},{"level":3,"text":"5. scope","anchor":"5-scope","htmlText":"5. scope"},{"level":3,"text":"6. serve","anchor":"6-serve","htmlText":"6. serve"},{"level":2,"text":"Dataset directory structure","anchor":"dataset-directory-structure","htmlText":"Dataset directory structure"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fenjalot%2Flatent-scope"}},{"displayName":"CODE_OF_CONDUCT.md","repoName":"latent-scope","refName":"main","path":"CODE_OF_CONDUCT.md","preferredFileType":"code_of_conduct","tabName":"Code of conduct","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fenjalot%2Flatent-scope"}},{"displayName":"LICENSE","repoName":"latent-scope","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"MIT","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fenjalot%2Flatent-scope"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-9f8a877aa99f.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"accessible_code_button":true}}}}</script> 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QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Latent Scope</h1><a id="user-content-latent-scope" class="anchor" aria-label="Permalink: Latent Scope" href="#latent-scope"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a href="https://discord.gg/x7NvpnM4pY" rel="nofollow"><img src="https://camo.githubusercontent.com/7d9d521bb23c7b7a975f016460fac0be22e40b08acd8ef07e397b41d28b77549/68747470733a2f2f646362616467652e76657263656c2e6170702f6170692f7365727665722f78374e76706e4d3470593f7374796c653d666c6174" alt="" data-canonical-src="https://dcbadge.vercel.app/api/server/x7NvpnM4pY?style=flat" style="max-width: 100%;"></a> <a href="https://pypi.org/project/latentscope/" rel="nofollow"><img src="https://camo.githubusercontent.com/dc381be35e6d71f14f7fd01a392121d5a0d426f8a797e990559755573314a989/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6c6174656e7473636f70652e737667" alt="PyPI version" data-canonical-src="https://img.shields.io/pypi/v/latentscope.svg" style="max-width: 100%;"></a></p> <p dir="auto">Quickly embed, project, cluster and explore a dataset with open models locally or via API. This project is a new kind of workflow + tool for visualizing and exploring datasets through the lens of latent spaces.</p> <p dir="auto"><a href="https://enjalot.github.io/latent-scope/" rel="nofollow">Docs</a> · <a href="https://latent.estate" rel="nofollow">Demos</a></p> <markdown-accessiblity-table><table> <thead> <tr> <th align="center"><a href="http://latent.estate/scope/enjalot/ls-fineweb-edu-100k/scopes-001" rel="nofollow"><img src="https://camo.githubusercontent.com/71a5523063e9574a2987fcf61cd81fe4a7b93e9282be0bcbcfab5c026f3eea75/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f66756e2d646174612f6c6174656e742d73636f70652f64656d6f732f656e6a616c6f742f6c732d66696e657765622d6564752d3130306b2f73636f7065732d3030312e706e67" alt="" data-canonical-src="https://storage.googleapis.com/fun-data/latent-scope/demos/enjalot/ls-fineweb-edu-100k/scopes-001.png" style="max-width: 100%;"></a></th> <th align="center"><a href="https://latent.estate/scope/enjalot/ls-dadabase/scopes-001" rel="nofollow"><img src="https://camo.githubusercontent.com/8e8b5e5946614e423623e69961dfcc0485647db79fdf1b38184878bf6bd9ceac/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f66756e2d646174612f6c6174656e742d73636f70652f64656d6f732f656e6a616c6f742f6c732d64616461626173652f73636f7065732d3030312e706e67" alt="" data-canonical-src="https://storage.googleapis.com/fun-data/latent-scope/demos/enjalot/ls-dadabase/scopes-001.png" style="max-width: 100%;"></a></th> <th align="center"><a href="https://latent.estate/scope/enjalot/ls-common-corpus-100k/scopes-001" rel="nofollow"><img src="https://camo.githubusercontent.com/746a89e61a1c0b523cbff6d270c3df71f98b0034d2cbd733a28868a43fd5aeac/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f66756e2d646174612f6c6174656e742d73636f70652f64656d6f732f656e6a616c6f742f6c732d636f6d6d6f6e2d636f727075732d3130306b2f73636f7065732d3030312e706e67" alt="" data-canonical-src="https://storage.googleapis.com/fun-data/latent-scope/demos/enjalot/ls-common-corpus-100k/scopes-001.png" style="max-width: 100%;"></a></th> <th align="center"><a href="https://latent.estate/scope/enjalot/ls-dataisplural/scopes-001" rel="nofollow"><img src="https://camo.githubusercontent.com/2df59eae90eff263f33a42a8f84a5bb3260e2cd06ef2e3165d1c70e55c08dc07/68747470733a2f2f73746f726167652e676f6f676c65617069732e636f6d2f66756e2d646174612f6c6174656e742d73636f70652f64656d6f732f656e6a616c6f742f6c732d646174616973706c7572616c2f73636f7065732d3030312e706e67" alt="" data-canonical-src="https://storage.googleapis.com/fun-data/latent-scope/demos/enjalot/ls-dataisplural/scopes-001.png" style="max-width: 100%;"></a></th> </tr> </thead> <tbody> <tr> <td align="center"><a href="http://latent.estate/scope/enjalot/ls-fineweb-edu-100k/scopes-001" rel="nofollow">Fineweb EDU</a></td> <td align="center"><a href="https://latent.estate/scope/enjalot/ls-dadabase/scopes-001" rel="nofollow">Dad Jokes</a></td> <td align="center"><a href="https://latent.estate/scope/enjalot/ls-common-corpus-100k/scopes-001" rel="nofollow">Common Corpus</a></td> <td align="center"><a href="https://latent.estate/scope/enjalot/ls-dataisplural/scopes-001" rel="nofollow">Data is Plural</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <p dir="auto">Latent Scope encodes a process that is increasingly common in AI and data science workflows: Embed unstructured data into high-dimensional vectors, reduce the dimensionality of those vectors, cluster the resulting points, label the clusters with an LLM and then explore the annotated data.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/enjalot/latent-scope/blob/main/documentation/process-crop.png?raw=true"><img src="https://github.com/enjalot/latent-scope/raw/main/documentation/process-crop.png?raw=true" alt="Setup your scope" style="max-width: 100%;"></a></p> <p dir="auto">In addition to making this process easier by providing a web interface for each step, Latent Scope provides an intuitive way to explore the resulting annotated data via an interactive visualization tightly coupled with the input data.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/enjalot/latent-scope/blob/main/documentation/explore.png?raw=true"><img src="https://github.com/enjalot/latent-scope/raw/main/documentation/explore.png?raw=true" alt="Explore and your data" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting started</h2><a id="user-content-getting-started" class="anchor" aria-label="Permalink: Getting started" href="#getting-started"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Follow the documentation guides to get started:</p> <ol dir="auto"> <li><a href="https://enjalot.github.io/latent-scope/install-and-config" rel="nofollow">Install and Configure</a></li> <li><a href="https://enjalot.github.io/latent-scope/your-first-scope" rel="nofollow">Your First Scope</a></li> <li><a href="https://enjalot.github.io/latent-scope/explore-and-curate" rel="nofollow">Explore and Curate</a></li> <li><a href="https://enjalot.github.io/latent-scope/exporting-data" rel="nofollow">Exporting Data</a></li> </ol> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Example Analysis</h2><a id="user-content-example-analysis" class="anchor" aria-label="Permalink: Example Analysis" href="#example-analysis"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">What can you do with Latent Scope? The following examples demonstrate the kinds of perspective and insights you can gain from your unstructured text data.</p> <ul dir="auto"> <li>Explore free-responses from surveys in this <a href="https://enjalot.github.io/latent-scope/datavis-survey" rel="nofollow">datavis survey analysis</a></li> <li>Cluster thousands of <a href="https://enjalot.github.io/latent-scope/plot-issues" rel="nofollow">GitHub issues and PRs</a></li> <li>Explore 50,000 <a href="https://enjalot.github.io/latent-scope/us-federal-laws" rel="nofollow">US Federal laws</a> spanning two hundred years.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Quick Start</h3><a id="user-content-quick-start" class="anchor" aria-label="Permalink: Quick Start" href="#quick-start"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Latent Scope works on Mac, Linux and Windows. Python 3.12 is the recommended python version.</p> <p dir="auto">To get started, install the <a href="https://pypi.org/project/latentscope/" rel="nofollow">latent-scope python module</a> and run the server via the Command Line:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -m venv venv source venv/bin/activate pip install latentscope ls-init ~/latent-scope-data --openai_key=XXX --mistral_key=YYY # optional api keys to enable API models ls-serve"><pre>python -m venv venv <span class="pl-c1">source</span> venv/bin/activate pip install latentscope ls-init <span class="pl-k">~</span>/latent-scope-data --openai_key=XXX --mistral_key=YYY <span class="pl-c"><span class="pl-c">#</span> optional api keys to enable API models</span> ls-serve</pre></div> <p dir="auto">Then open your browser to <a href="http://localhost:5001" rel="nofollow">http://localhost:5001</a> and start processing your first dataset!</p> <p dir="auto">See the <a href="https://enjalot.github.io/latent-scope/your-first-scope" rel="nofollow">Your First Scope</a> guide for a detailed walk-through of the process.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Python interface</h3><a id="user-content-python-interface" class="anchor" aria-label="Permalink: Python interface" href="#python-interface"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can also ingest data from a Pandas dataframe using the Python interface:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import latentscope as ls df = pd.read_parquet("...") ls.init("~/latent-scope-data") # you can also pass in openai_key="XXX", mistral_key="XXX" etc.) ls.ingest("dadabase", df, text_column="joke") ls.serve()"><pre><span class="pl-k">import</span> <span class="pl-s1">latentscope</span> <span class="pl-k">as</span> <span class="pl-s1">ls</span> <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">pd</span>.<span class="pl-c1">read_parquet</span>(<span class="pl-s">"..."</span>) <span class="pl-s1">ls</span>.<span class="pl-c1">init</span>(<span class="pl-s">"~/latent-scope-data"</span>) <span class="pl-c"># you can also pass in openai_key="XXX", mistral_key="XXX" etc.)</span> <span class="pl-s1">ls</span>.<span class="pl-c1">ingest</span>(<span class="pl-s">"dadabase"</span>, <span class="pl-s1">df</span>, <span class="pl-s1">text_column</span><span class="pl-c1">=</span><span class="pl-s">"joke"</span>) <span class="pl-s1">ls</span>.<span class="pl-c1">serve</span>()</pre></div> <p dir="auto">See these notebooks for detailed examples of using the Python interface to prepare and load data.</p> <ul dir="auto"> <li><a href="/enjalot/latent-scope/blob/main/notebooks/dvs-survey.ipynb">dvs-survey</a> - A small test dataset of 700 rows to quickly illustrate the process. This notebook shows how you can do every step of the process with the Python interface.</li> <li><a href="/enjalot/latent-scope/blob/main/notebooks/dadabase.ipynb">dadabase</a> - A more interesting (and funny) dataset of 50k rows. This notebook shows how you can preprocess a dataset, ingest it into latentscope and then use the web interface to complete the process.</li> <li><a href="/enjalot/latent-scope/blob/main/notebooks/dolly15k.ipynb">dolly15k</a> - Grab data from HuggingFace datasets and ingest into the process.</li> <li><a href="/enjalot/latent-scope/blob/main/notebooks/emotion.ipynb">emotion</a> - 400k rows of emotional tweets.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Command line quick start</h3><a id="user-content-command-line-quick-start" class="anchor" aria-label="Permalink: Command line quick start" href="#command-line-quick-start"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">When latent-scope is installed, it creates a suite of command line scripts that can be used to setup the scopes for exploring in the web application. The output of each step in the process is flat files stored in the data directory specified at init. These files are in standard formats that were designed to be ported into other pipelines or interfaces.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# like above, we make sure to install latent-scope python -m venv venv source venv/bin/activate pip install latent-scope # prepare some data wget "https://storage.googleapis.com/fun-data/latent-scope/examples/dvs-survey/datavis-misunderstood.csv" > ~/Downloads/datavis-misunderstood.csv ls-init "~/latent-scope-data" # ls-ingest dataset_id csv_path ls-ingest-csv "datavis-misunderstood" "~/Downloads/datavis-misunderstood.csv" # get a list of model ids available (lists both embedding and chat models available) ls-list-models # ls-embed dataset_id text_column model_id prefix ls-embed datavis-misunderstood "answer" transformers-intfloat___e5-small-v2 "" # ls-umap dataset_id embedding_id n_neighbors min_dist ls-umap datavis-misunderstood embedding-001 25 .1 # ls-cluster dataset_id umap_id samples min_samples ls-cluster datavis-misunderstood umap-001 5 5 # ls-label dataset_id text_column cluster_id model_id context ls-label datavis-misunderstood "answer" cluster-001 transformers-HuggingFaceH4___zephyr-7b-beta "" # ls-scope dataset_id embedding_id umap_id cluster_id cluster_labels_id label description ls-scope datavis-misunderstood cluster-001-labels-001 "E5 demo" "E5 embeddings summarized by Zephyr 7B" # start the server to explore your scope ls-serve"><pre><span class="pl-c"><span class="pl-c">#</span> like above, we make sure to install latent-scope</span> python -m venv venv <span class="pl-c1">source</span> venv/bin/activate pip install latent-scope <span class="pl-c"><span class="pl-c">#</span> prepare some data</span> wget <span class="pl-s"><span class="pl-pds">"</span>https://storage.googleapis.com/fun-data/latent-scope/examples/dvs-survey/datavis-misunderstood.csv<span class="pl-pds">"</span></span> <span class="pl-k">></span> <span class="pl-k">~</span>/Downloads/datavis-misunderstood.csv ls-init <span class="pl-s"><span class="pl-pds">"</span>~/latent-scope-data<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> ls-ingest dataset_id csv_path</span> ls-ingest-csv <span class="pl-s"><span class="pl-pds">"</span>datavis-misunderstood<span class="pl-pds">"</span></span> <span class="pl-s"><span class="pl-pds">"</span>~/Downloads/datavis-misunderstood.csv<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> get a list of model ids available (lists both embedding and chat models available)</span> ls-list-models <span class="pl-c"><span class="pl-c">#</span> ls-embed dataset_id text_column model_id prefix</span> ls-embed datavis-misunderstood <span class="pl-s"><span class="pl-pds">"</span>answer<span class="pl-pds">"</span></span> transformers-intfloat___e5-small-v2 <span class="pl-s"><span class="pl-pds">"</span><span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> ls-umap dataset_id embedding_id n_neighbors min_dist</span> ls-umap datavis-misunderstood embedding-001 25 .1 <span class="pl-c"><span class="pl-c">#</span> ls-cluster dataset_id umap_id samples min_samples</span> ls-cluster datavis-misunderstood umap-001 5 5 <span class="pl-c"><span class="pl-c">#</span> ls-label dataset_id text_column cluster_id model_id context</span> ls-label datavis-misunderstood <span class="pl-s"><span class="pl-pds">"</span>answer<span class="pl-pds">"</span></span> cluster-001 transformers-HuggingFaceH4___zephyr-7b-beta <span class="pl-s"><span class="pl-pds">"</span><span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> ls-scope dataset_id embedding_id umap_id cluster_id cluster_labels_id label description</span> ls-scope datavis-misunderstood cluster-001-labels-001 <span class="pl-s"><span class="pl-pds">"</span>E5 demo<span class="pl-pds">"</span></span> <span class="pl-s"><span class="pl-pds">"</span>E5 embeddings summarized by Zephyr 7B<span class="pl-pds">"</span></span> <span class="pl-c"><span class="pl-c">#</span> start the server to explore your scope</span> ls-serve</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Repository overview</h3><a id="user-content-repository-overview" class="anchor" aria-label="Permalink: Repository overview" href="#repository-overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This repository is currently meant to run locally, with a React frontend that communicates with a python server backend. We support several popular open source embedding models that can run locally as well as proprietary API embedding services. Adding new models and services should be quick and easy.</p> <p dir="auto">To learn more about contributing and the project roadmap see <a href="/enjalot/latent-scope/blob/main/CONTRIBUTION.md">CONTRIBUTION.md</a>, for technical details see <a href="/enjalot/latent-scope/blob/main/DEVELOPMENT.md">DEVELOPMENT.md</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Design principles</h3><a id="user-content-design-principles" class="anchor" aria-label="Permalink: Design principles" href="#design-principles"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This tool is meant to be a part of a larger process. Something that hopefully helps you see things in your data that you wouldn't otherwise have. That means it needs to be easy to get data in, and easily get useful data out.</p> <ol dir="auto"> <li>Flat files</li> </ol> <ul dir="auto"> <li>All of the data that drives the app is stored in flat files. This is so that both final and intermediate outputs can easily be exported for other uses. It also makes it easy to see the status of any part of the process.</li> </ul> <ol start="2" dir="auto"> <li>Remember everything</li> </ol> <ul dir="auto"> <li>This tool is intended to aid in research, the purpose is experimentation and exploration. I developed it because far too often I try a lot of things and then I forget what parameters lead me down a promising path in the first place. All choices you make in the process are recorded in metadata files along with the output of the process.</li> </ul> <ol start="3" dir="auto"> <li>It's all about the indices</li> </ol> <ul dir="auto"> <li>We consider an input dataset the source of truth, a list of rows that can be indexed into. So all downstream operations, whether its embeddings, pointing to nearest neighbors or assigning data points to clusters, all use indices into the input dataset.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Command Line Scripts: Detailed description</h2><a id="user-content-command-line-scripts-detailed-description" class="anchor" aria-label="Permalink: Command Line Scripts: Detailed description" href="#command-line-scripts-detailed-description"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you want to use the CLI instead of the web UI you can use the following scripts.</p> <p dir="auto">The scripts should be run in order once you have an <code>input.csv</code> file in your folder. Alternatively the Setup page in the web UI will run these scripts via API calls to the server for you.<br> These scripts expect at the least a <code>LATENT_SCOPE_DATA</code> environment variable with a path to where you want to store your data. If you run <code>ls-serve</code> it will set the variable and put it in a <code>.env</code> file. You can add API keys to the .env file to enable usage of the various API services, see <a href="/enjalot/latent-scope/blob/main/.env.example">.env.example</a> for the structure.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">0. ingest</h3><a id="user-content-0-ingest" class="anchor" aria-label="Permalink: 0. ingest" href="#0-ingest"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This script turns the <code>input.csv</code> into <code>input.parquet</code> and sets up the directories and <code>meta.json</code> which run the app.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# ls-ingest <dataset_name> ls-ingest database-curated"><pre><span class="pl-c"><span class="pl-c">#</span> ls-ingest <dataset_name></span> ls-ingest database-curated</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">1. embed</h3><a id="user-content-1-embed" class="anchor" aria-label="Permalink: 1. embed" href="#1-embed"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Take the text from the input and embed it. Default is to use <code>BAAI/bge-small-en-v1.5</code> locally via HuggingFace transformers. API services are supported as well, see <a href="/enjalot/latent-scope/blob/main/latentscope/models/embedding_models.json">latentscope/models/embedding_models.json</a> for model ids.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# you can get a list of models available with: ls-list-models # ls-embed <dataset_name> <text_column> <model_id> ls-embed dadabase joke transformers-intfloat___e5-small-v2"><pre><span class="pl-c"><span class="pl-c">#</span> you can get a list of models available with:</span> ls-list-models <span class="pl-c"><span class="pl-c">#</span> ls-embed <dataset_name> <text_column> <model_id></span> ls-embed dadabase joke transformers-intfloat___e5-small-v2</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">2. umap</h3><a id="user-content-2-umap" class="anchor" aria-label="Permalink: 2. umap" href="#2-umap"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Map the embeddings from high-dimensional space to 2D with UMAP. Will generate a thumbnail of the scatterplot.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# ls-umap <dataset_name> <embedding_id> <neighbors> <min_dist> ls-umap dadabase embedding-001 50 0.1"><pre><span class="pl-c"><span class="pl-c">#</span> ls-umap <dataset_name> <embedding_id> <neighbors> <min_dist></span> ls-umap dadabase embedding-001 50 0.1</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">3. cluster</h3><a id="user-content-3-cluster" class="anchor" aria-label="Permalink: 3. cluster" href="#3-cluster"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Cluster the UMAP points using HDBSCAN. This will label each point with a cluster label</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# ls-cluster <dataset_name> <umap_id> <samples> <min-samples> ls-cluster dadabase umap-001 5 3"><pre><span class="pl-c"><span class="pl-c">#</span> ls-cluster <dataset_name> <umap_id> <samples> <min-samples></span> ls-cluster dadabase umap-001 5 3</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">4. label</h3><a id="user-content-4-label" class="anchor" aria-label="Permalink: 4. label" href="#4-label"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We support auto-labeling clusters by summarizing them with an LLM. Supported models and APIs are listed in <a href="/enjalot/latent-scope/blob/main/latentscope/models/chat_models.json">latentscope/models/chat_models.json</a>. You can pass context that will be injected into the system prompt for your dataset.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# ls-label <dataset_id> <cluster_id> <chat_model_id> <context> ls-label dadabase "joke" cluster-001 openai-gpt-3.5-turbo """><pre><span class="pl-c"><span class="pl-c">#</span> ls-label <dataset_id> <cluster_id> <chat_model_id> <context></span> ls-label dadabase <span class="pl-s"><span class="pl-pds">"</span>joke<span class="pl-pds">"</span></span> cluster-001 openai-gpt-3.5-turbo <span class="pl-s"><span class="pl-pds">"</span><span class="pl-pds">"</span></span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">5. scope</h3><a id="user-content-5-scope" class="anchor" aria-label="Permalink: 5. scope" href="#5-scope"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The scope command ties together each step of the process to create an explorable configuration. You can have several scopes to view different choices, for example using different embeddings or even different parameters for UMAP and clustering. Switching between scopes in the UI is instant.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# ls-scope <dataset_id> <embedding_id> <umap_id> <cluster_id> <cluster_labels_id> <label> <description> ls-scope datavis-misunderstood cluster-001-labels-001 "E5 demo" "E5 embeddings summarized by GPT3.5-Turbo""><pre><span class="pl-c"><span class="pl-c">#</span> ls-scope <dataset_id> <embedding_id> <umap_id> <cluster_id> <cluster_labels_id> <label> <description></span> ls-scope datavis-misunderstood cluster-001-labels-001 <span class="pl-s"><span class="pl-pds">"</span>E5 demo<span class="pl-pds">"</span></span> <span class="pl-s"><span class="pl-pds">"</span>E5 embeddings summarized by GPT3.5-Turbo<span class="pl-pds">"</span></span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">6. serve</h3><a id="user-content-6-serve" class="anchor" aria-label="Permalink: 6. serve" href="#6-serve"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To start the web UI we run a small server. This also enables nearest neighbor similarity search and interactively querying subsets of the input data while exploring the scopes.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="ls-serve ~/latent-scope-data"><pre>ls-serve <span class="pl-k">~</span>/latent-scope-data</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Dataset directory structure</h2><a id="user-content-dataset-directory-structure" class="anchor" aria-label="Permalink: Dataset directory structure" href="#dataset-directory-structure"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Each dataset will have its own directory in data/ created when you ingest your CSV. All subsequent steps of setting up a dataset write their data and metadata to this directory. There are no databases in this tool, just flat files that are easy to copy and edit.</p> <pre>├── data/ | ├── dataset1/ | | ├── input.parquet # from ingest.py, the dataset | | ├── meta.json # from ingest.py, metadata for dataset, #rows, columns, text_column | | ├── embeddings/ | | | ├── embedding-001.h5 # from embed.py, embedding vectors | | | ├── embedding-001.json # from embed.py, parameters used to embed | | | ├── embedding-002... | | ├── umaps/ | | | ├── umap-001.parquet # from umap.py, x,y coordinates | | | ├── umap-001.json # from umap.py, params used | | | ├── umap-001.png # from umap.py, thumbnail of plot | | | ├── umap-002.... | | ├── clusters/ | | | ├── clusters-001.parquet # from cluster.py, cluster indices | | | ├── clusters-001-labels-default.parquet # from cluster.py, default labels | | | ├── clusters-001-labels-001.parquet # from label_clusters.py, LLM generated labels | | | ├── clusters-001.json # from cluster.py, params used | | | ├── clusters-001.png # from cluster.py, thumbnail of plot | | | ├── clusters-002... | | ├── scopes/ | | | ├── scopes-001.json # from scope.py, combination of embed, umap, clusters and label choice | | | ├── scopes-... | | ├── tags/ | | | ├── ❤️.indices # tagged by UI, powered by tags.py | | | ├── ... # can have arbitrary named tags | | ├── jobs/ | | | ├── 8980️-12345...json # created when job is run via web UI </pre> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="9PGY44ylymfOZ4ebVuL/U2Jho+3qwusJDXZF4BRxPWyMUk7tUeEtfukEUK7Qrt3YH5tKXxwHxH969DHcQu2SgA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> A scientific instrument for 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height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>658</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/enjalot/latent-scope/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>9</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/enjalot/latent-scope/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>25</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fenjalot%2Flatent-scope&report=enjalot+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/enjalot/latent-scope/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="20" data-view-component="true" class="Counter">20</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/enjalot/latent-scope/releases/tag/v0.6.0"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">0.6.0</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2025-02-28T20:56:24Z" class="no-wrap">Feb 28, 2025</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/enjalot/latent-scope/releases" data-view-component="true" class="Link">+ 19 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/users/enjalot/packages?repo_name=latent-scope" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span></a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/enjalot/latent-scope/used_by_list" accept="text/fragment+html"> 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data-hovercard-url="/users/AnukratiMehta/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/108215785?s=64&v=4" alt="@AnukratiMehta" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> </ul> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#f1e05a !important;;width: 39.9%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA5B0B !important;;width: 28.7%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3572A5 !important;;width: 22.6%;" itemprop="keywords" data-view-component="true" class="Progress-item 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data-view-component="true" class="octicon octicon-alert"> <path d="M6.457 1.047c.659-1.234 2.427-1.234 3.086 0l6.082 11.378A1.75 1.75 0 0 1 14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>