From 34d773f76318b6a30f13fc52457e6c4f89d01543 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Thu, 10 Aug 2023 16:05:18 +0000 Subject: [PATCH] deploy: ced9eb388a8a97f2e1def23f441ca2ede9e943bd --- 404.html | 4 ++-- assets/js/{6866e3af.67bccb24.js => 6866e3af.6a31b99f.js} | 2 +- assets/js/{c01ae077.0fc498b9.js => c01ae077.4271ac2d.js} | 2 +- .../{runtime~main.3d639c3c.js => runtime~main.bd0c61b9.js} | 2 +- getting-started/index.html | 6 +++--- index.html | 6 +++--- 6 files changed, 11 insertions(+), 11 deletions(-) rename assets/js/{6866e3af.67bccb24.js => 6866e3af.6a31b99f.js} (99%) rename assets/js/{c01ae077.0fc498b9.js => c01ae077.4271ac2d.js} (99%) rename assets/js/{runtime~main.3d639c3c.js => runtime~main.bd0c61b9.js} (99%) diff --git a/404.html b/404.html index e912113..90cfbcc 100644 --- a/404.html +++ b/404.html @@ -4,13 +4,13 @@ Page Not Found | eLLMental - +
Skip to main content

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- + \ No newline at end of file diff --git a/assets/js/6866e3af.67bccb24.js b/assets/js/6866e3af.6a31b99f.js similarity index 99% rename from assets/js/6866e3af.67bccb24.js rename to assets/js/6866e3af.6a31b99f.js index b5088f1..dbfb139 100644 --- a/assets/js/6866e3af.67bccb24.js +++ b/assets/js/6866e3af.6a31b99f.js @@ -227,7 +227,7 @@ __webpack_require__.r(__webpack_exports__); /* harmony import */ var _home_runner_work_eLLMental3_eLLMental3_docs_site_node_modules_babel_runtime_helpers_esm_extends_js__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(7462); /* harmony import */ var react__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(7294); /* harmony import */ var _mdx_js_react__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(3905); -/* @jsxRuntime classic */ /* @jsx mdx */ /* @jsxFrag React.Fragment */const frontMatter={slug:'/getting-started'};const contentTitle='Getting started';const metadata={"unversionedId":"getting_started","id":"getting_started","title":"Getting started","description":"","source":"@site/docs/02_getting_started.md","sourceDirName":".","slug":"/getting-started","permalink":"/getting-started","draft":false,"tags":[],"version":"current","lastUpdatedBy":"Juan José Rodríguez López","lastUpdatedAt":1691672425,"formattedLastUpdatedAt":"Aug 10, 2023","sidebarPosition":2,"frontMatter":{"slug":"/getting-started"}};const assets={};const toc=[];const layoutProps={toc};const MDXLayout="wrapper";function MDXContent(_ref){let{components,...props}=_ref;return (0,_mdx_js_react__WEBPACK_IMPORTED_MODULE_1__/* .mdx */ .kt)(MDXLayout,(0,_home_runner_work_eLLMental3_eLLMental3_docs_site_node_modules_babel_runtime_helpers_esm_extends_js__WEBPACK_IMPORTED_MODULE_2__/* ["default"] */ .Z)({},layoutProps,props,{components:components,mdxType:"MDXLayout"}),(0,_mdx_js_react__WEBPACK_IMPORTED_MODULE_1__/* .mdx */ .kt)("h1",{"id":"getting-started"},`Getting started`));};MDXContent.isMDXComponent=true; +/* @jsxRuntime classic */ /* @jsx mdx */ /* @jsxFrag React.Fragment */const frontMatter={slug:'/getting-started'};const contentTitle='Getting started';const metadata={"unversionedId":"getting_started","id":"getting_started","title":"Getting started","description":"","source":"@site/docs/02_getting_started.md","sourceDirName":".","slug":"/getting-started","permalink":"/getting-started","draft":false,"tags":[],"version":"current","lastUpdatedBy":"Juan José Rodríguez López","lastUpdatedAt":1691683455,"formattedLastUpdatedAt":"Aug 10, 2023","sidebarPosition":2,"frontMatter":{"slug":"/getting-started"}};const assets={};const toc=[];const layoutProps={toc};const MDXLayout="wrapper";function MDXContent(_ref){let{components,...props}=_ref;return (0,_mdx_js_react__WEBPACK_IMPORTED_MODULE_1__/* .mdx */ .kt)(MDXLayout,(0,_home_runner_work_eLLMental3_eLLMental3_docs_site_node_modules_babel_runtime_helpers_esm_extends_js__WEBPACK_IMPORTED_MODULE_2__/* ["default"] */ .Z)({},layoutProps,props,{components:components,mdxType:"MDXLayout"}),(0,_mdx_js_react__WEBPACK_IMPORTED_MODULE_1__/* .mdx */ .kt)("h1",{"id":"getting-started"},`Getting started`));};MDXContent.isMDXComponent=true; 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-
Skip to main content

Getting started

Last updated on by Juan José Rodríguez López
- +
Skip to main content

Getting started

Last updated on by Juan José Rodríguez López
+ \ No newline at end of file diff --git a/index.html b/index.html index daabb39..c6a3ea6 100644 --- a/index.html +++ b/index.html @@ -4,13 +4,13 @@ Introduction | eLLMental - +
-
Skip to main content

Introduction

eLLMental is the ultimate library of components for building LLM-driven projects in the JVM.

Wanna try? Go straight to the Getting Started Guide, or keep reading to know more about eLLMental.

What can you do with eLLMental?​

Embeddings Semantic Space Component​

Embedding models are a special kind of Large Language Models (LLMs) that allow, given a piece of text, to calculate a large vector that represents a point in what we call the semantics space. This semantics space has the property that two pieces of text that are semantically related will be placed close to each other, allowing us to calculate a semantic distance between any two given pieces of text. Embeddings can be used to implement powerful search features that go beyond keyword matching, find related documents in a large database, or detect duplicates even when they're written with completely different words.

The Embeddings Space Component provides straightforward interfaces to create and operate with embeddings, find the semantically closest documents to a given piece of text and many other operations. See the Embeddings Semantic Search Component documentation page for more details.

Text Generation Component​

Text generation is the reason why LLMs became mainstream. This component provides text generation features that enable use cases like text completion, summarization, translation or explanations. Access the Text Generation Component documentation page for more details.

eLLMental Principles​

These are the design principles behind eLLMental:

  1. Simplicity in Complexity: We aim to make the AI development process as simple and intuitive as developing any other software, hiding unneeded implementation details and glue code so the developer can focus on creating value.

  2. Readiness for Production: From development to deployment, all features of eLLMental are crafted with a production-ready mindset.

  3. Continuous Improvement: eLLMental continuously evolves for the better. With the support of our active community and dedicated team, we regularly implement improvements and introduce new features to keep expanding what is possible.

Join the movement!​

We'll need your help to build something that becomes really useful for everyone. There are many things you can do to contribute:

  1. Join the conversation in our Discord server.
  2. Send us suggestions, questions, or feature requests by creating a New Issue.
  3. Look at the Open Issues, choose one that interests you, and start contributing.
  4. Spread the word about eLLMental!
Last updated on by Juan José Rodríguez López
- +
Skip to main content

Introduction

eLLMental is the ultimate library of components for building LLM-driven projects in the JVM.

Wanna try? Go straight to the Getting Started Guide, or keep reading to know more about eLLMental.

What can you do with eLLMental?​

Embeddings Semantic Space Component​

Embedding models are a special kind of Large Language Models (LLMs) that allow, given a piece of text, to calculate a large vector that represents a point in what we call the semantics space. This semantics space has the property that two pieces of text that are semantically related will be placed close to each other, allowing us to calculate a semantic distance between any two given pieces of text. Embeddings can be used to implement powerful search features that go beyond keyword matching, find related documents in a large database, or detect duplicates even when they're written with completely different words.

The Embeddings Space Component provides straightforward interfaces to create and operate with embeddings, find the semantically closest documents to a given piece of text and many other operations. See the Embeddings Semantic Search Component documentation page for more details.

Text Generation Component​

Text generation is the reason why LLMs became mainstream. This component provides text generation features that enable use cases like text completion, summarization, translation or explanations. Access the Text Generation Component documentation page for more details.

eLLMental Principles​

These are the design principles behind eLLMental:

  1. Simplicity in Complexity: We aim to make the AI development process as simple and intuitive as developing any other software, hiding unneeded implementation details and glue code so the developer can focus on creating value.

  2. Readiness for Production: From development to deployment, all features of eLLMental are crafted with a production-ready mindset.

  3. Continuous Improvement: eLLMental continuously evolves for the better. With the support of our active community and dedicated team, we regularly implement improvements and introduce new features to keep expanding what is possible.

Join the movement!​

We'll need your help to build something that becomes really useful for everyone. There are many things you can do to contribute:

  1. Join the conversation in our Discord server.
  2. Send us suggestions, questions, or feature requests by creating a New Issue.
  3. Look at the Open Issues, choose one that interests you, and start contributing.
  4. Spread the word about eLLMental!
Last updated on by Juan José Rodríguez López
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