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Add AI-powered LLM answer plugin for SearXNG
Introduces a new SearXNG plugin that generates contextual, AI-powered answers using LangChain and an LLM, with results formatted in Markdown and rendered via a custom HTML template. Updates the README with usage details and adds supporting documentation and image assets.
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‎README.md‎

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<!-- Generated on 2025-07-16 09:14:32+00:00 -->
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<!-- Generated on 2025-08-02 05:15:44+00:00 -->
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<!-- Do not edit this file. Edit README.md/base.md.j2 instead. -->
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# Scripts
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| Title | Filename | Type | Description |
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| -------------------------------------- | ------------------------------------------------------------------------------------------------------ | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| SearXNG AI Search Assist | [`dashboard_services.py`](python/searxng-addons/search_answers_llm/plugins_langchain_llm.py) | SearXNG Addon | Get instant, AI-powered answers summarized from top search results, displayed directly on the page. [**`Demo 🖼️`**](/docs/search_llm_assist.png) |
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| SearXNG Self-Hosted Service Index | [`dashboard_services.py`](python/searxng-addons/dashboard_services.py) | SearXNG Addon | Integrates with [`gethomepage/homepage`](https://github.com/gethomepage/homepage) to search through your self-hosted services and applications. [**`Demo 🖼️`**](/docs/searxng-homepage.png) |
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| LiteLLM Spend Tracker | [`openfaas-function/`](openfaas-function/README.md) | Serverless | Hierarchical spend visibility across multiple time horizons is provided by a time-segmented API cost tracker that aggregates LLM usage data. |
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| Portainer Service Recovery Function | [`openfaas-function/`](openfaas-function/README.md) | Serverless | On-demand service stack recovery solution that intelligently identifies and remediates service disruptions in Portainer-managed containers. |

‎docs/scripts.xls‎

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‎docs/search_llm_assist.png‎

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‎docs/templates/scripts.md.j2‎

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| Title | Filename | Type | Description |
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| -------------------------------------- | ------------------------------------------------------------------------------------------------------ | ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| SearXNG AI Search Assist | [`dashboard_services.py`](python/searxng-addons/search_answers_llm/plugins_langchain_llm.py) | SearXNG Addon | Get instant, AI-powered answers summarized from top search results, displayed directly on the page. [**`Demo 🖼️`**](/docs/search_llm_assist.png) |
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| SearXNG Self-Hosted Service Index | [`dashboard_services.py`](python/searxng-addons/dashboard_services.py) | SearXNG Addon | Integrates with [`gethomepage/homepage`](https://github.com/gethomepage/homepage) to search through your self-hosted services and applications. [**`Demo 🖼️`**](/docs/searxng-homepage.png) |
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| LiteLLM Spend Tracker | [`openfaas-function/`](openfaas-function/README.md) | Serverless | Hierarchical spend visibility across multiple time horizons is provided by a time-segmented API cost tracker that aggregates LLM usage data. |
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| Portainer Service Recovery Function | [`openfaas-function/`](openfaas-function/README.md) | Serverless | On-demand service stack recovery solution that intelligently identifies and remediates service disruptions in Portainer-managed containers. |

‎python/searxng-addons/README.md‎

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## 🔍 Available Engines
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### 1. Homepage Dashboard Integration (`dashboard_services.py`)
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### 1. AI Search Assist (`search_answers_llm\plugins_langchain_llm.py`)
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This SearXNG plugin generates contextual, AI-powered answers by hooking into the `post_search` process. It programmatically executes a secondary, targeted search against Google and DuckDuckGo using `SearchQuery` and `EngineRef` to gather real-time context for the user's query. This context is then sent to a LLM. The plugin injects this response as a custom `Answer` result type, overriding the default template to use a custom one with the `|safe` filter, ensuring the rich text is rendered correctly on the results page.
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![SearXNG LLM Assist](/docs/search_llm_assist.png)
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### 2. Homepage Dashboard Integration (`dashboard_services.py`)
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Integrates with [`gethomepage/homepage`](https://github.com/gethomepage/homepage), a customizable home or startpage with Docker and service API support. This enables your home lab dashboard to become fully searchable using SearXNG.
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‎python/searxng-addons/search_answers_llm/llm_answer.html‎

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<div class="llm-answer-container">
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<div class="llm-answer-content">
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{{ answer.answer|safe }}
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</div>
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<style>
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.llm-answer-container {
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background: var(--color-answer-background);
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color: var(--color-answer-font);
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border-left: 4px solid var(--color-btn-background);
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padding: 16px;
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margin: 12px 0;
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border-radius: 6px;
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box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
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border: 1px solid var(--color-result-border);
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}
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/* Header styling */
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.llm-answer-container .llm-header {
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margin-bottom: 12px;
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display: flex;
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align-items: center;
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gap: 8px;
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}
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.llm-answer-container .llm-header strong {
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color: var(--color-btn-background);
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}
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.llm-answer-container .llm-header span {
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color: var(--color-base-font);
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opacity: 0.8;
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}
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/* Content styling */
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.llm-answer-container .llm-content {
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margin-bottom: 12px;
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line-height: 1.6;
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}
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/* Markdown-generated content styling using theme variables */
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.llm-answer-container h1,
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.llm-answer-container h2,
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.llm-answer-container h3,
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.llm-answer-container h4,
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.llm-answer-container h5,
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.llm-answer-container h6 {
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margin-top: 16px;
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margin-bottom: 8px;
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color: var(--color-answer-font);
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}
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.llm-answer-container h2 {
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font-size: 1.2em;
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border-bottom: 1px solid var(--color-result-border);
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padding-bottom: 4px;
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}
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.llm-answer-container h3 {
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font-size: 1.1em;
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}
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.llm-answer-container ul,
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.llm-answer-container ol {
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margin: 8px 0;
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padding-left: 20px;
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}
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.llm-answer-container li {
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margin: 4px 0;
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}
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.llm-answer-container p {
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margin: 8px 0;
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line-height: 1.5;
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}
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.llm-answer-container code {
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background: var(--color-doc-code-background);
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color: var(--color-doc-code);
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padding: 2px 4px;
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border-radius: 3px;
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font-family: 'Monaco', 'Menlo', 'Ubuntu Mono', monospace;
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font-size: 0.9em;
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}
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.llm-answer-container pre {
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background: var(--color-doc-code-background);
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color: var(--color-doc-code);
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border: 1px solid var(--color-result-border);
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border-radius: 4px;
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padding: 12px;
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overflow-x: auto;
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margin: 12px 0;
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}
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.llm-answer-container pre code {
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background: none;
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padding: 0;
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}
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.llm-answer-container blockquote {
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border-left: 3px solid var(--color-btn-background);
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margin: 12px 0;
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padding-left: 12px;
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color: var(--color-base-font);
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font-style: italic;
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opacity: 0.9;
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}
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.llm-answer-container strong {
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color: var(--color-answer-font);
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font-weight: 600;
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}
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.llm-answer-container em {
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color: var(--color-base-font);
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opacity: 0.9;
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}
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.llm-answer-container a {
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color: var(--color-result-link-font);
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text-decoration: underline;
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}
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.llm-answer-container a:hover {
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color: var(--color-result-link-font-highlight);
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}
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.llm-answer-container a:visited {
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color: var(--color-result-link-visited-font);
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}
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/* Footer styling */
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.llm-answer-container .llm-footer {
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border-top: 1px solid var(--color-result-border);
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padding-top: 8px;
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margin-top: 12px;
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font-size: 0.85em;
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color: var(--color-base-font);
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display: flex;
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justify-content: space-between;
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align-items: center;
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flex-wrap: wrap;
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gap: 8px;
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}
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.llm-answer-container .llm-footer .model-info {
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display: flex;
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align-items: center;
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gap: 12px;
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flex-wrap: wrap;
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}
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.llm-answer-container .llm-footer .model-name {
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font-weight: 600;
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color: var(--color-answer-font);
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}
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.llm-answer-container .llm-footer .disclaimer {
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opacity: 0.8;
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}
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/* GitHub button styling with theme support */
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.llm-answer-container .github-link {
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display: inline-flex;
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align-items: center;
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background: var(--color-toolkit-badge-background);
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color: var(--color-toolkit-badge-font);
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padding: 4px 8px;
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border-radius: 4px;
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text-decoration: none;
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font-size: 0.8em;
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transition: all 0.2s ease;
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border: 1px solid var(--color-result-border);
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}
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.llm-answer-container .github-link:hover {
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background: var(--color-btn-background);
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color: var(--color-btn-font);
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transform: translateY(-1px);
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text-decoration: none;
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}
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.llm-answer-container .github-link svg {
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width: 16px;
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height: 16px;
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margin-right: 4px;
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fill: currentColor;
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transition: transform 0.2s ease;
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}
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.llm-answer-container .github-link:hover svg {
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transform: scale(1.1);
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}
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/* Responsive design */
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@media (max-width: 50em) {
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.llm-answer-container .llm-footer {
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flex-direction: column;
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align-items: flex-start;
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}
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.llm-answer-container .llm-footer .model-info {
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flex-direction: column;
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align-items: flex-start;
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gap: 4px;
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}
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}
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</style>
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</div>

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