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posts/benchmarking-llms-on-jetson-orin-nano/index.html
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<!doctype html><html lang=en><head><title>Posts · Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta http-equiv=Content-Security-Policy content="upgrade-insecure-requests; block-all-mixed-content; default-src 'self'; child-src 'self'; font-src 'self' https://fonts.gstatic.com https://cdn.jsdelivr.net/; form-action 'self'; frame-src 'self' https://www.youtube.com https://disqus.com; img-src 'self' https://referrer.disqus.com https://c.disquscdn.com https://*.disqus.com; object-src 'none'; style-src 'self' 'unsafe-inline' https://fonts.googleapis.com/ https://cdn.jsdelivr.net/; script-src 'self' 'unsafe-inline' https://www.google-analytics.com https://cdn.jsdelivr.net/ https://pagead2.googlesyndication.com https://static.cloudflareinsights.com https://unpkg.com https://ericxliu-me.disqus.com https://disqus.com https://*.disqus.com https://*.disquscdn.com https://unpkg.com; connect-src 'self' https://www.google-analytics.com https://pagead2.googlesyndication.com https://cloudflareinsights.com ws://localhost:1313 ws://localhost:* wss://localhost:* https://links.services.disqus.com https://*.disqus.com;"><meta name=author content="Eric X. Liu"><meta name=description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=twitter:card content="summary"><meta name=twitter:title content="Posts"><meta name=twitter:description content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:url" content="https://ericxliu.me/posts/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="Posts"><meta property="og:description" content="Eric X. Liu - Software & Performance Engineer at Google. Sharing insights about software engineering, performance optimization, tech industry experiences, mountain biking adventures, Jeep overlanding, and outdoor activities."><meta property="og:locale" content="en"><meta property="og:type" content="website"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.4b392a85107b91dbdabc528edf014a6ab1a30cd44cafcd5325c8efe796794fca.css integrity="sha256-SzkqhRB7kdvavFKO3wFKarGjDNRMr81TJcjv55Z5T8o=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><link rel=alternate type=application/rss+xml href=/posts/index.xml title="Eric X. Liu's Personal Page"><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container list"><header><h1 class=title><a class=title-link href=https://ericxliu.me/posts/>Posts</a></h1></header><ul><li><span class=date>December 29, 2025</span>
<a class=title href=/posts/open-webui-openai-websearch/>How I Got Open WebUI Talking to OpenAI Web Search</a></li><li><span class=date>December 27, 2025</span>
<a class=title href=/posts/technical-deep-dive-llm-categorization/>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</a></li><li><span class=date>December 19, 2025</span>
<a class=title href=/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/>The Convergence of Fast Weights, Linear Attention, and State Space Models</a></li><li><span class=date>December 8, 2025</span>
<a class=title href=/posts/vattention/>vAttention</a></li><li><span class=date>November 15, 2025</span>
@@ -9,9 +10,8 @@
<a class=title href=/posts/flashing-jetson-orin-nano-in-virtualized-environments/>Flashing Jetson Orin Nano in Virtualized Environments</a></li><li><span class=date>September 28, 2025</span>
<a class=title href=/posts/openwrt-mwan3-wireguard-endpoint-exclusion/>OpenWrt: Fix WireGuard Connectivity with MWAN3 by Excluding the VPN Endpoint</a></li><li><span class=date>September 22, 2025</span>
<a class=title href=/posts/unifi-vlan-migration-to-zone-based-architecture/>UniFi VLAN Migration to Zone-Based Architecture</a></li><li><span class=date>August 19, 2025</span>
<a class=title href=/posts/quantization-in-llms/>Quantization in LLMs</a></li><li><span class=date>August 16, 2025</span>
<a class=title href=/posts/breville-barista-pro-maintenance/>Breville Barista Pro Maintenance</a></li></ul><ul class=pagination><li>1</li><li><a href=/posts/page/2/>2</a></li><li class=hidden><a href=/posts/page/2/>&#8250;</a></li><li><a href=/posts/page/2/>&#187;</a></li></ul></section></div><footer class=footer><section class=container>©
<a class=title href=/posts/quantization-in-llms/>Quantization in LLMs</a></li></ul><ul class=pagination><li>1</li><li><a href=/posts/page/2/>2</a></li><li class=hidden><a href=/posts/page/2/>&#8250;</a></li><li><a href=/posts/page/2/>&#187;</a></li></ul></section></div><footer class=footer><section class=container>©
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<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Posts on Eric X. Liu's Personal Page</title><link>https://ericxliu.me/posts/</link><description>Recent content in Posts on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sun, 28 Dec 2025 21:21:42 +0000</lastBuildDate><atom:link href="https://ericxliu.me/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</title><link>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</link><pubDate>Sat, 27 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</guid><description>&lt;p&gt;Running a family finance system is surprisingly complex. What starts as a simple spreadsheet often evolves into a web of rules, exceptions, and &amp;ldquo;wait, was this dinner or &lt;em&gt;vacation&lt;/em&gt; dinner?&amp;rdquo; questions.&lt;/p&gt;
<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Posts on Eric X. Liu's Personal Page</title><link>https://ericxliu.me/posts/</link><description>Recent content in Posts on Eric X. Liu's Personal Page</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 29 Dec 2025 07:15:58 +0000</lastBuildDate><atom:link href="https://ericxliu.me/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>How I Got Open WebUI Talking to OpenAI Web Search</title><link>https://ericxliu.me/posts/open-webui-openai-websearch/</link><pubDate>Mon, 29 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/open-webui-openai-websearch/</guid><description>&lt;p&gt;OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue &lt;a href="https://github.com/BerriAI/litellm/issues/13042" class="external-link" target="_blank" rel="noopener"&gt;#13042&lt;/a&gt; tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every &lt;code&gt;web_search_call&lt;/code&gt; before the UI ever sees it.&lt;/p&gt;
&lt;p&gt;This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself.&lt;/p&gt;</description></item><item><title>From Gemini-3-Flash to T5-Gemma-2 A Journey in Distilling a Family Finance LLM</title><link>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</link><pubDate>Sat, 27 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/technical-deep-dive-llm-categorization/</guid><description>&lt;p&gt;Running a family finance system is surprisingly complex. What starts as a simple spreadsheet often evolves into a web of rules, exceptions, and &amp;ldquo;wait, was this dinner or &lt;em&gt;vacation&lt;/em&gt; dinner?&amp;rdquo; questions.&lt;/p&gt;
&lt;p&gt;For years, I relied on a rule-based system to categorize our credit card transactions. It worked&amp;hellip; mostly. But maintaining &lt;code&gt;if &amp;quot;UBER&amp;quot; in description and amount &amp;gt; 50&lt;/code&gt; style rules is a never-ending battle against the entropy of merchant names and changing habits.&lt;/p&gt;</description></item><item><title>The Convergence of Fast Weights, Linear Attention, and State Space Models</title><link>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</link><pubDate>Fri, 19 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/</guid><description>&lt;p&gt;Modern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (&amp;ldquo;Fast Weights&amp;rdquo;) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).&lt;/p&gt;
&lt;p&gt;This article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV.&lt;/p&gt;</description></item><item><title>vAttention</title><link>https://ericxliu.me/posts/vattention/</link><pubDate>Mon, 08 Dec 2025 00:00:00 +0000</pubDate><guid>https://ericxliu.me/posts/vattention/</guid><description>&lt;p&gt;Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While &lt;strong&gt;PagedAttention&lt;/strong&gt; (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.&lt;/p&gt;
&lt;h4 id="the-status-quo-pagedattention-and-software-tables"&gt;

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Eric X. Liu
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<!doctype html><html lang=en><head><title>How I Got Open WebUI Talking to OpenAI Web Search · Eric X. Liu's Personal Page</title><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=color-scheme content="light dark"><meta http-equiv=Content-Security-Policy content="upgrade-insecure-requests; block-all-mixed-content; default-src 'self'; child-src 'self'; font-src 'self' https://fonts.gstatic.com https://cdn.jsdelivr.net/; form-action 'self'; frame-src 'self' https://www.youtube.com https://disqus.com; img-src 'self' https://referrer.disqus.com https://c.disquscdn.com https://*.disqus.com; object-src 'none'; style-src 'self' 'unsafe-inline' https://fonts.googleapis.com/ https://cdn.jsdelivr.net/; script-src 'self' 'unsafe-inline' https://www.google-analytics.com https://cdn.jsdelivr.net/ https://pagead2.googlesyndication.com https://static.cloudflareinsights.com https://unpkg.com https://ericxliu-me.disqus.com https://disqus.com https://*.disqus.com https://*.disquscdn.com https://unpkg.com; connect-src 'self' https://www.google-analytics.com https://pagead2.googlesyndication.com https://cloudflareinsights.com ws://localhost:1313 ws://localhost:* wss://localhost:* https://links.services.disqus.com https://*.disqus.com;"><meta name=author content="Eric X. Liu"><meta name=description content="OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue #13042 tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every web_search_call before the UI ever sees it.
This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself."><meta name=keywords content="software engineer,performance engineering,Google engineer,tech blog,software development,performance optimization,Eric Liu,engineering blog,mountain biking,Jeep enthusiast,overlanding,camping,outdoor adventures"><meta name=twitter:card content="summary"><meta name=twitter:title content="How I Got Open WebUI Talking to OpenAI Web Search"><meta name=twitter:description content="OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue #13042 tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every web_search_call before the UI ever sees it.
This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself."><meta property="og:url" content="https://ericxliu.me/posts/open-webui-openai-websearch/"><meta property="og:site_name" content="Eric X. Liu's Personal Page"><meta property="og:title" content="How I Got Open WebUI Talking to OpenAI Web Search"><meta property="og:description" content="OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue #13042 tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every web_search_call before the UI ever sees it.
This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself."><meta property="og:locale" content="en"><meta property="og:type" content="article"><meta property="article:section" content="posts"><meta property="article:published_time" content="2025-12-29T00:00:00+00:00"><meta property="article:modified_time" content="2025-12-29T07:15:58+00:00"><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=canonical href=https://ericxliu.me/posts/open-webui-openai-websearch/><link rel=preload href=/fonts/fa-brands-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-regular-400.woff2 as=font type=font/woff2 crossorigin><link rel=preload href=/fonts/fa-solid-900.woff2 as=font type=font/woff2 crossorigin><link rel=stylesheet href=/css/coder.min.4b392a85107b91dbdabc528edf014a6ab1a30cd44cafcd5325c8efe796794fca.css integrity="sha256-SzkqhRB7kdvavFKO3wFKarGjDNRMr81TJcjv55Z5T8o=" crossorigin=anonymous media=screen><link rel=stylesheet href=/css/coder-dark.min.a00e6364bacbc8266ad1cc81230774a1397198f8cfb7bcba29b7d6fcb54ce57f.css integrity="sha256-oA5jZLrLyCZq0cyBIwd0oTlxmPjPt7y6KbfW/LVM5X8=" crossorigin=anonymous media=screen><link rel=icon type=image/svg+xml href=/images/favicon.svg sizes=any><link rel=icon type=image/png href=/images/favicon-32x32.png sizes=32x32><link rel=icon type=image/png href=/images/favicon-16x16.png sizes=16x16><link rel=apple-touch-icon href=/images/apple-touch-icon.png><link rel=apple-touch-icon sizes=180x180 href=/images/apple-touch-icon.png><link rel=manifest href=/site.webmanifest><link rel=mask-icon href=/images/safari-pinned-tab.svg color=#5bbad5><script async src="https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js?client=ca-pub-3972604619956476" crossorigin=anonymous></script><script type=application/ld+json>{"@context":"http://schema.org","@type":"Person","name":"Eric X. Liu","url":"https:\/\/ericxliu.me\/","description":"Software \u0026 Performance Engineer at Google","sameAs":["https:\/\/www.linkedin.com\/in\/eric-x-liu-46648b93\/","https:\/\/git.ericxliu.me\/eric"]}</script><script type=application/ld+json>{"@context":"http://schema.org","@type":"BlogPosting","headline":"How I Got Open WebUI Talking to OpenAI Web Search","genre":"Blog","wordcount":"1087","url":"https:\/\/ericxliu.me\/posts\/open-webui-openai-websearch\/","datePublished":"2025-12-29T00:00:00\u002b00:00","dateModified":"2025-12-29T07:15:58\u002b00:00","description":"\u003cp\u003eOpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue \u003ca href=\u0022https:\/\/github.com\/BerriAI\/litellm\/issues\/13042\u0022 class=\u0022external-link\u0022 target=\u0022_blank\u0022 rel=\u0022noopener\u0022\u003e#13042\u003c\/a\u003e tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every \u003ccode\u003eweb_search_call\u003c\/code\u003e before the UI ever sees it.\u003c\/p\u003e\n\u003cp\u003eThis post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself.\u003c\/p\u003e","author":{"@type":"Person","name":"Eric X. Liu"}}</script></head><body class="preload-transitions colorscheme-auto"><div class=float-container><a id=dark-mode-toggle class=colorscheme-toggle><i class="fa-solid fa-adjust fa-fw" aria-hidden=true></i></a></div><main class=wrapper><nav class=navigation><section class=container><a class=navigation-title href=https://ericxliu.me/>Eric X. Liu's Personal Page
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<label class="menu-button float-right" for=menu-toggle><i class="fa-solid fa-bars fa-fw" aria-hidden=true></i></label><ul class=navigation-list><li class=navigation-item><a class=navigation-link href=/posts/>Posts</a></li><li class=navigation-item><a class=navigation-link href=https://chat.ericxliu.me>Chat</a></li><li class=navigation-item><a class=navigation-link href=https://git.ericxliu.me/user/oauth2/Authenitk>Git</a></li><li class=navigation-item><a class=navigation-link href=https://coder.ericxliu.me/api/v2/users/oidc/callback>Coder</a></li><li class=navigation-item><a class=navigation-link href=/about/>About</a></li><li class=navigation-item><a class=navigation-link href=/>|</a></li><li class=navigation-item><a class=navigation-link href=https://sso.ericxliu.me>Sign in</a></li></ul></section></nav><div class=content><section class="container post"><article><header><div class=post-title><h1 class=title><a class=title-link href=https://ericxliu.me/posts/open-webui-openai-websearch/>How I Got Open WebUI Talking to OpenAI Web Search</a></h1></div><div class=post-meta><div class=date><span class=posted-on><i class="fa-solid fa-calendar" aria-hidden=true></i>
<time datetime=2025-12-29T00:00:00Z>December 29, 2025
</time></span><span class=reading-time><i class="fa-solid fa-clock" aria-hidden=true></i>
6-minute read</span></div></div></header><div class=post-content><p>OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue <a href=https://github.com/BerriAI/litellm/issues/13042 class=external-link target=_blank rel=noopener>#13042</a> tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every <code>web_search_call</code> before the UI ever sees it.</p><p>This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself.</p><h2 id=why-open-webui-broke>Why Open WebUI Broke
<a class=heading-link href=#why-open-webui-broke><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><ol><li><strong>Wrong API surface.</strong> <code>/v1/chat/completions</code> still rejects <code>type: "web_search"</code> with <code>Invalid value: 'web_search'. Supported values are: 'function' and 'custom'.</code></li><li><strong>LiteLLM tooling gap.</strong> The OpenAI TypedDicts in <code>litellm/types/llms/openai.py</code> only allow <code>Literal["function"]</code>. Even if the backend call succeeded, streaming would crash when it saw a new tool type.</li><li><strong>Open WebUI assumptions.</strong> The UI eagerly parses every tool delta, so when LiteLLM streamed the raw <code>web_search_call</code> chunk, the UI tried to execute it, failed to parse the arguments, and aborted the chat.</li></ol><p>Fixing all three required touching both the proxy configuration and the LiteLLM transformation path.</p><h2 id=step-1--route-gpt5-through-the-responses-api>Step 1 – Route GPT‑5 Through the Responses API
<a class=heading-link href=#step-1--route-gpt5-through-the-responses-api><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><p>LiteLLM’s Responses bridge activates whenever the backend model name starts with <code>openai/responses/</code>. I added a dedicated alias, <code>gpt-5.2-search</code>, that hardcodes the Responses API plus web search metadata. Existing models (reasoning, embeddings, TTS) stay untouched.</p><div class=highlight><pre tabindex=0 style=color:#e6edf3;background-color:#0d1117;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-yaml data-lang=yaml><span style=display:flex><span><span style=color:#8b949e;font-style:italic># proxy-config.yaml (sanitized)</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#7ee787>model_list</span>:<span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span>- <span style=color:#7ee787>model_name</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>gpt-5.2-search</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>litellm_params</span>:<span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>model</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>openai/responses/openai/gpt-5.2</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>api_key</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>&lt;OPENAI_API_KEY&gt;</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>reasoning_effort</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>high</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>merge_reasoning_content_in_choices</span>:<span style=color:#6e7681> </span><span style=color:#79c0ff>true</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>tools</span>:<span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span>- <span style=color:#7ee787>type</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>web_search</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>user_location</span>:<span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>type</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>approximate</span><span style=color:#6e7681>
</span></span></span><span style=display:flex><span><span style=color:#6e7681> </span><span style=color:#7ee787>country</span>:<span style=color:#6e7681> </span><span style=color:#a5d6ff>US</span><span style=color:#6e7681>
</span></span></span></code></pre></div><p>Any client (Open WebUI included) can now request <code>model: "gpt-5.2-search"</code> over the standard <code>/v1/chat/completions</code> endpoint, and LiteLLM handles the Responses API hop transparently.</p><h2 id=step-2--mask-web_search_call-chunks-inside-litellm>Step 2 – Mask <code>web_search_call</code> Chunks Inside LiteLLM
<a class=heading-link href=#step-2--mask-web_search_call-chunks-inside-litellm><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><p>Even with the right API, LiteLLM still needs to stream deltas Open WebUI can digest. My <a href=https://ericxliu.me/hotfix.py class=external-link target=_blank rel=noopener>hotfix.py</a> script copies the LiteLLM source into <code>/tmp/patch/litellm</code>, then rewrites two files. This script runs as part of the Helm release’s init hook so I can inject fixes directly into the container filesystem at pod start. That saves me from rebuilding and pushing new images every time LiteLLM upstream changes (or refuses a patch), which is critical while waiting for issue #13042 to land. I’ll try to upstream the fix, but this is admittedly hacky, so timelines are uncertain.</p><ol><li><strong><code>openai.py</code> TypedDicts</strong>: extend the tool chunk definitions to accept <code>Literal["web_search"]</code>.</li><li><strong><code>litellm_responses_transformation/transformation.py</code></strong>: intercept every streaming item and short-circuit anything with <code>type == "web_search_call"</code>, returning an empty assistant delta instead of a tool call.</li></ol><div class=highlight><pre tabindex=0 style=color:#e6edf3;background-color:#0d1117;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-python data-lang=python><span style=display:flex><span><span style=color:#8b949e;font-style:italic># Excerpt from hotfix.py</span>
</span></span><span style=display:flex><span>tool_call_chunk_original <span style=color:#ff7b72;font-weight:700>=</span> (
</span></span><span style=display:flex><span> <span style=color:#a5d6ff>&#39;class ChatCompletionToolCallChunk(TypedDict): # result of /chat/completions call</span><span style=color:#79c0ff>\n</span><span style=color:#a5d6ff>&#39;</span>
</span></span><span style=display:flex><span> <span style=color:#a5d6ff>&#39; id: Optional[str]</span><span style=color:#79c0ff>\n</span><span style=color:#a5d6ff>&#39;</span>
</span></span><span style=display:flex><span> <span style=color:#a5d6ff>&#39; type: Literal[&#34;function&#34;]&#39;</span>
</span></span><span style=display:flex><span>)
</span></span><span style=display:flex><span>tool_call_chunk_patch <span style=color:#ff7b72;font-weight:700>=</span> tool_call_chunk_original<span style=color:#ff7b72;font-weight:700>.</span>replace(
</span></span><span style=display:flex><span> <span style=color:#a5d6ff>&#39;Literal[&#34;function&#34;]&#39;</span>, <span style=color:#a5d6ff>&#39;Literal[&#34;function&#34;, &#34;web_search&#34;]&#39;</span>
</span></span><span style=display:flex><span>)
</span></span><span style=display:flex><span><span style=color:#ff7b72;font-weight:700>...</span>
</span></span><span style=display:flex><span><span style=color:#ff7b72>if</span> tool_call_chunk_original <span style=color:#ff7b72;font-weight:700>in</span> content:
</span></span><span style=display:flex><span> content <span style=color:#ff7b72;font-weight:700>=</span> content<span style=color:#ff7b72;font-weight:700>.</span>replace(tool_call_chunk_original, tool_call_chunk_patch, <span style=color:#a5d6ff>1</span>)
</span></span></code></pre></div><div class=highlight><pre tabindex=0 style=color:#e6edf3;background-color:#0d1117;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-python data-lang=python><span style=display:flex><span>added_block <span style=color:#ff7b72;font-weight:700>=</span> <span style=color:#a5d6ff>&#34;&#34;&#34; elif output_item.get(&#34;type&#34;) == &#34;web_search_call&#34;:
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> # Mask the call: Open WebUI should never see tool metadata
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> action_payload = output_item.get(&#34;action&#34;)
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> verbose_logger.debug(
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> &#34;Chat provider: masking web_search_call (added) call_id=</span><span style=color:#a5d6ff>%s</span><span style=color:#a5d6ff> action=</span><span style=color:#a5d6ff>%s</span><span style=color:#a5d6ff>&#34;,
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> output_item.get(&#34;call_id&#34;),
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> action_payload,
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> )
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> return ModelResponseStream(
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> choices=[
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> StreamingChoices(
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> index=0,
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> delta=Delta(content=&#34;&#34;),
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> finish_reason=None,
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> )
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> ]
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> )
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff>&#34;&#34;&#34;</span>
</span></span></code></pre></div><p>These patches ensure LiteLLM never emits a <code>tool_calls</code> delta for <code>web_search</code>. Open WebUI only receives assistant text chunks, so it happily renders the model response and the inline citations the Responses API already provides.</p><h2 id=step-3--prove-it-with-curl-and-open-webui>Step 3 – Prove It with cURL (and Open WebUI)
<a class=heading-link href=#step-3--prove-it-with-curl-and-open-webui><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><p>I keep a simple smoke test (<code>litellm_smoke_test.sh</code>) that hits the public ingress with and without streaming. The only secrets are placeholders here, but the structure is the same.</p><div class=highlight><pre tabindex=0 style=color:#e6edf3;background-color:#0d1117;-moz-tab-size:4;-o-tab-size:4;tab-size:4><code class=language-bash data-lang=bash><span style=display:flex><span><span style=color:#8b949e;font-weight:700;font-style:italic>#!/usr/bin/env bash
</span></span></span><span style=display:flex><span>set -euo pipefail
</span></span><span style=display:flex><span>
</span></span><span style=display:flex><span>echo <span style=color:#a5d6ff>&#34;Testing non-streaming...&#34;</span>
</span></span><span style=display:flex><span>curl <span style=color:#a5d6ff>&#34;https://api.ericxliu.me/v1/chat/completions&#34;</span> <span style=color:#79c0ff>\
</span></span></span><span style=display:flex><span> -H <span style=color:#a5d6ff>&#34;Authorization: Bearer &lt;LITELLM_MASTER_KEY&gt;&#34;</span> <span style=color:#79c0ff>\
</span></span></span><span style=display:flex><span> -H <span style=color:#a5d6ff>&#34;Content-Type: application/json&#34;</span> <span style=color:#79c0ff>\
</span></span></span><span style=display:flex><span> -d <span style=color:#a5d6ff>&#39;{
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> &#34;model&#34;: &#34;gpt-5.2-search&#34;,
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> &#34;messages&#34;: [{&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: &#34;Find the sunset time in Tokyo today.&#34;}]
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> }&#39;</span>
</span></span><span style=display:flex><span>
</span></span><span style=display:flex><span>echo -e <span style=color:#a5d6ff>&#34;\n\nTesting streaming...&#34;</span>
</span></span><span style=display:flex><span>curl <span style=color:#a5d6ff>&#34;https://api.ericxliu.me/v1/chat/completions&#34;</span> <span style=color:#79c0ff>\
</span></span></span><span style=display:flex><span> -H <span style=color:#a5d6ff>&#34;Authorization: Bearer &lt;LITELLM_MASTER_KEY&gt;&#34;</span> <span style=color:#79c0ff>\
</span></span></span><span style=display:flex><span> -H <span style=color:#a5d6ff>&#34;Content-Type: application/json&#34;</span> <span style=color:#79c0ff>\
</span></span></span><span style=display:flex><span> -d <span style=color:#a5d6ff>&#39;{
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> &#34;model&#34;: &#34;gpt-5.2-search&#34;,
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> &#34;stream&#34;: true,
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> &#34;messages&#34;: [{&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: &#34;What is the weather in NYC right now?&#34;}]
</span></span></span><span style=display:flex><span><span style=color:#a5d6ff> }&#39;</span>
</span></span></code></pre></div><p>Each request now returns grounded answers with citations (<code>url_citation</code> annotations) via Open WebUI, and the SSE feed never stalls because the UI isn’t asked to interpret tool calls.</p><h2 id=lessons--pitfalls>Lessons & Pitfalls
<a class=heading-link href=#lessons--pitfalls><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><ul><li><strong>The Responses API is non-negotiable (and syntax-sensitive).</strong> <code>/v1/chat/completions</code> still rejects <code>web_search</code>. Always test against <code>/v1/responses</code> directly before wiring LiteLLM into the loop. Furthermore, the syntax for <code>reasoning</code> is different: while Chat Completions uses the top-level <code>reasoning_effort</code> parameter, the Responses API requires a nested object: <code>"reasoning": {"effort": "medium"}</code>.</li><li><strong>The Native Model Trap.</strong> Models like <code>gpt-5-search-api</code> exist and support web search via standard Chat Completions, but they are often less flexible—for instance, rejecting <code>reasoning_effort</code> entirely. Routing a standard model through LiteLLM&rsquo;s Responses bridge offers more control over formatting and fallbacks.</li><li><strong>Magic strings control routing.</strong> LiteLLM has hardcoded logic (deep in <code>main.py</code>) that only triggers the Responses-to-Chat bridge if the backend model name starts with <code>openai/responses/</code>. Without that specific prefix, LiteLLM bypasses its internal transformation layer entirely, leading to cryptic 404s or &ldquo;model not found&rdquo; errors.</li><li><strong>Synthesized Sovereignty: The Call ID Crisis.</strong> Open WebUI is a &ldquo;well-behaved&rdquo; OpenAI client, yet it often omits the <code>id</code> field in <code>tool_calls</code> when sending assistant messages back to the server. LiteLLM&rsquo;s Responses bridge initially exploded with a <code>KeyError: 'id'</code> because it assumed an ID would always be present. The fix: synthesizing predictable IDs like <code>auto_tool_call_N</code> on the fly to satisfy the server-side schema.</li><li><strong>The Argument Delta Void.</strong> In streaming mode, the Responses API sometimes skips sending <code>response.function_call_arguments.delta</code> entirely if the query is simple. If the proxy only waits for deltas, the client receives an empty <code>{}</code> for tool arguments. The solution is to fallback and synthesize the <code>arguments</code> string from the <code>action</code> payload (e.g., <code>output_item['action']['query']</code>) when deltas are missing.</li><li><strong>Streaming State Machines are Fragile.</strong> Open WebUI is highly sensitive to the exact state of a tool call. If it sees a <code>web_search_call</code> with <code>status: "in_progress"</code>, its internal parser chokes, assuming it&rsquo;s an uncompleted &ldquo;function&rdquo; call. These intermediate state chunks must be intercepted and handled before they reach the UI.</li><li><strong>Defensive Masking is the Final Boss.</strong> To stop Open WebUI from entering an infinite client-side loop (thinking it needs to execute a tool it doesn&rsquo;t have), LiteLLM must &ldquo;mask&rdquo; the <code>web_search_call</code> chunks. By emitting empty content deltas instead of tool chunks, we hide the server-side search mechanics from the UI, allowing it to stay focused on the final answer.</li></ul><p>With those guardrails in place, GPT‑5’s native web search works end-to-end inside Open WebUI, complete with citations, without waiting for LiteLLM upstream fixes.</p><h2 id=references>References
<a class=heading-link href=#references><i class="fa-solid fa-link" aria-hidden=true title="Link to heading"></i>
<span class=sr-only>Link to heading</span></a></h2><ul><li><a href=https://docs.litellm.ai/docs/proxy/openai_responses class=external-link target=_blank rel=noopener>LiteLLM Documentation - OpenAI Responses API Bridge</a></li><li><a href=https://platform.openai.com/docs/api-reference/responses class=external-link target=_blank rel=noopener>OpenAI Documentation - Responses API</a></li><li><a href=https://github.com/BerriAI/litellm/issues/13042 class=external-link target=_blank rel=noopener>LiteLLM GitHub Issue #13042</a></li><li><a href=https://docs.openwebui.com/ class=external-link target=_blank rel=noopener>Open WebUI Documentation</a></li><li><a href=https://ericxliu.me/hotfix.py class=external-link target=_blank rel=noopener>The hotfix.py Script</a></li></ul></div><footer><div id=disqus_thread></div><script>window.disqus_config=function(){},function(){if(["localhost","127.0.0.1"].indexOf(window.location.hostname)!=-1){document.getElementById("disqus_thread").innerHTML="Disqus comments not available by default when the website is previewed locally.";return}var t=document,e=t.createElement("script");e.async=!0,e.src="//ericxliu-me.disqus.com/embed.js",e.setAttribute("data-timestamp",+new Date),(t.head||t.body).appendChild(e)}(),document.addEventListener("themeChanged",function(){document.readyState=="complete"&&DISQUS.reset({reload:!0,config:disqus_config})})</script></footer></article><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/katex.min.css integrity=sha384-vKruj+a13U8yHIkAyGgK1J3ArTLzrFGBbBc0tDp4ad/EyewESeXE/Iv67Aj8gKZ0 crossorigin=anonymous><script defer src=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/katex.min.js integrity=sha384-PwRUT/YqbnEjkZO0zZxNqcxACrXe+j766U2amXcgMg5457rve2Y7I6ZJSm2A0mS4 crossorigin=anonymous></script><script defer src=https://cdn.jsdelivr.net/npm/katex@0.16.4/dist/contrib/auto-render.min.js integrity=sha384-+VBxd3r6XgURycqtZ117nYw44OOcIax56Z4dCRWbxyPt0Koah1uHoK0o4+/RRE05 crossorigin=anonymous onload='renderMathInElement(document.body,{delimiters:[{left:"$$",right:"$$",display:!0},{left:"$",right:"$",display:!1},{left:"\\(",right:"\\)",display:!1},{left:"\\[",right:"\\]",display:!0}]})'></script></section></div><footer class=footer><section class=container>©
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Eric X. Liu
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posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/index.html
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@@ -26,4 +26,4 @@ This article explores the mathematical equivalence between Hinton’s concept of
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2025
Eric X. Liu
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posts/transformer-s-core-mechanics/index.html
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@@ -36,4 +36,4 @@ In deep learning, a &ldquo;channel&rdquo; can be thought of as a feature dimensi
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2025
Eric X. Liu
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posts/unifi-vlan-migration-to-zone-based-architecture/index.html
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@@ -28,4 +28,4 @@ This article documents that journey. It details the pitfalls encountered, the co
2016 -
2025
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posts/useful/index.html
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@@ -9,4 +9,4 @@ One-minute read</span></div></div></header><div class=post-content><ul><li><a hr
2016 -
2025
Eric X. Liu
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posts/vattention/index.html
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@@ -31,4 +31,4 @@ The GPU TLB hierarchy is sensitive to page sizes.</p><ul><li><strong>4KB Pages:<
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2025
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