> ## Documentation Index
>
> Fetch the complete documentation index at: [/llms.txt](https://docs.cognigy.com/llms.txt)
>
> Use this file to discover all available pages before exploring further.
[Skip to main content](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#content-area)
[Updated in 2026.13](https://docs.cognigy.com/release-notes/2026.13) The table provides an overview of the LLM providers supported by Cognigy.AI, detailing the standard models and their compatibility with Cognigy.AI features.
You can also add a custom model that isn’t listed in the table.
To do so, select the **Custom Model** parameter when creating a model and specify both the model type and name.
For more information about adding a custom or standard model for a selected LLM provider,
refer to the [All LLM Providers](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/all-providers) page.
| Models | Design-Time Features[1](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote1) | AI Agent Node | Conversation Analyzer[8](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote8) | AI Enhanced Outputs | LLM Prompt Node | Answer Extraction | Knowledge Search | Sentiment Analysis | NLU Embedding Model |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **[Cognigy.AI](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/generative-ai#design-time-generative-ai-features)** | | | | | | | | | |
| Platform-Provided LLM | | | | | | | | | |
| **[Microsoft Azure OpenAI](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/microsoft-azure-openai)** | | | | | | | | | |
| `gpt-5-5`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2), [7](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote7)
`gpt-5-4`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-4-mini`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-4-nano`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-2`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-1`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-nano`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-mini`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-chat-latest` ( [deprecated](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models))
`gpt-4.1-nano` ( [deprecated](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models))
`gpt-4.1-mini`
`gpt-4.1`
`gpt-4o-mini`
`gpt-4o` | | | | | | | | | |
| `text-embedding-ada-002`
`text-embedding-3-small`[3](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote3)
`text-embedding-3-large`[3](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote3) | | | | | | | | | |
| **[OpenAI](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/openai)** | | | | | | | | | |
| `gpt-5-5`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2), [7](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote7)
`gpt-5-4`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-4-mini`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-4-nano`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-2`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-1`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-nano`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-mini`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gpt-5-chat-latest` ( [deprecated](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models))
`gpt-4.1-nano` ( [deprecated](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models))
`gpt-4.1-mini`
`gpt-4.1`
`gpt-4o-mini`
`gpt-4o` | | | | | | | | | |
| `text-embedding-ada-002`
`text-embedding-3-small`[3](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote3)
`text-embedding-3-large`[3](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote3) | | | | | | | | | |
| **[OpenAI-Compatible](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/openai-compatible)** | | | | | | | | | |
| OpenAI-compatible LLMs | | | | | | | | | |
| **[Anthropic](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/anthropic)** | | | | | | | | | |
| `claude-sonnet-4-6`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`claude-sonnet-4-5`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2) | | | | | | | | | |
| `claude-haiku-4-5`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2) | | | | | | | | | |
| **[Google Gemini](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/google-gemini)** | | | | | | | | | |
| `gemini-2.5-pro`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gemini-2.5-flash`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gemini-2.5-flash-lite`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2) ( [deprecated](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions#legacy-stable))
`gemini-2.0-flash` ( [deprecated](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/model-versions#legacy-stable)) | | | | | | | | | |
| `gemini-embedding-001` | | | | | | | | | |
| **[Google GenAI](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/google-genai)** | | | | | | | | | |
| `gemini-3.1-pro-preview`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gemini-3-flash-preview`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gemini-3.5-flash`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2)
`gemini-3.1-flash-lite`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2) | | | | | | | | | |
| `gemini-embedding-2` | | | | | | | | | |
| **[Aleph Alpha](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/aleph-alpha)** | | | | | | | | | |
| `luminous` ( [deprecated](https://docs.aleph-alpha.com/)) | | | | | | | | | |
| **[Amazon Bedrock](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/amazon-bedrock)** | | | | | | | | | |
| `anthropic.claude-3-5-sonnet-20240620-v1:0` ( [deprecated](https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle.html))
`amazon.nova-lite-v1:0`
`amazon.nova-pro-v1:0`
`amazon.nova-premier-v1:0`[2](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote2) ( [deprecated](https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle.html))
`amazon.nova-2-lite-v1:0` | | | | | | | | | |
| `amazon.titan-embed-text-v2:0`[5](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote5) | | | | | | | | | |
| `amazon.nova-micro-v1:0` | | | | | | | | | |
| [Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html)-compatible models | | Partially supported[6](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote6) | | | | | | | |
| **[Mistral AI](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/mistral)** | | | | | | | | | |
| `mistral-large-latest`[4](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote4)
`mistral-medium-latest` ( [deprecated](https://docs.mistral.ai/models/overview#legacy-models))[4](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote4)
`mistral-small-latest`[4](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote4)
`pixtral-large-latest`[4](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature#footnote4) | | | | | | | | | |
## [](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/model-support-by-feature\#more-information) More Information
- [Overview](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/llms)
- [All LLM Providers](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/all-providers)
- [Other LLM Operations](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/other-operations)
* * *
1 [Design-time\\
features](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/generative-ai#design-time-generative-ai-features)
include Intent Sentence Generation, Flow Generation, Adaptive Card Generation,
Lexicon Generation.
2 Reasoning models consume more tokens and may incur
higher costs. The models are optimized for tasks that require complex problem-solving
and logical reasoning. Before using these models in production, test token consumption in [debug mode](https://docs.cognigy.com/ai/agents/test/interaction-panel/chat#debug-mode) and use them with caution. To reduce costs, consider using a non-reasoning model such as `gpt-5-chat-latest`. For more information about reasoning models, refer to the [Microsoft Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/reasoning), [OpenAI](https://platform.openai.com/docs/guides/reasoning/advice-on-prompting), and [Google](https://cloud.google.com/vertex-ai/generative-ai/docs/models) documentation.
Anthropic’s `claude-sonnet-4-6` model doesn’t support [assistant message prefilling](https://platform.claude.com/docs/en/about-claude/models/migration-guide#breaking-changes-3).
To keep your Flows compatible, ensure that the last transcript step before the LLM call is a user message. For example, add an [Add Transcript Step Node](https://docs.cognigy.com/ai/agents/develop/node-reference/service/add-transcript-step) before the LLM call and set the role to `user`.
3 For Knowledge AI, we recommend using
`text-embedding-ada-002`. However, if you want to use `text-embedding-3-small`
and `text-embedding-3-large`, make sure that you familiarize yourself with the
restrictions of these models in [Which Model to\\
Choose?](https://docs.cognigy.com/ai/agents/develop/knowledge-ai/overview).
4 The `*-latest` suffix indicates that the model you
select in Cognigy.AI points to the latest version of the model. For more
information, read
[Anthropic’s](https://docs.anthropic.com/en/docs/about-claude/models/overview)
or [Mistral\\
AI’s](https://docs.mistral.ai/getting-started/models/models_overview/) models
documentation.
5 For Cognigy.AI 2025.10 and earlier versions, the
option to select this model is hidden behind the
`FEATURE_ENABLE_AWS_BEDROCK_EMBEDDING_LLM_WHITELIST` feature flag.
6 Note that some models from the [Converse\\
API](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html)
might not support the AI Agent Node feature.
7 This model supports only the Responses API. When you select this model, the Responses API is selected by default, and the **API Type** field is locked.8 The Conversation Analyzer uses the selected model to analyze session transcripts in [Cognigy Insights](https://docs.cognigy.com/insights/overview). If no model is explicitly selected in **Manage > Settings > Generative AI Settings**, the Project’s default LLM is used. Any chat model that supports the AI Agent Node also supports the Conversation Analyzer.
Last modified onJuly 10, 2026
[Previous](https://docs.cognigy.com/ai/agents/develop/gen-ai-and-llms/generative-ai) [All LLM Providers\\
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}
if (lastThemeClass === null && document.documentElement) {
lastThemeClass = document.documentElement.className;
}
if (!themeObserver && document.documentElement) {
themeObserver = new MutationObserver(function(mutations) {
for (let i = 0; i < mutations.length; i++) {
const mutation = mutations\[i\];
if (mutation.type === 'attributes' && mutation.attributeName === 'class') {
const currentThemeClass = document.documentElement.className;
if (lastThemeClass !== null && lastThemeClass !== currentThemeClass) {
themeToggleInProgress = true;
// Optimized: Lock containers synchronously
const contentArea = getContentArea();
if (contentArea) {
const containers = getContainers(true);
containers.forEach(function(container) {
if (container.getAttribute('data-position-locked') !== 'true') {
const rect = container.getBoundingClientRect();
Object.assign(container.style, {
position: 'fixed',
top: rect.top + 'px',
left: rect.left + 'px',
width: rect.width + 'px',
zIndex: '9999',
margin: '0'
});
container.setAttribute('data-position-locked', 'true');
containerPositions.set(container, {
top: rect.top,
left: rect.left,
width: rect.width,
height: rect.height
});
}
});
}
let repositionAttempts = 0;
const maxRepositionAttempts = 30;
const repositionInterval = setInterval(function() {
repositionAttempts++;
repositionContainersAfterThemeToggleCore(); // Use core function for immediate repositioning
if (repositionAttempts >= maxRepositionAttempts) {
clearInterval(repositionInterval);
}
}, 100);
setTimeout(function() {
clearInterval(repositionInterval);
themeToggleInProgress = false;
repositionContainersAfterThemeToggleCore(); // Use core function for final repositioning
unlockContainers();
}, 3000);
}
lastThemeClass = currentThemeClass;
}
}
});
themeObserver.observe(document.documentElement, {
attributes: true,
attributeFilter: \['class'\]
});
}
if (typeof MutationObserver !== 'undefined') {
setupObserver();
}
waitForContentArea(function() {
setTimeout(function() {
initSinglePageViewers();
setTimeout(function() {
const remaining = getUninitializedViewers(true);
if (remaining.length > 0) {
initSinglePageViewers();
}
}, INIT\_DELAY);
}, INIT\_DELAY);
});
}
// Optimized: Lock/unlock containers
function lockContainersInPlace() {
const contentArea = getContentArea();
if (!contentArea) return;
const containers = getContainers(true);
containerPositions.clear();
requestAnimationFrame(function() {
containers.forEach(function(container) {
if (container.getAttribute('data-position-locked') !== 'true') {
const rect = container.getBoundingClientRect();
Object.assign(container.style, {
position: 'fixed',
top: rect.top + 'px',
left: rect.left + 'px',
width: rect.width + 'px',
zIndex: '9999',
margin: '0'
});
container.setAttribute('data-position-locked', 'true');
containerPositions.set(container, {
top: rect.top,
left: rect.left,
width: rect.width,
height: rect.height
});
}
});
});
}
function unlockContainers() {
const contentArea = getContentArea();
if (!contentArea) return;
const containers = Array.from(
contentArea.querySelectorAll('.single-page-viewer-container\[data-position-locked="true"\]')
);
containers.forEach(function(container) {
Object.assign(container.style, {
position: '',
top: '',
left: '',
width: '',
zIndex: '',
margin: ''
});
container.removeAttribute('data-position-locked');
// Clean up container position from Map to prevent memory leak
containerPositions.delete(container);
});
containerPositions.clear();
}
// Optimized: Reposition containers with better DOM operations
// Core repositioning function (not debounced for immediate use)
function repositionContainersAfterThemeToggleCore() {
const contentArea = getContentArea();
if (!contentArea) return;
const containers = getContainers(true);
containers.forEach(function(container) {
const viewerId = container.getAttribute('data-viewer-id');
if (!viewerId) return;
let viewer = document.getElementById(viewerId);
if (!viewer) {
const iframe = container.querySelector('iframe');
if (iframe && iframe.src) {
const allViewers = contentArea.querySelectorAll('\[data-single-page-viewer\]');
for (let i = 0; i < allViewers.length; i++) {
const v = allViewers\[i\];
if (v.getAttribute('data-src') === iframe.src) {
viewer = v;
viewer.id = viewerId;
viewer.setAttribute('data-viewer-id', viewerId);
Object.assign(viewer.style, {
display: 'none',
visibility: 'hidden',
position: 'absolute',
width: '1px',
height: '1px',
overflow: 'hidden',
opacity: '0',
pointerEvents: 'none',
margin: '0',
padding: '0'
});
viewer.setAttribute('data-initialized', 'true');
viewer.setAttribute('data-viewer-replaced', 'true');
break;
}
}
}
}
if (viewer && viewer.parentNode) {
if (container.getAttribute('data-position-locked') === 'true') {
unlockContainers();
}
const viewerIndex = Array.from(viewer.parentNode.children).indexOf(viewer);
const containerIndex = Array.from(viewer.parentNode.children).indexOf(container);
if (containerIndex !== viewerIndex + 1) {
// CRITICAL: Check if container is actually a child before removing
if (container.parentNode && container.parentNode.contains(container)) {
try {
container.parentNode.removeChild(container);
} catch (e) {
return; // Skip repositioning if removal failed
}
}
if (viewer.nextSibling) {
viewer.parentNode.insertBefore(container, viewer.nextSibling);
} else {
viewer.parentNode.appendChild(container);
}
} else if (container.parentNode !== viewer.parentNode) {
// CRITICAL: Check if container is actually a child before removing
if (container.parentNode && container.parentNode.contains(container)) {
try {
container.parentNode.removeChild(container);
} catch (e) {
return; // Skip repositioning if removal failed
}
}
if (viewer.nextSibling) {
viewer.parentNode.insertBefore(container, viewer.nextSibling);
} else {
viewer.parentNode.appendChild(container);
}
}
}
});
invalidateQueryCache();
}
// Debounced version for non-critical repositioning
const repositionContainersAfterThemeToggle = debounce(repositionContainersAfterThemeToggleCore, REPOSITION\_DEBOUNCE);
// Optimized: Observer setup with better filtering
let observer = null;
let containerPositionObserver = null;
let fallbackObserver = null; // Track fallback observer for cleanup
function setupContainerPositionObserver() {
if (typeof MutationObserver === 'undefined') return;
const contentArea = getContentArea();
if (!contentArea \|\| !document.contains(contentArea)) {
setTimeout(setupContainerPositionObserver, 500);
return;
}
if (containerPositionObserver) {
containerPositionObserver.disconnect();
}
containerPositionObserver = new MutationObserver(function(mutations) {
if (themeToggleInProgress) {
// Use core function (not debounced) for immediate repositioning during theme toggle
requestAnimationFrame(repositionContainersAfterThemeToggleCore);
return;
}
let needsReposition = false;
for (let i = 0; i < mutations.length && !needsReposition; i++) {
if (mutations\[i\].type === 'childList') {
needsReposition = true;
}
}
if (needsReposition) {
clearTimeout(containerPositionObserver.\_timeout);
containerPositionObserver.\_timeout = setTimeout(repositionContainersAfterThemeToggle, REPOSITION\_DEBOUNCE);
}
});
containerPositionObserver.observe(contentArea, {
childList: true,
subtree: true,
attributes: false,
characterData: false
});
containerPositionObserver.\_timeout = null;
}
// Optimized: Main observer with better early exits
function setupObserver() {
if (typeof MutationObserver === 'undefined') return;
const contentArea = getContentArea();
if (!contentArea \|\| !document.contains(contentArea)) {
setTimeout(setupObserver, 500);
return;
}
if (contentArea === document.documentElement \|\| contentArea === document.body) {
return;
}
if (observer) {
const currentObserved = observer.\_observedElement;
if (currentObserved === contentArea && document.contains(contentArea)) {
return;
}
if (currentObserved) {
observer.disconnect();
}
observer = null;
}
observer = new MutationObserver(function(mutations) {
if (themeToggleInProgress) return;
let hasNewViewer = false;
for (let i = 0; i < mutations.length && !hasNewViewer; i++) {
const mutation = mutations\[i\];
if (mutation.addedNodes && mutation.addedNodes.length > 0) {
for (let j = 0; j < mutation.addedNodes.length && !hasNewViewer; j++) {
const node = mutation.addedNodes\[j\];
if (node.nodeType === 1 &&
node.hasAttribute &&
node.hasAttribute('data-single-page-viewer') &&
!node.hasAttribute('data-initialized') &&
!node.hasAttribute('data-viewer-replaced')) {
hasNewViewer = true;
}
}
}
}
if (hasNewViewer) {
// Use core function for immediate repositioning when new viewer detected
repositionContainersAfterThemeToggleCore();
setTimeout(repositionContainersAfterThemeToggleCore, 10);
setTimeout(repositionContainersAfterThemeToggleCore, 50);
setTimeout(repositionContainersAfterThemeToggleCore, 200);
}
clearTimeout(observerTimeout);
observerTimeout = setTimeout(function() {
if (themeToggleInProgress) return;
let hasNewViewer = false;
for (let i = 0; i < mutations.length && !hasNewViewer; i++) {
const mutation = mutations\[i\];
if (mutation.target === document.documentElement \|\| mutation.target === document.body) {
continue;
}
if (mutation.type === 'attributes' &&
mutation.attributeName === 'class' &&
mutation.target === document.documentElement) {
continue;
}
if (mutation.type === 'attributes' && mutation.attributeName === 'data-initialized') {
const target = mutation.target;
if (target && target.hasAttribute &&
target.hasAttribute('data-single-page-viewer') &&
!target.hasAttribute('data-initialized')) {
hasNewViewer = true;
}
}
if (mutation.addedNodes && mutation.addedNodes.length > 0) {
for (let j = 0; j < mutation.addedNodes.length && !hasNewViewer; j++) {
const node = mutation.addedNodes\[j\];
if (node.nodeType === 1 &&
node.hasAttribute &&
node.hasAttribute('data-single-page-viewer') &&
!node.hasAttribute('data-initialized')) {
hasNewViewer = true;
}
}
}
}
if (hasNewViewer) {
invalidateQueryCache();
initSinglePageViewers();
}
}, OBSERVER\_DEBOUNCE);
});
observer.observe(contentArea, {
childList: true,
subtree: true,
attributes: true,
attributeFilter: \['data-initialized'\],
characterData: false
});
observer.\_observedElement = contentArea;
setupContainerPositionObserver();
}
function start() {
setupObserver();
initialize();
}
function checkAndInitialize() {
if (isInitializing) return;
const currentUrl = window.location.href;
if (currentUrl === lastUrl && lastUrl !== null) {
return;
}
const contentArea = document.querySelector('#content-area') \|\| document.querySelector('.mdx-content');
if (contentArea) {
const viewers = getUninitializedViewers(true);
if (viewers.length > 0) {
cachedContentArea = contentArea;
setupObserver();
initialize();
}
}
}
if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', start);
} else {
setTimeout(start, 100);
}
if (typeof window !== 'undefined' && window.next) {
let initialLoadAttempts = 0;
const maxInitialAttempts = 4;
const attemptDelays = \[200, 500, 1000, 2000\];
const initialUrl = window.location.href;
attemptDelays.forEach(function(delay) {
setTimeout(function() {
if (initialLoadAttempts < maxInitialAttempts) {
initialLoadAttempts++;
if (window.location.href === initialUrl) {
checkAndInitialize();
}
}
}, delay);
});
}
window.addEventListener('popstate', function(e) {
const currentUrl = window.location.href;
if (currentUrl !== lastUrl) {
handleNavigation();
}
});
if (typeof window !== 'undefined') {
if (window.next && window.next.router && window.next.router.events) {
window.next.router.events.on('routeChangeComplete', function(url) {
lastUrl = window.location.href;
isInitializing = false;
pendingInitCall = false;
if (navigationTimeout) {
clearTimeout(navigationTimeout);
navigationTimeout = null;
}
invalidateContentAreaCache();
invalidateQueryCache();
setTimeout(function() {
setupObserver();
initialize();
}, 300);
});
window.next.router.events.on('routeChangeStart', function(url) {
isInitializing = false;
pendingInitCall = false;
});
}
if (!window.next \|\| !window.next.router) {
const originalPushState = history.pushState;
const originalReplaceState = history.replaceState;
history.pushState = function() {
originalPushState.apply(history, arguments);
setTimeout(function() {
if (window.location.href !== lastUrl) {
handleNavigation();
}
}, 0);
};
history.replaceState = function() {
originalReplaceState.apply(history, arguments);
setTimeout(function() {
if (window.location.href !== lastUrl) {
handleNavigation();
}
}, 0);
};
}
}
document.addEventListener('visibilitychange', function() {
if (!document.hidden) {
isInitializing = false;
startPeriodicCheck();
if (!pendingInitCall) {
pendingInitCall = true;
setTimeout(function() {
pendingInitCall = false;
initSinglePageViewers();
}, INIT\_DELAY / 2);
}
}
});
function startPeriodicCheck() {
if (periodicCheckInterval) return;
periodicCheckInterval = setInterval(function() {
const uninitialized = getUninitializedViewers(true);
if (uninitialized.length === 0) {
clearInterval(periodicCheckInterval);
periodicCheckInterval = null;
} else if (!isInitializing) {
initSinglePageViewers();
}
}, 2000);
}
setTimeout(startPeriodicCheck, INIT\_DELAY \* 2);
window.\_\_singlePageViewerInit = function() {
const currentUrl = window.location.href;
if (currentUrl === lastUrl) {
return;
}
isInitializing = false;
pendingInitCall = false;
lastUrl = currentUrl;
invalidateContentAreaCache();
invalidateQueryCache();
setupObserver();
initialize();
};
window.\_\_singlePageViewerCheck = function() {
invalidateQueryCache();
initSinglePageViewers();
};
})();
// Optimized: Second IIFE for navigation handling
(function() {
'use strict';
let lastPathname = window.location.pathname;
let lastInitializedPathname = window.location.pathname;
function checkAndInit() {
const currentPathname = window.location.pathname;
if (currentPathname === lastInitializedPathname) {
return;
}
lastInitializedPathname = currentPathname;
if (typeof window !== 'undefined') {
window.\_\_lastFullUrl = window.location.href;
}
if (window.\_\_singlePageViewerInit) {
window.\_\_singlePageViewerInit();
}
}
function runInit() {
if (window.\_\_singlePageViewerInit) {
setTimeout(checkAndInit, 100);
} else {
let attempts = 0;
const maxAttempts = 50;
const checkInterval = setInterval(function() {
attempts++;
if (window.\_\_singlePageViewerInit) {
clearInterval(checkInterval);
setTimeout(checkAndInit, 100);
} else if (attempts >= maxAttempts) {
clearInterval(checkInterval);
}
}, 100);
}
}
if (document.readyState === 'loading') {
document.addEventListener('DOMContentLoaded', runInit);
} else {
setTimeout(runInit, 100);
}
if (typeof window !== 'undefined' && window.next && window.next.router && window.next.router.events) {
window.next.router.events.on('routeChangeComplete', function(url) {
lastPathname = window.location.pathname;
setTimeout(checkAndInit, 300);
});
}
setInterval(function() {
const currentPathname = window.location.pathname;
if (currentPathname !== lastPathname) {
lastPathname = currentPathname;
setTimeout(checkAndInit, 300);
}
}, 500);
if (typeof MutationObserver !== 'undefined') {
let fallbackObserverTimeout = null;
// Store in outer scope for cleanup tracking
fallbackObserver = new MutationObserver(function(mutations) {
if (fallbackObserverTimeout) {
clearTimeout(fallbackObserverTimeout);
}
fallbackObserverTimeout = setTimeout(function() {
const contentArea = document.querySelector('#content-area');
if (contentArea) {
const uninitialized = contentArea.querySelectorAll(
'\[data-single-page-viewer\]:not(\[data-initialized\]):not(.single-page-viewer-container \[data-single-page-viewer\])'
);
if (uninitialized.length > 0) {
const currentPathname = window.location.pathname;
if (currentPathname !== lastInitializedPathname) {
setTimeout(checkAndInit, 200);
}
}
}
}, 500);
});
if (document.body) {
fallbackObserver.observe(document.body, { childList: true, subtree: true });
} else {
document.addEventListener('DOMContentLoaded', function() {
if (document.body) {
fallbackObserver.observe(document.body, { childList: true, subtree: true });
}
});
}
}
})();