DOC-3498: tinymceai on-prem documentation.#4137
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Add missing customer-facing content identified by comparing the original internal documentation against the current on-premises AsciiDoc pages: capabilities matrix on the overview page, Podman production runbook, performance characteristics table, expanded known limits reference, MySQL 8.4 caveat, Ollama systemd and Modelfile examples, and getting-started teardown and config update guidance.
Expand 18 acronyms (OCI, JWT, LLM, SSE, TLS, CORS, MCP, NTP, HPA, OTLP, IRSA, ADC, SSR, CSP, SIEM, PII, HA, mTLS) on first prose occurrence per page for readers unfamiliar with the terms.
| AI -->|MCP tools/call| MCP[MCP Server<br>knowledge-hub] | ||
| MCP -->|read| KB[Confluence ·<br>Notion ·<br>GitBook ·<br>internal wiki] |
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| AI -->|MCP tools/call| MCP[MCP Server<br>knowledge-hub] | |
| MCP -->|read| KB[Confluence ·<br>Notion ·<br>GitBook ·<br>internal wiki] | |
| AI <-->|MCP tools/call| MCP[MCP Server<br>knowledge-hub] | |
| MCP <-->|read| KB[Confluence ·<br>Notion ·<br>GitBook ·<br>internal wiki] |
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I like the idea, but this one has so much negative space because it's largely a single flow. Honestly wondering if it's better a just a list - "work through this list to check for common causes of errors" or something.
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| flowchart TD | |||
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Is this the same as the one I commented on just before??
| == Architecture | ||
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| image::tinymceai-on-premises/complete-guide-fig-1.svg[alt="Service architecture showing browser with TinyMCE token endpoint AI service database Redis and LLM providers",width=100%] |
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I like this diagram, but I worry that putting it on the Overview page, which might be people's first introduction to the product, could be overwhelming and/or give a false impression of the required complexity. Could we switch this for a simpler model that just gives the highlights, and use this somewhere we're talking about enterprise deployment?
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| [source,console] | ||
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| docker inspect ai-service | jq '.[0].Config.Image' |
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nitpick, but putting this after the other docker one is just a little neater? less jumping around if someone is running through the steps
| |Five-minute Docker Compose quick start. Stand up the AI service, database, Redis, token server, and a browser editor. | ||
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| |xref:tinymceai-on-premises-database.adoc[Database, Redis, and storage] | ||
| |MySQL and PostgreSQL setup, Redis configuration, container runtimes (Docker, Podman, Kubernetes, ECS), and reverse proxy with TLS. |
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we use the term "data layer" elsewhere - at least in the diagrams - so might as well use it here?
| |MySQL and PostgreSQL setup, Redis configuration, container runtimes (Docker, Podman, Kubernetes, ECS), and reverse proxy with TLS. | |
| |Data layer setup: MySQL and PostgreSQL setup, Redis configuration, container runtimes (Docker, Podman, Kubernetes, ECS), and reverse proxy with TLS. |
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although the proxy is application layer... doesn't fit with the "database, redis, and storage" title either. thoughts?
| |MySQL and PostgreSQL setup, Redis configuration, container runtimes (Docker, Podman, Kubernetes, ECS), and reverse proxy with TLS. | ||
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| |xref:tinymceai-on-premises-providers.adoc[LLM providers] | ||
| |OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock, Google Vertex AI, and self-hosted endpoints (Ollama, vLLM, LM Studio). Custom model catalog and API key rotation. |
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| |OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock, Google Vertex AI, and self-hosted endpoints (Ollama, vLLM, LM Studio). Custom model catalog and API key rotation. | |
| |Connect to any OpenAI-compatible model, such as OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock, Google Vertex AI, and self-hosted endpoints (Ollama, vLLM, LM Studio). Custom model catalog and API key rotation. |
trying to avoid implying a limitation
| |xref:tinymceai-on-premises-jwt.adoc[JWT authentication] | ||
| |HS256 signing model, required and optional claims, permissions reference, and token endpoint examples in 8 languages. | ||
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| |xref:tinymceai-on-premises-frameworks.adoc[Framework integration] |
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"framework integration"? that feels weird. do we usually use that kind of term? I'd keep it straightforward with "TinyMCE integration" unless we need it to be a wider term?
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| * The AI service is already running. See xref:tinymceai-on-premises-getting-started.adoc[Getting started] for setup instructions. | ||
| * A token endpoint exists that signs JSON Web Tokens (JWTs) for the AI service. See xref:tinymceai-on-premises-jwt.adoc[JWT authentication] for back-end implementations. | ||
| * The TinyMCE API key has the AI feature enabled. Retrieve or upgrade a key at https://www.tiny.cloud/my-account/integrate/. |
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API key = cloud, need to mention API key or license key I think? I assume license key is more likely here because on prem
Reduce edge clutter by connecting a single representative replica to downstream services and grouping the data layer into a subgraph. Fix SVG width to use a fixed pixel value consistent with other diagrams in the set.
Ticket
DOC-3498
Summary
Complete on-premises deployment documentation for the TinyMCE AI service. This introduces 10 new pages covering every aspect of self-hosted AI deployment, from initial setup through production hardening and advanced scenarios.
New pages (10)
Assets
.mmdsource files with pre-rendered.svgdiagrams-scripts/render-mermaid.shwidth=100%for responsive scalingNavigation
nav.adocupdated with a new "On-premises deployment" section containing all 10 pages in logical reading order.Pre-checks
feature/<version>/modules/ROOT/nav.adochas been updatedrelease noteentry for anyNew product featuresproductminorversioninantora.ymland added new supported versions entry inmodules/ROOT/partials/misc/supported-versions.adocReview