another batch of low-hanging fruit

Author: josh-heyer

advocacy_docs/edb-postgres-ai/ai-factory/gen-ai/builder/libraries/index.mdx

@@ -37,7 +37,7 @@ Data Sources → Libraries → Knowledge Bases → Retrievers → Assistants →
 
 For background on key concepts, see:
 
--   [AI Factory Concepts](../../learn/explained/ai-factory-concepts)
+-   [AI Factory Concepts](/edb-postgres-ai/ai-factory/learn/explained/ai-factory-concepts/)
 -   [Knowledge Bases explained](/edb-postgres-ai/ai-factory/learn/explained/knowledge-bases-explained/)
 -   [Retrievers explained](../../learn/explained/retrievers-explained)
 -   [Retrieval Augmented Generation (RAG)](/edb-postgres-ai/ai-factory/learn/explained/terminology/#retrieval-augmented-generation-rag)
@@ -114,7 +114,7 @@ For procedural steps, see the How-to Guides in this section.
 
 ## Related topics
 
--   [AI Factory Concepts](../../learn/explained/ai-factory-concepts)
+-   [AI Factory Concepts](/edb-postgres-ai/ai-factory/learn/explained/ai-factory-concepts/)
 -   [Knowledge Bases explained](/edb-postgres-ai/ai-factory/learn/explained/knowledge-bases-explained/)
 -   [Retrievers explained](../../learn/explained/retrievers-explained)
 -   [Retrieval Augmented Generation (RAG)](/edb-postgres-ai/ai-factory/learn/explained/terminology/#retrieval-augmented-generation-rag)

advocacy_docs/edb-postgres-ai/ai-factory/learn/explained/terminology.mdx

@@ -37,23 +37,23 @@ Natural language processing (NLP) enables computers to understand and generate h
 
 ### Large language models (LLMs)
 
-LLMs are large deep learning models trained on massive text corpora. In AI Factory, they drive [Assistants](assistants-explained), Retrieval-Augmented Generation (RAG), and various model pipelines, deployed using [Model Serving](../model/serving).
+LLMs are large deep learning models trained on massive text corpora. In AI Factory, they drive [Assistants](assistants-explained), Retrieval-Augmented Generation (RAG), and various model pipelines, deployed using [Model Serving](/edb-postgres-ai/ai-factory/model/serving/).
 
 [Learn more](http://huggingface.co/blog/llm)
 
 ### Embeddings
 
-Embeddings are vector representations of data that capture semantic meaning. AI Factory Pipelines create embeddings used in [Knowledge Bases](knowledge-bases-explained) and served through the [Vector Engine](../vector-engine) to enable semantic search and RAG.
+Embeddings are vector representations of data that capture semantic meaning. AI Factory Pipelines create embeddings used in [Knowledge Bases](knowledge-bases-explained) and served through the [Vector Engine](/edb-postgres-ai/ai-factory/vector-engine/) to enable semantic search and RAG.
 
 [Learn more](http://sebastianraschka.com/blog/2023/llm-embeddings.html)
 
 ### Vector databases
 
-Vector databases store embeddings and enable fast similarity search. AI Factory provides this through the [Vector Engine](../vector-engine), built on the open-source [pgvector](http://github.com/pgvector/pgvector) extension, integrated directly with Postgres.
+Vector databases store embeddings and enable fast similarity search. AI Factory provides this through the [Vector Engine](/edb-postgres-ai/ai-factory/vector-engine/), built on the open-source [pgvector](http://github.com/pgvector/pgvector) extension, integrated directly with Postgres.
 
 ### Retrieval-augmented generation (RAG)
 
-RAG combines vector search with LLM generation to ground model responses in relevant documents. In AI Factory, it is implemented through [Knowledge Bases](knowledge-bases-explained), [Retrievers](retrievers-explained), and [Model Serving](../model/serving).
+RAG combines vector search with LLM generation to ground model responses in relevant documents. In AI Factory, it is implemented through [Knowledge Bases](knowledge-bases-explained), [Retrievers](retrievers-explained), and [Model Serving](/edb-postgres-ai/ai-factory/model/serving/).
 
 [Intro to RAG](http://huggingface.co/blog/rag)
 
@@ -63,19 +63,19 @@ RAG combines vector search with LLM generation to ground model responses in rele
 
 ### Intelligent database management
 
-Intelligent database management applies AI to optimize Postgres performance and operations. AI Factory extends this with intelligent retrieval and search using [Vector Engine](../vector-engine) and Pipelines.
+Intelligent database management applies AI to optimize Postgres performance and operations. AI Factory extends this with intelligent retrieval and search using [Vector Engine](/edb-postgres-ai/ai-factory/vector-engine/) and Pipelines.
 
 ### In-database machine learning (In-DB ML)
 
-In-DB ML enables running vector search and ML pipelines inside Postgres, reducing data movement and latency. AI Factory implements this through [Vector Engine](../vector-engine) and [Pipelines](../pipeline).
+In-DB ML enables running vector search and ML pipelines inside Postgres, reducing data movement and latency. AI Factory implements this through [Vector Engine](/edb-postgres-ai/ai-factory/vector-engine/) and [Pipelines](/edb-postgres-ai/ai-factory/pipeline/).
 
 ### Vector search in Postgres
 
-Vector search allows you to query embeddings directly within Postgres. AI Factory uses [pgvector](http://github.com/pgvector/pgvector) to power this capability through the [Vector Engine](../vector-engine), supporting Knowledge Bases and RAG.
+Vector search allows you to query embeddings directly within Postgres. AI Factory uses [pgvector](http://github.com/pgvector/pgvector) to power this capability through the [Vector Engine](/edb-postgres-ai/ai-factory/vector-engine/), supporting Knowledge Bases and RAG.
 
 ### AIDB
 
-AIDB (AI-in-Database) brings vector search, embedding pipelines, and future ML capabilities to HCP-managed Postgres clusters. It is the foundation for AI Factory [Pipelines](../pipeline) and Knowledge Bases.
+AIDB (AI-in-Database) brings vector search, embedding pipelines, and future ML capabilities to HCP-managed Postgres clusters. It is the foundation for AI Factory [Pipelines](/edb-postgres-ai/ai-factory/pipeline/) and Knowledge Bases.
 
 ### Natural language interfaces to databases
 
@@ -87,21 +87,21 @@ Natural language interfaces enable users to query Postgres using natural languag
 
 ### AI-accelerated hardware
 
-AI Factory uses GPU-accelerated Kubernetes clusters to serve deep learning models and high-throughput inference. Model workloads in [Model Serving](../model/serving) run on GPU-enabled nodes.
+AI Factory uses GPU-accelerated Kubernetes clusters to serve deep learning models and high-throughput inference. Model workloads in [Model Serving](/edb-postgres-ai/ai-factory/model/serving/) run on GPU-enabled nodes.
 
 [Learn more](http://developer.nvidia.com/cuda-gpus)
 
 ### KServe
 
-KServe is the open-source Kubernetes-native framework AI Factory uses to deploy and manage ML models. It provides InferenceServices, autoscaling, and observability for AI Factory [Model Serving](../model/serving).
+KServe is the open-source Kubernetes-native framework AI Factory uses to deploy and manage ML models. It provides InferenceServices, autoscaling, and observability for AI Factory [Model Serving](/edb-postgres-ai/ai-factory/model/serving/).
 
 [KServe documentation](http://kserve.github.io/website/)
 
 ### Model Serving
 
 Model Serving deploys AI models as production-grade inference services, using KServe under the hood. It supports LLMs, embedding models, vision models, and custom AI workloads.
 
-See: [Model Serving](../model/serving), [Model Serving explained](model-serving-explained)
+See: [Model Serving](/edb-postgres-ai/ai-factory/model/serving/), [Model Serving explained](model-serving-explained)
 
 * * *
 
@@ -135,7 +135,7 @@ See: [Hybrid Manager AI Factory](/edb-postgres-ai/hybrid-manager/ai-factory)
 
 Pipelines automate the creation of embeddings and Knowledge Bases from data sources in your control, ensuring transparency and auditability.
 
-See: [Pipelines overview](../pipeline)
+See: [Pipelines overview](/edb-postgres-ai/ai-factory/pipeline/)
 
 ### Knowledge Bases
 
@@ -145,19 +145,19 @@ See: [Knowledge Bases explained](knowledge-bases-explained)
 
 ### Model Serving
 
-Model Serving deploys models using Kubernetes-native KServe and integrates with the [Model Library](.edb-postgres-ai/ai-factory/model/library). It powers Assistants, Knowledge Bases, and custom AI applications.
+Model Serving deploys models using Kubernetes-native KServe and integrates with the [Model Library](/edb-postgres-ai/ai-factory/model/library/). It powers Assistants, Knowledge Bases, and custom AI applications.
 
-See: [Model Serving](../model/serving)
+See: [Model Serving](/edb-postgres-ai/ai-factory/model/serving/)
 
 * * *
 
 ## Additional concepts
 
 ### Image and Model Library
 
-The Image and Model Library in Hybrid Manager manages container images for both Postgres and AI model deployments. The [Model Library](.edb-postgres-ai/ai-factory/model/library) provides an AI-focused view, supporting Model Serving and governed image workflows.
+The Image and Model Library in Hybrid Manager manages container images for both Postgres and AI model deployments. The [Model Library](/edb-postgres-ai/ai-factory/model/library/) provides an AI-focused view, supporting Model Serving and governed image workflows.
 
-See: [Model Image Library explained](/edb-postgres-ai/hybrid-manager/learn/explained/model-image-library)
+See: [Model Image Library explained](/edb-postgres-ai/hybrid-manager/ai-factory/learn/explained/model-image-library-explained/)
 
 ### NVIDIA NIM
 

advocacy_docs/edb-postgres-ai/ai-factory/learn/how-to/model-library/define-repository-rules.mdx

@@ -10,39 +10,39 @@ This guide explains how to define **Repository Rules** to control which model im
 
 Repository Rules help you manage:
 
-- **Scope** of image discovery
-- **Governance** over which images are exposed
-- **Version control** by controlling which tags are visible
+-   **Scope** of image discovery
+-   **Governance** over which images are exposed
+-   **Version control** by controlling which tags are visible
 
 ## Who should use this guide?
 
-- AI platform admins integrating private registries
-- DevOps engineers managing container registry integrations
-- Security/compliance teams governing AI model image usage
+-   AI platform admins integrating private registries
+-   DevOps engineers managing container registry integrations
+-   Security/compliance teams governing AI model image usage
 
 ## What this enables
 
-- You can precisely control which image repositories HCP syncs.
-- You can filter which tags within each repository are exposed.
-- You can govern which images are available in the Model Library for deployment.
+-   You can precisely control which image repositories HCP syncs.
+-   You can filter which tags within each repository are exposed.
+-   You can govern which images are available in the Model Library for deployment.
 
 ## Estimated time to complete
 
 5–10 minutes per Repository Rule.
 
 ## Prerequisites
 
-- You must have already [Integrated your Private Registry](integrate-private-registry).
-- Your registry must appear in **AI Factory > Model Library > Manage Repositories**.
+-   You must have already [Integrated your Private Registry](integrate-private-registry).
+-   Your registry must appear in **AI Factory > Model Library > Manage Repositories**.
 
 ## Understanding Repository Rules
 
 Repository Rules are attached to each configured registry.
 
 They allow you to define:
 
-- **Which repositories** to discover
-- **Which tags** within those repositories to expose
+-   **Which repositories** to discover
+-   **Which tags** within those repositories to expose
 
 This prevents accidental exposure of test images, unvetted model builds, or entire registry contents.
 
@@ -52,16 +52,16 @@ Repository Rules are **evaluated periodically** — newly pushed images matching
 
 Each rule typically includes:
 
-| Field | Description |
-|-------|-------------|
-| Repository Name | Name of the repository to include (exact match or pattern) |
-| Tag Filter | (Optional) Filter for tag names (exact match, prefix, regex) |
-| Rule Enabled | Whether this rule is currently active |
+| Field           | Description                                                  |
+| --------------- | ------------------------------------------------------------ |
+| Repository Name | Name of the repository to include (exact match or pattern)   |
+| Tag Filter      | (Optional) Filter for tag names (exact match, prefix, regex) |
+| Rule Enabled    | Whether this rule is currently active                        |
 
 Example:
 
-| Repository Name | Tag Filter |
-|-----------------|------------|
+| Repository Name            | Tag Filter    |
+| -------------------------- | ------------- |
 | `my-company/llama3-models` | `^release-.*` |
 
 → Only tags starting with `release-` in `my-company/llama3-models` will appear.
@@ -70,22 +70,23 @@ Example:
 
 ### 1. Navigate to Repository Rules
 
-- Go to:
+-   Go to:
 
 `AI Factory > Model Library > Manage Repositories`
 
-- Click **Manage Rules** for your desired registry.
+-   Click **Manage Rules** for your desired registry.
 
 ### 2. Add a Repository Rule
 
-- Click **Add Rule**.
-- Fill in:
+-   Click **Add Rule**.
+
+-   Fill in:
 
-  - **Repository Name** — required
-  - **Tag Filter** — optional
-  - Enable/Disable toggle
+    -   **Repository Name** — required
+    -   **Tag Filter** — optional
+    -   Enable/Disable toggle
 
-- Click **Save**.
+-   Click **Save**.
 
 The rule will appear in the rules list.
 
@@ -95,73 +96,73 @@ HCP will apply rules on the next registry sync cycle.
 
 If your UI supports manual sync:
 
-- Click **Sync Now** after adding/updating a rule.
+-   Click **Sync Now** after adding/updating a rule.
 
 ### 4. Verify Model Library content
 
-- Go to **AI Factory > Model Library**.
-- Use filters to browse images from your registry.
-- Confirm only desired repositories/tags appear.
+-   Go to **AI Factory > Model Library**.
+-   Use filters to browse images from your registry.
+-   Confirm only desired repositories/tags appear.
 
 ## Example Use Cases
 
 ### Basic: Restrict to a single repo
 
-| Repository Name | Tag Filter |
-|-----------------|------------|
+| Repository Name     | Tag Filter         |
+| ------------------- | ------------------ |
 | `my-org/nim-models` | (blank — all tags) |
 
 → All tags in `my-org/nim-models` will appear.
 
 ### Advanced: Filter to production tags
 
 | Repository Name | Tag Filter |
-|-----------------|------------|
-| `models/gpt4` | `^prod-.*` |
+| --------------- | ---------- |
+| `models/gpt4`   | `^prod-.*` |
 
 → Only tags starting with `prod-` will appear.
 
 ### Multi-repo governance
 
-- Add multiple Repository Rules per registry as needed.
-- Use Tag Filters to align with your model promotion pipelines (dev, staging, prod).
+-   Add multiple Repository Rules per registry as needed.
+-   Use Tag Filters to align with your model promotion pipelines (dev, staging, prod).
 
 ## Tips & Best Practices
 
-- **Be explicit** — add Repository Rules for only the repositories you want to expose.
-- Use **Tag Filters** to enforce version governance and CI/CD promotion stages.
-- Regularly **audit Repository Rules** to ensure compliance with internal image standards.
-- If your registry uses structured tagging (e.g., `prod-`, `test-`), leverage Tag Filters to separate experimental from production models.
+-   **Be explicit** — add Repository Rules for only the repositories you want to expose.
+-   Use **Tag Filters** to enforce version governance and CI/CD promotion stages.
+-   Regularly **audit Repository Rules** to ensure compliance with internal image standards.
+-   If your registry uses structured tagging (e.g., `prod-`, `test-`), leverage Tag Filters to separate experimental from production models.
 
 ## Troubleshooting
 
 ### Images not appearing
 
-- Check that Repository Rule exists for the desired repo.
-- If using Tag Filter — verify tags match the filter exactly.
-- Check when the last sync ran; trigger manual sync if needed.
+-   Check that Repository Rule exists for the desired repo.
+-   If using Tag Filter — verify tags match the filter exactly.
+-   Check when the last sync ran; trigger manual sync if needed.
 
 ### Too many images appearing
 
-- Refine Repository Rules.
-- Add Tag Filters to narrow scope.
+-   Refine Repository Rules.
+-   Add Tag Filters to narrow scope.
 
 ### Repository Rule has no effect
 
-- Confirm repository name is exact (case sensitive).
-- Ensure Rule is enabled.
-- Trigger manual sync and check HCP logs if issue persists.
+-   Confirm repository name is exact (case sensitive).
+-   Ensure Rule is enabled.
+-   Trigger manual sync and check HCP logs if issue persists.
 
 ## Summary
 
-- Repository Rules govern which image repositories and tags appear in Model Library.
-- You can scope exposure by repository name and tag filters.
-- This supports governance, version control, and security best practices.
-- Fine-grained control helps ensure only vetted model images are deployed.
+-   Repository Rules govern which image repositories and tags appear in Model Library.
+-   You can scope exposure by repository name and tag filters.
+-   This supports governance, version control, and security best practices.
+-   Fine-grained control helps ensure only vetted model images are deployed.
 
 ## Related Links
 
-- [Model Library Explained](/edb-postgres-ai/ai-factory/learn/explained/model-library-explained)
-- [Integrate Private Registry with Model Library](integrate-private-registry)
-- [Deploy AI Models from Model Library](deploy-ai-models)
-- [Image and Model Library Explained](/edb-postgres-ai/hybrid-manager/learn/explained/model-image-library)
+-   [Model Library Explained](/edb-postgres-ai/ai-factory/learn/explained/model-library-explained)
+-   [Integrate Private Registry with Model Library](integrate-private-registry)
+-   [Deploy AI Models from Model Library](deploy-ai-models)
+-   [Image and Model Library Explained](/edb-postgres-ai/hybrid-manager/ai-factory/learn/explained/model-image-library-explained/)