[Ref Arch] Hyperlint Automation: Broken Link Fixes (#19857)

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Co-authored-by: hyperlint-ai[bot] <154288675+hyperlint-ai[bot]@users.noreply.github.com>

Author: hyperlint-ai[bot]

src/content/docs/reference-architecture/diagrams/ai/ai-asset-creation.mdx

@@ -35,12 +35,12 @@ Example uses of such compositions of AI models can be employed to generation vis
 
 1. **Client upload**: Send POST request with content to API endpoint.
 2. **Prompt generation**: Generate prompt for later-stage text-to-image model by calling [Workers AI](/workers-ai/) [text generation models](/workers-ai/models/) with content as input.
-3. **Safety check**: Check for compliance with safety guidelines by calling [Workers AI](/workers-ai/) [text classification models](/workers-ai/models/#text-classification) with the previously generated prompt as input.
-4. **Image generation**: Generate image by calling [Workers AI](/workers-ai/) [text-to-image models](/workers-ai/models/#text-to-image) previously generated prompt.
+3. **Safety check**: Check for compliance with safety guidelines by calling [Workers AI](/workers-ai/) [text classification models](/workers-ai/models/) with the previously generated prompt as input.
+4. **Image generation**: Generate image by calling [Workers AI](/workers-ai/) [text-to-image models](/workers-ai/models/) previously generated prompt.
 
 ## Related resources
 
 - [Community project: content-based asset creation demo](https://auto-asset.pages.dev/)
 - [Workers AI: Text generation models](/workers-ai/models/)
-- [Workers AI: Text-to-image models](/workers-ai/models/#text-to-image)
+- [Workers AI: Text-to-image models](/workers-ai/models/)
 - [Workers AI: llamaguard-7b-awq](/workers-ai/models/llamaguard-7b-awq/)

src/content/docs/reference-architecture/diagrams/ai/ai-rag.mdx

@@ -32,7 +32,7 @@ In the context of Retrieval-Augmented Generation (RAG), knowledge seeding involv
 1. **Client upload**: Send POST request with documents to API endpoint.
 2. **Input processing**: Process incoming request using [Workers](/workers/) and send messages to [Queues](/queues/) to add processing backlog.
 3. **Batch processing**: Use [Queues](/queues/) to trigger a [consumer](/queues/reference/how-queues-works/#consumers) that process input documents in batches to prevent downstream overload.
-4. **Embedding generation**: Generate embedding vectors by calling [Workers AI](/workers-ai/) [text embedding models](/workers-ai/models/#text-embeddings) for the documents.
+4. **Embedding generation**: Generate embedding vectors by calling [Workers AI](/workers-ai/) [text embedding models](/workers-ai/models/) for the documents.
 5. **Vector storage**: Insert the embedding vectors to [Vectorize](/vectorize/).
 6. **Document storage**: Insert documents to [D1](/d1/) for persistent storage.
 7. **Ack/Retry mechanism**: Signal success/error by using the [Queues Runtime API](/queues/configuration/javascript-apis/#message) in the consumer for each document. [Queues](/queues/) will schedule retries, if needed.
@@ -42,13 +42,13 @@ In the context of Retrieval-Augmented Generation (RAG), knowledge seeding involv
 ![Figure 2: Knowledge queries](~/assets/images/reference-architecture/rag-ref-architecture-diagrams/rag-architecture-query.svg "Figure 2: Knowledge queries")
 
 1. **Client query**: Send GET request with query to API endpoint.
-2. **Embedding generation**: Generate embedding vectors by calling [Workers AI](/workers-ai/) [text embedding models](/workers-ai/models/#text-embeddings) for the incoming query.
+2. **Embedding generation**: Generate embedding vectors by calling [Workers AI](/workers-ai/) [text embedding models](/workers-ai/models/) for the incoming query.
 3. **Vector search**: Query [Vectorize](/vectorize/) using the vector representation of the query to retrieve related vectors.
 4. **Document lookup**: Retrieve related documents from [D1](/d1/) based on search results from [Vectorize](/vectorize/).
 5. **Text generation**: Pass both the original query and the retrieved documents as context to [Workers AI](/workers-ai/) [text generation models](/workers-ai/models/#text-generation) to generate a response.
 
 ## Related resources
 
 - [Tutorial: Build a RAG AI](/workers-ai/tutorials/build-a-retrieval-augmented-generation-ai/)
-- [Workers AI: Text embedding models](/workers-ai/models/#text-embeddings)
+- [Workers AI: Text embedding models](/workers-ai/models/)
 - [Workers AI: Text generation models](/workers-ai/models/#text-generation)