docs(ci): expand linter scope (supabase#30678)

Expand linter scope to cover `ai` directory as well.

.github/workflows/docs-lint-v2.yml

@@ -28,10 +28,10 @@ jobs:
             ~/.cargo/registry/index/
             ~/.cargo/registry/cache/
             ~/.cargo/git/db/
-          key: 9fd8c8fa7487a3d454a676e6e3d7409af34fc715
+          key: e23c860111349d8bac17b78b1fe632afe84223f9
       - name: install linter
         if: steps.cache-cargo.outputs.cache-hit != 'true'
-        run: cargo install --locked --git https://github.com/supabase-community/supa-mdx-lint --rev 9fd8c8fa7487a3d454a676e6e3d7409af34fc715
+        run: cargo install --locked --git https://github.com/supabase-community/supa-mdx-lint --rev e23c860111349d8bac17b78b1fe632afe84223f9
       - name: install reviewdog
         uses: reviewdog/action-setup@3f401fe1d58fe77e10d665ab713057375e39b887 # v1.3.0
         with:
@@ -41,4 +41,4 @@ jobs:
           REVIEWDOG_GITHUB_API_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: |
           set -o pipefail
-          supa-mdx-lint apps/docs/content/guides/getting-started --format rdf | reviewdog -f=rdjsonl -reporter=github-pr-review
+          supa-mdx-lint apps/docs/content/guides/getting-started apps/docs/content/guides/ai --format rdf | reviewdog -f=rdjsonl -reporter=github-pr-review

apps/docs/content/guides/ai/examples/semantic-image-search-amazon-titan.mdx

@@ -28,7 +28,7 @@ Then initialize a new project:
 poetry new aws_bedrock_image_search
 ```
 
-## Spin up a Postgres Database with pgvector
+## Spin up a Postgres database with pgvector
 
 If you haven't already, head over to [database.new](https://database.new) and create a new project. Every Supabase project comes with a full Postgres database and the [pgvector extension](/docs/guides/database/extensions/pgvector) preconfigured.
 

apps/docs/content/guides/ai/integrations/amazon-bedrock.mdx

@@ -10,7 +10,7 @@ tocVideo: 'A3uND5sgiO0'
 
 This guide will walk you through an example using Amazon Bedrock SDK with `vecs`. We will create embeddings using the Amazon Titan Embeddings G1 – Text v1.2 (amazon.titan-embed-text-v1) model, insert these embeddings into a PostgreSQL database using vecs, and then query the collection to find the most similar sentences to a given query sentence.
 
-## Create an Environment
+## Create an environment
 
 First, you need to set up your environment. You will need Python 3.7+ with the `vecs` and `boto3` libraries installed.
 
@@ -25,7 +25,7 @@ You'll also need:
 - [Credentials to your AWS account](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html)
 - [A Postgres Database with the pgvector extension](hosting.md)
 
-## Create Embeddings
+## Create embeddings
 
 Next, we will use Amazon’s Titan Embedding G1 - Text v1.2 model to create embeddings for a set of sentences.
 
@@ -67,7 +67,7 @@ for sentence in dataset:
 
 ```
 
-### Store the Embeddings with vecs
+### Store the embeddings with vecs
 
 Now that we have our embeddings, we can insert them into a PostgreSQL database using vecs.
 
@@ -90,7 +90,7 @@ sentences.upsert(records=embeddings)
 sentences.create_index()
 ```
 
-### Querying for Most Similar Sentences
+### Querying for most similar sentences
 
 Now, we query the `sentences` collection to find the most similar sentences to a sample query sentence. First need to create an embedding for the query sentence. Next, we query the collection we created earlier to find the most similar sentences.
 

supa-mdx-lint.config.toml

@@ -5,32 +5,42 @@ ignore_patterns = ["**/_*.mdx"]
 # Words that may be uppercased even if they are not the first word in the sentence.
 # Can also specify a regex that is compatible with the [Rust regex crate](https://docs.rs/regex/latest/regex/).
 may_uppercase = [
-    "[A-Z]{3,5}",
+    "[A-Z0-9]{2,5}",
     "Android",
     "Angular",
     "Apple",
     "Auth",
+    "ChatGPT",
     "Content Delivery Network",
     "Dart",
-    "Edge Functions",
+    "Edge Functions?",
     "Flutter",
     "GoTrue",
     "Google",
     "GraphQL",
+    "Hugging Face",
+    "I",
+    "IVFFlat",
     "Ionic Angular",
     "Ionic React",
     "Ionic Vue",
     "JavaScript",
     "Kotlin",
+    "Navigable Small World",
     "Next.js",
     "Nuxt",
     "OpenAI",
+    "Poetry",
     "Postgres",
     "PostgreSQL",
     "PostgREST",
+    "Python",
     "React",
     "React Native",
+    "Reciprocal Ranked Fusion",
     "RedwoodJS",
+    "Retrieval Plugin",
+    "Roboflow Inference",
     "Row Level Security",
     "Server-Side Auth",
     "Single Sign-On",
@@ -40,8 +50,11 @@ may_uppercase = [
     "SvelteKit",
     "Swift",
     "SwiftUI",
+    "TypeScript",
     "Xcode",
+    "Vecs",
     "Vue",
+    "Wrappers",
 ]
 # Words that may be lowercased even if they are the first word in the sentence.
 # Can also specify a regex that is compatible with the [Rust regex crate](https://docs.rs/regex/latest/regex/).