diff --git a/docs/src/embedding.md b/docs/src/embedding.md
index 61118404..0657f9bd 100644
--- a/docs/src/embedding.md
+++ b/docs/src/embedding.md
@@ -46,7 +46,7 @@ You can also use an external API like OpenAI to generate embeddings
 
         def embed_func(c):
             rs = openai.Embedding.create(input=c, engine="text-embedding-ada-002")
-        return [record["embedding"] for record in rs["data"]]
+            return [record["embedding"] for record in rs["data"]]
       ```
 
 === "Javascript"
@@ -126,7 +126,7 @@ belong in the same latent space and your results will be nonsensical.
 === "Javascript"
      ```javascript
       const results = await table
-        .search('What's the best pizza topping?')
+        .search("What's the best pizza topping?")
         .limit(10)
         .execute()
      ```
diff --git a/docs/src/examples/transformerjs_embedding_search_nodejs.md b/docs/src/examples/transformerjs_embedding_search_nodejs.md
index 77413bb5..38875d52 100644
--- a/docs/src/examples/transformerjs_embedding_search_nodejs.md
+++ b/docs/src/examples/transformerjs_embedding_search_nodejs.md
@@ -1,4 +1,8 @@
-# Vector embedding search using TransformersJS and NodeJS
+# Vector embedding search using TransformersJS
+
+## Embed and query data from LacneDB using TransformersJS
+
+<img id="splash" width="400" alt="transformersjs" src="https://github.com/lancedb/lancedb/assets/43097991/88a31e30-3d6f-4eef-9216-4b7c688f1b4f">
 
 This example shows how to use the [transformers.js](https://github.com/xenova/transformers.js) library to perform vector embedding search using LanceDB's Javascript API.