diff --git a/docs/50-prepare-the-data/4-embed-data.mdx b/docs/50-prepare-the-data/4-embed-data.mdx
index c9fe5029..c0511bc3 100644
--- a/docs/50-prepare-the-data/4-embed-data.mdx
+++ b/docs/50-prepare-the-data/4-embed-data.mdx
@@ -12,7 +12,7 @@ The answers for code blocks in this section are as follows:
Answer
```python
-SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
+SentenceTransformer("thenlper/gte-small")
```
@@ -35,9 +35,13 @@ return embedding.tolist()
Answer
```python
-for doc in split_docs:
+for doc in tqdm(split_docs):
doc["embedding"] = get_embedding(doc["body"])
embedded_docs.append(doc)
```
-
\ No newline at end of file
+
+
+:::caution
+If the embedding generation is taking too long (> 2-3 min), kill/interrupt the cell and move on to the next step with the documents that have been embedded up until that point.
+:::
\ No newline at end of file
diff --git a/docs/60-perform-semantic-search/2-create-vector-index.mdx b/docs/60-perform-semantic-search/2-create-vector-index.mdx
index e4a71b99..530b4c9a 100644
--- a/docs/60-perform-semantic-search/2-create-vector-index.mdx
+++ b/docs/60-perform-semantic-search/2-create-vector-index.mdx
@@ -23,7 +23,7 @@ Select the `mongodb_rag_lab` database and the `knowledge` collection, change the
{
"type": "vector",
"path": "embedding",
- "numDimensions": 1024,
+ "numDimensions": 384,
"similarity": "cosine"
}
]
@@ -31,5 +31,5 @@ Select the `mongodb_rag_lab` database and the `knowledge` collection, change the
```
:::info
-The number of dimensions in the index definition is 1024 since we are using Mixedbread AI's open-source [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) model to generate embeddings in this lab.
+The number of dimensions in the index definition is 384 since we are using the [gte-small](https://huggingface.co/thenlper/gte-small) model to generate embeddings in this lab.
:::
\ No newline at end of file