diff --git a/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb b/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb
index dbe153fc..b75af159 100644
--- a/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb
+++ b/examples/Search_augmented_by_query_generation_and_embeddings_reranking.ipynb
@@ -12,7 +12,7 @@
     "There are two prominent approaches to using language models for information retrieval:\n",
     "\n",
     "1. **Mimicking Human Browsing:** [GPT triggers a search](https://openai.com/blog/chatgpt-plugins#browsing), evaluates the results, and modifies the search query if necessary. It can also follow up on specific search results to form a chain of thought, much like a human user would do.\n",
-    "2. **Retrieval with Embeddings:** Calculating [embeddings](https://platform.openai.com/docs/guides/embeddings) for your content, and then using a metric like cosine distance between the user query and the embedded data to sort and [retrieve information](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb). This technique is [used heavily](https://blog.google/products/search/search-language-understanding-bert/) by search engines like Google.\n",
+    "2. **Retrieval with Embeddings:** Calculating [embeddings](https://platform.openai.com/docs/guides/embeddings) for your content, and then using a metric like cosine distance between the user query and the embedded data to sort and [retrieve information](Question_answering_using_embeddings.ipynb). This technique is [used heavily](https://blog.google/products/search/search-language-understanding-bert/) by search engines like Google.\n",
     "\n",
     "These approaches are both promising, but each has their shortcomings: the first one can be slow due to its iterative nature and the second one requires embedding your entire knowledge base in advance, continuously embedding new content and maintaining a vector database.\n",
     "\n",