Merge pull request #970 from vespa-engine/kkraune/more-cta

Link to blog posts

docs/sphinx/source/examples/Matryoshka_embeddings_in_Vespa-cloud.ipynb

@@ -15,10 +15,12 @@
                 "\n",
                 "# Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa\n",
                 "\n",
-                "This notebook demonstrates the effectiveness of using the recently released(As of January 2024) OpenAI `text-embedding-3` embeddings with Vespa.\n",
+                "This notebook demonstrates the effectiveness of using the recently released (as of January 2024) OpenAI `text-embedding-3` embeddings with Vespa.\n",
                 "\n",
                 "Specifically, we are interested in the [Matryoshka Representation Learning](https://aniketrege.github.io/blog/2024/mrl/) technique used in training, which lets us \"shorten embeddings (i.e. remove some numbers from the end of the sequence) without the embedding losing its concept-representing properties\". This allow us to trade off a small amount of accuracy in exchange for much smaller embedding sizes, so we can store more documents and search them faster.\n",
                 "\n",
+                "[Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa](https://blog.vespa.ai/matryoshka-embeddings-in-vespa/) and [Matryoshka 🤝 Binary vectors: Slash vector search costs with Vespa](https://blog.vespa.ai/combining-matryoshka-with-binary-quantization-using-embedder/) are good reads on this subject.\n",
+                "\n",
                 "By using [phased ranking](https://docs.vespa.ai/en/phased-ranking.html), we can re-rank the top K results with the full embeddings in a second step. This produces accuracy on par with using the full embeddings!\n",
                 "\n",
                 "We'll use a standard information retrieval benchmark to evaluate result quality with different embedding sizes and retrieval/ranking strategies.\n",