Merge 71af7b16b1 into dc0e64aedf

examples/vector_databases/elasticsearch/elasticsearch-retrieval-augmented-generation.ipynb

@@ -306,14 +306,14 @@
     "## Encode a question with OpenAI embedding model\n",
     "\n",
     "To perform kNN search, we need to encode queries with the same embedding model used to encode the documents at index time.\n",
-    "In this example, we need to use the `text-embedding-3-small` model.\n",
+    "In this example, we need to use the `text-embedding-ada-002` model, as the zip file containing the articles was generated using this embedding model.\n",
     "\n",
     "You'll need your OpenAI [API key](https://platform.openai.com/account/api-keys) to generate the embeddings."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "57385c69",
    "metadata": {},
    "outputs": [],
@@ -325,7 +325,7 @@
     "openai.api_key = OPENAI_API_KEY\n",
     "\n",
     "# Define model\n",
-    "EMBEDDING_MODEL = \"text-embedding-3-small\"\n",
+    "EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
     "\n",
     "# Define question\n",
     "question = 'Is the Atlantic the biggest ocean in the world?'\n",

examples/vector_databases/elasticsearch/elasticsearch-semantic-search.ipynb

@@ -314,7 +314,7 @@
     "## Encode a question with OpenAI embedding model\n",
     "\n",
     "To perform semantic search, we need to encode queries with the same embedding model used to encode the documents at index time.\n",
-    "In this example, we need to use the `text-embedding-3-small` model.\n",
+    "In this example, we need to use the `text-embedding-ada-002` model, as the zip file containing the articles was generated using this embedding model.\n",
     "\n",
     "You'll need your OpenAI [API key](https://platform.openai.com/account/api-keys) to generate the embeddings."
    ]