fix: Update Embedding Model to text-embedding-004 (GoogleCloudPlatf…

…orm#1408)
eliasecchig · Nov 14, 2024 · dd9e36b · dd9e36b
1 parent 1e481eb
commit dd9e36b
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Showing 2 changed files with 6 additions and 7 deletions.
diff --git a/gemini/use-cases/retrieval-augmented-generation/rag_vector_embedding_in_bigquery.ipynb b/gemini/use-cases/retrieval-augmented-generation/rag_vector_embedding_in_bigquery.ipynb
@@ -70,9 +70,9 @@
         "id": "0ExfeGcm9Iu5"
       },
       "source": [
-        "| | | |\n",
-        "|-|-|-|\n",
-        "|Author(s) | [Logan Ramalingam](https://github.com/logan-google)"
+        "| | |\n",
+        "|-|-|\n",
+        "|Author(s) | [Logan Ramalingam](https://github.com/logan-google) |"
       ]
     },
     {
@@ -255,10 +255,10 @@
         "LLM_MODEL_ID = \"llm_gemini\"  # @param {type:\"string\"}\n",
         "\n",
         "# Embeddings Model to use\n",
-        "EMBEDDINGS_ENDPOINT_TYPE = \"textembedding-gecko@002\"  # @param {type:\"string\"}\n",
+        "EMBEDDINGS_ENDPOINT_TYPE = \"text-embedding-004\"  # @param {type:\"string\"}\n",
         "\n",
         "# LLM Model to use\n",
-        "LLM_ENDPOINT_TYPE = \"gemini-pro\"  # @param {type:\"string\"}"
+        "LLM_ENDPOINT_TYPE = \"gemini-1.5-pro\"  # @param {type:\"string\"}"
       ]
     },
     {

diff --git a/search/custom-embeddings/custom_embeddings.ipynb b/search/custom-embeddings/custom_embeddings.ipynb
@@ -295,7 +295,7 @@
       "outputs": [],
       "source": [
         "# Load the text embeddings model\n",
-        "model = TextEmbeddingModel.from_pretrained(\"textembedding-gecko@003\")"
+        "model = TextEmbeddingModel.from_pretrained(\"text-embedding-004\")"
       ]
     },
     {
@@ -313,7 +313,6 @@
         "    embs = []\n",
         "    for i in tqdm(range(0, len(texts), batch_size)):\n",
         "        # Create embeddings optimized for document retrieval\n",
-        "        # (supported in textembedding-gecko@002 and later)\n",
         "        result = model.get_embeddings(\n",
         "            [\n",
         "                TextEmbeddingInput(text=text, task_type=\"RETRIEVAL_DOCUMENT\")\n",