Updating VS lab

Author: ajosh0504

labs/vector-search-lab.ipynb

@@ -188,7 +188,6 @@
     "# Use the `multimodal_embed` method of the Voyage AI API with the following arguments to embed the image:\n",
     "# inputs: The image wrapped in a list of lists\n",
     "# model: `voyage-multimodal-3`\n",
-    "# input_type: \"query\" or \"document\", doesn't matter for this example\n",
     "embedding = <CODE_BLOCK_1>"
    ]
   },

solutions/vector-search-lab.ipynb

@@ -188,7 +188,6 @@
     "# Use the `multimodal_embed` method of the Voyage AI API with the following arguments to embed the image:\n",
     "# inputs: The image wrapped in a list of lists\n",
     "# model: `voyage-multimodal-3`\n",
-    "# input_type: \"query\" or \"document\", doesn't matter for this example\n",
     "embedding = vo.multimodal_embed(inputs=[[image]], model=\"voyage-multimodal-3\")"
    ]
   },
@@ -322,7 +321,7 @@
     "    # Use the `get_embeddings` function defined above to embed the `content`\n",
     "    # Note that `content` is the cover image of the book, so set the `mode` accordingly\n",
     "    # `input_type` should be set to \"document\" since we are embedding the \"documents\" we want to search\n",
-    "    embedding = get_embeddings(content, mode=\"image\", input_type=\"document\")\n",
+    "    embedding = get_embeddings(content, \"image\", \"document\")\n",
     "    # Filter for the document where the `_id` field is equal to the `_id` of the current document\n",
     "    filter = {\"_id\": result[\"_id\"]}\n",
     "    # Set the `embedding_field` field to the value `embedding` using the `$set` operator\n",