qdrant · NirantK · Oct 30, 2023 · Oct 30, 2023 · Oct 30, 2023 · Oct 30, 2023
diff --git a/docs/examples/Usage_With_Qdrant.ipynb b/docs/examples/Usage_With_Qdrant.ipynb
@@ -178,6 +178,13 @@
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Behind the scenes, Qdrant Client uses the FastEmbed library to make a passage embedding and then uses the Qdrant API to upsert the documents with metadata, put together as a Points into the collection."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,

diff --git a/docs/experimental/Binary Quantization with Qdrant.ipynb b/docs/experimental/Binary Quantization with Qdrant.ipynb
@@ -46,7 +46,7 @@
    "source": [
     "## 2. Download and Slice Dataset\n",
     "\n",
-    "We will be using the [dbpedia-entitis-openai-1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) dataset from the [HuggingFace Datasets](https://huggingface.co/datasets) library. This contains 1M vectors of 1536 dimensions each. We will be using the first 10K vectors here."
+    "We will be using the [dbpedia-entitis-openai-1M](https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M) dataset from the [HuggingFace Datasets](https://huggingface.co/datasets) library. This contains 1M vectors of 1536 dimensions each. We will be using the first 100K vectors here."
    ]
   },
   {

diff --git a/...perimental/Throughput_Across_Models.ipynb → ...ing_around/Throughput_Across_Models.ipynb b/...perimental/Throughput_Across_Models.ipynb → ...ing_around/Throughput_Across_Models.ipynb