Updated text to include Qdrant in guide

examples/vector_databases/Using_vector_databases_for_embeddings_search.ipynb

@@ -30,6 +30,10 @@
     "    - *Setup*: Here we setup the Python client for Weaviate. For more details go [here](https://weaviate.io/developers/weaviate/current/client-libraries/python.html)\n",
     "    - *Index Data*: We'll create an index with __title__ search vectors in it\n",
     "    - *Search Data*: We'll run a few searches to confirm it works\n",
+    "- **Qdrant**\n",
+    "    - *Setup*: Here we setup the Python client for Qdrant. For more details go [here](https://github.com/qdrant/qdrant_client)\n",
+    "    - *Index Data*: We'll create a collection with vectors for __titles__ and __content__\n",
+    "    - *Search Data*: We'll run a few searches to confirm it works\n",
     "\n",
     "Once you've run through this notebook you should have a basic understanding of how to setup and use vector databases, and can move on to more complex use cases making use of our embeddings."
    ]
@@ -46,7 +50,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "8d8810f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here we install the clients for all vector databases\n",
+    "!pip install pinecone-client\n",
+    "!pip install weaviate-client\n",
+    "!pip install qdrant-client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "id": "5be94df6",
    "metadata": {},
    "outputs": [],
@@ -1051,11 +1068,12 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9cfaed9d",
    "metadata": {},
    "source": [
     "## Qdrant\n",
     "\n",
-    "The last vector database we'll consider in **[Qdrant](https://qdrant.tech/)**. This is a high-performant vector search database written in Rust. It offers both on-premise and cloud version, but for the purposes of that example we're going to use the local deployment mode.\n",
+    "The last vector database we'll consider is **[Qdrant](https://qdrant.tech/)**. This is a high-performant vector search database written in Rust. It offers both on-premise and cloud version, but for the purposes of that example we're going to use the local deployment mode.\n",
     "\n",
     "Setting everything up will require:\n",
     "- Spinning up a local instance of Qdrant\n",
@@ -1065,6 +1083,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "38774565",
    "metadata": {},
    "source": [
     "### Setup\n",
@@ -1077,6 +1096,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
+   "id": "76d697e9",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:28:38.928205Z",
@@ -1091,6 +1111,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
+   "id": "1deeb539",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:29:19.806639Z",
@@ -1115,6 +1136,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "bc006b6f",
    "metadata": {},
    "source": [
     "### Index data\n",
@@ -1127,6 +1149,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
+   "id": "1a84ee1d",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:29:22.530121Z",
@@ -1141,6 +1164,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
+   "id": "00876f92",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:31:14.413334Z",
@@ -1169,6 +1193,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
+   "id": "f24e76ab",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:36:28.597535Z",
@@ -1207,6 +1232,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
+   "id": "d1188a12",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:58:13.825886Z",
@@ -1232,6 +1258,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "06ed119b",
    "metadata": {},
    "source": [
     "### Search Data\n",
@@ -1242,6 +1269,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
+   "id": "f1bac4ef",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:50:35.265647Z",
@@ -1272,6 +1300,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
+   "id": "aa92f3d3",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:50:46.545145Z",
@@ -1315,6 +1344,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
+   "id": "7ed116b8",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2023-01-18T09:53:11.038910Z",
@@ -1358,6 +1388,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "55afccbf",
    "metadata": {},
    "source": [
     "Thanks for following along, you're now equipped to set up your own vector databases and use embeddings to do all kinds of cool things - enjoy! For more complex use cases please continue to work through other cookbook examples in this repo."