Merge pull request #202 from dandv/patch-1

Weaviate: update links, minor corrections

examples/vector_databases/Using_vector_databases_for_embeddings_search.ipynb

@@ -597,7 +597,7 @@
     "* for any CRUD operations\n",
     "* for semantic search\n",
     "\n",
-    "Check out the [Getting Started with Weaviate and OpenAI module cookbook](./weaviate/getting-started-with-text2vec-openai.ipynb) to learn step by step how to import and vectorize data in one step."
+    "Check out the [Getting Started with Weaviate and OpenAI module cookbook](./weaviate/getting-started-with-weaviate-and-openai.ipynb) to learn step by step how to import and vectorize data in one step."
    ]
   },
   {
@@ -608,7 +608,7 @@
    "source": [
     "### Setup\n",
     "\n",
-    "To run Weaviate locally, you'll need [Docker](https://www.docker.com/). Following the instructions contained in the Weaviate documentation [here](https://weaviate.io/developers/weaviate/current/installation/docker-compose.html), we created an example docker-compose.yml file in this repo saved at [./weaviate/docker-compose.yml](./weaviate/docker-compose.yml).\n",
+    "To run Weaviate locally, you'll need [Docker](https://www.docker.com/). Following the instructions contained in the Weaviate documentation [here](https://weaviate.io/developers/weaviate/installation/docker-compose), we created an example docker-compose.yml file in this repo saved at [./weaviate/docker-compose.yml](./weaviate/docker-compose.yml).\n",
     "\n",
     "After starting Docker, you can start Weaviate locally by navigating to the `examples/vector_databases/weaviate/` directory and running `docker-compose up -d`.\n",
     "\n",
@@ -620,7 +620,7 @@
     "    * Weaviate Version: Use default (latest)\n",
     "    * OIDC Authentication: `Disabled`\n",
     "3. your instance should be ready in a minute or two\n",
-    "4. make a note of the `Cluster Id`. The link will take you to the full path of your cluster (you will need it later to connect to it). It should be something like: `https://your-project-name.weaviate.network` "
+    "4. make a note of the `Cluster Id`. The link will take you to the full path of your cluster (you will need it later to connect to it). It should be something like: `https://your-project-name-suffix.weaviate.network` "
    ]
   },
   {
@@ -816,7 +816,7 @@
    "id": "46050ca9",
    "metadata": {},
    "source": [
-    "### Search Data\n",
+    "### Search data\n",
     "\n",
     "As above, we'll fire some queries at our new Index and get back results based on the closeness to our existing vectors"
    ]
@@ -841,7 +841,7 @@
     "    # Queries input schema with vectorised user query\n",
     "    query_result = (\n",
     "        client.query\n",
-    "        .get(collection_name,[\"title\",\"content\", \"_additional {certainty distance}\"])\n",
+    "        .get(collection_name, [\"title\", \"content\", \"_additional {certainty distance}\"])\n",
     "        .with_near_vector(near_vector)\n",
     "        .with_limit(top_k)\n",
     "        .do()\n",
@@ -857,7 +857,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query_result = query_weaviate(\"modern art in Europe\",\"Article\")\n",
+    "query_result = query_weaviate(\"modern art in Europe\", \"Article\")\n",
     "counter = 0\n",
     "for article in query_result[\"data\"][\"Get\"][\"Article\"]:\n",
     "    counter += 1\n",
@@ -871,7 +871,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "query_result = query_weaviate(\"Famous battles in Scottish history\",\"Article\")\n",
+    "query_result = query_weaviate(\"Famous battles in Scottish history\", \"Article\")\n",
     "counter = 0\n",
     "for article in query_result[\"data\"][\"Get\"][\"Article\"]:\n",
     "    counter += 1\n",