Minor language improvements/fixes (#625)

docs/sphinx/source/getting-started-pyvespa-cloud.ipynb

@@ -161,7 +161,7 @@
     "\n",
     "Authenticate to generate a tenant level control-plane API key for deploying the applications to Vespa Cloud, and save the path to it. \n",
     "<div style=\"background-color: #ffcc00; padding: 10px; border: 1px solid #ff9900; font-weight: bold;\">\n",
-    "    <strong>Warning:</strong>The generated tenant api key must be added in the Vespa Console before attemting to deploy the application.\n",
+    "    <strong>Warning:</strong>The generated tenant api key must be added in the Vespa Console before attempting to deploy the application.",
     "</div>\n",
     "\n",
     "The following step will print the following message: \n",
@@ -286,7 +286,7 @@
    "source": [
     "## Deploy to Vespa Cloud\n",
     "\n",
-    "The app with is now defined and ready to deploy to Vespa Cloud. \n",
+    "The app is now defined and ready to deploy to Vespa Cloud. \n",
     "\n",
     "Deploy `package` to Vespa Cloud, by creating an instance of\n",
     "[VespaCloud](https://pyvespa.readthedocs.io/en/latest/reference-api.html#vespa.deployment.VespaCloud):"
@@ -407,7 +407,7 @@
    "metadata": {},
    "source": [
     "Now we can feed to Vespa using `feed_iterable` which accepts any `Iterable` and an optional callback function where we can\n",
-    "check the outcome of each operation. The application is configured to use to use [embedding](https://docs.vespa.ai/en/embedding.html)\n",
+    "check the outcome of each operation. The application is configured to use [embedding](https://docs.vespa.ai/en/embedding.html)\n",
     "functionality, that produce a vector embedding using a concatenation of the title and the body input fields. This step is resource intensive. \n",
     "\n",
     "Read more about embedding inference in Vespa in the [Accelerating Transformer-based Embedding Retrieval with Vespa](https://blog.vespa.ai/accelerating-transformer-based-embedding-retrieval-with-vespa/)\n",
@@ -441,7 +441,7 @@
     "\n",
     "Using the [Vespa Query language](https://docs.vespa.ai/en/query-language.html) we can query the indexed data. \n",
     "\n",
-    "- Using a context manager `with app.syncio() as session` to handle connection pooling ([best practises](https://cloud.vespa.ai/en/http-best-practices))\n",
+    "- Using a context manager `with app.syncio() as session` to handle connection pooling ([best practices](https://cloud.vespa.ai/en/http-best-practices))\n",
     "- The query method accepts any valid Vespa [query api parameter](https://docs.vespa.ai/en/reference/query-api-reference.html) in `**kwargs`\n",
     "- Vespa api parameter names that contains `.` must be sent as `dict` parameters in the `body` method argument\n",
     "\n",
@@ -543,7 +543,7 @@
     "[cross-hits feature normalization and reciprocal rank fusion](https://docs.vespa.ai/en/phased-ranking.html#cross-hit-normalization-including-reciprocal-rank-fusion). This\n",
     "functionality is exposed in the context of `global` re-ranking, after the distributed query retrieval execution which might span 1000s of nodes. \n",
     "\n",
-    "#### Hybrid search the or query operator\n",
+    "#### Hybrid search with the OR query operator\n",
     "\n",
     "This combines the two methods using logical disjunction (OR). Note that the first-phase expression in our `fusion` expression is only using the semantic score, this \n",
     "because usually semantic search provides better recall than sparse keyword search alone. "
@@ -575,7 +575,7 @@
    "id": "8d811509",
    "metadata": {},
    "source": [
-    "#### Hybrid search the rank query operator\n",
+    "#### Hybrid search with the RANK query operator\n",
     "\n",
     "This combines the two methods using the [rank](https://docs.vespa.ai/en/reference/query-language-reference.html#rank) query operator. In this case\n",
     "we express that we want to retrieve the top-1000 documents using vector search, and then have sparse features like BM25 calculated as well (second operand \n",
@@ -608,7 +608,7 @@
    "id": "b544ef07",
    "metadata": {},
    "source": [
-    "#### Hybrid with filters\n",
+    "#### Hybrid search with filters\n",
     "\n",
     "In this example we add another query term to the yql, restricting the nearest neighbor search to only consider documents that have vegetable in the title."
    ]

docs/sphinx/source/getting-started-pyvespa.ipynb

@@ -204,7 +204,7 @@
    "metadata": {},
    "source": [
     "Now we can feed to Vespa using `feed_iterable` which accepts any `Iterable` and an optional callback function where we can\n",
-    "check the outcome of each operation. The application is configured to use to use [embedding](https://docs.vespa.ai/en/embedding.html)\n",
+    "check the outcome of each operation. The application is configured to use [embedding](https://docs.vespa.ai/en/embedding.html)\n",
     "functionality, that produce a vector embedding using a concatenation of the title and the body input fields. This step is computionally expensive. Read more\n",
     "about embedding inference in Vespa in the [Accelerating Transformer-based Embedding Retrieval with Vespa](https://blog.vespa.ai/accelerating-transformer-based-embedding-retrieval-with-vespa/)."
    ]
@@ -234,7 +234,7 @@
     "\n",
     "Using the [Vespa Query language](https://docs.vespa.ai/en/query-language.html) we can query the indexed data. \n",
     "\n",
-    "- Using a context manager `with app.syncio() as session` to handle connection pooling ([best practises](https://cloud.vespa.ai/en/http-best-practices))\n",
+    "- Using a context manager `with app.syncio() as session` to handle connection pooling ([best practices](https://cloud.vespa.ai/en/http-best-practices))\n",
     "- The query method accepts any valid Vespa [query api parameter](https://docs.vespa.ai/en/reference/query-api-reference.html) in `**kwargs`\n",
     "- Vespa api parameter names that contains `.` must be sent as `dict` parameters in the `body` method argument\n",
     "\n",
@@ -330,7 +330,7 @@
     "[cross-hits feature normalization and reciprocal rank fusion](https://docs.vespa.ai/en/phased-ranking.html#cross-hit-normalization-including-reciprocal-rank-fusion). This\n",
     "functionality is exposed in the context of `global` re-ranking, after the distributed query retrieval execution which might span 1000s of nodes. \n",
     "\n",
-    "#### Hybrid search the or query operator\n",
+    "#### Hybrid search with the OR query operator\n",
     "\n",
     "This combines the two methods using logical disjunction (OR). Note that the first-phase expression in our `fusion` expression is only using the semantic score, this \n",
     "because usually semantic search provides better recall than sparse keyword search alone. \n",
@@ -363,7 +363,7 @@
    "id": "95fa4eb0",
    "metadata": {},
    "source": [
-    "#### Hybrid search the rank query operator"
+    "#### Hybrid search with the RANK query operator"
    ]
   },
   {
@@ -402,7 +402,7 @@
    "id": "1457aefc",
    "metadata": {},
    "source": [
-    "#### Hybrid with filters\n",
+    "#### Hybrid search with filters\n",
     "\n",
     "In this example we add another query term to the yql, restricting the nearest neighbor search to only consider documents that have vegetable in the title."
    ]