Fix: Fix typos and update codespell config

libs/azure-ai/docs/azure_cosmos_db.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Azure Cosmos DB Mongo vCore\n",
     "\n",
-    "This notebook shows you how to leverage this integrated [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) to store documents in collections, create indicies and perform vector search queries using approximate nearest neighbor algorithms such as COS (cosine distance), L2 (Euclidean distance), and IP (inner product) to locate documents close to the query vectors. \n",
+    "This notebook shows you how to leverage this integrated [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) to store documents in collections, create indices and perform vector search queries using approximate nearest neighbor algorithms such as COS (cosine distance), L2 (Euclidean distance), and IP (inner product) to locate documents close to the query vectors. \n",
     "    \n",
     "Azure Cosmos DB is the database that powers OpenAI's ChatGPT service. It offers single-digit millisecond response times, automatic and instant scalability, along with guaranteed speed at any scale. \n",
     "\n",

libs/azure-ai/docs/azure_cosmos_db_no_sql.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Azure Cosmos DB No SQL\n",
     "\n",
-    "This notebook shows you how to leverage this integrated [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) to store documents in collections, create indicies and perform vector search queries using approximate nearest neighbor algorithms such as COS (cosine distance), L2 (Euclidean distance), and IP (inner product) to locate documents close to the query vectors. \n",
+    "This notebook shows you how to leverage this integrated [vector database](https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database) to store documents in collections, create indices and perform vector search queries using approximate nearest neighbor algorithms such as COS (cosine distance), L2 (Euclidean distance), and IP (inner product) to locate documents close to the query vectors. \n",
     "    \n",
     "Azure Cosmos DB is the database that powers OpenAI's ChatGPT service. It offers single-digit millisecond response times, automatic and instant scalability, along with guaranteed speed at any scale. \n",
     "\n",
@@ -23,36 +23,36 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "ad3c1e88",
    "metadata": {
     "jupyter": {
      "is_executing": true
     }
    },
+   "outputs": [],
    "source": [
     "%pip install --upgrade --quiet azure-cosmos langchain-openai langchain-community"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "c507b0e8",
    "metadata": {
     "jupyter": {
      "is_executing": true
     }
    },
+   "outputs": [],
    "source": [
     "OPENAI_API_KEY = \"\"\n",
     "OPENAI_API_TYPE = \"azure\"\n",
     "OPENAI_API_VERSION = \"2024-07-01-preview\"\n",
     "OPENAI_API_BASE = \"\"\n",
     "OPENAI_EMBEDDINGS_MODEL_NAME = \"text-embedding-3-small\"\n",
     "OPENAI_EMBEDDINGS_MODEL_DEPLOYMENT = \"text-embedding-3-small\""
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
@@ -64,21 +64,21 @@
   },
   {
    "cell_type": "code",
+   "execution_count": null,
    "id": "8205cd27",
    "metadata": {
     "jupyter": {
      "is_executing": true
     }
    },
+   "outputs": [],
    "source": [
     "from langchain_community.document_loaders import PyPDFLoader\n",
     "\n",
     "# Load the PDF\n",
     "loader = PyPDFLoader(\"https://arxiv.org/pdf/2303.08774.pdf\")\n",
     "data = loader.load()"
-   ],
-   "outputs": [],
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "code",
@@ -238,7 +238,9 @@
    "cell_type": "markdown",
    "id": "3f5ff6adb7c8ad48",
    "metadata": {},
-   "source": "## Vector Search"
+   "source": [
+    "## Vector Search"
+   ]
   },
   {
    "cell_type": "code",
@@ -284,7 +286,9 @@
    "cell_type": "markdown",
    "id": "30d77484c71fe192",
    "metadata": {},
-   "source": "## Vector Search with Score"
+   "source": [
+    "## Vector Search with Score"
+   ]
   },
   {
    "cell_type": "code",
@@ -343,7 +347,9 @@
    "cell_type": "markdown",
    "id": "9c4ffb492375192d",
    "metadata": {},
-   "source": "## Vector Search with filtering"
+   "source": [
+    "## Vector Search with filtering"
+   ]
   },
   {
    "cell_type": "code",
@@ -410,7 +416,9 @@
    "cell_type": "markdown",
    "id": "9fd7b4932ed5f42a",
    "metadata": {},
-   "source": "## Full Text Search"
+   "source": [
+    "## Full Text Search"
+   ]
   },
   {
    "cell_type": "code",
@@ -473,7 +481,9 @@
    "cell_type": "markdown",
    "id": "2831548f1fb4eb90",
    "metadata": {},
-   "source": "## Full Text Search BM 25 Ranking"
+   "source": [
+    "## Full Text Search BM 25 Ranking"
+   ]
   },
   {
    "cell_type": "code",
@@ -534,7 +544,9 @@
    "cell_type": "markdown",
    "id": "920e4eb20a141031",
    "metadata": {},
-   "source": "## Hybrid Search"
+   "source": [
+    "## Hybrid Search"
+   ]
   },
   {
    "cell_type": "code",
@@ -601,7 +613,9 @@
    "cell_type": "markdown",
    "id": "1178119f59653565",
    "metadata": {},
-   "source": "## Hybrid Search with filtering"
+   "source": [
+    "## Hybrid Search with filtering"
+   ]
   },
   {
    "cell_type": "code",

libs/azure-ai/pyproject.toml

@@ -99,3 +99,6 @@ convention = "google"
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"
+
+[tool.codespell]
+ignore-words-list = "nin"