embaas title

docs/modules/models/text_embedding/examples/embaas.ipynb

@@ -2,142 +2,127 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
+    "# Embaas\n",
+    "\n",
     "[embaas](https://embaas.io) is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a [variety of pre-trained models](https://embaas.io/docs/models/embeddings).\n",
     "\n",
     "In this tutorial, we will show you how to use the embaas Embeddings API to generate embeddings for a given text.\n",
     "\n",
     "### Prerequisites\n",
     "Create your free embaas account at [https://embaas.io/register](https://embaas.io/register) and generate an [API key](https://embaas.io/dashboard/api-keys)."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Set API key\n",
     "embaas_api_key = \"YOUR_API_KEY\"\n",
     "# or set environment variable\n",
     "os.environ[\"EMBAAS_API_KEY\"] = \"YOUR_API_KEY\""
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.embeddings import EmbaasEmbeddings"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "embeddings = EmbaasEmbeddings()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T11:17:55.940265Z",
+     "start_time": "2023-06-10T11:17:55.938517Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Create embeddings for a single document\n",
     "doc_text = \"This is a test document.\"\n",
     "doc_text_embedding = embeddings.embed_query(doc_text)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-06-10T11:17:55.938517Z",
-     "end_time": "2023-06-10T11:17:55.940265Z"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Print created embedding\n",
     "print(doc_text_embedding)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T11:19:25.237161Z",
+     "start_time": "2023-06-10T11:19:25.235320Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Create embeddings for multiple documents\n",
     "doc_texts = [\"This is a test document.\", \"This is another test document.\"]\n",
     "doc_texts_embeddings = embeddings.embed_documents(doc_texts)"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-06-10T11:19:25.235320Z",
-     "end_time": "2023-06-10T11:19:25.237161Z"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Print created embeddings\n",
     "for i, doc_text_embedding in enumerate(doc_texts_embeddings):\n",
     "    print(f\"Embedding for document {i + 1}: {doc_text_embedding}\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T11:22:26.139769Z",
+     "start_time": "2023-06-10T11:22:26.138357Z"
+    }
+   },
    "outputs": [],
    "source": [
     "# Using a different model and/or custom instruction\n",
     "embeddings = EmbaasEmbeddings(model=\"instructor-large\", instruction=\"Represent the Wikipedia document for retrieval\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-06-10T11:22:26.138357Z",
-     "end_time": "2023-06-10T11:22:26.139769Z"
-    }
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "For more detailed information about the embaas Embeddings API, please refer to [the official embaas API documentation](https://embaas.io/api-reference)."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -155,5 +140,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }