Update colab urls

Author: leemthompo

README.md

@@ -8,7 +8,7 @@ This repository contains executable Python notebooks, sample apps, and resources
 
 # Python notebooks 📒
 
-The [`notebooks` folder](notebooks/README.md) contains a range of executable Python notebooks, so you can test these features out for yourself. Colab provides an easy-to-use Python virtual environment in the browser.
+The [`notebooks`](notebooks/README.md) folder contains a range of executable Python notebooks, so you can test these features out for yourself. Colab provides an easy-to-use Python virtual environment in the browser.
 
 # Example apps 💻
 

example-apps/relevance-workbench/README.md

@@ -1,24 +1,24 @@
 # Relevance workbench
 
-This application allows you to compare results ranking between the ELSER model and BM25. 
+This application allows you to compare results ranking between the [Elastic Learned Sparse EncodeR](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-elser.html) model and BM25.
 
 ![relevance-workbench](./images/relevance-workbench.png)
 
-You can also try with your own data by forking this repo and plugging the application to your own Elasticsearch deployment. 
+You can also try with your own data by forking this repo and plugging the application into your own Elasticsearch deployment.
 
 # Deploy with default dataset
 
 ## Pre-requisites
 
-To run this demo successfully, you will need an Elasticsearch deployment (> 8.8) with the ELSER model deployed. The easiest way for this is to use Elastic Cloud as described in the next part but you can also deploy Elasticsearch locally. 
+To run this demo successfully, you will need an Elasticsearch deployment (> 8.8) with the ELSER model deployed. The easiest way for this is to use Elastic Cloud as described in the next part but you can also deploy Elasticsearch locally.
 
 ## Deploy Elasticsearch in Elastic Cloud
 
 If you don't have an Elastic Cloud account, you can start by signing up for a [free Elastic Cloud trial](https://cloud.elastic.co/registration). After creating an account, you’ll have an active subscription, and you’ll be prompted to create your first deployment.
 
 Follow the steps to Create a new deployment. For more details, refer to [Create a deployment](https://www.elastic.co/guide/en/cloud/current/ec-create-deployment.html) in the Elastic Cloud documentation.
 
-For that demo, you will need to have an Elasticsearch deployment with Enterprise Search enabled and a ML node with at least 4Gb of memory. 
+For that demo, you will need to have an Elasticsearch deployment with Enterprise Search enabled and a ML node with at least 4Gb of memory.
 
 Once created don't forget to save the default admin password in a safe place.
 
@@ -32,23 +32,23 @@ You can follow this [documentation](https://www.elastic.co/guide/en/machine-lear
 
 The best approach is to use Enterprise Search to create a new index and configure the ingest pipeline to enrich the data.
 
-From the landing page in Kibana, navigate to Enterprise Search. 
+From the landing page in Kibana, navigate to Enterprise Search.
 
 ![ent-search-landing](./images/ent-search-landing.png)
 
-### Create the index 
+### Create the index
 
-Here, click on Create an Elasticsearch index and choose the API method. 
+Here, click on Create an Elasticsearch index and choose the API method.
 
-Name the index `search-movies` (notice the existing prefix `search-`), and click on Create index. 
+Name the index `search-movies` (notice the existing prefix `search-`), and click on Create index.
 
 ### Configure the ingest pipeline
 
 On the index configuration screen, navigate to the Pipelines tab and click on Copy and customize.
 
 ![copy-customize](./images/copy-customize.png)
 
-Now click on Add Inference pipeline to configure the inference pipeline. 
+Now click on Add Inference pipeline to configure the inference pipeline.
 
 Name the inference pipeline `ml-inference-movies` (notice the existing prefix `ml-inference-`) and select the ELSER model. If it's not available, you can deploy it on the previous screen. Click continue.
 
@@ -58,15 +58,15 @@ On the next screen, add the fields `overview` and `title` as custom options.
 
 ![fields-mapping](./images/fields-mapping.png)
 
-Then click on Continue. 
+Then click on Continue.
 
 ![add-inference-fields](./images/add-inference-fields.png)
 
-Click Continue to review the changes and then Create pipeline. 
+Click Continue to review the changes and then Create pipeline.
 
 ### Run the script to ingest data
 
-Go to the folder `data` and run the python script `index-data.py` to ingest the movies dataset. 
+Go to the folder `data` and run the python script `index-data.py` to ingest the movies dataset.
 
 In order to connect it to the correct Elastic Cloud instance, we need the default admin password you saved after creating the deployment and the Cloud ID for your deployment.
 
@@ -87,33 +87,33 @@ python3 index-data.py --es_password=<ELASTICSEARCH_PASSWORD> --cloud_id=<CLOUD_I
 - ELASTICSEARCH_PASSWORD: Use the default admin password previously saved
 - CLOUD_ID: See instructions above to retrieve it
 
-Note that by default, only subset of the dataset (100 movies) is indexed. If you're interested in indexing the whole data (7918 movies), you can select the `movies.json.gz` file by adding the option `--gzip_file=movies.json.gz` to the command line. Note that it might take up to 1 hour to index the full dataset. 
+Note that by default, only subset of the dataset (100 movies) is indexed. If you're interested in indexing the whole data (7918 movies), you can select the `movies.json.gz` file by adding the option `--gzip_file=movies.json.gz` to the command line. Note that it might take up to 1 hour to index the full dataset.
 
 ## Run the application
 
-Once the data have been succesfully indexed, you can run the application to start comparing relevance models. 
+Once the data have been successfully indexed, you can run the application to start comparing relevance models.
 
-The application is composed of a backend Python API and a React frontend. You can run the whole application locally using Docker compose. 
+The application is composed of a backend Python API and a React frontend. You can run the whole application locally using Docker compose.
 
-Edit the `docker-compose.yml` file to replace values for. Reuse the same information that you use for loading the data. 
+Edit the `docker-compose.yml` file to replace values for. Reuse the same information that you use for loading the data.
 - CLOUD_ID=<CLOUD_ID>
 - ELASTICSEARCH_PASSWORD=<ELASTICSEARCH_PASSWORD>
 
-Run `docker-compose up` to start the application. 
+Run `docker-compose up` to start the application.
 
 Open [localhost:3000](http://localhost:3000) to access the application.
 
 # Use your own dataset
 
-To use your own dataset, you first need to ingest it and then configure the backend API to use it. 
+To use your own dataset, you first need to ingest it and then configure the backend API to use it.
 
 ## Load your own data
 
-The first part described in the chapter below can be used similarly to load your own data. 
+The first part described in the chapter below can be used similarly to load your own data.
 
-Use Enterprise Search to create a new index and configure an ML Inference pipeline. In this case, you'll need to choose yourself the fields to generate text expansion, note that ELSER inference works across text fields, and best on shorter spans of text. Those are the fields that the relevance workbench will query. 
+Use Enterprise Search to create a new index and configure an ML Inference pipeline. In this case, you'll need to choose yourself the fields to generate text expansion, note that ELSER inference works across text fields, and best on shorter spans of text. Those are the fields that the relevance workbench will query.
 
-Once the index is ready, use the same Python script to ingest the data, with additional options. 
+Once the index is ready, use the same Python script to ingest the data, with additional options.
 
 ```
 python3 index-data.py --es_password=<ELASTICSEARCH_PASSWORD> --cloud_id=<CLOUD_ID> --index_name=<INDEX_NAME> --gzip_file=<GZIP_FILE_NAME>
@@ -122,13 +122,13 @@ python3 index-data.py --es_password=<ELASTICSEARCH_PASSWORD> --cloud_id=<CLOUD_I
 - ELASTICSEARCH_PASSWORD: Use the default admin password previously saved
 - CLOUD_ID: You can find this information in your Elastic Cloud admin console
 - INDEX_NAME: Your own custom index
-- GZIP_FILE_NAME: The name of the GZIP JSON file containing your dataset. To be placed under `data` folder. 
+- GZIP_FILE_NAME: The name of the GZIP JSON file containing your dataset. To be placed under `data` folder.
 
 ## Configure the backend API
 
-At the beginning of the file `app-api/app.py`, you can find an object that configure the datasets to use. 
+At the beginning of the file `app-api/app.py`, you can find an object that configure the datasets to use.
 
-By default, it looks like this: 
+By default, it looks like this:
 
 ```
 datasets = {
@@ -144,7 +144,7 @@ datasets = {
 }
 ```
 
-To add a new dataset, simply adds a new entry in the datasets object. 
+To add a new dataset, simply adds a new entry in the datasets object.
 
 ```
 datasets = {
@@ -169,15 +169,15 @@ datasets = {
 }
 ```
 
-In the configuration of the new dataset, provides the following informations: 
+In the configuration of the new dataset, provides the following informations:
 - index: Name of the index
 - search_fields: Fields to query for BM25
 - elser_search_fields: Fields to query for ELSER
 - result_fields: Fields to return
 - mapping_fields: Mapping between returned fields and fields expected by the front-end
 
-Then save the file `app.py` and run the application using docker-compose. 
+Then save the file `app.py` and run the application using docker-compose.
 
-## Credits 
+## Credits
 
 <img src="./images/tmdb-logo.svg" width="40"> This product uses the TMDB API but is not endorsed or certified by TMDB.

notebooks/generative-ai/question-answering.ipynb

@@ -9,7 +9,7 @@
  "source": [
  "# Question Answering with Langchain and OpenAI\n",
  "\n",
- "<a target=\"_blank\" href=\"https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/colab-notebooks-examples/generative-ai/question-answering.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+ "<a target=\"_blank\" href=\"https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/generative-ai/question-answering.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
  "\n",
  "This interactive notebook uses Langchain to split fictional workplace documents into passages and uses OpenAI to transform these passages into embeddings and store them into Elasticsearch.\n",
  "\n",

notebooks/search/00-quick-start.ipynb

@@ -9,7 +9,7 @@
  "source": [
  "# Elasticsearch quick start: embeddings, semantic search, and hybrid search\n",
  "\n",
- "<a target=\"_blank\" href=\"https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/colab-notebooks-examples/search/00-quick-start.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+ "<a target=\"_blank\" href=\"https://colab.research.google.com/github/elastic/elasticsearch-labs/blob/main/notebooks/search/00-quick-start.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
  "\n",
  "This interactive notebook will introduce you to some basic operations with Elasticsearch, using the official [Elasticsearch Python client](https://www.elastic.co/guide/en/elasticsearch/client/python-api/current/connecting.html).\n",
  "You'll perform semantic search using [Sentence Transformers](https://www.sbert.net) for text embedding. Learn how to integrate traditional text-based search with semantic search, for a hybrid search system."
@@ -239,7 +239,7 @@
  "source": [
  "### Index test data\n",
  "\n",
- "Run the following command to upload some test data, containing information about 10 popular programming books from this [dataset](https://raw.githubusercontent.com/elastic/elasticsearch-labs/notebooks-guides/colab-notebooks-examples/search/data.json).\n",
+ "Run the following command to upload some test data, containing information about 10 popular programming books from this [dataset](https://raw.githubusercontent.com/elastic/elasticsearch-labs/blob/main/notebooks/search/data.json).\n",
  "`model.encode` will encode the text into a vector on the fly, using the model we initialized earlier."
  ]
  },
@@ -255,7 +255,7 @@
  "import json\n",
  "from urllib.request import urlopen\n",
  "\n",
- "url = \"https://raw.githubusercontent.com/elastic/elasticsearch-labs/notebooks-guides/colab-notebooks-examples/search/data.json\"\n",
+ "url = \"https://raw.githubusercontent.com/elastic/elasticsearch-labs/blob/main/search/data.json\"\n",
  "response = urlopen(url)\n",
  "books = json.loads(response.read())\n",
  "\n",