Merge pull request #142 from cohere-ai/llmu-updates

LLMU - update links and images

Author: mrmer1

notebooks/guides/Analyzing_Hacker_News_with_Six_Language_Understanding_Methods.ipynb

@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/guides/Analyzing_Hacker_News_with_Six_Language_Understanding_Methods.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",

notebooks/llmu/Fine_Tuning_for_Classify.ipynb

@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/cohere-ai/notebooks/blob/main/notebooks/llmu/Fine_Tuning_for_Classify.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

notebooks/llmu/Introduction_to_RAG.ipynb

@@ -26,7 +26,7 @@
         "id": "TSkzr2WGmeQe"
       },
       "source": [
-        "*Note: To run the notebook, you must first deploy your own Google Drive connector as a web-based REST API (the steps are outlined in [this article](https://txt.cohere.com/rag-chatbot-quickstart/)).:*"
+        "*Note: To run the notebook, you must first deploy your own Google Drive connector as a web-based REST API (the steps are outlined in [this article](https://txt.cohere.com/rag-quickstart-connectors/#build-and-deploy-the-connector)).*"
       ]
     },
     {
@@ -35,7 +35,9 @@
         "id": "yOv1E6lBg_Qj"
       },
       "source": [
-        "This notebook shows how to build a RAG-powered chatbot with Cohere's Chat endpoint using connectors. \n",
+        "This notebook shows how to build a RAG-powered chatbot with Cohere's Chat endpoint using connectors.\n",
+        "\n",
+        "Read the accompanying [article here](https://txt.cohere.com/rag-large-scale-data/).\n",
         "\n",
         "In particular, this notebook shows how to use connectors at scale, such as connecting to multiple datastores, working with large volumes of documents, and handling long documents. Enterprises need a RAG system that can efficiently handle vast amounts of data from diverse sources, and in this chapter, you’ll learn about how this can be automated with the Chat endpoint.\n",
         "\n",

notebooks/llmu/RAG_over_Large_Scale_Data.ipynb

@@ -23,6 +23,8 @@
         "\n",
         "This notebook shows how to build a RAG-powered chatbot with Cohere's Chat endpoint using connectors. The chatbot can extract relevant information from external documents and produce verifiable, inline citations in its responses.\n",
         "\n",
+        "Read the accompanying [article here](https://txt.cohere.com/rag-connectors/).\n",
+        "\n",
         "Connectors are ways of connecting to data sources. These data sources could be internal documents, document databases, the broader internet, or any other source of context which can inform the replies generated by the model.\n",
         "\n",
         "We'll use the web search connector, a Cohere-managed connector that you can use without additional setup.\n",

notebooks/llmu/RAG_with_Chat_Embed_and_Rerank.ipynb

@@ -26,9 +26,9 @@
         "id": "yZzk64vUhC8T"
       },
       "source": [
-        "*Note: To run the notebook, you must first deploy your own Google Drive connector as a web-based REST API (the steps are outlined in [this article](https://txt.cohere.com/rag-chatbot-quickstart/))*\n",
+        "*Note: To run the notebook, you must first deploy your own Google Drive connector as a web-based REST API (the steps are outlined [here](https://txt.cohere.com/rag-quickstart-connectors/#build-and-deploy-the-connector)).*\n",
         "\n",
-        "*The connector implementation code is [available here](utils/rag_quickstart_connectors).*"
+        "*The connector implementation code is [available here](examples/rag_quickstart_connectors/gdrive).*"
       ]
     },
     {
@@ -37,7 +37,9 @@
         "id": "LgnnKlvWgfi8"
       },
       "source": [
-        "This notebook shows how to build a RAG-powered chatbot with Cohere's Chat endpoint using one of 80+ pre-built quickstart connectors. We’ll use it to connect a chatbot to a Google Drive, enabling the chatbot to use the Google Drive API to find answers to a user’s question by searching documents in the Google Drive."
+        "This notebook shows how to build a RAG-powered chatbot with Cohere's Chat endpoint using one of 80+ pre-built quickstart connectors. We’ll use it to connect a chatbot to a Google Drive, enabling the chatbot to use the Google Drive API to find answers to a user’s question by searching documents in the Google Drive.\n",
+        "\n",
+        "Read the accompanying [article here](https://txt.cohere.com/rag-quickstart-connectors/)."
       ]
     },
     {