cohere-ai · fern-support · Nov 4, 2024 · Nov 4, 2024 · Nov 4, 2024
@@ -58,7 +58,7 @@ If you're running this in a jupyter notebook, you'll need to prepend a `!` to th
 !pip install --upgrade cohere
 ```
 
-Get your Cohere API key by [signing up here](https://os.cohere.ai/register). Paste it in `api_key` below.
+Get your Cohere API key by [signing up here](https://dashboard.cohere.ai/register). Paste it in `api_key` below.
 
 #### 1a. Import the Necessary Dependencies to Run this Example
 

@@ -14,7 +14,7 @@ Human collaboration runs on language. The firehose of documents, emails, and mes
 - Analysis of financial filings and reports
 
 ![](../../assets/images/698b185-Text_Classification_Visual_1.svg)
-The Cohere platform aims to help you build classifiers for these and other use cases. You can [start trying Cohere for free when you sign up](https://os.cohere.ai/) and automatically receive $75 in free credits.
+The Cohere platform aims to help you build classifiers for these and other use cases. You can [start trying Cohere for free when you sign up](https://dashboard.cohere.ai/) and automatically receive $75 in free credits.
 
 ## Training classifiers with Cohere’s language models
 

@@ -28,7 +28,7 @@ updatedAt: "Wed Sep 14 2022 15:18:32 GMT+0000 (Coordinated Universal Time)"
 Use the command `co key create [key_name]`.
 
 <Info> 
-You must be [signed in](auth) to use this command.
+You must be [signed in](/docs/auth) to use this command.
 </Info>
 
 # Retrieving an API Key

@@ -23,7 +23,7 @@ This page shows some common CLI Commands. For more info about a specific command
 co auth login --email string="[email protected]"
 ```
 
-You will be prompted to put in your password. For more commands related to your Cohere account, see the [Auth](auth) and [User](user) commands.
+You will be prompted to put in your password. For more commands related to your Cohere account, see the [Auth](/docs/auth) and [User](/docs/user) commands.
 
 <Info> 
 Currently, SSO accounts cannot log in via the CLI.
@@ -55,7 +55,7 @@ co model generate --max-tokens n '[Your Prompt Here]'
 ```
 
 <Note title="Using Finetuned Models in the CLI">  
- If you want to use a finetuned model, you can do so by using the model's full ID with the `model` flag. See the [Model reference page](model) for more details.
+ If you want to use a finetuned model, you can do so by using the model's full ID with the `model` flag. See the [Model reference page](/docs/model) for more details.
 </Note>
 
 ## Finetune a Model

@@ -27,7 +27,7 @@ We’ll cover three groups of tasks that you will typically work on when dealing
 - Classifying text
 - Analyzing text
 
-The first step is to install the Cohere Python SDK. Next, create an API key, which you can generate from the Cohere [dashboard](https://dashboard.cohere.com/register) or [CLI tool](https://docs.cohere.ai/cli-key).
+The first step is to install the Cohere Python SDK. Next, create an API key, which you can generate from the Cohere [dashboard](https://dashboard.cohere.com/register) or [CLI tool](/docs/key).
 
 ```python PYTHON
 ! pip install cohere altair umap-learn -q

@@ -10,27 +10,27 @@ updatedAt: "Thu Apr 18 2024 10:45:10 GMT+0000 (Coordinated Universal Time)"
 
 Use the embed endpoint with Langchain to efficiently build semantic search applications on top of Cohere’s multilingual model.
 
-### <a target="_blank" href="/page/multilingual-movie-search">Multilingual Movie Search</a>
+### <a target="_blank" href="/docs/multilingual-movie-search">Multilingual Movie Search</a>
 
 Build a movie search and recommendation app using the embed endpoint, similarity, and nearest neighbors.
 
-### <a target="_blank" href="/page/topic-modeling">Topic Modeler</a>
+### <a target="_blank" href="/docs/topic-modeling">Topic Modeler</a>
 
 Using the embed endpoint and clustering, build an app that extracts the main topics from a large collection of text documents.
 
-### <a target="_blank" href="/page/invoice-extractor">Invoice Extractor</a>
+### <a target="_blank" href="/docs/invoice-extractor">Invoice Extractor</a>
 
 Using the generate endpoint, build an app that extracts important information from invoices.
 
-### <a target="_blank" href="/page/news-article-recommender">News Article Recommender</a>
+### <a target="_blank" href="/docs/news-article-recommender">News Article Recommender</a>
 
 Build an article recommender app using the embed endpoint and similarity to find the closest articles to a given article. Then classify each article by category and extract tags from the articles.
 
-### <a target="_blank" href="/page/multilingual-sentiment-analysis">Multilingual Sentiment Analysis</a>
+### <a target="_blank" href="/docs/multilingual-sentiment-analysis">Multilingual Sentiment Analysis</a>
 
 Using embeddings, build a sentiment analysis classifier that works in many languages.
 
-### <a target="_blank" href="/page/document-question-answering">Document Question Answering</a>
+### <a target="_blank" href="/docs/document-question-answering">Document Question Answering</a>
 
 Build an app that takes a user's question about a given document and generates an answer by searching the most semantically similar sections of the document.
 

@@ -9,7 +9,7 @@ createdAt: "Mon May 01 2023 14:37:21 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Wed Apr 03 2024 19:03:21 GMT+0000 (Coordinated Universal Time)"
 ---
 ![](../../../assets/images/0d1dd7c-image.png)
-In Module 1 you learned about text <a target="_blank" href="/docs/text-embeddings">embeddings</a>, and how they are a very useful way to turn text into numbers that capture its meaning and context. In this chapter you'll learn how to put them in practice using the <a target="_blank" href="/reference/embed">Embed</a> endpoint. You'll use it to explore a dataset of sentences, and be able to plot them in the plane and observe graphically that indeed similar sentences are mapped to close points in the embedding.
+In Module 1 you learned about text <a target="_blank" href="/docs/embeddings">embeddings</a>, and how they are a very useful way to turn text into numbers that capture its meaning and context. In this chapter you'll learn how to put them in practice using the <a target="_blank" href="/reference/embed">Embed</a> endpoint. You'll use it to explore a dataset of sentences, and be able to plot them in the plane and observe graphically that indeed similar sentences are mapped to close points in the embedding.
 
 ### Colab Notebook
 

@@ -29,7 +29,7 @@ Example use cases:
 
 Foundational model: Command
 
-[API Reference](/reference/chat-1)
+[API Reference](/reference/chat)
 
 ### Embed
 

@@ -229,7 +229,7 @@ warnings.filterwarnings('ignore')
 pd.set_option('display.max_colwidth', None)
 ```
 
-Fill in your Cohere API key in the next cell. To do this, begin by [signing up to Cohere](https://os.cohere.ai/) (for free!) if you haven't yet. Then get your API key [here](https://dashboard.cohere.com/api-keys).
+Fill in your Cohere API key in the next cell. To do this, begin by [signing up to Cohere](https://dashboard.cohere.ai/) (for free!) if you haven't yet. Then get your API key [here](https://dashboard.cohere.com/api-keys).
 
 ```python PYTHON
 co = cohere.Client("COHERE_API_KEY") # Insert your Cohere API key

@@ -151,7 +151,7 @@ And if you're running an older version of the SDK, you might need to upgrade it
 #!pip install --upgrade cohere
 ```
 
-Get your Cohere API key by [signing up here](https://os.cohere.ai/register). Paste it in the cell below.
+Get your Cohere API key by [signing up here](https://dashboard.cohere.ai/register). Paste it in the cell below.
 
 ## 1. Getting Set Up
 
@@ -173,7 +173,7 @@ warnings.filterwarnings('ignore')
 pd.set_option('display.max_colwidth', None)
 ```
 
-You'll need your API key for this next cell. [Sign up to Cohere](https://os.cohere.ai/) and get one if you haven't yet.
+You'll need your API key for this next cell. [Sign up to Cohere](https://dashboard.cohere.ai/) and get one if you haven't yet.
 
 ```python PYTHON
 model_name = "embed-english-v3.0"

@@ -146,7 +146,7 @@ We’ll cover three groups of tasks that you will typically work on when dealing
 - Classifying text
 - Analyzing text
 
-The first step is to install the Cohere Python SDK. Next, create an API key, which you can generate from the Cohere [dashboard](https://os.cohere.ai/register) or [CLI tool](https://docs.cohere.ai/cli-key).
+The first step is to install the Cohere Python SDK. Next, create an API key, which you can generate from the Cohere [dashboard](https://dashboard.cohere.ai/register) or [CLI tool](https://docs.cohere.ai/cli-key).
 
 ```python PYTHON
 ! pip install cohere altair umap-learn -q

@@ -245,7 +245,7 @@ labels_test = labels_test[:95]
 
 ## 2. Set up the Cohere client and get the embeddings of the reviews
 
-We're now ready to retrieve the embeddings from the API. You'll need your API key for this next cell. [Sign up to Cohere](https://os.cohere.ai/) and get one if you haven't yet.
+We're now ready to retrieve the embeddings from the API. You'll need your API key for this next cell. [Sign up to Cohere](https://dashboard.cohere.ai/) and get one if you haven't yet.
 
 ```python PYTHON
 model_name = "embed-english-v3.0"