Remove redundant addpath calls

Since we recommend users get this code via Add-On Manager, which adds everything to the path,
all those `addpath` calls are redundant.

Author: ccreutzi

examples/AnalyzeScientificPapersUsingFunctionCalls.md

@@ -14,7 +14,6 @@ To run this example, you need a valid API key from a paid OpenAI™ API account.
 
 ```matlab
 loadenv(".env")
-addpath('../..')
 ```
 # Initialize OpenAI API Function and Chat
 

examples/AnalyzeSentimentinTextUsingChatGPTwithStructuredOutput.md

@@ -10,7 +10,6 @@ To run this example, you need a valid API key from a paid OpenAI™ API account.
 
 ```matlab
 loadenv(".env")
-addpath('../..')
 ```
 
 Define some text to analyze the sentiment.
@@ -50,6 +49,7 @@ prototype = struct with fields:
 
 ```
 
+
 Create a chat object and set `ResponseFormat` to `prototype`.
 
 ```matlab
@@ -78,6 +78,8 @@ T = struct2table(scores);
 T.text = inputText;
 T = movevars(T,"text","Before","sentiment")
 ```
+
+
 | |text|sentiment|confidence|
 |:--:|:--:|:--:|:--:|
 |1|"I can't stand homework."|negative|0.9500|
@@ -90,5 +92,6 @@ T = movevars(T,"text","Before","sentiment")
 |8|"More work. Great!"|positive|0.9000|
 
 
+
 *Copyright 2024 The MathWorks, Inc.*
 

examples/CreateSimpleChatBot.md

@@ -15,6 +15,9 @@ When you run this example, an interactive AI chat starts in the MATLAB® Command
 
 To run this example, you need a valid API key from a paid OpenAI™ API account.
 
+```matlab
+loadenv(".env")
+```
 # Setup Model
 
 Set the maximum allowable number of words per chat session and define the keyword that, when entered by the user, ends the chat session. This example uses the model o1.
@@ -123,6 +126,7 @@ AI: Time is the dimension in which events occur sequentially from past to future
 User: end
 AI: Closing the chat. Have a great day!
 ```
+
 # `countNumWords` function
 
 Function to count the number of words in a text string

examples/DescribeImagesUsingChatGPT.md

@@ -7,7 +7,6 @@ This example shows how to generate image descriptions using the addUserMessageWi
 
 ```matlab
 loadenv(".env")
-addpath('../..')
 ```
 # Load and Display Image Data
 
@@ -54,6 +53,7 @@ wrappedText =
      is bright and cloudy, creating a serene and tranquil atmosphere."
 
 ```
+
 # Helper function
 ```matlab
 function wrappedText = wrapText(text)

examples/InformationRetrievalUsingOpenAIDocumentEmbedding.md

@@ -1,5 +1,5 @@
 
-# Information Retrieval Using OpenAI™ Document Embedding
+# Information Retrieval Using OpenAI Document Embedding
 
 To run the code shown on this page, open the MLX file in MATLAB®: [mlx-scripts/InformationRetrievalUsingOpenAIDocumentEmbedding.mlx](mlx-scripts/InformationRetrievalUsingOpenAIDocumentEmbedding.mlx) 
 
@@ -19,11 +19,10 @@ This process is sometimes referred to as Retrieval\-Augmented Generation (RAG),
 This example requires Text Analytics Toolbox™. 
 
 
-To run this example, you need a valid API key from a paid OpenAI API account.
+To run this example, you need a valid API key from a paid OpenAI™ API account.
 
 ```matlab
 loadenv(".env")
-addpath('../..') 
 ```
 # Embed Query Document
 
@@ -40,6 +39,7 @@ ans = 1x5
    -0.0051   -0.0005    0.0362   -0.0069    0.0534
 
 ```
+
 # Download and Embed Source Text
 
 In this example, we will scrape content from several MATLAB documentation pages. 
@@ -106,6 +106,7 @@ ans =
 
 ```
 
+
 Pass the question and the context for generation to get a contextualized answer.
 
 ```matlab
@@ -120,6 +121,7 @@ ans =
      This makes tables an ideal data structure for organizing and manipulating data in a tabular format."
 
 ```
+
 # Helper Function
 
 Helper function to wrap text for easier reading in the live script.

examples/ProcessGeneratedTextinRealTimebyUsingChatGPTinStreamingMode.md

@@ -1,9 +1,9 @@
 
-# Process Generated Text in Real Time by Using ChatGPT™ in Streaming Mode
+# Process Generated Text in Real Time by Using ChatGPT in Streaming Mode
 
 To run the code shown on this page, open the MLX file in MATLAB®: [mlx-scripts/ProcessGeneratedTextinRealTimebyUsingChatGPTinStreamingMode.mlx](mlx-scripts/ProcessGeneratedTextinRealTimebyUsingChatGPTinStreamingMode.mlx) 
 
-This example shows how to process generated text in real time by using ChatGPT in streaming mode.
+This example shows how to process generated text in real time by using ChatGPT™ in streaming mode.
 
 
 By default, when you pass a prompt to ChatGPT, it generates a response internally and then outputs it in full at the end. To print out and format generated text as the model is generating it, use the `StreamFun` name\-value argument of the `openAIChat` class. The streaming function is a custom function handle that tells the model what to do with the output.
@@ -18,7 +18,6 @@ To run this example, you need a valid API key from a paid OpenAI™ API account.
 
 ```matlab
 loadenv(".env")
-addpath('../..') 
 ```
 # Print Stream Directly to Screen
 
@@ -49,6 +48,7 @@ generate(chat, prompt, MaxNumTokens=500);
 ```matlabTextOutput
 Model-Based Design is an approach to system development that uses graphical models to design and simulate systems before implementing them in hardware or software. It involves creating models that represent the behavior and interactions of system components, and using these models to analyze, validate, and optimize the system before building it. Model-Based Design can help to improve the efficiency, reliability, and quality of system development by enabling engineers to explore design alternatives, detect errors early in the development process, and facilitate collaboration between different teams working on the same project.
 ```
+
 # Print Stream to HTML UI Component
 
 In this example, the streamed output is printed to the HTML component. 

examples/RetrievalAugmentedGenerationUsingChatGPTandMATLAB.md

@@ -1,5 +1,5 @@
 
-# Retrieval\-Augmented Generation Using ChatGPT™ and MATLAB
+# Retrieval\-Augmented Generation Using ChatGPT and MATLAB
 
 To run the code shown on this page, open the MLX file in MATLAB®: [mlx-scripts/RetrievalAugmentedGenerationUsingChatGPTandMATLAB.mlx](mlx-scripts/RetrievalAugmentedGenerationUsingChatGPTandMATLAB.mlx) 
 
@@ -10,7 +10,7 @@ The example contains three steps:
 
 -  Download and preprocess documents. 
 -  Find documents relevant to a query using keyword search. 
--  Generate a response using ChatGPT based on the both the query and the most relevant source document. \-> title "Generate Response" 
+-  Generate a response using ChatGPT™ based on the both the query and the most relevant source document. 
 
 This example requires Text Analytics Toolbox™. 
 
@@ -19,7 +19,6 @@ To run this example, you need a valid API key from a paid OpenAI™ API account.
 
 ```matlab
 loadenv(".env")
-addpath('../..')
 ```
 # Download and Preprocess Documents
 
@@ -138,6 +137,7 @@ ans =
      connection of DPV installations to the grid efficiently and effectively."
 
 ```
+
 # Helper Functions
 ```matlab
 function allDocs = preprocessDocuments(str)

examples/SummarizeLargeDocumentsUsingChatGPTandMATLAB.md

@@ -1,9 +1,9 @@
 
-# Summarize Large Documents Using ChatGPT™ and MATLAB®
+# Summarize Large Documents Using ChatGPT and MATLAB
 
 To run the code shown on this page, open the MLX file in MATLAB®: [mlx-scripts/SummarizeLargeDocumentsUsingChatGPTandMATLAB.mlx](mlx-scripts/SummarizeLargeDocumentsUsingChatGPTandMATLAB.mlx) 
 
-This example shows how to use ChatGPT to summarize documents that are too large to be summarized at once.
+This example shows how to use ChatGPT™ to summarize documents that are too large to be summarized at once.
 
 
 To summarize short documents using ChatGPT, you can pass the documents directly as a prompt together with an instruction to summarize them. However, ChatGPT can only process prompts of limited size.
@@ -24,7 +24,6 @@ To run this example, you need a valid API key from a paid OpenAI™ API account.
 
 ```matlab
 loadenv(".env")
-addpath('../..')
 ```
 # Download Text Data
 
@@ -107,6 +106,7 @@ ans =
      The narrative is filled with illogical and imaginative elements, capturing readers' imaginations with its colorful and eccentric storytelling."
 
 ```
+
 # `createChunks` function
 
 This function segments a long text into smaller parts of a predefined size to facilitate easier summarization. It preserves the structure of sentences. The `chunkSize` should be large enough to fit at least one sentence.

examples/UsingDALLEToEditImages.md

@@ -1,16 +1,15 @@
 
-# Using DALL·E™ to Edit Images
+# Using DALL·E to Edit Images
 
 To run the code shown on this page, open the MLX file in MATLAB®: [mlx-scripts/UsingDALLEToEditImages.mlx](mlx-scripts/UsingDALLEToEditImages.mlx) 
 
 This example shows how to generate and edit images using the `openAIImages` object.
 
 
-To run this example, you need a valid OpenAI™ API key. Creating images using DALL•E may incur a fee.
+To run this example, you need a valid OpenAI™ API key. Creating images using DALL•E™ may incur a fee.
 
 ```matlab
 loadenv(".env")
-addpath("../..")
 ```
 
 We want to load images files relative to the project directory below: