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Improving example text: summarization and streaming
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‎examples/ExampleStreaming.m‎

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%% Process Generated Text in Real Time by Using ChatGPT in Streaming Mode
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% This example shows how to process generated text in real time by using ChatGPT
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% in streaming mode.
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%
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% By default, when you pass a prompt to ChatGPT, it generates a response internally
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% and then outputs it in full at the end. To print out and format generated text
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% as the model is generating it, use the |StreamFun| name-value argument of the
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% |openAIChat| class. The streaming function is a custom function handle that
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% tells the model what to do with the output.
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%
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% The example includes two parts:
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%%
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% * First, define and use a custom streaming function to print out generated
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% text directly as the model generates it.
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% * Then, create an HTML UI Component and define and use a custom streaming
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% function to update the UI Component in real time as the model generates text.
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%%
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% To run this example, you need a valid API key from a paid OpenAI API account.
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loadenv(".env")
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addpath('..')
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%% Print Stream Directly to Screen
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% In this example, the streamed output is printed directly to the screen.
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%
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% Define the function to print the returned tokens.
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function printToken(token)
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fprintf("%s",token);
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end
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%%
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% Create the chat object with the defined function as a handle.
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chat = openAIChat(StreamFun=@printToken);
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%%
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% Generate response to a prompt in streaming mode.
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prompt = "What is Model-Based Design?";
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generate(chat, prompt, MaxNumTokens=500);
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%% Print Stream to HTML UI Component
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% In this example, the streamed output is printed to the HTML component.
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%
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% Create the HTML UI component.
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fig = uifigure;
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h = uihtml(fig,Position=[50,10,450,400]);
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%%
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% Initialize the content of the HTML UI component.
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resetTable(h);
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%%
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% Create the chat object with the function handle, which requires the |uihtml|
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% object created earlier.
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chat = openAIChat(StreamFun=@(x)printStream(h,x));
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%%
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% Add the user prompt to the table in the HTML UI component.
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userPrompt = "Tell me 5 jokes.";
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addChat(h,"user",userPrompt,"new")
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%%
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% Generate response to a prompt in streaming mode.
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[txt, message, response] = generate(chat,userPrompt);
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%%
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% Update the last row with the final output. This is necessary if further update
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% is needed to support additional HTML formatting.
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addChat(h,"assistant",txt,"current")
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%% Helper functions
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% |resetTable|:
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%%
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% # Adds the basic HTML structure and the JavaScript that process the data change
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% in MATLAB.
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% # The JavaScript gets a reference to the table and changed data and if the
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% 3rd element in the data is "new", adds a new row.
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% # It populates the new row with two cells and update the cells from the first
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% two elements of the data.
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% # The new row is then appended to the table.
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% # Otherwise, the JavaScript gets reference to the last cell of the last row
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% of the table, and update it with the 2nd element of the data.
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function resetTable(obj)
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%RESETTABLE initialize the HTML UI component in the input argument.
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mustBeA(obj,'matlab.ui.control.HTML')
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obj.HTMLSource = ['<html><body><table>' ...
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'<tr><th>Role</th><th>Content</th></tr></table><script>', ...
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'function setup(htmlComponent) {', ...
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'htmlComponent.addEventListener("DataChanged", function(event) {', ...
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'var table = document.querySelector("table");' ...
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'var changedData = htmlComponent.Data;', ...
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'if (changedData[2] == "new") {', ...
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'var newRow = document.createElement("tr");', ...
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'var cell1 = document.createElement("td");', ...
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'var cell2 = document.createElement("td");', ...
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'cell1.innerHTML = changedData[0];', ...
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'cell2.innerHTML = changedData[1];', ...
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'newRow.appendChild(cell1);', ...
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'newRow.appendChild(cell2);', ...
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'table.appendChild(newRow);', ...
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'} else { ', ...
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'var lastRow = table.rows[table.rows.length - 1];', ...
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'var lastCell = lastRow.cells[lastRow.cells.length - 1];', ...
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'lastCell.innerHTML = changedData[1];', ...
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'}});}</script></body></html>'];
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obj.Data = [];
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drawnow
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end
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%%
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% |addRow| adds a new row to the table in the HTML UI component
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function addChat(obj,role,content,row)
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%ADDCHAT adds a new row or updates the last row of the table
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mustBeA(obj,'matlab.ui.control.HTML')
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content = replace(content,newline,"<br>");
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obj.Data = {role,content,row};
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drawnow
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end
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%%
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% |printStream| is the streaming function and prints the stream in the table
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% in the HTML UI component
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function printStream(h,x)
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%PRINTSTREAM prints the stream in a new row in the table
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if strlength(x) == 0
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% if the first token is 0 length, add a new row
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tokens = string(x);
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h.Data = {"assistant",tokens,"new"};
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else
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% otherwise append the new token to the previous tokens
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% if the new token contains a line break, replace
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% it with <br>
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if contains(x,newline)
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x = replace(x,newline,"<br>");
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end
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tokens = h.Data{2} + string(x);
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% update the existing row.
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h.Data = {"assistant",tokens,"current"};
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end
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drawnow
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end
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%%
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% _Copyright 2024 The MathWorks, Inc._

‎examples/ExampleStreaming.mlx‎

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‎examples/ExampleSummarization.m‎

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%% Summarize Large Documents Using ChatGPT and MATLAB
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% This example shows how to use ChatGPT to summarize documents that are too
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% large to be summarized at once.
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%
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% To summarize short documents using ChatGPT, you can pass the documents directly
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% as a prompt together with an instruction to summarize them. However, ChatGPT
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% can only process prompts of limited size.
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%
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% To summarize documents that are larger than this limit, split the documents
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% up into smaller documents. Summarize the smaller document chunks, then pass
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% all of the summaries to ChatGPT to generate one overall summary.
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%%
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% * This example includes four steps:
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% * Download the complete text of "Alice in Wonderland" by Lewis Carroll from
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% Project Gutenberg.
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% * Split the documents up into chunks of less than 3000 words. (Section title:
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% "Split Document Into Chunks")
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% * Use ChatGPT to create summaries of each chunk. ("Summarize Chunks")
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% * Then use ChatGPT to create a summary of all of the summaries. ("Summarize
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% Document")
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%%
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% To run this example, you need Text Analytics Toolbox™.
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%
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% To run this example, you need a valid API key from a paid OpenAI™ API account.
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loadenv(".env")
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addpath('..')
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%% Download Text Data
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% Download and read the content from Alice's Adventures in Wonderland by Lewis
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% Carroll from Project Gutenberg.
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%
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% First read the contents of the webpage.
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options = weboptions(Timeout=30);
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code = webread("https://www.gutenberg.org/files/11/11-h/11-h.htm", options);
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longText = extractHTMLText(string(code));
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%% Split Document Into Chunks
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% Large language models have a limit in terms of how much text they can accept
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% as input, so if you try to summarize the complete book, you will likely get
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% an error. A workaround is splitting the book into chunks and summarize each
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% chunk individually. The chunk size is defined in |limitChunkWords|, which restricts
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% the numbers of words in a chunk.
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incrementalSummary = longText;
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limitChunkWords = 3000;
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chunks = createChunks(incrementalSummary, limitChunkWords);
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%% Summarize Chunks
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% Initialize a ChatGPT session with the role of summarizing text
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summarizer = openAIChat("You are a professional summarizer.");
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%%
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% Looping process to gradually summarize the text chunk by chunk, reducing the
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% chunk size with each iteration.
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numCalls = 0;
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while numel(chunks)>1
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summarizedChunks = strings(size(chunks));
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numCalls = numCalls + numel(chunks);
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%%
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% Add a limit to the number of calls, to ensure you are not making more calls
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% than what is expected. You can change this value to match what is needed for
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% your application.
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if numCalls > 20
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error("Document is too long to be summarized.")
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end
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for i = 1:length(chunks)
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summarizedChunks(i) = generate(summarizer, "Summarize this content:" + newline + chunks(i));
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end
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%%
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% Merge the summarized chunks to serve as the base for the next iteration.
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incrementalSummary = join(summarizedChunks);
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%%
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% Form new chunks with a reduced size for the subsequent iteration.
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chunks = createChunks(incrementalSummary, limitChunkWords);
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end
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%% Summarize Document
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% Compile the final summary by combining the summaries from all the chunks.
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fullSummary = generate(summarizer, "The following text is a combination of summaries. " + ...
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"Provide a cohese and coherent summary combining these smaller summaries, preserving as much information as possible:" + newline + incrementalSummary);
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wrapText(fullSummary)
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%% |createChunks| function
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% This function segments a long text into smaller parts of a predefined size
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% to facilitate easier summarization. It preserves the structure of sentences.
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% The |chunkSize| should be large enough to fit at least one sentence.
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function chunks = createChunks(text, chunkSize)
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% Tokenizing the input text for processing
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text = tokenizedDocument(text);
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% Splitting the tokenized text into individual sentences
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text = splitSentences(text);
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chunks = [];
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currentChunk = "";
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currentChunkSize = 0;
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% Iterating through the sentences to aggregate them into chunks until the chunk
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% attains the predefined size, after which a new chunk is started
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for i=1:length(text)
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newChunkSize = currentChunkSize + doclength(text(i));
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if newChunkSize < chunkSize
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currentChunkSize = currentChunkSize + doclength(text(i));
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currentChunk = currentChunk + " " + joinWords(text(i));
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else
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chunks = [chunks; currentChunk]; %#ok
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currentChunkSize = doclength(text(i));
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currentChunk = joinWords(text(i));
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end
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end
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end
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%% |wrapText| function
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% This function splits text into sentences and then concatenates them again
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% using |newline| to make it easier to visualize text in this example
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function wrappedText = wrapText(text)
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wrappedText = splitSentences(text);
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wrappedText = join(wrappedText,newline);
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end
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%%
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% _Copyright 2023 The MathWorks, Inc._

‎examples/ExampleSummarization.mlx‎

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