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RetrievalAugmentedGenerationUsingChatGPTandMATLAB.md

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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

This example shows how to use retrieval-augmented generation to generate answers to queries based on information contained in a document corpus.

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"

This example requires Text Analytics Toolbox™.

To run this example, you need a valid API key from a paid OpenAI™ API account.

loadenv(".env")
addpath('../..')

Download and Preprocess Documents

Specify the URLs of the reports.

url = ["https://openknowledge.worldbank.org/bitstreams/0c18c872-91f0-51a4-ba91-c36b98893b4a/download"
    "https://openknowledge.worldbank.org/bitstreams/476f037b-a17e-484f-9cc2-282a2e5a929f/download"
    "https://openknowledge.worldbank.org/bitstreams/0c18c872-91f0-51a4-ba91-c36b98893b4a/download"];

Define the local path where the reports will be saved and download the reports using the provided URLs and save them to the specified local path.

localpath = "./data/";
if ~exist(localpath, "dir")
    mkdir(localpath);
end
numFiles = numel(url);
for i = 1:numFiles
    filename = "WBD_" + i + ".pdf";
    local_file_name = fullfile(localpath, filename);
    if ~exist(local_file_name,"file")
        websave(local_file_name, url{i}, weboptions(Timeout=30));
    end
end

Define the function to read the text from the downloaded files.

readFcn = @extractFileText;
file_pattern = [".txt",".pdf",".docx",".html",".htm"];
fds = fileDatastore(localpath,'FileExtensions',file_pattern,'ReadFcn',readFcn);

str = readall(fds);
str = [str{:}];

Split the text data into paragraphs with the helper function preprocessDocuments.

documents = preprocessDocuments(str);

Initialize the chatbot with a system prompt and API key. Include your API key in the environment variable OPENAI_API_KEY or pass your key using the APIKey name-value pair.

chat = openAIChat("You are a helpful assistant. You will get a " + ...
    "context for each question, but only use the information " + ...
    "in the context if that makes sense to answer the question. " + ...
    "Let's think step-by-step, explaining how you reached the answer.");

Retrieve Relevant Documents

Define the query, then retrieve and filter the relevant documents based on the query.

query = "What technical criteria can be used to streamline new approvals for grid-friendly DPV?";

Tokenize the query and find similarity scores between the query and documents.

embQuery = bm25Similarity(documents, tokenizedDocument(query));

Sort the documents in descending order of similarity scores.

[~, idx] = sort(embQuery, "descend");
limitWords = 1000;
selectedDocs = [];
totalWords = 0;

Iterate over sorted document indices until word limit is reached

i = 1;
while totalWords <= limitWords && i <= length(idx)
    totalWords = totalWords + doclength(documents(idx(i)));
    selectedDocs = [selectedDocs; joinWords(documents(idx(i)))];
    i = i + 1;
end

Generate Response

Define the prompt for the chatbot and generate a response.

prompt = "Context:" + join(selectedDocs, " ") + newline + ...
    "Answer the following question: " + query;
response = generate(chat, prompt);

Wrap the text for easier visualization.

wrapText(response)
ans = 
    "The context provides information on how technical criteria can be used to 
     streamline new approvals for grid-friendly DPV. It mentions that technical 
     approvals for DPV installations to connect to the grid can be streamlined with 
     prudent screening criteria for systems that meet certain specifications. 
     Additionally, it emphasizes the importance of having a grid code that reflects 
     expected future growth of distributed energy resources.
     
     Therefore, the technical criteria that can be used to streamline new approvals 
     for grid-friendly DPV include having prudent screening criteria based on 
     specific specifications and ensuring that the grid code is in line with the 
     expected growth of distributed resources. This helps in facilitating the 
     connection of DPV installations to the grid efficiently and effectively."

Helper Functions

function allDocs = preprocessDocuments(str)
tokenized = tokenizedDocument(join(str,[newline newline]));
allDocs = splitParagraphs(tokenized);
end

function wrappedText = wrapText(text)
s = textwrap(text,80);
wrappedText = string(join(s,newline));
end

References

Energy Sector Management Assistance Program (ESMAP). 2023. From Sun to Roof to Grid: Power Systems and Distributed PV. Technical Report. Washington, DC: World Bank. License: Creative Commons Attribution CC BY 3.0 IGO

Copyright 2024 The MathWorks, Inc.