ChatGPT folder + Azure Function example (#1231)
Max Reid
4 months ago
committed by
GitHub
parent
f0bbde1e8d
commit
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@ -0,0 +1,240 @@
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const { Client } = require('@microsoft/microsoft-graph-client');
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const pdfParse = require('pdf-parse');
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const { Buffer } = require('buffer');
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const path = require('path');
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const axios = require('axios');
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const qs = require('querystring');
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const { OpenAI } = require("openai");
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//// --------- ENVIRONMENT CONFIGURATION AND INITIALIZATION ---------
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// Function to initialize Microsoft Graph client
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const initGraphClient = (accessToken) => {
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return Client.init({
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authProvider: (done) => {
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done(null, accessToken); // Pass the access token for Graph API calls
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}
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});
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};
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//// --------- AUTHENTICATION AND TOKEN MANAGEMENT ---------
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// Function to obtain OBO token. This will take the access token in request header (scoped to this Function App) and generate a new token to use for Graph API
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const getOboToken = async (userAccessToken) => {
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const { TENANT_ID, CLIENT_ID, MICROSOFT_PROVIDER_AUTHENTICATION_SECRET } = process.env;
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const scope = 'https://graph.microsoft.com/.default';
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const oboTokenUrl = `https://login.microsoftonline.com/${TENANT_ID}/oauth2/v2.0/token`;
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const params = {
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client_id: CLIENT_ID,
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client_secret: MICROSOFT_PROVIDER_AUTHENTICATION_SECRET,
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grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',
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assertion: userAccessToken,
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requested_token_use: 'on_behalf_of',
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scope: scope
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};
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try {
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const response = await axios.post(oboTokenUrl, qs.stringify(params), {
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headers: {
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'Content-Type': 'application/x-www-form-urlencoded'
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}
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});
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return response.data.access_token; // OBO token
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} catch (error) {
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console.error('Error obtaining OBO token:', error.response?.data || error.message);
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throw error;
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}
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};
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//// --------- DOCUMENT PROCESSING ---------
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// Function to fetch drive item content and convert to text
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const getDriveItemContent = async (client, driveId, itemId, name) => {
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try {
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const fileType = path.extname(name).toLowerCase();
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// the below files types are the ones that are able to be converted to PDF to extract the text. See https://learn.microsoft.com/en-us/graph/api/driveitem-get-content-format?view=graph-rest-1.0&tabs=http
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const allowedFileTypes = ['.pdf', '.doc', '.docx', '.odp', '.ods', '.odt', '.pot', '.potm', '.potx', '.pps', '.ppsx', '.ppsxm', '.ppt', '.pptm', '.pptx', '.rtf'];
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// filePath changes based on file type, adding ?format=pdf to convert non-pdf types to pdf for text extraction, so all files in allowedFileTypes above are converted to pdf
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const filePath = `/drives/${driveId}/items/${itemId}/content` + ((fileType === '.pdf' || fileType === '.txt' || fileType === '.csv') ? '' : '?format=pdf');
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if (allowedFileTypes.includes(fileType)) {
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response = await client.api(filePath).getStream();
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// The below takes the chunks in response and combines
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let chunks = [];
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for await (let chunk of response) {
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chunks.push(chunk);
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}
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let buffer = Buffer.concat(chunks);
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// the below extracts the text from the PDF.
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const pdfContents = await pdfParse(buffer);
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return pdfContents.text;
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} else if (fileType === '.txt') {
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// If the type is txt, it does not need to create a stream and instead just grabs the content
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response = await client.api(filePath).get();
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return response;
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} else if (fileType === '.csv') {
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response = await client.api(filePath).getStream();
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let chunks = [];
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for await (let chunk of response) {
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chunks.push(chunk);
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}
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let buffer = Buffer.concat(chunks);
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let dataString = buffer.toString('utf-8');
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return dataString
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} else {
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return 'Unsupported File Type';
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}
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} catch (error) {
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console.error('Error fetching drive content:', error);
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throw new Error(`Failed to fetch content for ${name}: ${error.message}`);
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}
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};
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// Function to get relevant parts of text using gpt-3.5-turbo.
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const getRelevantParts = async (text, query) => {
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try {
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// We use your OpenAI key to initialize the OpenAI client
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const openAIKey = process.env["OPENAI_API_KEY"];
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const openai = new OpenAI({
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apiKey: openAIKey,
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});
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const response = await openai.chat.completions.create({
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// Using gpt-3.5-turbo due to speed to prevent timeouts. You can tweak this prompt as needed
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model: "gpt-3.5-turbo-0125",
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messages: [
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{"role": "system", "content": "You are a helpful assistant that finds relevant content in text based on a query. You only return the relevant sentences, and you return a maximum of 10 sentences"},
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{"role": "user", "content": `Based on this question: **"${query}"**, get the relevant parts from the following text:*****\n\n${text}*****. If you cannot answer the question based on the text, respond with 'No information provided'`}
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],
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// using temperature of 0 since we want to just extract the relevant content
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temperature: 0,
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// using max_tokens of 1000, but you can customize this based on the number of documents you are searching.
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max_tokens: 1000
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});
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return response.choices[0].message.content;
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} catch (error) {
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console.error('Error with OpenAI:', error);
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return 'Error processing text with OpenAI' + error;
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}
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};
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//// --------- AZURE FUNCTION LOGIC ---------
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// Below is what the Azure Function executes
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module.exports = async function (context, req) {
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const query = req.query.query || (req.body && req.body.query);
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const searchTerm = req.query.searchTerm || (req.body && req.body.searchTerm);
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if (!req.headers.authorization) {
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context.res = {
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status: 400,
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body: 'Authorization header is missing'
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};
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return;
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}
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/// The below takes the token passed to the function, to use to get an OBO token.
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const bearerToken = req.headers.authorization.split(' ')[1];
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let accessToken;
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try {
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accessToken = await getOboToken(bearerToken);
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} catch (error) {
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context.res = {
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status: 500,
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body: `Failed to obtain OBO token: ${error.message}`
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};
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return;
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}
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// Initialize the Graph Client using the initGraphClient function defined above
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let client = initGraphClient(accessToken);
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// this is the search body to be used in the Microsft Graph Search API: https://learn.microsoft.com/en-us/graph/search-concept-files
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const requestBody = {
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requests: [
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{
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entityTypes: ['driveItem'],
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query: {
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queryString: searchTerm
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},
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from: 0,
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// the below is set to summarize the top 10 search results from the Graph API, but can configure based on your documents.
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size: 10
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}
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]
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};
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try {
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// Function to tokenize content (e.g., based on words).
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const tokenizeContent = (content) => {
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return content.split(/\s+/);
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};
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// Function to break tokens into 10k token windows for gpt-3.5-turbo
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const breakIntoTokenWindows = (tokens) => {
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const tokenWindows = []
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const maxWindowTokens = 10000; // 10k tokens
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let startIndex = 0;
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while (startIndex < tokens.length) {
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const window = tokens.slice(startIndex, startIndex + maxWindowTokens);
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tokenWindows.push(window);
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startIndex += maxWindowTokens;
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}
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return tokenWindows;
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};
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// This is where we are doing the search
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const list = await client.api('/search/query').post(requestBody);
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const processList = async () => {
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// This will go through and for each search response, grab the contents of the file and summarize with gpt-3.5-turbo
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const results = [];
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await Promise.all(list.value[0].hitsContainers.map(async (container) => {
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for (const hit of container.hits) {
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if (hit.resource["@odata.type"] === "#microsoft.graph.driveItem") {
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const { name, id } = hit.resource;
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// We use the below to grab the URL of the file to include in the response
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const webUrl = hit.resource.webUrl.replace(/\s/g, "%20");
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// The Microsoft Graph API ranks the reponses, so we use this to order it
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const rank = hit.rank;
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// The below is where the file lives
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const driveId = hit.resource.parentReference.driveId;
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const contents = await getDriveItemContent(client, driveId, id, name);
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if (contents !== 'Unsupported File Type') {
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// Tokenize content using function defined previously
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const tokens = tokenizeContent(contents);
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// Break tokens into 10k token windows
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const tokenWindows = breakIntoTokenWindows(tokens);
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// Process each token window and combine results
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const relevantPartsPromises = tokenWindows.map(window => getRelevantParts(window.join(' '), query));
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const relevantParts = await Promise.all(relevantPartsPromises);
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const combinedResults = relevantParts.join('\n'); // Combine results
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results.push({ name, webUrl, rank, contents: combinedResults });
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}
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else {
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results.push({ name, webUrl, rank, contents: 'Unsupported File Type' });
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}
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}
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}
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}));
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return results;
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};
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let results;
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if (list.value[0].hitsContainers[0].total == 0) {
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// Return no results found to the API if the Microsoft Graph API returns no results
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results = 'No results found';
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} else {
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// If the Microsoft Graph API does return results, then run processList to iterate through.
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results = await processList();
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results.sort((a, b) => a.rank - b.rank);
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}
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context.res = {
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status: 200,
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body: results
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};
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} catch (error) {
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context.res = {
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status: 500,
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body: `Error performing search or processing results: ${error.message}`,
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};
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}
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};
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@ -0,0 +1,240 @@
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const { Client } = require('@microsoft/microsoft-graph-client');
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const pdfParse = require('pdf-parse');
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const { Buffer } = require('buffer');
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const path = require('path');
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const axios = require('axios');
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const qs = require('querystring');
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const { OpenAI } = require("openai");
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//// --------- ENVIRONMENT CONFIGURATION AND INITIALIZATION ---------
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// Function to initialize Microsoft Graph client
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const initGraphClient = (accessToken) => {
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return Client.init({
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authProvider: (done) => {
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done(null, accessToken); // Pass the access token for Graph API calls
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}
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});
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};
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//// --------- AUTHENTICATION AND TOKEN MANAGEMENT ---------
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// Function to obtain OBO token. This will take the access token in request header (scoped to this Function App) and generate a new token to use for Graph API
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const getOboToken = async (userAccessToken) => {
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const { TENANT_ID, CLIENT_ID, MICROSOFT_PROVIDER_AUTHENTICATION_SECRET } = process.env;
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const scope = 'https://graph.microsoft.com/.default';
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const oboTokenUrl = `https://login.microsoftonline.com/${TENANT_ID}/oauth2/v2.0/token`;
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const params = {
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client_id: CLIENT_ID,
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client_secret: MICROSOFT_PROVIDER_AUTHENTICATION_SECRET,
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grant_type: 'urn:ietf:params:oauth:grant-type:jwt-bearer',
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assertion: userAccessToken,
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requested_token_use: 'on_behalf_of',
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scope: scope
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};
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try {
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const response = await axios.post(oboTokenUrl, qs.stringify(params), {
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headers: {
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'Content-Type': 'application/x-www-form-urlencoded'
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}
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});
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return response.data.access_token; // OBO token
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} catch (error) {
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console.error('Error obtaining OBO token:', error.response?.data || error.message);
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throw error;
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}
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};
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//// --------- DOCUMENT PROCESSING ---------
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// Function to fetch drive item content and convert to text
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const getDriveItemContent = async (client, driveId, itemId, name) => {
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try {
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const fileType = path.extname(name).toLowerCase();
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// the below files types are the ones that are able to be converted to PDF to extract the text. See https://learn.microsoft.com/en-us/graph/api/driveitem-get-content-format?view=graph-rest-1.0&tabs=http
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const allowedFileTypes = ['.pdf', '.doc', '.docx', '.odp', '.ods', '.odt', '.pot', '.potm', '.potx', '.pps', '.ppsx', '.ppsxm', '.ppt', '.pptm', '.pptx', '.rtf'];
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// filePath changes based on file type, adding ?format=pdf to convert non-pdf types to pdf for text extraction, so all files in allowedFileTypes above are converted to pdf
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const filePath = `/drives/${driveId}/items/${itemId}/content` + ((fileType === '.pdf' || fileType === '.txt' || fileType === '.csv') ? '' : '?format=pdf');
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if (allowedFileTypes.includes(fileType)) {
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response = await client.api(filePath).getStream();
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// The below takes the chunks in response and combines
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let chunks = [];
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for await (let chunk of response) {
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chunks.push(chunk);
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}
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let buffer = Buffer.concat(chunks);
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// the below extracts the text from the PDF.
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const pdfContents = await pdfParse(buffer);
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return pdfContents.text;
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} else if (fileType === '.txt') {
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// If the type is txt, it does not need to create a stream and instead just grabs the content
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response = await client.api(filePath).get();
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return response;
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} else if (fileType === '.csv') {
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response = await client.api(filePath).getStream();
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let chunks = [];
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for await (let chunk of response) {
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chunks.push(chunk);
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}
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let buffer = Buffer.concat(chunks);
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let dataString = buffer.toString('utf-8');
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return dataString
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} else {
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return 'Unsupported File Type';
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}
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} catch (error) {
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console.error('Error fetching drive content:', error);
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throw new Error(`Failed to fetch content for ${name}: ${error.message}`);
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}
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};
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|
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// Function to get relevant parts of text using gpt-3.5-turbo.
|
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const getRelevantParts = async (text, query) => {
|
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try {
|
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// We use your OpenAI key to initialize the OpenAI client
|
||||
const openAIKey = process.env["OPENAI_API_KEY"];
|
||||
const openai = new OpenAI({
|
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apiKey: openAIKey,
|
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});
|
||||
const response = await openai.chat.completions.create({
|
||||
// Using gpt-3.5-turbo due to speed to prevent timeouts. You can tweak this prompt as needed
|
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model: "gpt-3.5-turbo-0125",
|
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messages: [
|
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{"role": "system", "content": "You are a helpful assistant that finds relevant content in text based on a query. You only return the relevant sentences, and you return a maximum of 10 sentences"},
|
||||
{"role": "user", "content": `Based on this question: **"${query}"**, get the relevant parts from the following text:*****\n\n${text}*****. If you cannot answer the question based on the text, respond with 'No information provided'`}
|
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],
|
||||
// using temperature of 0 since we want to just extract the relevant content
|
||||
temperature: 0,
|
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// using max_tokens of 1000, but you can customize this based on the number of documents you are searching.
|
||||
max_tokens: 1000
|
||||
});
|
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return response.choices[0].message.content;
|
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} catch (error) {
|
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console.error('Error with OpenAI:', error);
|
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return 'Error processing text with OpenAI' + error;
|
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}
|
||||
};
|
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|
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//// --------- AZURE FUNCTION LOGIC ---------
|
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// Below is what the Azure Function executes
|
||||
module.exports = async function (context, req) {
|
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const query = req.query.query || (req.body && req.body.query);
|
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const searchTerm = req.query.searchTerm || (req.body && req.body.searchTerm);
|
||||
if (!req.headers.authorization) {
|
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context.res = {
|
||||
status: 400,
|
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body: 'Authorization header is missing'
|
||||
};
|
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return;
|
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}
|
||||
/// The below takes the token passed to the function, to use to get an OBO token.
|
||||
const bearerToken = req.headers.authorization.split(' ')[1];
|
||||
let accessToken;
|
||||
try {
|
||||
accessToken = await getOboToken(bearerToken);
|
||||
} catch (error) {
|
||||
context.res = {
|
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status: 500,
|
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body: `Failed to obtain OBO token: ${error.message}`
|
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};
|
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return;
|
||||
}
|
||||
// Initialize the Graph Client using the initGraphClient function defined above
|
||||
let client = initGraphClient(accessToken);
|
||||
// this is the search body to be used in the Microsft Graph Search API: https://learn.microsoft.com/en-us/graph/search-concept-files
|
||||
const requestBody = {
|
||||
requests: [
|
||||
{
|
||||
entityTypes: ['driveItem'],
|
||||
query: {
|
||||
queryString: searchTerm
|
||||
},
|
||||
from: 0,
|
||||
// the below is set to summarize the top 10 search results from the Graph API, but can configure based on your documents.
|
||||
size: 10
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
try {
|
||||
// Function to tokenize content (e.g., based on words).
|
||||
const tokenizeContent = (content) => {
|
||||
return content.split(/\s+/);
|
||||
};
|
||||
|
||||
// Function to break tokens into 10k token windows for gpt-3.5-turbo
|
||||
const breakIntoTokenWindows = (tokens) => {
|
||||
const tokenWindows = []
|
||||
const maxWindowTokens = 10000; // 10k tokens
|
||||
let startIndex = 0;
|
||||
|
||||
while (startIndex < tokens.length) {
|
||||
const window = tokens.slice(startIndex, startIndex + maxWindowTokens);
|
||||
tokenWindows.push(window);
|
||||
startIndex += maxWindowTokens;
|
||||
}
|
||||
|
||||
return tokenWindows;
|
||||
};
|
||||
// This is where we are doing the search
|
||||
const list = await client.api('/search/query').post(requestBody);
|
||||
|
||||
const processList = async () => {
|
||||
// This will go through and for each search response, grab the contents of the file and summarize with gpt-3.5-turbo
|
||||
const results = [];
|
||||
|
||||
await Promise.all(list.value[0].hitsContainers.map(async (container) => {
|
||||
for (const hit of container.hits) {
|
||||
if (hit.resource["@odata.type"] === "#microsoft.graph.driveItem") {
|
||||
const { name, id } = hit.resource;
|
||||
// We use the below to grab the URL of the file to include in the response
|
||||
const webUrl = hit.resource.webUrl.replace(/\s/g, "%20");
|
||||
// The Microsoft Graph API ranks the reponses, so we use this to order it
|
||||
const rank = hit.rank;
|
||||
// The below is where the file lives
|
||||
const driveId = hit.resource.parentReference.driveId;
|
||||
const contents = await getDriveItemContent(client, driveId, id, name);
|
||||
if (contents !== 'Unsupported File Type') {
|
||||
// Tokenize content using function defined previously
|
||||
const tokens = tokenizeContent(contents);
|
||||
|
||||
// Break tokens into 10k token windows
|
||||
const tokenWindows = breakIntoTokenWindows(tokens);
|
||||
|
||||
// Process each token window and combine results
|
||||
const relevantPartsPromises = tokenWindows.map(window => getRelevantParts(window.join(' '), query));
|
||||
const relevantParts = await Promise.all(relevantPartsPromises);
|
||||
const combinedResults = relevantParts.join('\n'); // Combine results
|
||||
|
||||
results.push({ name, webUrl, rank, contents: combinedResults });
|
||||
}
|
||||
else {
|
||||
results.push({ name, webUrl, rank, contents: 'Unsupported File Type' });
|
||||
}
|
||||
}
|
||||
}
|
||||
}));
|
||||
|
||||
return results;
|
||||
};
|
||||
let results;
|
||||
if (list.value[0].hitsContainers[0].total == 0) {
|
||||
// Return no results found to the API if the Microsoft Graph API returns no results
|
||||
results = 'No results found';
|
||||
} else {
|
||||
// If the Microsoft Graph API does return results, then run processList to iterate through.
|
||||
results = await processList();
|
||||
results.sort((a, b) => a.rank - b.rank);
|
||||
}
|
||||
context.res = {
|
||||
status: 200,
|
||||
body: results
|
||||
};
|
||||
} catch (error) {
|
||||
context.res = {
|
||||
status: 500,
|
||||
body: `Error performing search or processing results: ${error.message}`,
|
||||
};
|
||||
}
|
||||
};
|
||||
|
||||
Loading…
Reference in New Issue