Usando os MongoDB Atlas Triggers para resumir as avaliações do Airbnb com OpenAI

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"reviews": [ { "_id": "2663437", 
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"date": { "$date": "2012-10-20T04:00:00.000Z" }, \
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"listing_id": "664017",
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 "reviewer_id": "633940", 
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"reviewer_name": "Patricia", 
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"comments": "I booked the room at Marinete's apartment for my husband. He was staying in Rio for a week because he was studying Portuguese. He loved the place. Marinete was very helpfull, the room was nice and clean. \r\nThe location is perfect. He loved the time there. \r\n\r\n" },
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 { "_id": "2741592", 
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"date": { "$date": "2012-10-28T04:00:00.000Z" }, 
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"listing_id": "664017",
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 "reviewer_id": "3932440", 
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"reviewer_name": "Carolina", 
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"comments": "Es una muy buena anfitriona, preocupada de que te encuentres cómoda y te sugiere que actividades puedes realizar. Disfruté mucho la estancia durante esos días, el sector es central y seguro." }, ... ]

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listingsAndReviews.updateOne({"_id" : "1129303"}, { $push : { "reviews" : new_review } , $set : { "process" : false" }});

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function sampleReviews(reviews) {
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    if (reviews.length <= 50) {
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        return reviews;
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    }
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    const sampledReviews = [];
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    const seenIndices = new Set();
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    while (sampledReviews.length < 50) {
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        const randomIndex = Math.floor(Math.random() * reviews.length);
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        if (!seenIndices.has(randomIndex)) {
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            seenIndices.add(randomIndex);
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            sampledReviews.push(reviews[randomIndex]);
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        }
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    }
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    return sampledReviews;
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}

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exports = async function(changeEvent) {
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  // A Database Trigger will always call a function with a changeEvent.
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  // Documentation on ChangeEvents: https://www.mongodb.com/pt-br/docs/manual/reference/change-events
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  // This sample function will listen for events and replicate them to a collection in a different Database
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function sampleReviews(reviews) {
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// Logic above...
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   if (reviews.length <= 50) {
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        return reviews;
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    }
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    const sampledReviews = [];
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    const seenIndices = new Set();
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    while (sampledReviews.length < 50) {
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        const randomIndex = Math.floor(Math.random() * reviews.length);
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        if (!seenIndices.has(randomIndex)) {
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            seenIndices.add(randomIndex);
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            sampledReviews.push(reviews[randomIndex]);
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        }
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    }
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    return sampledReviews;
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}
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  // Access the _id of the changed document:
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  const docId = changeEvent.documentKey._id;
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  const doc= changeEvent.fullDocument;
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  // Get the MongoDB service you want to use (see "Linked Data Sources" tab)
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  const serviceName = "mongodb-atlas";
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  const databaseName = "sample_airbnb";
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  const collection = context.services.get(serviceName).db(databaseName).collection(changeEvent.ns.coll);
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 // This function is the endpoint's request handler. 
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    // URL to make the request to the OpenAI API.
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    const url = 'https://api.openai.com/v1/chat/completions';
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    // Fetch the OpenAI key stored in the context values.
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    const openai_key = context.values.get("openAIKey");
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    const reviews = doc.reviews.map((review) => {return {"comments" : review.comments}});
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    const sampledReviews= sampleReviews(reviews);
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    // Prepare the request string for the OpenAI API.
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    const reqString = `Summerize the reviews provided here: ${JSON.stringify(sampledReviews)} | instructions example:\n\n [{"comment" : "Very Good bed"} ,{"comment" : "Very bad smell"} ] \nOutput: {"overall_review": "Overall good beds and bad smell" , "neg_tags" : ["bad smell"], pos_tags : ["good bed"]}. No explanation. No 'Output:' string in response. Valid JSON. `;
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    console.log(`reqString: ${reqString}`);
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    // Call OpenAI API to get the response.
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    let resp = await context.http.post({
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        url: url,
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        headers: {
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            'Authorization': [`Bearer ${openai_key}`],
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            'Content-Type': ['application/json']
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        },
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        body: JSON.stringify({
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            model: "gpt-4",
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            temperature: 0,
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            messages: [
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                {
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                    "role": "system",
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                    "content": "Output json generator follow only provided example on the current reviews"
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                },
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                {
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                    "role": "user",
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                    "content": reqString
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                }
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            ]
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        })
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    });
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    // Parse the JSON response
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    let responseData = JSON.parse(resp.body.text());
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    // Check the response status.
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    if(resp.statusCode === 200) {
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        console.log("Successfully received code.");
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        console.log(JSON.stringify(responseData));
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        const code = responseData.choices[0].message.content;
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        // Get the required data to be added into the document
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        const updateDoc = JSON.parse(code)
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        // Set a flag that this document does not need further re-processing 
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        updateDoc.process = true
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        await collection.updateOne({_id : docId}, {$set : updateDoc});
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    } else {
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        console.error("Failed to generate filter JSON.");
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        console.log(JSON.stringify(responseData));
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        return {};
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    }
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};

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"process": true, 
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"overall_review": "Overall, guests had a positive experience at Marinete's apartment. They praised the location, cleanliness, and hospitality. However, some guests mentioned issues with the dog and language barrier.",
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"neg_tags": [ "language barrier", "dog issues" ], 
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"pos_tags": [ "great location", "cleanliness", "hospitality" ]

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<div v-if="listing.overall_review" class="overall-review" >
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     <b>Overall Review (ai based) :</b> {{ listing.overall_review }}
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      <div v-for="tag in listing.pos_tags" >
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          <span class="badge-pos">{{tag}}</span>
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      </div>
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      <div v-for="tag in listing.neg_tags" >
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        <span class="badge-neg">{{tag}}</span>
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      </div>
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  </div>