This problem seems like a rerun of a nightmare for me. The project is to train a chatbot using gpt3 and I'm experimenting with embeddings.
I've got document, and I'm trying to create embeddings for the different sections. getEmbeddings() appears to return a value according to my testing. However, when my loop ends, I don't seem to have an embedding on my section (there's only one... it costs money to make embeddings so I'm only running the loop once until I get it working).
What am I doing wrong?
begin();
async function begin(){
    let sections = [];
    let inputFileName = "sourceDocument.jsonl";
    let filecontents = fs.readFileSync(inputFileName,{encoding:'utf-8'})
    let inputRows = [] = String(filecontents).split(/\r?\n/);
        for(let i = 0; i < inputRows.length; i++) {
            let row = inputRows[i];
            if(row.trim().length == 0) continue;
            let dataObject = JSON.parse(row);
            
            let text = dataObject.completion;
            let section = new Section();
            let titleend =  text.indexOf("'>")
            
            //console.log(dataObject);
    
            section.title = text.substring(1,titleend);
            section.text = String(text).substring( (titleend + 2), (text.length - 10) );
            section.embedding = await getEmbedding(section.text);
            sections.push(section);
            
            break;
        }
    console.log(sections[0]);   
}
async function getEmbedding(textSample){
    const embeddingModel = "text-search-davinci-doc-001";
    if(DEBUGGING){
        console.log(`DEBUG: getEmbedding(${textSample})`);
    }
    const OPENAI_REQEST_HEADERS = {
        "Content-Type":"application/json",
        "Authorization":`Bearer ${OPENAI_API_KEY}`
    }
    let data = {
        "input"         :   textSample,
        "model"         :   embeddingModel
    };
    let res;
    await axios.post(EMBEDDINGS, data, {headers:OPENAI_REQEST_HEADERS})
    .then((response) => {
        if(DEBUGGING) {
            console.log("   Embedding:  " + response.data.data[0].embedding);
        }
        let embedding = response.data.data[0].embedding;
        return embedding;
    })
    .catch((error) => {
        console.log("Error posting to OpenAI:");
        console.log(error.response.data);
    })
}
 
    