// Price to run from zero(create embeddings and request to LLM): 0,015$
// Price to re-run if database is exists: 0,0004$
// Dependencies: LangChain, LangChain.Databases.Sqlite, LangChain.Sources.Pdf
var gpt35 = new Gpt35TurboModel("OPENAI_API_KEY");

if (!File.Exists("vectors.db"))
{
    var documents = await PdfPigPdfSource.FromUriAsync(
        new Uri("https://canonburyprimaryschool.co.uk/wp-content/uploads/2016/01/Joanne-K.-Rowling-Harry-Potter-Book-1-Harry-Potter-and-the-Philosophers-Stone-EnglishOnlineClub.com_.pdf"));
    
    await SQLiteVectorStore.CreateIndexFromDocuments(
        embeddings: gpt35,
        documents: documents,
        filename: "vectors.db",
        tableName: "vectors",
        textSplitter: new RecursiveCharacterTextSplitter(
            chunkSize: 200,
            chunkOverlap: 50));
}

var database = new SQLiteVectorStore(
    filename: "vectors.db",
    tableName: "vectors",
    embeddings: gpt35);
const string question = "Who was drinking a unicorn blood?";
var similarDocuments = await database.GetSimilarDocuments(question, amount: 5);

var answer = await gpt35.GenerateAsync(
    $"""
     Use the following pieces of context to answer the question at the end.
     If the answer is not in context then just say that you don't know, don't try to make up an answer.
     Keep the answer as short as possible.

     {similarDocuments.AsString()}

     Question: {question}
     Helpful Answer:
     """, CancellationToken.None).ConfigureAwait(false);

Console.WriteLine($"LLM answer: {answer}"); // The cloaked figure.
Console.WriteLine($"Total usage: {gpt35.TotalUsage}");