// 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

// Initialize models
var provider = new OpenAiProvider(
    Environment.GetEnvironmentVariable("OPENAI_API_KEY") ??
    throw new InconclusiveException("OPENAI_API_KEY is not set"));
var llm = new Gpt35TurboModel(provider);
var embeddings = new TextEmbeddingV3SmallModel(provider);

// Create vector database from Harry Potter book pdf
var source = await PdfPigPdfSource.CreateFromUriAsync(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"));
var index = await SQLiteVectorStore.GetOrCreateIndexAsync(embeddings, source);

// Now we have two ways: use the async methods or use the chains
// 1. Async methods

// Find similar documents for the question
const string question = "Who was drinking a unicorn blood?";
var similarDocuments = await index.Store.GetSimilarDocuments(question, amount: 5);

// Use similar documents and LLM to answer the question
var answer = await llm.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: CancellationToken.None).ConfigureAwait(false);

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

// 2. Chains
var promptTemplate =
    @"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. Always quote the context in your answer.
{context}
Question: {text}
Helpful Answer:";

var chain =
    Set("Who was drinking a unicorn blood?")     // set the question (default key is "text")
    | RetrieveSimilarDocuments(index, amount: 5) // take 5 most similar documents
    | CombineDocuments(outputKey: "context")     // combine documents together and put them into context
    | Template(promptTemplate)                   // replace context and question in the prompt with their values
    | LLM(llm.UseConsoleForDebug());             // send the result to the language model
var chainAnswer = await chain.Run("text");  // get chain result

Console.WriteLine("Chain Answer:"+ chainAnswer);       // print the result
        
Console.WriteLine($"LLM usage: {llm.Usage}");    // Print usage and price
Console.WriteLine($"Embeddings usage: {embeddings.Usage}");   // Print usage and price