Build a RAG command line chatbot (C#)

dotnet add package DataStax.AstraDB.DataApi OpenAI

using System.Text;
using DataStax.AstraDB.DataApi;
using DataStax.AstraDB.DataApi.Admin;
using DataStax.AstraDB.DataApi.Collections;
using DataStax.AstraDB.DataApi.Core;
using DataStax.AstraDB.DataApi.Core.Query;
using OpenAI.Chat;

public class Program
{
  public static async Task Main()
  {
    const string keyspace = "default_keyspace"; (1)
    const string collectionName = "quickstart_collection"; (2)

    string? endpoint = Environment.GetEnvironmentVariable("API_ENDPOINT"); (3)
    string? applicationToken = Environment.GetEnvironmentVariable(
      "APPLICATION_TOKEN"
    );
    string? openaiApiKey = Environment.GetEnvironmentVariable(
      "OPENAI_API_KEY"
    );

    if (
      string.IsNullOrEmpty(endpoint)
      || string.IsNullOrEmpty(applicationToken)
      || string.IsNullOrEmpty(openaiApiKey)
    )
    {
      throw new InvalidOperationException(
        "Environment variables API_ENDPOINT, APPLICATION_TOKEN, OPENAI_API_KEY must be defined."
      );
    }

    // Instantiate the DataAPIClient and get a reference to your collection
    var client = new DataAPIClient();
    var database = client.GetDatabase(
      endpoint,
      new GetDatabaseOptions()
      {
        Token = applicationToken,
        Keyspace = keyspace,
      }
    );
    var collection = database.GetCollection(collectionName);

    // Instantiate the OpenAI client
    var openai = new ChatClient(
      model: "gpt-4o-mini",
      apiKey: openaiApiKey
    );

    // This list of messages will be sent to OpenAI with every query.
    // It starts with a single system prompt and grows as the chat progresses.
    var messages = new List<ChatMessage>
    {
      new SystemChatMessage(
        "You are an AI assistant that can answer questions based on the the context you are given. "
          + "Don't mention the context, just use it to inform your answers."
      ),
    };

    // Start the chat by writing a message to the CLI
    Console.Write(
      "Greetings! I am an AI assistant that is ready to help you with your questions. "
        + "You can ask me anything you like.\n"
        + "If you want to exit, type \".exit\".\n\n> "
    );

    string? userInput = Console.ReadLine();

    // Run this loop continuously until the user inputs the exit command
    while (
      userInput != null
      && !userInput.Equals(".exit", StringComparison.OrdinalIgnoreCase)
    )
    {
      // If the user didn't input text, re-prompt them
      if (string.IsNullOrWhiteSpace(userInput))
      {
        Console.Write("> ");
        userInput = Console.ReadLine();
        continue;
      }

      try
      {
        // Perform a vector search in your collection,
        // using the user input as the search string to vectorize.
        // Limit the search to 10 documents.
        // Use a projection to return just the $vectorize field of each document.
        var response = collection.Find(
          new CollectionFindOptions<Document>()
          {
            Sort = Builders<Document>.CollectionSort.Vectorize(userInput),
            Projection = Builders<Document>.Projection.Include(
              "$vectorize"
            ),
          }
        );

        // Join the $vectorize fields of the returned documents into a single string
        var contextBuilder = new StringBuilder();
        await foreach (var document in response)
        {
          if (
            document.TryGetValue("$vectorize", out var value)
            && value is string text
          )
          {
            contextBuilder.AppendLine(text);
          }
        }
        string context = contextBuilder.ToString().TrimEnd();

        // Combine the user question with the context from vector search
        var ragMessage = new UserChatMessage(
          $"{context}\n---\nGiven the above context, answer the following question:\n{userInput}"
        );

        // Send the list of previous messages, plus the context augmented question
        // to OpenAI and stream the response
        var requestMessages = new List<ChatMessage>(messages)
        {
          ragMessage,
        };
        var stream = openai.CompleteChatStreamingAsync(requestMessages);

        // Write OpenAI's response to the CLI as it comes in,
        // and also record it in a string
        var message = new StringBuilder();
        await foreach (var update in stream)
        {
          if (update.ContentUpdate.Count > 0)
          {
            string chunk = update.ContentUpdate[0].Text ?? "";
            Console.Write(chunk);
            message.Append(chunk);
          }
        }

        // Record the user question, without the added context, in the list of messages
        messages.Add(new UserChatMessage(userInput));

        // Record the OpenAI response in the list of messages
        messages.Add(new AssistantChatMessage(message.ToString()));

        // Prompt the user for their next question
        Console.Write("\n\n> ");
        userInput = Console.ReadLine();
      }
      catch (Exception ex)
      {
        Console.Error.WriteLine(ex.Message);
        Console.Write("\nSomething went wrong, try asking again\n\n> ");
        userInput = Console.ReadLine();
      }
    }
  }
}