llm-example.ts

// pipe is a utility function that passes the result of previous step to the next
// pipe(a, b, c) === c(b(a))
pipe(
  // This function returns an array of messages for chat completion
  getTurnPromptMessages(state, action),
  // This turns an array into the CC request object
  mkCCRequestFromMessages,
  pipe(
    // flow returns a function that combines passed functions into it
    // flow(a, b, c) = (x) => c(b(a(x)))
    flow(
      // this adds the schema that GPT should answer in to the request
      addSchemaToRequest<Omit<CreateChatCompletionRequest, 'model'>>(turnS),
      // this is the OpenAI's handler for chat completion
      // it is an async operation (Task) that can fail (Either),
      // so next steps deal with TaskEither (TE)
      handleChatCompletion,
      // this extracts first completion choice
      // (or fails if there are no choices)
      TE.flatMapEither(extractFirstCompletionChoice),
      // this extracts completion message
      // (or fails if there's no message)
      TE.flatMapEither(extractChatCompletionMessage),
      // this adds an empty message to errors from previous steps
      // now any resulting error has an optional message
      TE.mapLeft(mkMessageError(undefined)),
      // this handles the retrieved completion message
      TE.flatMap((message) =>
        pipe(
          // this decodes the message according to schema mentioned earlier
          // (or fails if LLM doesn't build valid json)
          decodeSchema(message),
          // this uses another LLM request to detect a location in a story
          TE.flatMap(detectLocation(state)),
          // this adds the retrieved message to errors from previous steps
          TE.mapLeft(mkMessageError(message))
        )
      )
    ),
    // and this function accepts the handler above and makes it retriable, up to 3 times
    // on every retry it adds GPT messages from above and errors to chat history
    mkResilient(3)
  )
)

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