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feat(tutorial): scripted conversational apps
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docs/pages/tutorials/semi-scripted-conversational-applications/index.md
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| --- | ||
| layout: dm:tutorial | ||
| parent: index | ||
| title: Semi-Scripted Conversational Applications | ||
| description: > | ||
| Large Language Models, when used as a user interface, enable users to | ||
| create advanced applications that can replace most traditional interfaces. | ||
| --- | ||
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| ## What Are Conversational Applications? | ||
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| Conversational applications are more than just chatbots. Large Language Models, | ||
| when used as a user interface, enables users to create advanced applications that | ||
| can replace most of the traditional interfaces. | ||
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| They can use LLM (Large Language Models) or other means of processing | ||
| human input as a front-end. The simplest example is a custom GPT that can use | ||
| your API to interact with your application. | ||
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| Is it a silver bullet to solve all the problems? Of course not. Currently most | ||
| of AI integrations act as copilots or assistants. What if we try to | ||
| inverse that dynamic where CUI (Conversational User Interface) is an add-on to | ||
| the Graphical User Interface and make GUI components a complimentary feature | ||
| instead? | ||
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| ### How It Started | ||
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| Ok, maybe [Zork](https://en.wikipedia.org/wiki/Zork) is not really a | ||
| conversational application, but it is really symbolic for me. | ||
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| All the interactions with the game are done through a series of specific | ||
| commands. That kind of interface is rigorous in its usage because | ||
| although it theoretically allows us to input any text, we still have to | ||
| guess our options. | ||
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| It is strict both on input and output data with no room for interpretation - | ||
| like using some scripting programming language. | ||
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|  | ||
| *image source: [lemon64](https://www.lemon64.com/game/zork-1-golden-edition)* | ||
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| ### How Is It Going | ||
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| Several observations: | ||
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| 1. We have an AI that can recreate the Zork experience in seconds without | ||
| requiring almost any human labor. | ||
| 2. AI understands any input that we can throw at it. | ||
| 3. AI responds in the style and tone we asked, making the experience | ||
| uniquely personalized. | ||
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| Now, we can create a conversational application that can | ||
| understand any input and provide a unique experience for | ||
| each user. It is, however, very general in its use cases - which is both a | ||
| blessing and a curse in disguise (see: | ||
| [infamous Chevrolet chatbot incident](https://www.reddit.com/r/ChatGPT/comments/18kvlzc/i_gaslit_the_chevrolet_support_bot_into_thinking/)). | ||
|
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|  | ||
| *image source: [ChatGPT](https://chat.openai.com/)* | ||
|
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| ### Where Is It Going? | ||
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| What if we could mold a general-use AI to fit our specific applications and use | ||
| cases while benefiting from its reasoning and language capabilities? To | ||
| be flexible when accepting inputs and simultaneously avoiding hallucinations. | ||
| How about building the applications themselves without writing code? | ||
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| ## Semi-Scripted Dialogues | ||
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| Let us meet the AI halfway. We will create a semi-scripted dialogue to list | ||
| predefined dialogue nodes and potential responses. The system will accept any | ||
| input and try to match it with one of the predefined responses. This allows us | ||
| flexibility when accepting inputs and be strict and predictable about our o | ||
| utputs. | ||
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| ### Ingredients | ||
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| 1. [Swoole](https://swoole.com/) | ||
| 1. {{docs/features/ai/server/llama-cpp/extractors/index}} | ||
| (to interpret the user's input and allow the AI to make further decisions based | ||
| on it) | ||
| 1. {{docs/features/conversational-applications/dialogue-nodes/index}} | ||
| (to create a conversational flow) | ||
| 1. {{docs/features/websockets/index}} (to actually serve the application) | ||
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| ### What Are We Going to Build? | ||
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| We will create a simple CRUD task management system. User will be able to name | ||
| a new task, add a description, and list available tasks through a conversational | ||
| interface. | ||
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| We are going to use an LLM (through {{docs/features/ai/server/llama-cpp/index}}) | ||
| to match user inputs with specific conversational nodes. | ||
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| The application itself will alternate between handling potential responses from | ||
| the user and executing side effects. | ||
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| ```graphviz render | ||
| digraph { | ||
| dialogue_node [label="Dialogue Node"]; | ||
| response [label="Response"]; | ||
| side_effects [label="Side Effects"]; | ||
| dialogue_node -> response; | ||
| response -> side_effects; | ||
| response -> dialogue_node; | ||
| } | ||
| ``` | ||
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| We will model the following conversation in a semi-scripted way so it will be | ||
| capable of handling pretty much any user input: | ||
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| ``` | ||
| System: | ||
| Message: Hello! How can I help you with your tasks? | ||
| Potential Responses: | ||
| - I want to create a task | ||
| - I want to list all my tasks. | ||
| ``` | ||
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| You can serve it in any way, either as a standalone application, a | ||
| [Telegram](https://telegram.org/) bot or a web application. We will focus on | ||
| the core of the conversational application and skip the delivery method for | ||
| now. | ||
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| ### Dialogue Controllers | ||
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| We are all familiar with HTTP {{docs/features/http/controllers}} who mediate | ||
| between the user and the application. Why don't we reuse a similar concept and | ||
| create a dialogue controller? | ||
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| It should keep the general state of the conversation and be able to react to | ||
| user requests. | ||
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| First, let us define the initial dialogue node and all the potential responses: | ||
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| ```php file:app/DialogueCOntroller/TaskConversationController.php | ||
| #[Singleton] | ||
| readonly class TaskConversationController extends DialogueController | ||
| { | ||
| private DialogueNodeInterface $rootNode; | ||
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| public function __construct( | ||
| private DoctrineEntityManagerRepository $doctrineEntityManagerRepository, | ||
| private HelpfulAssistant $helpfulAssistantPersona, | ||
| private LlamaCppClientInterface $llamaCppClient, | ||
| LlamaCppExtractWhenInterface $llamaCppExtractWhen, | ||
| private LlamaCppExtractSubject $llamaCppExtractSubject, | ||
| ) { | ||
| $this->rootNode = DialogueNode::withMessage('Hello! How can I help you with your tasks?'); | ||
| $this->rootNode->addPotentialResponse(new CatchAllResponse( | ||
| followUp: $this->welcomeNode, | ||
| )); | ||
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| $this->welcomeNode->addPotentialResponse(new LlamaCppExtractWhenResponse( | ||
| llamaCppExtractWhen: $llamaCppExtractWhen, | ||
| condition: 'User wants to create a new task', | ||
| persona: $helpfulAssistantPersona, | ||
| whenProvided: $this->whenUserWantsToCreateTask(...) | ||
| )); | ||
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| $this->welcomeNode->addPotentialResponse(new LlamaCppExtractWhenResponse( | ||
| llamaCppExtractWhen: $llamaCppExtractWhen, | ||
| condition: 'User wants to list all their tasks', | ||
| persona: $helpfulAssistantPersona, | ||
| whenProvided: $this->whenUserWantsToListAllTasks(...) | ||
| )); | ||
| } | ||
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| public function getRootDialogueNode(): DialogueNodeInterface | ||
| { | ||
| return $this->rootNode; | ||
| } | ||
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| // (...) | ||
| ``` | ||
|
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| Now, we need to handle all the predefined responses. Let's start with the | ||
| one to create a new task. Let us try to extract the task name. If that is | ||
| successful, let us move forward and add the task; in any other case, ask the | ||
| user to specify further: | ||
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| ```php file:app/DialogueCOntroller/TaskConversationController.php | ||
| // (...) | ||
|
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| private function whenUserWantsToCreateTask(LlamaCppExtractWhenResult $response): Generator | ||
| { | ||
| if ($response->result->isNo()) { | ||
| return DialogueResponseResolution::cannotRespond(); | ||
| } | ||
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| $result = $this->llamaCppExtractSubject->extract( | ||
| input: $response->input, | ||
| topic: 'task name', | ||
| ); | ||
|
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| //user provided a name of the task | ||
| if ($result->content) { | ||
| $followUp->addSideEffect(new CreateTask( | ||
| taskName: $result->content, | ||
| )); | ||
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| $followUp = new DialogueNode(new LlamaCppPromptMessageProducer( | ||
| llamaCppClient: $this->llamaCppClient, | ||
| request: new LlamaCppCompletionRequest( | ||
| llmChatHistory: new LlmChatHistory([ | ||
| new LlmChatMessage( | ||
| actor: 'system', | ||
| message: 'Let user know that their task is added', | ||
| ), | ||
| ]), | ||
| ), | ||
| )); | ||
| } else { | ||
| $followUp = new DialogueNode(new LlamaCppPromptMessageProducer( | ||
| llamaCppClient: $this->llamaCppClient, | ||
| request: new LlamaCppCompletionRequest( | ||
| llmChatHistory: new LlmChatHistory([ | ||
| new LlmChatMessage( | ||
| actor: 'system', | ||
| message: 'Ask user to specify the name of the task', | ||
| ), | ||
| ]), | ||
| ), | ||
| )); | ||
| } | ||
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| // Go back to the primary dialogue node in any case | ||
| $followUp->copyResponsesFrom($this->rootNode); | ||
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| return DialogueResponseResolution::canRespond($followUp); | ||
| } | ||
|
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| // (...) | ||
| ``` | ||
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| When a user wants to list all tasks, just produce a response in a similar way | ||
| you would produce an HTML view: | ||
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| ```php file:app/DialogueCOntroller/TaskConversationController.php | ||
| // (...) | ||
|
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| private function whenUserWantsToCreateTask(LlamaCppExtractWhenResult $response): Generator | ||
| { | ||
| if ($response->result->isNo()) { | ||
| return DialogueResponseResolution::cannotRespond(); | ||
| } | ||
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| $responseMessage = "Your tasks:\n%s"; | ||
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| $tasks = $this | ||
| ->doctrineEntityManagerRepository | ||
| ->withEntityManager(function (EntityManagerInterface $entityManager) { | ||
| // ...obtain user tasks somehow | ||
| }) | ||
| ; | ||
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| foreach ($tasks as $task) { | ||
| $responseMessage .= "- {$task->name}\n"; | ||
| } | ||
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| $followUp = DialogueNode::withMessage($responseMessage); | ||
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| // Go back to the primary dialogue node in any case | ||
| $followUp->copyResponsesFrom($this->rootNode); | ||
|
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| return DialogueResponseResolution::canRespond($followUp); | ||
| } | ||
| } | ||
| ``` | ||
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| ## Summary | ||
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| It was a simple example of a conversational application; it's like writing a | ||
| basic "Hello, world!". I hope it inspires you to create something incredible! |
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