Awesome

This is an sarah.UserContextStorage implementation that use Redis as its primary storage.

Basic idea of user's conversational state: sarah.UserContext

One outstanding feature that Sarah offers is the ability to store user's conversational context, sarah.UserContext, initiated by sarah.Command. With this feature, sarah.Command developer can let messaging user stay in the sarah.Command's conversation without adding any change to sarah.Bot or sarah.Runner logic.

When a sarah.Command returns sarah.CommandResponose with sarah.UserContext, Sarah considers the user is in the middle of sarah.Command's conversational context and stores this context information in designated sarah.Storage. When the user sends next input within a pre-configured timeout window, the input is passed to the function defined by stored sarah.UserContext.

This is how a sarah.Command turns typical one-response-per-input bot interaction to conversational one so users can input series of arguments in a more user-friendly conversational manner.

sarah.UserContextStorage

Pre-defined default storage is provided and can be initialized via sarah.NewUserContextStorage, but developers may replace it with preferred storage since sarah.UserContextStorage is merely an interface. A use of alternative storage is indeed recommended for production environment for two reasons:

• Default storage internally uses a map to store sarah.UserContext in the process memory space, which means all stored contexts are vanished on process restart.
• While some chat services such as Slack and Gitter let bot initiate a connection against chat server, some such as LINE let chat services' server initiate HTTP request against bot server. With this model, to handle larger amount of HTTP requests, bot may consist of multiple server instances. Therefore multiple sarah.Bot processes over multiple server instances must be capable of sharing sarah.UserContextStorage to let user continue her conversation.

This repository provides one solution to store serialized sarah.UserContext in Redis. One limitation to use external KVS is that arguments to the callback function must be serializable; while default storage does not require so because it casually stores callback functions in Golang's map structure.

Getting started

Below is a simple example that describes how to provide sarah.UserContext-to-callback mapping, configure, and initialize storage.

// Provide context-to-callback mapping
// When serialized argument and function ID of "callbackFuncID" are stored with the key of sarah.Input.SenderKey(),
// the function defined here is considered to correspond because this function is set with ID of "callbackFuncID" as second argument shows.
rediscontext.SetFunc(
        line.LINE,
        "callbackFuncID",
        reflect.TypeOf(&Hello{}).Elem(), // A type of argument to deserialize
        func(_ context.Context, input sarah.Input, arg interface{}) (*sarah.CommandResponse, error) {
                // Do something with given input and stored argument.
                return nil, nil
        },
)

// Configure
lineStorage := rediscontext.NewUserContextStorage(
        line.LINE,
        rediscontext.NewConfig(),
        &redis.Options{
                Addr:     "localhost:6379",
                Password: "",
                DB:       0,
        },
)

To store sarah.UserContext that kicks callbackFuncID on next user input, return sarah.UserContext as sarah.Command response.

res :=  &sarah.CommandResponse{
        // Response content.
        Content: []linebot.Message{
                linebot.NewTextMessage("Hello! This is immediately returned to user."),
		},
		
		// Here defines the conversational context to be stored.
		UserContext: &sarah.UserContext{
                Serializable: &sarah.SerializableArgument{
                        FuncIdentifier: "callbackFuncID",
                        Argument: Hello{
                                Input: input.Message(),
                        },
                },
        }