As you may have noticed in the welcome guide, every interaction with your LLM starts by instantiating a Chat object. In the following sections, we talk about which configuration options it has, and when to use them.w

Creating a Chat

There are two main ways of instantiating a Chat object and the difference lies in when the model file is loaded. The simplest way is using Chat.fromPath like so:

final chat = await nobodywho.Chat.fromPath(modelPath: "./model.gguf");

This function is async since loading a model can take a bit of time, but this should not block the any of your UI. Another way to achieve the same thing is to load the model seperately and then use the Chat constructor:

final model = await nobodywho.Model.load(modelPath: "./model.gguf");
final chat = nobodywho.Chat(model : model);

This allows for sharing the model between several Chat instances.

Prompts and responses

The Chat.ask() function is central to NobodyWho. This function sends your message to the LLM, which then starts generating a response.

final chat = await nobodywho.Chat.fromPath(modelPath: "./model.gguf");
final response = await chat.ask("Is water wet?");

The return type of ask is a TokenStream. If you want to start reading the response as soon as possible, you can just iterate over the TokenStream. Each token is either an individual word or fragments of a word.

await for (final token in response) {
   print(token);
}
print("\n");

If you just want to get the complete response, you can call TokenStream.completed(). This will return the entire response string once the model is done generating its entire response.

final fullResponse = await response.completed();

All of your messages and the model's responses are stored in the Chat object, so the next time you call Chat.ask(), it will remember the previous messages.

Chat history

If you want to inspect the messages inside the Chat object, you can use getChatHistory.

final msgs = await chat.getChatHistory();
print(msgs[0].content); // "Is water wet?"

Similarly, if you want to edit what messages are in the context, you can use setChatHistory:

await chat.setChatHistory([
  nobodywho.Message.message(role: nobodywho.Role.user, content: "What is water?")
]);

System prompt

A system prompt is a special message put into the chat context, which should guide its overall behavior. Some models ship with a built-in system prompt. If you don't specify a system prompt yourself, NobodyWho will fall back to using the model's default system prompt.

You can specify a system prompt when initializing a Chat:

import 'package:nobodywho_dart/nobodywho_dart.dart' as nobodywho;

final chat = await nobodywho.Chat.fromPath(
  modelPath: "./model.gguf",
  systemPrompt: "You are a mischievous assistant!"
);

This systemPrompt is then persisted until the chat context is reset.

Context

The context is the text window which the LLM currently considers. Specifically this is the number of tokens the LLM keeps in memory for your current conversation. As bigger context size means more computational overhead, it makes sense to constrain it. This can be done with contextSize setting, again at the time of creation:

final chat = await nobodywho.Chat.fromPath(
  modelPath: "./model.gguf",
  contextSize: 4096
);

The default value is 4096, however this is mainly useful for short and simple conversations. Choosing the right context size is quite important and depends heavily on your use case. A good place to start is to look at your selected models documentation and see what their recommended context size is.

Even with properly selected context size it might happen that you fill up your entire context during a conversation. When this happens, NobodyWho will shrink the context for you. Currently this is done by removing old messages (apart from the system prompt and the first user message) from the chat history, until the size reaches contextSize / 2. The KV cache is also updated automatically. In the future we plan on adding more advanced methods of context shrinking.

Again, contextSize is fixed to the Chat instance, so it is currently not possible to change the size after Chat is created. To reset the current context content, just call resetContext() with the new system prompt and potentially changed tools.

await chat.resetContext(systemPrompt: "New system prompt", tools: []);

If you don't want to change the already set defaults (systemPrompt, tools), but only reset the context, then go for resetHistory.

Sharing model between contexts

There are scenarios where you would like to keep separate chat contexts (e.g. for every user of your app), but have only one model loaded. In this case you must load the model seperately from creating the Chat instance.

For this use case, instead of the path to the .gguf model, you can pass in Model object, which can be shared between multiple Chat instances.

import 'package:nobodywho_dart/nobodywho_dart.dart' as nobodywho;

final model = await nobodywho.Model.load(modelPath: './model.gguf');
final chat1 = nobodywho.Chat(model: model);
final chat2 = nobodywho.Chat(model: model);
...

NobodyWho will then take care of the separation, such that your chat histories won't collide or interfere with each other, while having only one model loaded.

GPU

When instantiating Model or using Chat.fromPath you have the option to disable/enable GPU acceleration. This can be done as:

final model = await nobodywho.Model.load(modelPath: './model.gguf', useGpu: true);

or

final chat = await nobodywho.Chat.fromPath(modelPath: './model.gguf', useGpu : false);

By defualt useGpu is set to true. So far, NobodyWho relies purely on Vulkan, however support of more architectures is planned (for details check out our issues or join us on Discord).

Reasoning

To enhance cognitive performance, allowing reasoning can be a good idea:

final chat = await nobodywho.Chat.fromPath(
  modelPath: "./model.gguf",
  allowThinking: true
);

Note that Chat has thinking set to true by default. You can also change the setting of an already existing chat instance:

await chat.setAllowThinking(true);

With the next message send, the setting will be propagated.