zed/crates/assistant_tooling

Assistant Tooling

Bringing Language Model tool calling to GPUI.

This unlocks:

• Structured Extraction of model responses
• Validation of model inputs
• Execution of chosen tools

Overview

Language Models can produce structured outputs that are perfect for calling functions. The most famous of these is OpenAI's tool calling. When making a chat completion you can pass a list of tools available to the model. The model will choose 0..n tools to help them complete a user's task. It's up to you to create the tools that the model can call.

User: "Hey I need help with implementing a collapsible panel in GPUI"

Assistant: "Sure, I can help with that. Let me see what I can find."

tool_calls: ["name": "query_codebase", arguments: "{ 'query': 'GPUI collapsible panel' }"]

result: "['crates/gpui/src/panel.rs:12: impl Panel { ... }', 'crates/gpui/src/panel.rs:20: impl Panel { ... }']"

Assistant: "Here are some excerpts from the GPUI codebase that might help you."

This library is designed to facilitate this interaction mode by allowing you to go from struct to tool with two simple traits, LanguageModelTool and ToolView.

Using the Tool Registry

let mut tool_registry = ToolRegistry::new();
tool_registry
    .register(WeatherTool { api_client },
    })
    .unwrap(); // You can only register one tool per name

let completion = cx.update(|cx| {
    CompletionProvider::get(cx).complete(
        model_name,
        messages,
        Vec::new(),
        1.0,
        // The definitions get passed directly to OpenAI when you want
        // the model to be able to call your tool
        tool_registry.definitions(),
    )
});

let mut stream = completion?.await?;

let mut message = AssistantMessage::new();

while let Some(delta) = stream.next().await {
    // As messages stream in, you'll get both assistant content
    if let Some(content) = &delta.content {
        message
            .body
            .update(cx, |message, cx| message.append(&content, cx));
    }

    // And tool calls!
    for tool_call_delta in delta.tool_calls {
        let index = tool_call_delta.index as usize;
        if index >= message.tool_calls.len() {
            message.tool_calls.resize_with(index + 1, Default::default);
        }
        let tool_call = &mut message.tool_calls[index];

        // Build up an ID
        if let Some(id) = &tool_call_delta.id {
            tool_call.id.push_str(id);
        }

        tool_registry.update_tool_call(
            tool_call,
            tool_call_delta.name.as_deref(),
            tool_call_delta.arguments.as_deref(),
            cx,
        );
    }
}

Once the stream of tokens is complete, you can execute the tool call by calling tool_registry.execute_tool_call(tool_call, cx), which returns a Task<Result<()>>.

As the tokens stream in and tool calls are executed, your ToolView will get updates. Render each tool call by passing that tool_call in to tool_registry.render_tool_call(tool_call, cx). The final message for the model can be pulled by calling self.tool_registry.content_for_tool_call( tool_call, &mut project_context, cx, ).