ZIm/crates/assistant2/src/completion_provider.rs

use anyhow::Result;
use assistant_tooling::ToolFunctionDefinition;
use client::{proto, Client};
use futures::{future::BoxFuture, stream::BoxStream, FutureExt, StreamExt};
use gpui::{AppContext, Global};
use std::sync::Arc;

pub use open_ai::RequestMessage as CompletionMessage;

#[derive(Clone)]
pub struct CompletionProvider(Arc<dyn CompletionProviderBackend>);

impl CompletionProvider {
    pub fn get(cx: &AppContext) -> &Self {
        cx.global::<CompletionProvider>()
    }

    pub fn new(backend: impl CompletionProviderBackend) -> Self {
        Self(Arc::new(backend))
    }

    pub fn default_model(&self) -> String {
        self.0.default_model()
    }

    pub fn available_models(&self) -> Vec<String> {
        self.0.available_models()
    }

    pub fn complete(
        &self,
        model: String,
        messages: Vec<CompletionMessage>,
        stop: Vec<String>,
        temperature: f32,
        tools: &[ToolFunctionDefinition],
    ) -> BoxFuture<'static, Result<BoxStream<'static, Result<proto::LanguageModelResponseMessage>>>>
    {
        self.0.complete(model, messages, stop, temperature, tools)
    }
}

impl Global for CompletionProvider {}

pub trait CompletionProviderBackend: 'static {
    fn default_model(&self) -> String;
    fn available_models(&self) -> Vec<String>;
    fn complete(
        &self,
        model: String,
        messages: Vec<CompletionMessage>,
        stop: Vec<String>,
        temperature: f32,
        tools: &[ToolFunctionDefinition],
    ) -> BoxFuture<'static, Result<BoxStream<'static, Result<proto::LanguageModelResponseMessage>>>>;
}

pub struct CloudCompletionProvider {
    client: Arc<Client>,
}

impl CloudCompletionProvider {
    pub fn new(client: Arc<Client>) -> Self {
        Self { client }
    }
}

impl CompletionProviderBackend for CloudCompletionProvider {
    fn default_model(&self) -> String {
        "gpt-4-turbo".into()
    }

    fn available_models(&self) -> Vec<String> {
        vec!["gpt-4-turbo".into(), "gpt-4".into(), "gpt-3.5-turbo".into()]
    }

    fn complete(
        &self,
        model: String,
        messages: Vec<CompletionMessage>,
        stop: Vec<String>,
        temperature: f32,
        tools: &[ToolFunctionDefinition],
    ) -> BoxFuture<'static, Result<BoxStream<'static, Result<proto::LanguageModelResponseMessage>>>>
    {
        let client = self.client.clone();
        let tools: Vec<proto::ChatCompletionTool> = tools
            .iter()
            .filter_map(|tool| {
                Some(proto::ChatCompletionTool {
                    variant: Some(proto::chat_completion_tool::Variant::Function(
                        proto::chat_completion_tool::FunctionObject {
                            name: tool.name.clone(),
                            description: Some(tool.description.clone()),
                            parameters: Some(serde_json::to_string(&tool.parameters).ok()?),
                        },
                    )),
                })
            })
            .collect();

        let tool_choice = match tools.is_empty() {
            true => None,
            false => Some("auto".into()),
        };

        async move {
            let stream = client
                .request_stream(proto::CompleteWithLanguageModel {
                    model,
                    messages: messages
                        .into_iter()
                        .map(|message| match message {
                            CompletionMessage::Assistant {
                                content,
                                tool_calls,
                            } => proto::LanguageModelRequestMessage {
                                role: proto::LanguageModelRole::LanguageModelAssistant as i32,
                                content: content.unwrap_or_default(),
                                tool_call_id: None,
                                tool_calls: tool_calls
                                    .into_iter()
                                    .map(|tool_call| match tool_call.content {
                                        open_ai::ToolCallContent::Function { function } => {
                                            proto::ToolCall {
                                                id: tool_call.id,
                                                variant: Some(proto::tool_call::Variant::Function(
                                                    proto::tool_call::FunctionCall {
                                                        name: function.name,
                                                        arguments: function.arguments,
                                                    },
                                                )),
                                            }
                                        }
                                    })
                                    .collect(),
                            },
                            CompletionMessage::User { content } => {
                                proto::LanguageModelRequestMessage {
                                    role: proto::LanguageModelRole::LanguageModelUser as i32,
                                    content,
                                    tool_call_id: None,
                                    tool_calls: Vec::new(),
                                }
                            }
                            CompletionMessage::System { content } => {
                                proto::LanguageModelRequestMessage {
                                    role: proto::LanguageModelRole::LanguageModelSystem as i32,
                                    content,
                                    tool_calls: Vec::new(),
                                    tool_call_id: None,
                                }
                            }
                            CompletionMessage::Tool {
                                content,
                                tool_call_id,
                            } => proto::LanguageModelRequestMessage {
                                role: proto::LanguageModelRole::LanguageModelTool as i32,
                                content,
                                tool_call_id: Some(tool_call_id),
                                tool_calls: Vec::new(),
                            },
                        })
                        .collect(),
                    stop,
                    temperature,
                    tool_choice,
                    tools,
                })
                .await?;

            Ok(stream
                .filter_map(|response| async move {
                    match response {
                        Ok(mut response) => Some(Ok(response.choices.pop()?.delta?)),
                        Err(error) => Some(Err(error)),
                    }
                })
                .boxed())
        }
        .boxed()
    }
}