b1872e3afd
Done automatically with
> ast-grep -p '$A.background_executor().spawn($B)' -r
'$A.background_spawn($B)' --update-all --globs "\!crates/gpui"
Followed by:
* `cargo fmt`
* Unexpected need to remove some trailing whitespace.
* Manually adding imports of `gpui::{AppContext as _}` which provides
`background_spawn`
* Added `AppContext as _` to existing use of `AppContext`
Release Notes:
- N/A
371 lines
14 KiB
Rust
371 lines
14 KiB
Rust
use anyhow::{anyhow, Result};
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use assistant_slash_command::{
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ArgumentCompletion, SlashCommand, SlashCommandOutput, SlashCommandOutputSection,
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SlashCommandResult,
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};
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use feature_flags::FeatureFlag;
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use futures::StreamExt;
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use gpui::{App, AsyncApp, Task, WeakEntity, Window};
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use language::{CodeLabel, LspAdapterDelegate};
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use language_model::{
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LanguageModelCompletionEvent, LanguageModelRegistry, LanguageModelRequest,
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LanguageModelRequestMessage, Role,
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};
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use semantic_index::{FileSummary, SemanticDb};
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use smol::channel;
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use std::sync::{atomic::AtomicBool, Arc};
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use ui::{prelude::*, BorrowAppContext};
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use util::ResultExt;
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use workspace::Workspace;
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use crate::create_label_for_command;
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pub struct AutoSlashCommandFeatureFlag;
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impl FeatureFlag for AutoSlashCommandFeatureFlag {
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const NAME: &'static str = "auto-slash-command";
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}
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pub struct AutoCommand;
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impl SlashCommand for AutoCommand {
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fn name(&self) -> String {
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"auto".into()
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}
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fn description(&self) -> String {
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"Automatically infer what context to add".into()
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}
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fn icon(&self) -> IconName {
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IconName::Wand
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}
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fn menu_text(&self) -> String {
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self.description()
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}
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fn label(&self, cx: &App) -> CodeLabel {
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create_label_for_command("auto", &["--prompt"], cx)
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}
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fn complete_argument(
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self: Arc<Self>,
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_arguments: &[String],
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_cancel: Arc<AtomicBool>,
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workspace: Option<WeakEntity<Workspace>>,
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_window: &mut Window,
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cx: &mut App,
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) -> Task<Result<Vec<ArgumentCompletion>>> {
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// There's no autocomplete for a prompt, since it's arbitrary text.
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// However, we can use this opportunity to kick off a drain of the backlog.
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// That way, it can hopefully be done resummarizing by the time we've actually
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// typed out our prompt. This re-runs on every keystroke during autocomplete,
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// but in the future, we could instead do it only once, when /auto is first entered.
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let Some(workspace) = workspace.and_then(|ws| ws.upgrade()) else {
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log::warn!("workspace was dropped or unavailable during /auto autocomplete");
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return Task::ready(Ok(Vec::new()));
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};
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let project = workspace.read(cx).project().clone();
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let Some(project_index) =
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cx.update_global(|index: &mut SemanticDb, cx| index.project_index(project, cx))
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else {
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return Task::ready(Err(anyhow!("No project indexer, cannot use /auto")));
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};
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let cx: &mut App = cx;
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cx.spawn(|cx: gpui::AsyncApp| async move {
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let task = project_index.read_with(&cx, |project_index, cx| {
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project_index.flush_summary_backlogs(cx)
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})?;
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cx.background_spawn(task).await;
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anyhow::Ok(Vec::new())
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})
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}
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fn requires_argument(&self) -> bool {
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true
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}
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fn run(
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self: Arc<Self>,
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arguments: &[String],
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_context_slash_command_output_sections: &[SlashCommandOutputSection<language::Anchor>],
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_context_buffer: language::BufferSnapshot,
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workspace: WeakEntity<Workspace>,
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_delegate: Option<Arc<dyn LspAdapterDelegate>>,
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window: &mut Window,
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cx: &mut App,
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) -> Task<SlashCommandResult> {
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let Some(workspace) = workspace.upgrade() else {
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return Task::ready(Err(anyhow::anyhow!("workspace was dropped")));
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};
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if arguments.is_empty() {
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return Task::ready(Err(anyhow!("missing prompt")));
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};
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let argument = arguments.join(" ");
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let original_prompt = argument.to_string();
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let project = workspace.read(cx).project().clone();
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let Some(project_index) =
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cx.update_global(|index: &mut SemanticDb, cx| index.project_index(project, cx))
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else {
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return Task::ready(Err(anyhow!("no project indexer")));
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};
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let task = window.spawn(cx, |cx| async move {
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let summaries = project_index
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.read_with(&cx, |project_index, cx| project_index.all_summaries(cx))?
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.await?;
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commands_for_summaries(&summaries, &original_prompt, &cx).await
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});
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// As a convenience, append /auto's argument to the end of the prompt
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// so you don't have to write it again.
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let original_prompt = argument.to_string();
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cx.background_spawn(async move {
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let commands = task.await?;
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let mut prompt = String::new();
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log::info!(
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"Translating this response into slash-commands: {:?}",
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commands
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);
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for command in commands {
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prompt.push('/');
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prompt.push_str(&command.name);
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prompt.push(' ');
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prompt.push_str(&command.arg);
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prompt.push('\n');
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}
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prompt.push('\n');
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prompt.push_str(&original_prompt);
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Ok(SlashCommandOutput {
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text: prompt,
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sections: Vec::new(),
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run_commands_in_text: true,
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}
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.to_event_stream())
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})
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}
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}
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const PROMPT_INSTRUCTIONS_BEFORE_SUMMARY: &str = include_str!("prompt_before_summary.txt");
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const PROMPT_INSTRUCTIONS_AFTER_SUMMARY: &str = include_str!("prompt_after_summary.txt");
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fn summaries_prompt(summaries: &[FileSummary], original_prompt: &str) -> String {
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let json_summaries = serde_json::to_string(summaries).unwrap();
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format!("{PROMPT_INSTRUCTIONS_BEFORE_SUMMARY}\n{json_summaries}\n{PROMPT_INSTRUCTIONS_AFTER_SUMMARY}\n{original_prompt}")
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}
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/// The slash commands that the model is told about, and which we look for in the inference response.
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const SUPPORTED_SLASH_COMMANDS: &[&str] = &["search", "file"];
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#[derive(Debug, Clone)]
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struct CommandToRun {
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name: String,
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arg: String,
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}
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/// Given the pre-indexed file summaries for this project, as well as the original prompt
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/// string passed to `/auto`, get a list of slash commands to run, along with their arguments.
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///
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/// The prompt's output does not include the slashes (to reduce the chance that it makes a mistake),
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/// so taking one of these returned Strings and turning it into a real slash-command-with-argument
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/// involves prepending a slash to it.
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///
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/// This function will validate that each of the returned lines begins with one of SUPPORTED_SLASH_COMMANDS.
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/// Any other lines it encounters will be discarded, with a warning logged.
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async fn commands_for_summaries(
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summaries: &[FileSummary],
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original_prompt: &str,
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cx: &AsyncApp,
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) -> Result<Vec<CommandToRun>> {
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if summaries.is_empty() {
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log::warn!("Inferring no context because there were no summaries available.");
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return Ok(Vec::new());
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}
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// Use the globally configured model to translate the summaries into slash-commands,
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// because Qwen2-7B-Instruct has not done a good job at that task.
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let Some(model) = cx.update(|cx| LanguageModelRegistry::read_global(cx).active_model())? else {
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log::warn!("Can't infer context because there's no active model.");
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return Ok(Vec::new());
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};
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// Only go up to 90% of the actual max token count, to reduce chances of
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// exceeding the token count due to inaccuracies in the token counting heuristic.
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let max_token_count = (model.max_token_count() * 9) / 10;
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// Rather than recursing (which would require this async function use a pinned box),
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// we use an explicit stack of arguments and answers for when we need to "recurse."
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let mut stack = vec![summaries];
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let mut final_response = Vec::new();
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let mut prompts = Vec::new();
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// TODO We only need to create multiple Requests because we currently
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// don't have the ability to tell if a CompletionProvider::complete response
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// was a "too many tokens in this request" error. If we had that, then
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// we could try the request once, instead of having to make separate requests
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// to check the token count and then afterwards to run the actual prompt.
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let make_request = |prompt: String| LanguageModelRequest {
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messages: vec![LanguageModelRequestMessage {
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role: Role::User,
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content: vec![prompt.into()],
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// Nothing in here will benefit from caching
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cache: false,
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}],
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tools: Vec::new(),
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stop: Vec::new(),
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temperature: None,
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};
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while let Some(current_summaries) = stack.pop() {
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// The split can result in one slice being empty and the other having one element.
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// Whenever that happens, skip the empty one.
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if current_summaries.is_empty() {
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continue;
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}
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log::info!(
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"Inferring prompt context using {} file summaries",
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current_summaries.len()
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);
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let prompt = summaries_prompt(¤t_summaries, original_prompt);
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let start = std::time::Instant::now();
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// Per OpenAI, 1 token ~= 4 chars in English (we go with 4.5 to overestimate a bit, because failed API requests cost a lot of perf)
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// Verifying this against an actual model.count_tokens() confirms that it's usually within ~5% of the correct answer, whereas
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// getting the correct answer from tiktoken takes hundreds of milliseconds (compared to this arithmetic being ~free).
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// source: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them
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let token_estimate = prompt.len() * 2 / 9;
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let duration = start.elapsed();
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log::info!(
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"Time taken to count tokens for prompt of length {:?}B: {:?}",
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prompt.len(),
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duration
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);
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if token_estimate < max_token_count {
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prompts.push(prompt);
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} else if current_summaries.len() == 1 {
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log::warn!("Inferring context for a single file's summary failed because the prompt's token length exceeded the model's token limit.");
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} else {
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log::info!(
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"Context inference using file summaries resulted in a prompt containing {token_estimate} tokens, which exceeded the model's max of {max_token_count}. Retrying as two separate prompts, each including half the number of summaries.",
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);
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let (left, right) = current_summaries.split_at(current_summaries.len() / 2);
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stack.push(right);
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stack.push(left);
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}
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}
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let all_start = std::time::Instant::now();
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let (tx, rx) = channel::bounded(1024);
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let completion_streams = prompts
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.into_iter()
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.map(|prompt| {
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let request = make_request(prompt.clone());
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let model = model.clone();
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let tx = tx.clone();
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let stream = model.stream_completion(request, &cx);
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(stream, tx)
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})
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.collect::<Vec<_>>();
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cx.background_spawn(async move {
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let futures = completion_streams
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.into_iter()
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.enumerate()
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.map(|(ix, (stream, tx))| async move {
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let start = std::time::Instant::now();
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let events = stream.await?;
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log::info!("Time taken for awaiting /await chunk stream #{ix}: {:?}", start.elapsed());
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let completion: String = events
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.filter_map(|event| async {
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if let Ok(LanguageModelCompletionEvent::Text(text)) = event {
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Some(text)
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} else {
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None
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}
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})
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.collect()
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.await;
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log::info!("Time taken for all /auto chunks to come back for #{ix}: {:?}", start.elapsed());
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for line in completion.split('\n') {
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if let Some(first_space) = line.find(' ') {
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let command = &line[..first_space].trim();
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let arg = &line[first_space..].trim();
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tx.send(CommandToRun {
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name: command.to_string(),
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arg: arg.to_string(),
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})
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.await?;
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} else if !line.trim().is_empty() {
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// All slash-commands currently supported in context inference need a space for the argument.
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log::warn!(
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"Context inference returned a non-blank line that contained no spaces (meaning no argument for the slash command): {:?}",
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line
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);
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}
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}
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anyhow::Ok(())
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})
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.collect::<Vec<_>>();
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let _ = futures::future::try_join_all(futures).await.log_err();
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let duration = all_start.elapsed();
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eprintln!("All futures completed in {:?}", duration);
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})
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.await;
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drop(tx); // Close the channel so that rx.collect() won't hang. This is safe because all futures have completed.
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let results = rx.collect::<Vec<_>>().await;
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eprintln!(
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"Finished collecting from the channel with {} results",
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results.len()
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);
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for command in results {
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// Don't return empty or duplicate commands
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if !command.name.is_empty()
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&& !final_response
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.iter()
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.any(|cmd: &CommandToRun| cmd.name == command.name && cmd.arg == command.arg)
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{
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if SUPPORTED_SLASH_COMMANDS
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.iter()
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.any(|supported| &command.name == supported)
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{
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final_response.push(command);
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} else {
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log::warn!(
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"Context inference returned an unrecognized slash command: {:?}",
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command
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);
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}
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}
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}
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// Sort the commands by name (reversed just so that /search appears before /file)
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final_response.sort_by(|cmd1, cmd2| cmd1.name.cmp(&cmd2.name).reverse());
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Ok(final_response)
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}
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