use std::{ fmt::Write as _, ops::DerefMut, sync::{atomic::AtomicBool, Arc},}; use anyhow::{anyhow, Result};use assistant_slash_command::{ ArgumentCompletion, SlashCommand, SlashCommandOutput, SlashCommandOutputSection, SlashCommandResult,};use feature_flags::FeatureFlag;use gpui::{App, Task, WeakEntity};use language::{Anchor, CodeLabel, LspAdapterDelegate};use language_model::{LanguageModelRegistry, LanguageModelTool};use prompt_store::PromptBuilder;use schemars::JsonSchema;use semantic_index::SemanticDb;use serde::Deserialize;use ui::prelude::*;use workspace::Workspace; use super::{create_label_for_command, search_command::add_search_result_section}; pub struct ProjectSlashCommandFeatureFlag; impl FeatureFlag for ProjectSlashCommandFeatureFlag { const NAME: &'static str = "project-slash-command";} pub struct ProjectSlashCommand { prompt_builder: Arc<PromptBuilder>,} impl ProjectSlashCommand { pub fn new(prompt_builder: Arc<PromptBuilder>) -> Self { Self { prompt_builder } }} impl SlashCommand for ProjectSlashCommand { fn name(&self) -> String { "project".into() } fn label(&self, cx: &App) -> CodeLabel { create_label_for_command("project", &[], cx) } fn description(&self) -> String { "Generate a semantic search based on context".into() } fn icon(&self) -> IconName { IconName::Folder } fn menu_text(&self) -> String { self.description() } fn requires_argument(&self) -> bool { false } fn complete_argument( self: Arc<Self>, _arguments: &[String], _cancel: Arc<AtomicBool>, _workspace: Option<WeakEntity<Workspace>>, _window: &mut Window, _cx: &mut App, ) -> Task<Result<Vec<ArgumentCompletion>>> { Task::ready(Ok(Vec::new())) } fn run( self: Arc<Self>, _arguments: &[String], _context_slash_command_output_sections: &[SlashCommandOutputSection<Anchor>], context_buffer: language::BufferSnapshot, workspace: WeakEntity<Workspace>, _delegate: Option<Arc<dyn LspAdapterDelegate>>, window: &mut Window, cx: &mut App, ) -> Task<SlashCommandResult> { let model_registry = LanguageModelRegistry::read_global(cx); let current_model = model_registry.active_model(); let prompt_builder = self.prompt_builder.clone(); let Some(workspace) = workspace.upgrade() else { return Task::ready(Err(anyhow::anyhow!("workspace was dropped"))); }; let project = workspace.read(cx).project().clone(); let fs = project.read(cx).fs().clone(); let Some(project_index) = cx.update_global(|index: &mut SemanticDb, cx| index.project_index(project, cx)) else { return Task::ready(Err(anyhow::anyhow!("no project indexer"))); }; window.spawn(cx, |mut cx| async move { let current_model = current_model.ok_or_else(|| anyhow!("no model selected"))?; let prompt = prompt_builder.generate_project_slash_command_prompt(context_buffer.text())?; let search_queries = current_model .use_tool::<SearchQueries>( language_model::LanguageModelRequest { messages: vec![language_model::LanguageModelRequestMessage { role: language_model::Role::User, content: vec![language_model::MessageContent::Text(prompt)], cache: false, }], tools: vec![], stop: vec![], temperature: None, }, cx.deref_mut(), ) .await? .search_queries; let results = project_index .read_with(&cx, |project_index, cx| { project_index.search(search_queries.clone(), 25, cx) })? .await?; let results = SemanticDb::load_results(results, &fs, &cx).await?; cx.background_spawn(async move { let mut output = "Project context:\n".to_string(); let mut sections = Vec::new(); for (ix, query) in search_queries.into_iter().enumerate() { let start_ix = output.len(); writeln!(&mut output, "Results for {query}:").unwrap(); let mut has_results = false; for result in &results { if result.query_index == ix { add_search_result_section(result, &mut output, &mut sections); has_results = true; } } if has_results { sections.push(SlashCommandOutputSection { range: start_ix..output.len(), icon: IconName::MagnifyingGlass, label: query.into(), metadata: None, }); output.push('\n'); } else { output.truncate(start_ix); } } sections.push(SlashCommandOutputSection { range: 0..output.len(), icon: IconName::Book, label: "Project context".into(), metadata: None, }); Ok(SlashCommandOutput { text: output, sections, run_commands_in_text: true, } .to_event_stream()) }) .await }) }} #[derive(JsonSchema, Deserialize)]struct SearchQueries { /// An array of semantic search queries. /// /// These queries will be used to search the user's codebase. /// The function can only accept 4 queries, otherwise it will error. /// As such, it's important that you limit the length of the search_queries array to 5 queries or less. search_queries: Vec<String>,} impl LanguageModelTool for SearchQueries { fn name() -> String { "search_queries".to_string() } fn description() -> String { "Generate semantic search queries based on context".to_string() }}
use std::{
fmt::Write as _,
ops::DerefMut,
sync::{atomic::AtomicBool, Arc},
};
use anyhow::{anyhow, Result};
use assistant_slash_command::{
ArgumentCompletion, SlashCommand, SlashCommandOutput, SlashCommandOutputSection,
SlashCommandResult,
};
use feature_flags::FeatureFlag;
use gpui::{App, Task, WeakEntity};
use language::{Anchor, CodeLabel, LspAdapterDelegate};
use language_model::{LanguageModelRegistry, LanguageModelTool};
use prompt_store::PromptBuilder;
use schemars::JsonSchema;
use semantic_index::SemanticDb;
use serde::Deserialize;
use ui::prelude::*;
use workspace::Workspace;
use super::{create_label_for_command, search_command::add_search_result_section};
pub struct ProjectSlashCommandFeatureFlag;
impl FeatureFlag for ProjectSlashCommandFeatureFlag {
const NAME: &'static str = "project-slash-command";
}
pub struct ProjectSlashCommand {
prompt_builder: Arc<PromptBuilder>,
}
impl ProjectSlashCommand {
pub fn new(prompt_builder: Arc<PromptBuilder>) -> Self {
Self { prompt_builder }
}
}
impl SlashCommand for ProjectSlashCommand {
fn name(&self) -> String {
"project".into()
}
fn label(&self, cx: &App) -> CodeLabel {
create_label_for_command("project", &[], cx)
}
fn description(&self) -> String {
"Generate a semantic search based on context".into()
}
fn icon(&self) -> IconName {
IconName::Folder
}
fn menu_text(&self) -> String {
self.description()
}
fn requires_argument(&self) -> bool {
false
}
fn complete_argument(
self: Arc<Self>,
_arguments: &[String],
_cancel: Arc<AtomicBool>,
_workspace: Option<WeakEntity<Workspace>>,
_window: &mut Window,
_cx: &mut App,
) -> Task<Result<Vec<ArgumentCompletion>>> {
Task::ready(Ok(Vec::new()))
}
fn run(
self: Arc<Self>,
_arguments: &[String],
_context_slash_command_output_sections: &[SlashCommandOutputSection<Anchor>],
context_buffer: language::BufferSnapshot,
workspace: WeakEntity<Workspace>,
_delegate: Option<Arc<dyn LspAdapterDelegate>>,
window: &mut Window,
cx: &mut App,
) -> Task<SlashCommandResult> {
let model_registry = LanguageModelRegistry::read_global(cx);
let current_model = model_registry.active_model();
let prompt_builder = self.prompt_builder.clone();
let Some(workspace) = workspace.upgrade() else {
return Task::ready(Err(anyhow::anyhow!("workspace was dropped")));
};
let project = workspace.read(cx).project().clone();
let fs = project.read(cx).fs().clone();
let Some(project_index) =
cx.update_global(|index: &mut SemanticDb, cx| index.project_index(project, cx))
else {
return Task::ready(Err(anyhow::anyhow!("no project indexer")));
};
window.spawn(cx, |mut cx| async move {
let current_model = current_model.ok_or_else(|| anyhow!("no model selected"))?;
let prompt =
prompt_builder.generate_project_slash_command_prompt(context_buffer.text())?;
let search_queries = current_model
.use_tool::<SearchQueries>(
language_model::LanguageModelRequest {
messages: vec![language_model::LanguageModelRequestMessage {
role: language_model::Role::User,
content: vec![language_model::MessageContent::Text(prompt)],
cache: false,
}],
tools: vec![],
stop: vec![],
temperature: None,
},
cx.deref_mut(),
)
.await?
.search_queries;
let results = project_index
.read_with(&cx, |project_index, cx| {
project_index.search(search_queries.clone(), 25, cx)
})?
.await?;
let results = SemanticDb::load_results(results, &fs, &cx).await?;
cx.background_spawn(async move {
let mut output = "Project context:\n".to_string();
let mut sections = Vec::new();
for (ix, query) in search_queries.into_iter().enumerate() {
let start_ix = output.len();
writeln!(&mut output, "Results for {query}:").unwrap();
let mut has_results = false;
for result in &results {
if result.query_index == ix {
add_search_result_section(result, &mut output, &mut sections);
has_results = true;
}
}
if has_results {
sections.push(SlashCommandOutputSection {
range: start_ix..output.len(),
icon: IconName::MagnifyingGlass,
label: query.into(),
metadata: None,
});
output.push('\n');
} else {
output.truncate(start_ix);
}
}
sections.push(SlashCommandOutputSection {
range: 0..output.len(),
icon: IconName::Book,
label: "Project context".into(),
metadata: None,
});
Ok(SlashCommandOutput {
text: output,
sections,
run_commands_in_text: true,
}
.to_event_stream())
})
.await
})
}
}
#[derive(JsonSchema, Deserialize)]
struct SearchQueries {
/// An array of semantic search queries.
///
/// These queries will be used to search the user's codebase.
/// The function can only accept 4 queries, otherwise it will error.
/// As such, it's important that you limit the length of the search_queries array to 5 queries or less.
search_queries: Vec<String>,
}
impl LanguageModelTool for SearchQueries {
fn name() -> String {
"search_queries".to_string()
}
fn description() -> String {
"Generate semantic search queries based on context".to_string()
}
}