This PR partially implements a knowledge distillation data pipeline. `zeta distill` gets a dataset of chronologically ordered commits and generates synthetic predictions with a teacher model (one-shot Claude Sonnet). `zeta distill --batches cache.db` will enable Message Batches API. Under the first run, this command will collect all LLM requests and upload a batch of them to Anthropic. On subsequent runs, it will check the batch status. If ready, it will download the result and put them into the local cache. Release Notes: - N/A --------- Co-authored-by: Piotr Osiewicz <24362066+osiewicz@users.noreply.github.com> Co-authored-by: Ben Kunkle <ben@zed.dev>
527 lines
15 KiB
Rust
527 lines
15 KiB
Rust
mod evaluate;
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mod example;
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mod headless;
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mod metrics;
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mod paths;
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mod predict;
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mod source_location;
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mod training;
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mod util;
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use crate::{
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evaluate::run_evaluate,
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example::{ExampleFormat, NamedExample},
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headless::ZetaCliAppState,
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predict::run_predict,
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source_location::SourceLocation,
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training::{context::ContextType, distill::run_distill},
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util::{open_buffer, open_buffer_with_language_server},
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};
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use ::util::{ResultExt, paths::PathStyle};
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use anyhow::{Result, anyhow};
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use clap::{Args, Parser, Subcommand, ValueEnum};
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use cloud_llm_client::predict_edits_v3;
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use edit_prediction::udiff::DiffLine;
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use edit_prediction_context::EditPredictionExcerptOptions;
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use gpui::{Application, AsyncApp, Entity, prelude::*};
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use language::{Bias, Buffer, BufferSnapshot, Point};
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use metrics::delta_chr_f;
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use project::{Project, Worktree, lsp_store::OpenLspBufferHandle};
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use reqwest_client::ReqwestClient;
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use std::io::{self};
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use std::{collections::HashSet, path::PathBuf, str::FromStr, sync::Arc};
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#[derive(Parser, Debug)]
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#[command(name = "zeta")]
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struct ZetaCliArgs {
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#[arg(long, default_value_t = false)]
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printenv: bool,
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#[command(subcommand)]
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command: Option<Command>,
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}
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#[derive(Subcommand, Debug)]
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enum Command {
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Context(ContextArgs),
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Predict(PredictArguments),
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Eval(EvaluateArguments),
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Distill(DistillArguments),
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ConvertExample {
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path: PathBuf,
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#[arg(long, value_enum, default_value_t = ExampleFormat::Md)]
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output_format: ExampleFormat,
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},
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Score {
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golden_patch: PathBuf,
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actual_patch: PathBuf,
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},
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Clean,
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}
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#[derive(Debug, Args)]
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struct ContextArgs {
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#[arg(long)]
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provider: ContextProvider,
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#[arg(long)]
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worktree: PathBuf,
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#[arg(long)]
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cursor: SourceLocation,
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#[arg(long)]
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use_language_server: bool,
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#[arg(long)]
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edit_history: Option<FileOrStdin>,
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#[clap(flatten)]
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zeta2_args: Zeta2Args,
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}
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#[derive(clap::ValueEnum, Default, Debug, Clone, Copy)]
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enum ContextProvider {
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Zeta1,
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#[default]
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Zeta2,
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}
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#[derive(Clone, Debug, Args)]
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struct Zeta2Args {
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#[arg(long, default_value_t = 8192)]
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max_prompt_bytes: usize,
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#[arg(long, default_value_t = 2048)]
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max_excerpt_bytes: usize,
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#[arg(long, default_value_t = 1024)]
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min_excerpt_bytes: usize,
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#[arg(long, default_value_t = 0.66)]
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target_before_cursor_over_total_bytes: f32,
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#[arg(long, default_value_t = 1024)]
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max_diagnostic_bytes: usize,
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#[arg(long, value_enum, default_value_t = PromptFormat::default())]
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prompt_format: PromptFormat,
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#[arg(long, value_enum, default_value_t = Default::default())]
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output_format: OutputFormat,
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#[arg(long, default_value_t = 42)]
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file_indexing_parallelism: usize,
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#[arg(long, default_value_t = false)]
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disable_imports_gathering: bool,
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#[arg(long, default_value_t = u8::MAX)]
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max_retrieved_definitions: u8,
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}
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#[derive(Debug, Args)]
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pub struct PredictArguments {
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#[clap(long, short, value_enum, default_value_t = PredictionsOutputFormat::Md)]
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format: PredictionsOutputFormat,
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example_path: PathBuf,
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#[clap(flatten)]
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options: PredictionOptions,
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}
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#[derive(Debug, Args)]
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pub struct DistillArguments {
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split_commit_dataset: PathBuf,
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#[clap(long, value_enum, default_value_t = ContextType::CurrentFile)]
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context_type: ContextType,
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#[clap(long)]
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batch: Option<String>,
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}
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#[derive(Clone, Debug, Args)]
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pub struct PredictionOptions {
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#[clap(flatten)]
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zeta2: Zeta2Args,
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#[clap(long)]
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provider: PredictionProvider,
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#[clap(long, value_enum, default_value_t = CacheMode::default())]
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cache: CacheMode,
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}
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#[derive(Debug, ValueEnum, Default, Clone, Copy, PartialEq)]
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pub enum CacheMode {
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/// Use cached LLM requests and responses, except when multiple repetitions are requested
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#[default]
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Auto,
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/// Use cached LLM requests and responses, based on the hash of the prompt and the endpoint.
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#[value(alias = "request")]
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Requests,
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/// Ignore existing cache entries for both LLM and search.
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Skip,
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/// Use cached LLM responses AND search results for full determinism. Fails if they haven't been cached yet.
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/// Useful for reproducing results and fixing bugs outside of search queries
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Force,
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}
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impl CacheMode {
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fn use_cached_llm_responses(&self) -> bool {
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self.assert_not_auto();
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matches!(self, CacheMode::Requests | CacheMode::Force)
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}
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fn use_cached_search_results(&self) -> bool {
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self.assert_not_auto();
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matches!(self, CacheMode::Force)
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}
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fn assert_not_auto(&self) {
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assert_ne!(
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*self,
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CacheMode::Auto,
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"Cache mode should not be auto at this point!"
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);
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}
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}
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#[derive(clap::ValueEnum, Debug, Clone)]
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pub enum PredictionsOutputFormat {
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Json,
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Md,
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Diff,
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}
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#[derive(Debug, Args)]
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pub struct EvaluateArguments {
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example_paths: Vec<PathBuf>,
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#[clap(flatten)]
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options: PredictionOptions,
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#[clap(short, long, default_value_t = 1, alias = "repeat")]
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repetitions: u16,
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#[arg(long)]
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skip_prediction: bool,
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}
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#[derive(clap::ValueEnum, Default, Debug, Clone, Copy, PartialEq)]
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enum PredictionProvider {
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Zeta1,
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#[default]
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Zeta2,
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Sweep,
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}
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fn zeta2_args_to_options(args: &Zeta2Args) -> edit_prediction::ZetaOptions {
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edit_prediction::ZetaOptions {
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context: EditPredictionExcerptOptions {
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max_bytes: args.max_excerpt_bytes,
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min_bytes: args.min_excerpt_bytes,
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target_before_cursor_over_total_bytes: args.target_before_cursor_over_total_bytes,
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},
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max_prompt_bytes: args.max_prompt_bytes,
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prompt_format: args.prompt_format.into(),
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}
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}
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#[derive(clap::ValueEnum, Default, Debug, Clone, Copy)]
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enum PromptFormat {
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OnlySnippets,
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#[default]
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OldTextNewText,
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Minimal,
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MinimalQwen,
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SeedCoder1120,
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}
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impl Into<predict_edits_v3::PromptFormat> for PromptFormat {
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fn into(self) -> predict_edits_v3::PromptFormat {
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match self {
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Self::OnlySnippets => predict_edits_v3::PromptFormat::OnlySnippets,
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Self::OldTextNewText => predict_edits_v3::PromptFormat::OldTextNewText,
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Self::Minimal => predict_edits_v3::PromptFormat::Minimal,
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Self::MinimalQwen => predict_edits_v3::PromptFormat::MinimalQwen,
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Self::SeedCoder1120 => predict_edits_v3::PromptFormat::SeedCoder1120,
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}
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}
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}
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#[derive(clap::ValueEnum, Default, Debug, Clone)]
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enum OutputFormat {
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#[default]
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Prompt,
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Request,
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Full,
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}
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#[derive(Debug, Clone)]
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enum FileOrStdin {
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File(PathBuf),
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Stdin,
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}
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impl FileOrStdin {
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async fn read_to_string(&self) -> Result<String, std::io::Error> {
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match self {
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FileOrStdin::File(path) => smol::fs::read_to_string(path).await,
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FileOrStdin::Stdin => smol::unblock(|| std::io::read_to_string(std::io::stdin())).await,
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}
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}
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}
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impl FromStr for FileOrStdin {
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type Err = <PathBuf as FromStr>::Err;
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fn from_str(s: &str) -> Result<Self, Self::Err> {
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match s {
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"-" => Ok(Self::Stdin),
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_ => Ok(Self::File(PathBuf::from_str(s)?)),
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}
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}
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}
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struct LoadedContext {
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full_path_str: String,
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snapshot: BufferSnapshot,
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clipped_cursor: Point,
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worktree: Entity<Worktree>,
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project: Entity<Project>,
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buffer: Entity<Buffer>,
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lsp_open_handle: Option<OpenLspBufferHandle>,
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}
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async fn load_context(
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args: &ContextArgs,
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app_state: &Arc<ZetaCliAppState>,
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cx: &mut AsyncApp,
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) -> Result<LoadedContext> {
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let ContextArgs {
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worktree: worktree_path,
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cursor,
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use_language_server,
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..
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} = args;
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let worktree_path = worktree_path.canonicalize()?;
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let project = cx.update(|cx| {
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Project::local(
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app_state.client.clone(),
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app_state.node_runtime.clone(),
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app_state.user_store.clone(),
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app_state.languages.clone(),
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app_state.fs.clone(),
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None,
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cx,
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)
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})?;
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let worktree = project
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.update(cx, |project, cx| {
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project.create_worktree(&worktree_path, true, cx)
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})?
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.await?;
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let mut ready_languages = HashSet::default();
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let (lsp_open_handle, buffer) = if *use_language_server {
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let (lsp_open_handle, _, buffer) = open_buffer_with_language_server(
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project.clone(),
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worktree.clone(),
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cursor.path.clone(),
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&mut ready_languages,
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cx,
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)
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.await?;
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(Some(lsp_open_handle), buffer)
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} else {
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let buffer =
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open_buffer(project.clone(), worktree.clone(), cursor.path.clone(), cx).await?;
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(None, buffer)
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};
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let full_path_str = worktree
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.read_with(cx, |worktree, _| worktree.root_name().join(&cursor.path))?
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.display(PathStyle::local())
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.to_string();
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let snapshot = cx.update(|cx| buffer.read(cx).snapshot())?;
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let clipped_cursor = snapshot.clip_point(cursor.point, Bias::Left);
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if clipped_cursor != cursor.point {
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let max_row = snapshot.max_point().row;
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if cursor.point.row < max_row {
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return Err(anyhow!(
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"Cursor position {:?} is out of bounds (line length is {})",
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cursor.point,
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snapshot.line_len(cursor.point.row)
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));
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} else {
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return Err(anyhow!(
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"Cursor position {:?} is out of bounds (max row is {})",
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cursor.point,
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max_row
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));
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}
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}
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Ok(LoadedContext {
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full_path_str,
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snapshot,
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clipped_cursor,
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worktree,
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project,
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buffer,
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lsp_open_handle,
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})
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}
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async fn zeta2_context(
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args: ContextArgs,
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app_state: &Arc<ZetaCliAppState>,
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cx: &mut AsyncApp,
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) -> Result<String> {
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let LoadedContext {
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worktree,
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project,
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buffer,
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clipped_cursor,
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lsp_open_handle: _handle,
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..
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} = load_context(&args, app_state, cx).await?;
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// wait for worktree scan before starting zeta2 so that wait_for_initial_indexing waits for
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// the whole worktree.
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worktree
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.read_with(cx, |worktree, _cx| {
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worktree.as_local().unwrap().scan_complete()
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})?
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.await;
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let output = cx
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.update(|cx| {
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let store = cx.new(|cx| {
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edit_prediction::EditPredictionStore::new(
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app_state.client.clone(),
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app_state.user_store.clone(),
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cx,
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)
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});
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store.update(cx, |store, cx| {
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store.set_options(zeta2_args_to_options(&args.zeta2_args));
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store.register_buffer(&buffer, &project, cx);
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});
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cx.spawn(async move |cx| {
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let updates_rx = store.update(cx, |store, cx| {
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let cursor = buffer.read(cx).snapshot().anchor_before(clipped_cursor);
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store.set_use_context(true);
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store.refresh_context(&project, &buffer, cursor, cx);
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store.project_context_updates(&project).unwrap()
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})?;
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updates_rx.recv().await.ok();
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let context = store.update(cx, |store, cx| {
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store.context_for_project(&project, cx).to_vec()
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})?;
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anyhow::Ok(serde_json::to_string_pretty(&context).unwrap())
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})
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})?
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.await?;
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Ok(output)
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}
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async fn zeta1_context(
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args: ContextArgs,
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app_state: &Arc<ZetaCliAppState>,
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cx: &mut AsyncApp,
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) -> Result<edit_prediction::zeta1::GatherContextOutput> {
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let LoadedContext {
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full_path_str,
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snapshot,
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clipped_cursor,
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..
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} = load_context(&args, app_state, cx).await?;
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let events = match args.edit_history {
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Some(events) => events.read_to_string().await?,
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None => String::new(),
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};
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let prompt_for_events = move || (events, 0);
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cx.update(|cx| {
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edit_prediction::zeta1::gather_context(
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full_path_str,
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&snapshot,
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clipped_cursor,
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prompt_for_events,
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cloud_llm_client::PredictEditsRequestTrigger::Cli,
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cx,
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)
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})?
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.await
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}
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fn main() {
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zlog::init();
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zlog::init_output_stderr();
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let args = ZetaCliArgs::parse();
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let http_client = Arc::new(ReqwestClient::new());
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let app = Application::headless().with_http_client(http_client);
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app.run(move |cx| {
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let app_state = Arc::new(headless::init(cx));
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cx.spawn(async move |cx| {
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match args.command {
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None => {
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if args.printenv {
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::util::shell_env::print_env();
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} else {
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panic!("Expected a command");
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}
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}
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Some(Command::Context(context_args)) => {
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let result = match context_args.provider {
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ContextProvider::Zeta1 => {
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let context =
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zeta1_context(context_args, &app_state, cx).await.unwrap();
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serde_json::to_string_pretty(&context.body).unwrap()
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}
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ContextProvider::Zeta2 => {
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zeta2_context(context_args, &app_state, cx).await.unwrap()
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}
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};
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println!("{}", result);
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}
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Some(Command::Predict(arguments)) => {
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run_predict(arguments, &app_state, cx).await;
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}
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Some(Command::Eval(arguments)) => {
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run_evaluate(arguments, &app_state, cx).await;
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}
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Some(Command::Distill(arguments)) => {
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let _guard = cx
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.update(|cx| gpui_tokio::Tokio::handle(cx))
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.unwrap()
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.enter();
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run_distill(arguments).await.log_err();
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}
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Some(Command::ConvertExample {
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path,
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output_format,
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}) => {
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let example = NamedExample::load(path).unwrap();
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example.write(output_format, io::stdout()).unwrap();
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}
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Some(Command::Score {
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golden_patch,
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actual_patch,
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}) => {
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let golden_content = std::fs::read_to_string(golden_patch).unwrap();
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let actual_content = std::fs::read_to_string(actual_patch).unwrap();
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let golden_diff: Vec<DiffLine> = golden_content
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.lines()
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.map(|line| DiffLine::parse(line))
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.collect();
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let actual_diff: Vec<DiffLine> = actual_content
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.lines()
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.map(|line| DiffLine::parse(line))
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.collect();
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let score = delta_chr_f(&golden_diff, &actual_diff);
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println!("{:.2}", score);
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}
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Some(Command::Clean) => {
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std::fs::remove_dir_all(&*crate::paths::TARGET_ZETA_DIR).unwrap()
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}
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};
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let _ = cx.update(|cx| cx.quit());
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})
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|
.detach();
|
|
});
|
|
}
|