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feat: chat context-usage display, /compact slash command, auto-compact

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Adds visibility into how much of the model context window the chat agent
is using and a way to free space when it fills up.

Backend
- New ContextUsage{used_chars, budget_chars} returned from chat_send
  alongside messages (return type ChatTurnResult). Computed by running
  build_history once at end of turn and counting char bytes — same data
  path as the actual LLM call, so the count is exact for the chosen
  budget unit.
- CONTEXT_BUDGET_CHARS = 24,000 (~6-8K tokens). Tuned for Ollama
  defaults; can be exposed via AiSettings later.
- New chat_compact Tauri command. Splits the thread at the last user
  turn, LLM-summarises everything before it (3-6 bullet points,
  language-aware, < 800 chars), and returns a thread of
  [Assistant("📋 Compacted N messages: …"), <last_user_turn?>]. The
  recent user turn is preserved untouched so the agent can keep
  answering it.
- render_thread_for_summary skips QueryResult.rows entirely so a single
  large run_query can't blow the summariser's context.
- 3 new unit tests (last_user_turn_index, render skipping rows, empty
  thread no-op).

Frontend
- ChatPanel header gets a usage badge: progress bar + `Xk / Yk tok ·
  P%`, color-coded green (<30%) / muted (<60%) / amber (<85%) / red
  (≥85%). Tooltip explains and nudges /compact when ≥60%.
- Compact button next to Clear in the header.
- Slash commands in ChatComposer: /compact, /clear.
- Empty-state shows the slash-command hint.
- Auto-compact: if the previous turn pushed usage past 85% AND the
  thread has more than one message, the next user turn first runs
  chat_compact transparently before chat_send. The compaction surfaces
  as a visible Assistant("📋 Compacted …") message so the user can see
  what the agent kept.
- app-store gets chatUsage map per tab + replaceChatThread + setChatUsage
  actions; closeTab and clearChatThread clean up usage too.

Verification: cargo check clean, cargo test --lib 53 pass (+3),
tsc --noEmit clean, vitest run 20 pass.
This commit is contained in:
Alexey Shakhmatov
2026-05-06 19:44:11 +03:00
parent b41c84dab8
commit 27fed0dbf8
8 changed files with 454 additions and 31 deletions
Download Patch File Download Diff File Expand all files Collapse all files

240
src-tauri/src/commands/chat.rs
View File

@@ -7,7 +7,7 @@ use crate::commands::memory::{append_memory_core, read_memory_core};
use crate::commands::queries::execute_query_core;
use crate::error::{TuskError, TuskResult};
use crate::models::ai::OllamaChatMessage;
use crate::models::chat::ChatMessage;
use crate::models::chat::{ChatMessage, ChatTurnResult, ContextUsage};
use crate::models::query_result::QueryResult;
use crate::state::AppState;
use chrono::Utc;
@@ -26,6 +26,10 @@ const RUN_QUERY_SAMPLE_ROWS: usize = 10;
const CELL_CHAR_CAP: usize = 200;
/// Per text-tool-result character cap (list_tables, get_columns, etc).
const TEXT_TOOL_CHAR_CAP: usize = 10_000;
/// Soft cap on serialized history+system prompt characters before the user
/// is nudged to /compact. Tuned for Ollama defaults (~4-8K tokens).
/// Token estimate ≈ chars / 3 for mixed Cyrillic/ASCII content.
const CONTEXT_BUDGET_CHARS: u64 = 24_000;
// ---------------------------------------------------------------------------
// Action protocol
@@ -418,13 +422,38 @@ fn build_history(
// chat_send
// ---------------------------------------------------------------------------
/// Estimate how many characters the next LLM call will serialize to history
/// (system prompt + conversation, after compression). This is the same data
/// path as the actual call, so the count is exact for the chosen budget unit.
async fn compute_usage(
state: &AppState,
app: &AppHandle,
connection_id: &str,
working: &[ChatMessage],
) -> ContextUsage {
let overview = build_overview_context(state, connection_id)
.await
.unwrap_or_default();
let memory = read_memory_core(app, connection_id).unwrap_or_default();
let history = build_history(working, &overview, &memory);
// role string ("system"/"user"/"assistant") ≤ 9 chars + content + JSON envelope overhead
let used: u64 = history
.iter()
.map(|m| (m.role.len() + m.content.len() + 16) as u64)
.sum();
ContextUsage {
used_chars: used,
budget_chars: CONTEXT_BUDGET_CHARS,
}
}
#[tauri::command]
pub async fn chat_send(
app: AppHandle,
state: State<'_, Arc<AppState>>,
connection_id: String,
messages: Vec<ChatMessage>,
) -> TuskResult<Vec<ChatMessage>> {
) -> TuskResult<ChatTurnResult> {
let mut new_messages: Vec<ChatMessage> = Vec::new();
let mut working: Vec<ChatMessage> = messages;
@@ -456,7 +485,11 @@ pub async fn chat_send(
};
new_messages.push(msg.clone());
working.push(msg);
return Ok(new_messages);
let usage = compute_usage(&state, &app, &connection_id, &working).await;
return Ok(ChatTurnResult {
messages: new_messages,
usage,
});
}
};
@@ -469,7 +502,11 @@ pub async fn chat_send(
};
new_messages.push(msg.clone());
working.push(msg);
return Ok(new_messages);
let usage = compute_usage(&state, &app, &connection_id, &working).await;
return Ok(ChatTurnResult {
messages: new_messages,
usage,
});
}
AgentAction::RunQuery { sql } => {
push_tool_call(
@@ -604,8 +641,13 @@ pub async fn chat_send(
),
created_at: now_ms(),
};
new_messages.push(msg);
Ok(new_messages)
new_messages.push(msg.clone());
working.push(msg);
let usage = compute_usage(&state, &app, &connection_id, &working).await;
Ok(ChatTurnResult {
messages: new_messages,
usage,
})
}
fn push_tool_call(
@@ -654,6 +696,143 @@ fn run_text_tool(outcome: TuskResult<String>, tool: &str) -> ChatMessage {
}
}
// ---------------------------------------------------------------------------
// chat_compact
// ---------------------------------------------------------------------------
/// Render the older-history portion of the thread as a compact text block
/// for LLM-driven summarization. Skips QueryResult.rows (huge), keeps only
/// columns + row_count + sample.
fn render_thread_for_summary(messages: &[ChatMessage]) -> String {
let mut out = String::new();
for m in messages {
match m {
ChatMessage::User { text, .. } => {
out.push_str(&format!("USER: {}\n\n", text));
}
ChatMessage::Assistant { text, .. } => {
out.push_str(&format!("ASSISTANT: {}\n\n", text));
}
ChatMessage::ToolCall { tool, input_json, .. } => {
out.push_str(&format!("TOOL_CALL [{}]: {}\n\n", tool, input_json));
}
ChatMessage::ToolResult {
tool,
is_error,
text,
result,
..
} => {
if *is_error {
out.push_str(&format!(
"TOOL_ERROR [{}]: {}\n\n",
tool,
text.as_deref().unwrap_or("")
));
continue;
}
if let Some(qr) = result {
out.push_str(&format!(
"TOOL_RESULT [{}]: {} rows; columns={}\n\n",
tool,
qr.row_count,
qr.columns.join(", ")
));
} else if let Some(t) = text {
let snippet: String = t.chars().take(800).collect();
out.push_str(&format!("TOOL_RESULT [{}]: {}\n\n", tool, snippet));
}
}
}
}
out
}
/// Find the index of the last User message; returns messages.len() if no user message.
fn last_user_turn_index(messages: &[ChatMessage]) -> usize {
for (i, m) in messages.iter().enumerate().rev() {
if matches!(m, ChatMessage::User { .. }) {
return i;
}
}
messages.len()
}
/// LLM-summarise the older portion of a chat thread.
/// Returns thread = [ Assistant("📋 Compacted: …") , <last_user_turn_if_any> ].
/// If the thread has nothing to compact, returns it unchanged.
#[tauri::command]
pub async fn chat_compact(
app: AppHandle,
state: State<'_, Arc<AppState>>,
connection_id: String,
messages: Vec<ChatMessage>,
) -> TuskResult<ChatTurnResult> {
if messages.is_empty() {
let usage = compute_usage(&state, &app, &connection_id, &messages).await;
return Ok(ChatTurnResult { messages, usage });
}
// Preserve the user's most recent question (if any) untouched so the
// model can continue from it after compaction. Everything before goes
// into the summary.
let split_at = last_user_turn_index(&messages);
let (older, recent): (&[ChatMessage], &[ChatMessage]) = if split_at == messages.len() {
(&messages[..], &[])
} else {
(&messages[..split_at], &messages[split_at..])
};
if older.is_empty() {
let usage = compute_usage(&state, &app, &connection_id, &messages).await;
return Ok(ChatTurnResult { messages, usage });
}
let convo = render_thread_for_summary(older);
let system = "You are a precise summarizer of a database analysis dialogue. \
Produce a SHORT summary in the SAME language the user spoke. \
Use 3-6 bullet points covering: the user's goal, key tables/columns/queries used, \
numerical findings, conclusions reached, any open questions. \
Be concrete with numbers and identifiers. Total length < 800 chars. \
Output the bullets directly with no preamble, no JSON, no markdown fences.";
let llm_messages = vec![
OllamaChatMessage {
role: "system".to_string(),
content: system.to_string(),
},
OllamaChatMessage {
role: "user".to_string(),
content: convo,
},
];
let summary = call_ollama_chat_messages(&app, &state, llm_messages, None)
.await
.map_err(|e| TuskError::Ai(format!("Compact failed: {}", e)))?;
let cleaned = summary.trim();
let compacted_msg = ChatMessage::Assistant {
id: new_id("asst"),
text: format!(
"📋 Compacted {} earlier message{}:\n\n{}",
older.len(),
if older.len() == 1 { "" } else { "s" },
cleaned
),
created_at: now_ms(),
};
let mut out: Vec<ChatMessage> = Vec::with_capacity(1 + recent.len());
out.push(compacted_msg);
out.extend(recent.iter().cloned());
let usage = compute_usage(&state, &app, &connection_id, &out).await;
Ok(ChatTurnResult {
messages: out,
usage,
})
}
// ---------------------------------------------------------------------------
// tests
// ---------------------------------------------------------------------------
@@ -833,6 +1012,55 @@ mod tests {
assert!(parse_agent_action(r#"{"action":"find_queries","text":""}"#).is_err());
}
#[test]
fn last_user_turn_index_finds_last_user() {
let msgs = vec![
ChatMessage::User { id: "u1".into(), text: "first".into(), created_at: 1 },
ChatMessage::Assistant { id: "a1".into(), text: "ans".into(), created_at: 2 },
ChatMessage::User { id: "u2".into(), text: "second".into(), created_at: 3 },
ChatMessage::Assistant { id: "a2".into(), text: "ans2".into(), created_at: 4 },
];
assert_eq!(last_user_turn_index(&msgs), 2);
}
#[test]
fn last_user_turn_index_returns_len_when_no_user() {
let msgs = vec![ChatMessage::Assistant {
id: "a1".into(),
text: "alone".into(),
created_at: 1,
}];
assert_eq!(last_user_turn_index(&msgs), msgs.len());
}
#[test]
fn render_thread_for_summary_includes_roles_and_skips_rows() {
let msgs = vec![
ChatMessage::User { id: "u1".into(), text: "find users".into(), created_at: 1 },
ChatMessage::ToolCall { id: "c1".into(), tool: "run_query".into(), input_json: r#"{"sql":"SELECT 1"}"#.into(), created_at: 2 },
ChatMessage::ToolResult {
id: "r1".into(),
tool: "run_query".into(),
is_error: false,
text: None,
result: Some(QueryResult {
columns: vec!["id".into(), "name".into()],
types: vec!["INT4".into(), "TEXT".into()],
rows: vec![vec![Value::Number(1.into()), Value::String("alice".into())]; 1000],
row_count: 1000,
execution_time_ms: 12,
}),
created_at: 3,
},
];
let rendered = render_thread_for_summary(&msgs);
assert!(rendered.contains("USER: find users"));
assert!(rendered.contains("TOOL_CALL [run_query]"));
assert!(rendered.contains("1000 rows"));
// Must NOT include the actual rows
assert!(!rendered.contains("alice"));
}
#[test]
fn rejects_legacy_get_schema() {
assert!(parse_agent_action(r#"{"action":"get_schema"}"#).is_err());