// context.rs — Context window management // // Token counting, conversation trimming, and error classification. // Journal entries are loaded from the memory graph store, not from // a flat file — the parse functions are gone. use crate::agent::api::*; use chrono::{DateTime, Utc}; use serde::{Deserialize, Serialize}; use tiktoken_rs::CoreBPE; use crate::agent::tools::working_stack; // --- Context state types --- /// Conversation entry — either a regular message or memory content. /// Memory entries preserve the original message for KV cache round-tripping. #[derive(Debug, Clone, PartialEq)] pub enum ConversationEntry { /// System prompt or system-level instruction. System(Message), Message(Message), Memory { key: String, message: Message, score: Option }, /// DMN heartbeat/autonomous prompt — evicted aggressively during compaction. Dmn(Message), /// Debug/status log line — written to conversation log for tracing, /// skipped on read-back. Log(String), } /// Entry in the context window — wraps a ConversationEntry with cached metadata. #[derive(Debug, Clone)] pub struct ContextEntry { pub entry: ConversationEntry, /// Cached token count (0 for Log entries). pub tokens: usize, /// When this entry was added to the context. pub timestamp: Option>, } /// A named section of the context window with cached token total. #[derive(Debug, Clone)] pub struct ContextSection { pub name: String, /// Cached sum of entry tokens. tokens: usize, entries: Vec, } impl ContextSection { pub fn new(name: impl Into) -> Self { Self { name: name.into(), tokens: 0, entries: Vec::new() } } pub fn entries(&self) -> &[ContextEntry] { &self.entries } pub fn tokens(&self) -> usize { self.tokens } pub fn len(&self) -> usize { self.entries.len() } pub fn is_empty(&self) -> bool { self.entries.is_empty() } /// Push an entry, updating the cached token total. pub fn push(&mut self, entry: ContextEntry) { self.tokens += entry.tokens; self.entries.push(entry); } /// Replace an entry at `index`, adjusting the token total. pub fn set(&mut self, index: usize, entry: ContextEntry) { self.tokens -= self.entries[index].tokens; self.tokens += entry.tokens; self.entries[index] = entry; } /// Remove an entry at `index`, adjusting the token total. pub fn del(&mut self, index: usize) -> ContextEntry { let removed = self.entries.remove(index); self.tokens -= removed.tokens; removed } /// Replace the message inside an entry, recomputing its token count. pub fn set_message(&mut self, index: usize, tokenizer: &CoreBPE, msg: Message) { let old_tokens = self.entries[index].tokens; *self.entries[index].entry.message_mut() = msg; let new_tokens = msg_token_count(tokenizer, self.entries[index].entry.api_message()); self.entries[index].tokens = new_tokens; self.tokens = self.tokens - old_tokens + new_tokens; } /// Set the score on a Memory entry. No token change. pub fn set_score(&mut self, index: usize, score: Option) { if let ConversationEntry::Memory { score: s, .. } = &mut self.entries[index].entry { *s = score; } } /// Bulk replace all entries, recomputing token total. pub fn set_entries(&mut self, entries: Vec) { self.tokens = entries.iter().map(|e| e.tokens).sum(); self.entries = entries; } /// Dedup and trim entries to fit within context budget. pub fn trim(&mut self, budget: &ContextBudget, tokenizer: &CoreBPE) { let result = trim_entries(&self.entries, tokenizer, budget); self.entries = result; self.tokens = self.entries.iter().map(|e| e.tokens).sum(); } /// Clear all entries. pub fn clear(&mut self) { self.entries.clear(); self.tokens = 0; } } #[derive(Clone)] pub struct ContextState { pub system: ContextSection, pub identity: ContextSection, pub journal: ContextSection, pub conversation: ContextSection, /// Working stack — separate from identity because it's managed /// by its own tool, not loaded from personality files. pub working_stack: Vec, } impl ContextState { /// Total tokens across all sections. pub fn total_tokens(&self) -> usize { self.system.tokens() + self.identity.tokens() + self.journal.tokens() + self.conversation.tokens() } /// All sections as a slice for iteration. pub fn sections(&self) -> [&ContextSection; 4] { [&self.system, &self.identity, &self.journal, &self.conversation] } } /// Context window size in tokens (from config). pub fn context_window() -> usize { crate::config::get().api_context_window } /// Context budget in tokens: 80% of the model's context window. /// The remaining 20% is reserved for model output. fn context_budget_tokens() -> usize { context_window() * 80 / 100 } /// Dedup and trim conversation entries to fit within the context budget. /// /// 1. Dedup: if the same memory key appears multiple times, keep only /// the latest render (drop the earlier Memory entry and its /// corresponding assistant tool_call message). /// 2. Trim: drop oldest entries until the conversation fits, snapping /// to user message boundaries. fn trim_entries( entries: &[ContextEntry], _tokenizer: &CoreBPE, budget: &ContextBudget, ) -> Vec { let fixed_tokens = budget.system + budget.identity + budget.journal; // --- Phase 1: dedup memory entries by key (keep last) --- let mut seen_keys: std::collections::HashMap<&str, usize> = std::collections::HashMap::new(); let mut drop_indices: std::collections::HashSet = std::collections::HashSet::new(); for (i, ce) in entries.iter().enumerate() { if let ConversationEntry::Memory { key, .. } = &ce.entry { if let Some(prev) = seen_keys.insert(key.as_str(), i) { drop_indices.insert(prev); } } } let deduped: Vec = entries.iter().enumerate() .filter(|(i, _)| !drop_indices.contains(i)) .map(|(_, e)| e.clone()) .collect(); // --- Phase 2: trim to fit context budget --- let max_tokens = context_budget_tokens(); let msg_costs: Vec = deduped.iter().map(|e| e.tokens).collect(); let entry_total: usize = msg_costs.iter().sum(); let total: usize = fixed_tokens + entry_total; let mem_tokens: usize = deduped.iter() .filter(|ce| ce.entry.is_memory()) .map(|ce| ce.tokens).sum(); let conv_tokens: usize = entry_total - mem_tokens; dbglog!("[trim] max_tokens={} fixed={} mem={} conv={} total={} entries={}", max_tokens, fixed_tokens, mem_tokens, conv_tokens, total, deduped.len()); // Phase 2a: evict all DMN entries first — they're ephemeral let mut drop = vec![false; deduped.len()]; let mut trimmed = total; let mut cur_mem = mem_tokens; for i in 0..deduped.len() { if deduped[i].entry.is_dmn() { drop[i] = true; trimmed -= msg_costs[i]; } } // Phase 2b: if memories > 50% of context, evict lowest-scored first if cur_mem > conv_tokens && trimmed > max_tokens { let mut mem_indices: Vec = (0..deduped.len()) .filter(|&i| !drop[i] && deduped[i].entry.is_memory()) .collect(); mem_indices.sort_by(|&a, &b| { let sa = match &deduped[a].entry { ConversationEntry::Memory { score, .. } => score.unwrap_or(0.0), _ => 0.0, }; let sb = match &deduped[b].entry { ConversationEntry::Memory { score, .. } => score.unwrap_or(0.0), _ => 0.0, }; sa.partial_cmp(&sb).unwrap_or(std::cmp::Ordering::Equal) }); for i in mem_indices { if cur_mem <= conv_tokens { break; } if trimmed <= max_tokens { break; } drop[i] = true; trimmed -= msg_costs[i]; cur_mem -= msg_costs[i]; } } // Phase 2c: if still over, drop oldest conversation entries for i in 0..deduped.len() { if trimmed <= max_tokens { break; } if drop[i] { continue; } drop[i] = true; trimmed -= msg_costs[i]; } // Walk forward to include complete conversation boundaries let mut result: Vec = Vec::new(); let mut skipping = true; for (i, ce) in deduped.into_iter().enumerate() { if skipping { if drop[i] { continue; } // Snap to user message boundary if ce.entry.message().role != Role::User { continue; } skipping = false; } result.push(ce); } dbglog!("[trim] result={} trimmed_total={}", result.len(), trimmed); result } /// Count the token footprint of a message using BPE tokenization. pub fn msg_token_count(tokenizer: &CoreBPE, msg: &Message) -> usize { let count = |s: &str| tokenizer.encode_with_special_tokens(s).len(); let content = msg.content.as_ref().map_or(0, |c| match c { MessageContent::Text(s) => count(s), MessageContent::Parts(parts) => parts.iter() .map(|p| match p { ContentPart::Text { text } => count(text), ContentPart::ImageUrl { .. } => 85, }) .sum(), }); let tools = msg.tool_calls.as_ref().map_or(0, |calls| { calls.iter() .map(|c| count(&c.function.arguments) + count(&c.function.name)) .sum() }); content + tools } /// Detect context window overflow errors from the API. pub fn is_context_overflow(err: &anyhow::Error) -> bool { let msg = err.to_string().to_lowercase(); msg.contains("context length") || msg.contains("token limit") || msg.contains("too many tokens") || msg.contains("maximum context") || msg.contains("prompt is too long") || msg.contains("request too large") || msg.contains("input validation error") || msg.contains("content length limit") || (msg.contains("400") && msg.contains("tokens")) } /// Detect model/provider errors delivered inside the SSE stream. pub fn is_stream_error(err: &anyhow::Error) -> bool { err.to_string().contains("model stream error") } // Custom serde: serialize Memory with a "memory_key" field added to the message, // plain messages serialize as-is. This keeps the conversation log readable. impl Serialize for ConversationEntry { fn serialize(&self, s: S) -> Result { use serde::ser::SerializeMap; match self { Self::System(m) | Self::Message(m) | Self::Dmn(m) => m.serialize(s), Self::Memory { key, message, score } => { let json = serde_json::to_value(message).map_err(serde::ser::Error::custom)?; let mut map = s.serialize_map(None)?; if let serde_json::Value::Object(obj) = json { for (k, v) in obj { map.serialize_entry(&k, &v)?; } } map.serialize_entry("memory_key", key)?; if let Some(s) = score { map.serialize_entry("memory_score", s)?; } map.end() } Self::Log(text) => { use serde::ser::SerializeMap; let mut map = s.serialize_map(Some(1))?; map.serialize_entry("log", text)?; map.end() } } } } impl<'de> Deserialize<'de> for ConversationEntry { fn deserialize>(d: D) -> Result { let mut json: serde_json::Value = serde_json::Value::deserialize(d)?; // Log entries — skip on read-back if json.get("log").is_some() { let text = json["log"].as_str().unwrap_or("").to_string(); return Ok(Self::Log(text)); } if let Some(key) = json.as_object_mut().and_then(|o| o.remove("memory_key")) { let key = key.as_str().unwrap_or("").to_string(); let score = json.as_object_mut() .and_then(|o| o.remove("memory_score")) .and_then(|v| v.as_f64()); let message: Message = serde_json::from_value(json).map_err(serde::de::Error::custom)?; Ok(Self::Memory { key, message, score }) } else { let message: Message = serde_json::from_value(json).map_err(serde::de::Error::custom)?; Ok(Self::Message(message)) } } } impl ConversationEntry { /// Get the API message for sending to the model. /// Panics on Log entries (which should be filtered before API calls). pub fn api_message(&self) -> &Message { match self { Self::System(m) | Self::Message(m) | Self::Dmn(m) => m, Self::Memory { message, .. } => message, Self::Log(_) => panic!("Log entries have no API message"), } } pub fn is_memory(&self) -> bool { matches!(self, Self::Memory { .. }) } pub fn is_dmn(&self) -> bool { matches!(self, Self::Dmn(_)) } pub fn is_log(&self) -> bool { matches!(self, Self::Log(_)) } /// Get a reference to the inner message. /// Panics on Log entries. pub fn message(&self) -> &Message { match self { Self::System(m) | Self::Message(m) | Self::Dmn(m) => m, Self::Memory { message, .. } => message, Self::Log(_) => panic!("Log entries have no message"), } } /// Get a mutable reference to the inner message. /// Panics on Log entries. pub fn message_mut(&mut self) -> &mut Message { match self { Self::System(m) | Self::Message(m) | Self::Dmn(m) => m, Self::Memory { message, .. } => message, Self::Log(_) => panic!("Log entries have no message"), } } } impl ContextState { /// Render journal entries into a single text block. pub fn render_journal(&self) -> String { if self.journal.is_empty() { return String::new(); } let mut text = String::from("[Earlier — from your journal]\n\n"); for e in self.journal.entries() { use std::fmt::Write; if let Some(ts) = &e.timestamp { writeln!(text, "## {}\n{}\n", ts.format("%Y-%m-%dT%H:%M"), e.entry.message().content_text()).ok(); } else { text.push_str(&e.entry.message().content_text()); text.push_str("\n\n"); } } text } /// Render identity files + working stack into a single user message. pub fn render_context_message(&self) -> String { let mut parts: Vec = self.identity.entries().iter() .map(|e| e.entry.message().content_text().to_string()) .collect(); let instructions = std::fs::read_to_string(working_stack::instructions_path()).unwrap_or_default(); let mut stack_section = instructions; if self.working_stack.is_empty() { stack_section.push_str("\n## Current stack\n\n(empty)\n"); } else { stack_section.push_str("\n## Current stack\n\n"); for (i, item) in self.working_stack.iter().enumerate() { if i == self.working_stack.len() - 1 { stack_section.push_str(&format!("→ {}\n", item)); } else { stack_section.push_str(&format!(" [{}] {}\n", i, item)); } } } parts.push(stack_section); parts.join("\n\n---\n\n") } } /// Token budget per context category — cheap to compute, no formatting. pub struct ContextBudget { pub system: usize, pub identity: usize, pub journal: usize, pub memory: usize, pub conversation: usize, } impl ContextBudget { pub fn total(&self) -> usize { self.system + self.identity + self.journal + self.memory + self.conversation } pub fn format(&self) -> String { let window = context_window(); if window == 0 { return String::new(); } let used = self.total(); let free = window.saturating_sub(used); let pct = |n: usize| if n == 0 { 0 } else { ((n * 100) / window).max(1) }; format!("sys:{}% id:{}% jnl:{}% mem:{}% conv:{}% free:{}%", pct(self.system), pct(self.identity), pct(self.journal), pct(self.memory), pct(self.conversation), pct(free)) } }