consciousness/src/agent/context.rs

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// 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<f64> },
/// 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<DateTime<Utc>>,
}
/// 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<ContextEntry>,
}
impl ContextSection {
pub fn new(name: impl Into<String>) -> 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<f64>) {
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<ContextEntry>) {
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<String>,
}
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<ContextEntry> {
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<usize> = 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<ContextEntry> = 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<usize> = 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<usize> = (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<ContextEntry> = 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<S: serde::Serializer>(&self, s: S) -> Result<S::Ok, S::Error> {
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: serde::Deserializer<'de>>(d: D) -> Result<Self, D::Error> {
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<String> = 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))
}
}