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273 changed files with 3447 additions and 12471 deletions
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914
Cargo.lock generated
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16
Cargo.toml
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@ -18,12 +18,8 @@ name = "consciousness"
version.workspace = true
edition.workspace = true
[features]
nightly-diagnostics = []
[dependencies]
anyhow = "1"
html2md = "0.2"
crossterm = { version = "0.29", features = ["event-stream", "bracketed-paste", "osc52"] }
clap = { version = "4", features = ["derive"] }
figment = { version = "0.10", features = ["env"] }
@ -33,8 +29,7 @@ log = "0.4"
serde = { version = "1", features = ["derive"] }
serde_json = "1"
json-five = "0.3"
notify-debouncer-mini = "0.7"
json5 = "1.3"
ratatui = { version = "0.30", features = ["unstable-rendered-line-info"] }
tui-markdown = { git = "https://github.com/koverstreet/tui-markdown", subdirectory = "tui-markdown" }
@ -64,11 +59,6 @@ futures = "0.3"
capnp = "0.25"
capnp-rpc = "0.25"
tonic = { version = "0.12", features = ["tls", "tls-roots"] }
prost = "0.13"
async-stream = "0.3"
tokio-stream = "0.1"
tokenizers = "0.22"
http = "1"
@ -77,18 +67,14 @@ hyper-util = { version = "0.1", features = ["tokio"], default-features = false }
http-body-util = "0.1"
bytes = "1"
base64 = "0.22"
imagesize = "0.14"
rustls = "0.23"
tokio-rustls = "0.26"
rustls-native-certs = "0.8"
rustls-pemfile = "2"
serde_urlencoded = "0.7"
[build-dependencies]
capnpc = "0.25"
tonic-build = { version = "0.12", default-features = false, features = ["prost", "transport"] }
protoc-bin-vendored = "3"
[lib]
name = "consciousness"

17
build.rs
View file

@ -13,21 +13,4 @@ fn main() {
.file("schema/channel.capnp")
.run()
.expect("capnp compile failed (channel.capnp)");
// Generate salience.v1 gRPC client + message types from proto.
// Server side (python) is generated separately via grpcio-tools.
// Use vendored protoc so we don't require a system install.
let protoc = protoc_bin_vendored::protoc_bin_path()
.expect("vendored protoc not available for this platform");
// SAFETY: build script is single-threaded at this point; setting env
// before invoking tonic_build is the documented way to point it at a
// non-PATH protoc.
unsafe { std::env::set_var("PROTOC", protoc); }
tonic_build::configure()
.build_server(false)
.build_client(true)
.compile_protos(&["proto/salience.proto"], &["proto"])
.expect("tonic_build compile failed (salience.proto)");
println!("cargo:rerun-if-changed=proto/salience.proto");
}

60
channels/irc/src/main.rs
View file

@ -237,19 +237,11 @@ impl State {
async fn send_privmsg(&mut self, target: &str, msg: &str) -> io::Result<()> {
// Send PRIVMSG, which is used for both private and channel messages.
// Splits into multiple fragments if necessary.
//
// Two constraints:
// 1. IRC max line = 512 bytes including CRLF. The server prepends
// our prefix when relaying: ":nick!~user@host PRIVMSG target :msg\r\n"
// So per-PRIVMSG message content must fit in 512 - overhead.
// 2. Embedded '\n' in the message would be interpreted by the
// server as an end-of-command marker, truncating us. Split
// on newlines first and send each line as its own PRIVMSG.
//
// IRC max line = 512 bytes including CRLF. The server prepends
// our prefix when relaying: ":nick!~user@host PRIVMSG target :msg\r\n"
// User is often ~nick (nick_len + 1). Host is up to 63 bytes.
// Cloaked OFTC hosts can be longer - pad the budget.
let nick_len = self.config.nick.len();
let overhead = 1 + nick_len + 1 + (nick_len + 1) + 1 + 80
let overhead = 1 + nick_len + 2 + nick_len + 1 + 63
+ " PRIVMSG ".len() + target.len() + " :".len() + 2;
let max_msg = 512_usize.saturating_sub(overhead);
@ -257,34 +249,24 @@ impl State {
return Err(io::Error::new(io::ErrorKind::InvalidInput, "target too long"));
}
for line in msg.split('\n') {
let mut remaining = line;
// Empty lines (blank paragraph breaks) can't be sent as empty
// PRIVMSGs - most IRC servers reject them. Skip.
if remaining.is_empty() { continue; }
loop {
let split_at = if remaining.len() <= max_msg {
remaining.len()
} else {
// Find last char boundary at or before max_msg.
let mut i = max_msg;
while i > 0 && !remaining.is_char_boundary(i) { i -= 1; }
// Prefer splitting at a word boundary - look back up to
// max_msg/4 chars for a space. With dense content (code)
// we may not find one; fall back to the char boundary.
let lookback = max_msg / 4;
let bytes = remaining.as_bytes();
let mut j = i;
while j > 0 && (i - j) < lookback && bytes[j - 1] != b' ' {
j -= 1;
}
if j > 0 && bytes[j - 1] == b' ' { j } else { i }
};
let (chunk, rest) = remaining.split_at(split_at);
self.send_raw(&format!("PRIVMSG {target} :{chunk}")).await?;
remaining = rest;
if remaining.is_empty() { break; }
}
// Split on UTF-8 char boundaries
let mut remaining = msg;
while !remaining.is_empty() {
let split_at = if remaining.len() <= max_msg {
remaining.len()
} else {
// Find last char boundary at or before max_msg
let mut i = max_msg;
while i > 0 && !remaining.is_char_boundary(i) { i -= 1; }
// To avoid splitting mid-word, see if there was a space recently
let mut j = i;
while j > 1 && j > i-10 && remaining.as_bytes()[j] != b' ' { j -= 1; }
if remaining.as_bytes()[j] == b' ' { j }
else if i == 0 { max_msg } else { i }
};
let (chunk, rest) = remaining.split_at(split_at);
self.send_raw(&format!("PRIVMSG {target} :{chunk}")).await?;
remaining = rest;
}
Ok(())
}

126
channels/telegram/src/main.rs
View file

@ -181,8 +181,6 @@ struct TelegramMessage {
chat_id: i64,
sender: String,
text: String,
/// Absolute path to a downloaded media file (photo, etc.), if any.
media_path: Option<String>,
}
/// Fetch and parse pending updates from Telegram via long polling.
@ -208,115 +206,19 @@ async fn get_updates(
let sender = msg["from"]["first_name"].as_str().unwrap_or("unknown").to_string();
let chat_id = msg["chat"]["id"].as_i64().unwrap_or(0);
// Photo: array of PhotoSize, largest is last. Download largest,
// surface message with [image: <path>] marker so the multimodal
// model can Read the image.
let (text, media_path) = if let Some(sizes) = msg["photo"].as_array() {
let caption = msg["caption"].as_str().unwrap_or("").to_string();
let largest = sizes.last();
let file_id = largest
.and_then(|s| s["file_id"].as_str())
.unwrap_or("");
if file_id.is_empty() {
error!("telegram photo: missing file_id in update {update_id}");
(caption, None)
} else {
// Bound the download — HttpClient::request_timeout only covers
// send_request, not body collect, so an indefinitely-slow body
// would otherwise stall every subsequent poll.
let dl = tokio::time::timeout(
std::time::Duration::from_secs(60),
download_telegram_file(client, token, file_id),
).await
.unwrap_or_else(|_| Err("download timed out after 60s".into()));
match dl {
Ok(path) => (caption, Some(path)),
Err(e) => {
error!("telegram photo download failed (file_id={file_id}): {e}");
// Surface what we have: caption plus a marker that
// a photo was sent but couldn't be fetched.
let marker = format!("[image: download failed: {e}]");
let combined = if caption.is_empty() {
marker
} else {
format!("{marker}\n{caption}")
};
(combined, None)
}
}
}
} else if let Some(text) = msg["text"].as_str() {
(text.to_string(), None)
} else {
// Other media types (voice, video, sticker, etc.) — skip for now,
// but log so we can extend later.
let kind = ["voice", "video", "sticker", "document", "audio", "animation"]
.iter()
.find(|k| !msg[**k].is_null())
.copied()
.unwrap_or("unknown");
info!("telegram: skipping non-text/photo message (kind={kind}, update_id={update_id})");
continue;
};
messages.push(TelegramMessage {
update_id,
chat_id,
sender,
text,
media_path,
});
if let Some(text) = msg["text"].as_str() {
messages.push(TelegramMessage {
update_id,
chat_id,
sender,
text: text.to_string(),
});
}
}
}
Ok(messages)
}
/// Resolve a Telegram file_id to a downloadable URL path via getFile.
async fn get_file_path(
client: &HttpClient,
token: &str,
file_id: &str,
) -> Result<String, Box<dyn std::error::Error>> {
let url = format!(
"https://api.telegram.org/bot{}/getFile?file_id={}",
token, file_id,
);
let response = client.get(&url).await?;
let body = response.text().await?;
let resp: serde_json::Value = serde_json::from_str(&body)
.map_err(|e| format!("getFile JSON parse error: {e}"))?;
if !resp["ok"].as_bool().unwrap_or(false) {
return Err(format!("getFile failed: {}", resp["description"].as_str().unwrap_or("?")).into());
}
let file_path = resp["result"]["file_path"].as_str()
.ok_or("getFile: missing result.file_path")?;
Ok(file_path.to_string())
}
/// Download a Telegram file by file_id into the channel media dir.
/// Returns the absolute local path on success.
async fn download_telegram_file(
client: &HttpClient,
token: &str,
file_id: &str,
) -> Result<String, Box<dyn std::error::Error>> {
let file_path = get_file_path(client, token, file_id).await?;
let url = format!("https://api.telegram.org/file/bot{}/{}", token, file_path);
let response = client.get(&url).await?;
let status = response.status();
if !status.is_success() {
return Err(format!("file download failed: {status}").into());
}
let bytes = response.bytes().await?;
let ext = file_path.rsplit('.').next().filter(|e| !e.contains('/')).unwrap_or("dat");
let media_dir = log_dir().join("media");
std::fs::create_dir_all(&media_dir)?;
let dest = media_dir.join(format!("{file_id}.{ext}"));
std::fs::write(&dest, &bytes)?;
Ok(dest.to_string_lossy().to_string())
}
/// Send a text message to a Telegram chat.
async fn send_message(
client: &HttpClient,
@ -467,19 +369,11 @@ async fn poll_once(
let sender_lower = msg.sender.to_lowercase();
let channel = format!("telegram.{}", sender_lower);
// If the message has media, prepend an [image: <abs_path>] marker
// so the multimodal model can Read the file directly.
let body = match &msg.media_path {
Some(path) if msg.text.is_empty() => format!("[image: {path}]"),
Some(path) => format!("[image: {path}]\n{}", msg.text),
None => msg.text.clone(),
};
channel_log::append_disk_log(&log_dir(), &sender_lower, &msg.sender, &body);
channel_log::append_disk_log(&log_dir(), &sender_lower, &msg.sender, &msg.text);
let mut s = state.borrow_mut();
s.config.chat_ids.insert(sender_lower, msg.chat_id);
let line = format!("[{}] {}", msg.sender, body);
let line = format!("[{}] {}", msg.sender, msg.text);
s.push_message(line, 2, &channel);
}

182
channels/tmux/src/main.rs
View file

@ -26,12 +26,10 @@ use consciousness::thalamus::channel_log::ChannelLog;
#[derive(Clone, serde::Serialize, serde::Deserialize)]
struct PaneConfig {
/// Human-readable label: becomes the channel name "tmux.<label>",
/// and the tmux pane title / window name the live pane id is
/// resolved from. The pane id is deliberately not stored — it is
/// ephemeral (recycled across pane and tmux-server restarts), so it
/// is looked up fresh on every connect attempt.
/// Human-readable label, becomes the channel name "tmux.<label>"
label: String,
/// Tmux pane ID, e.g. "%5"
pane_id: String,
}
#[derive(Clone, serde::Serialize, serde::Deserialize)]
@ -88,9 +86,11 @@ impl State {
}
}
/// Whether a pane with this label is registered.
fn has_pane(&self, label: &str) -> bool {
self.config.panes.iter().any(|p| p.label == label)
/// Get pane_id for a label
fn get_pane(&self, label: &str) -> Option<&str> {
self.config.panes.iter()
.find(|p| p.label == label)
.map(|p| p.pane_id.as_str())
}
/// Check if a pane is connected
@ -103,124 +103,98 @@ impl State {
self.connected.insert(label.to_string(), connected);
}
/// Register a pane and persist.
fn add_pane(&mut self, label: String) {
/// Add a pane and persist
fn add_pane(&mut self, label: String, pane_id: String) {
if !self.config.panes.iter().any(|p| p.label == label) {
self.config.panes.push(PaneConfig { label });
self.config.panes.push(PaneConfig { label, pane_id });
save_config(&self.config);
}
}
/// Unregister a pane and persist. Returns whether it was registered.
fn remove_pane(&mut self, label: &str) -> bool {
/// Remove a pane and persist
fn remove_pane(&mut self, label: &str) -> Option<String> {
if let Some(idx) = self.config.panes.iter().position(|p| p.label == label) {
self.config.panes.remove(idx);
let pane = self.config.panes.remove(idx);
self.connected.remove(label);
save_config(&self.config);
true
Some(pane.pane_id)
} else {
false
None
}
}
}
// ── Pipe-Pane Reader ──────────────────────────────────────────
/// Wait between connect attempts for a pane that is not yet reachable.
const RETRY_INTERVAL: std::time::Duration = std::time::Duration::from_secs(2);
/// Keep a pane streamed into its channel log for as long as it stays
/// registered. The pane id is resolved fresh by label on every connect
/// attempt — tmux pane ids are ephemeral, so the label (pane title /
/// window name) is the durable identity. Retries until the pane exists
/// and pipe-pane succeeds, and reconnects the same way if the pipe
/// later drops. Returns once close() unregisters the pane.
async fn pipe_pane_reader(state: SharedState, label: String) {
/// Set up pipe-pane for a single pane, reading output into the channel log.
async fn pipe_pane_reader(state: SharedState, pane: PaneConfig) {
let pipe_dir = dirs::home_dir()
.unwrap_or_default()
.join(".consciousness/channels/tmux-pipes");
std::fs::create_dir_all(&pipe_dir).ok();
let pipe_path = pipe_dir.join(format!("{}.pipe", label));
let channel_key = format!("tmux.{}", label);
loop {
if !state.borrow().has_pane(&label) {
return;
}
let pipe_path = pipe_dir.join(format!("{}.pipe", pane.label));
let _ = std::fs::remove_file(&pipe_path);
connect_and_stream(&state, &label, &pipe_path, &channel_key).await;
state.borrow_mut().set_connected(&label, false);
if !state.borrow().has_pane(&label) {
return;
}
tokio::time::sleep(RETRY_INTERVAL).await;
}
}
/// One connect attempt: resolve the pane's live id by label, point its
/// output at the FIFO with pipe-pane, and stream lines into the channel
/// log. Returns on the first failure, or when the stream ends.
async fn connect_and_stream(
state: &SharedState,
label: &str,
pipe_path: &std::path::Path,
channel_key: &str,
) {
let pane_id = match find_pane_by_name(label) {
Some(id) => id,
None => return,
};
// Fresh FIFO for this attempt.
let _ = std::fs::remove_file(pipe_path);
// Create a named pipe (FIFO)
unsafe {
let c_path = std::ffi::CString::new(pipe_path.to_str().unwrap()).unwrap();
libc::mkfifo(c_path.as_ptr(), 0o644);
}
// Point the pane's output at our FIFO.
let pipe_cmd = format!("cat >> {}", pipe_path.to_string_lossy());
match std::process::Command::new("tmux")
.args(["pipe-pane", "-t", &pane_id, &pipe_cmd])
.output()
{
Ok(o) if o.status.success() => {}
Ok(o) => {
warn!("pipe-pane failed for {} ({}): {}", label, pane_id,
String::from_utf8_lossy(&o.stderr));
// Tell tmux to pipe this pane's output to our FIFO
let pipe_path_str = pipe_path.to_string_lossy().to_string();
let result = std::process::Command::new("tmux")
.args(["pipe-pane", "-t", &pane.pane_id, &format!("cat >> {}", pipe_path_str)])
.output();
match result {
Ok(output) if output.status.success() => {
info!("pipe-pane set up for {} ({})", pane.label, pane.pane_id);
}
Ok(output) => {
error!("pipe-pane failed for {}: {}", pane.label,
String::from_utf8_lossy(&output.stderr));
state.borrow_mut().set_connected(&pane.label, false);
return;
}
Err(e) => {
error!("running tmux pipe-pane for {}: {}", label, e);
error!("failed to run tmux pipe-pane for {}: {}", pane.label, e);
state.borrow_mut().set_connected(&pane.label, false);
return;
}
}
let file = match tokio::fs::File::open(pipe_path).await {
// Open the FIFO and read lines
let file = match tokio::fs::File::open(&pipe_path).await {
Ok(f) => f,
Err(e) => {
warn!("opening pipe for {}: {}", label, e);
error!("failed to open pipe for {}: {}", pane.label, e);
state.borrow_mut().set_connected(&pane.label, false);
return;
}
};
info!("connected channel tmux.{} (pane {})", label, pane_id);
state.borrow_mut().set_connected(label, true);
// Mark as connected once pipe is open
state.borrow_mut().set_connected(&pane.label, true);
let reader = tokio::io::BufReader::new(file);
let mut lines = reader.lines();
let channel_key = format!("tmux.{}", pane.label);
let mut lines = tokio::io::BufReader::new(file).lines();
while let Ok(Some(line)) = lines.next_line().await {
if line.trim().is_empty() {
continue;
}
let mut s = state.borrow_mut();
s.channel_logs
.entry(channel_key.to_string())
.or_insert_with(ChannelLog::new)
.push(line);
let log = s.channel_logs
.entry(channel_key.clone())
.or_insert_with(ChannelLog::new);
log.push(line);
}
warn!("pipe-pane stream ended for {}", label);
warn!("pipe-pane reader ended for {}", pane.label);
state.borrow_mut().set_connected(&pane.label, false);
}
// ── ChannelServer Implementation ───────────────────────────────
@ -270,10 +244,10 @@ impl channel_server::Server for ChannelServerImpl {
let channel = pry!(pry!(params.get_channel()).to_str()).to_string();
let message = pry!(pry!(params.get_message()).to_str()).to_string();
// Send to tmux pane via send-keys — resolve the live pane id by
// label (it is not stored).
// Send to tmux pane via send-keys
let label = channel.strip_prefix("tmux.").unwrap_or(&channel);
if let Some(pane_id) = find_pane_by_name(label) {
let pane_id = self.state.borrow().get_pane(label).map(String::from);
if let Some(pane_id) = pane_id {
let _ = std::process::Command::new("tmux")
.args(["send-keys", "-t", &pane_id, &message, "Enter"])
.output();
@ -328,22 +302,28 @@ impl channel_server::Server for ChannelServerImpl {
let params = pry!(params.get());
let label = pry!(pry!(params.get_label()).to_str()).to_string();
// Already registered — nothing to do.
if self.state.borrow().has_pane(&label) {
// Check if already open
if self.state.borrow().get_pane(&label).is_some() {
return std::future::ready(Ok(()));
}
info!("opening channel tmux.{}", label);
// Find the tmux pane by name (window or pane title)
let pane_id = match find_pane_by_name(&label) {
Some(id) => id,
None => return std::future::ready(Err(capnp::Error::failed(
format!("no tmux pane named '{}'", label)))),
};
// Register the label and persist. The pane id is not stored —
// the reader resolves it by label on every connect attempt, so
// this succeeds even if the pane does not exist yet; the reader
// connects once it appears.
self.state.borrow_mut().add_pane(label.clone());
info!("opening channel tmux.{} (pane {})", label, pane_id);
// Register in state and persist
self.state.borrow_mut().add_pane(label.clone(), pane_id.clone());
// Start pipe-pane reader
let pane = PaneConfig { label, pane_id };
let reader_state = self.state.clone();
tokio::task::spawn_local(async move {
pipe_pane_reader(reader_state, label).await;
pipe_pane_reader(reader_state, pane).await;
});
std::future::ready(Ok(()))
@ -359,18 +339,14 @@ impl channel_server::Server for ChannelServerImpl {
let label = channel.strip_prefix("tmux.").unwrap_or(&channel).to_string();
let mut s = self.state.borrow_mut();
if s.remove_pane(&label) {
if let Some(pane_id) = s.remove_pane(&label) {
info!("closing channel tmux.{}", label);
s.channel_logs.remove(&format!("tmux.{}", label));
// Stop piping if the pane is still around (if it is gone the
// pipe is already dead). The reader then sees the pane
// unregistered and exits.
if let Some(pane_id) = find_pane_by_name(&label) {
let _ = std::process::Command::new("tmux")
.args(["pipe-pane", "-t", &pane_id])
.output();
}
// Disconnect pipe-pane
let _ = std::process::Command::new("tmux")
.args(["pipe-pane", "-t", &pane_id])
.output();
}
std::future::ready(Ok(()))
@ -421,13 +397,11 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
tokio::task::LocalSet::new()
.run_until(async move {
// Start a pipe-pane reader for each configured pane; each
// resolves its live pane id by label and retries until
// connected.
// Start a pipe-pane reader for each configured pane
for pane in state.borrow().config.panes.clone() {
let reader_state = state.clone();
tokio::task::spawn_local(async move {
pipe_pane_reader(reader_state, pane.label).await;
pipe_pane_reader(reader_state, pane).await;
});
}

27
flake.lock generated
View file

@ -1,27 +0,0 @@
{
"nodes": {
"nixpkgs": {
"locked": {
"lastModified": 1781074563,
"narHash": "sha256-md8WlXOlfnIeHeOScMTTHFyf2d6iaTwPl2apR5EQ3P4=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "9ae611a455b90cf061d8f332b977e387bda8e1ca",
"type": "github"
},
"original": {
"owner": "NixOS",
"ref": "nixos-unstable",
"repo": "nixpkgs",
"type": "github"
}
},
"root": {
"inputs": {
"nixpkgs": "nixpkgs"
}
}
},
"root": "root",
"version": 7
}

42
flake.nix
View file

@ -1,42 +0,0 @@
{
description = "Development shell for consciousness";
inputs = {
nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
};
outputs = { nixpkgs, ... }:
let
systems = [
"x86_64-linux"
"aarch64-linux"
];
forAllSystems = nixpkgs.lib.genAttrs systems;
in
{
devShells = forAllSystems (system:
let
pkgs = import nixpkgs { inherit system; };
in
{
default = pkgs.mkShell {
packages = with pkgs; [
cargo
rustc
rustfmt
clippy
rust-analyzer
capnproto
pkg-config
jq
sqlite
python3
];
RUST_BACKTRACE = "1";
};
});
};
}

276
proto/salience.proto
View file

@ -1,276 +0,0 @@
// salience.proto — stateful generation + per-token concept readout over gRPC.
//
// Shape:
// - One server-streaming RPC (Generate) for inference. Every other
// operation is unary. This is the minimum streaming we need —
// tokens arrive one at a time with optional readouts / logprobs —
// and keeping everything else unary makes the client dramatically
// simpler than a single bidi state machine did.
//
// - Server-side sessions hold the token list and image binaries.
// Sessions exist for bandwidth: at 200K tokens we'd otherwise
// re-ship ~800KB every turn, which hurts badly over a WAN link.
// vLLM's prefix cache holds the KV; the session just gives the
// client a handle so it can send deltas.
//
// - The client is the source of truth for prompt content. The server
// is the source of truth for image token expansion (how many
// IMAGE_PAD tokens an image becomes under this model). The client
// never writes vision tokens itself — AppendImage appends the whole
// <|vision_start|> + IMAGE_PAD×N + <|vision_end|> block server-side.
//
// - Every mutation carries (offset, truncating): the client's view of
// the server's current length, plus whether the client is deliberately
// rewriting history. Server validates on each call and rejects drift.
// No silent divergence, no migration bugs.
//
// - Errors use gRPC status codes. NOT_FOUND for missing sessions,
// FAILED_PRECONDITION for offset drift or image-block splits,
// RESOURCE_EXHAUSTED for context overflow, ABORTED for "session busy".
//
// Not in v1:
// - Authentication beyond a shared bearer token in gRPC metadata.
// - Multi-tenant session namespacing.
// - Sampling traces beyond top-k logprobs.
syntax = "proto3";
package salience.v1;
// ============================================================
// Service
// ============================================================
service Salience {
// Create a fresh session. Client uses session_id on every subsequent
// RPC until CloseSession or TTL eviction (default 30 min idle). To
// refresh TTL across a long pause, issue a no-op Generate (empty
// append_tokens, max_tokens=0, no ranges).
rpc OpenSession(OpenSessionRequest) returns (OpenSessionResponse);
// Release the session's tokens + images. Idempotent.
rpc CloseSession(CloseSessionRequest) returns (CloseSessionResponse);
// Branch a session at a given token position. The new session
// inherits tokens [0, at_position) and any images whose vision
// block lies fully in that range. Rejected with FAILED_PRECONDITION
// if at_position falls inside an image block (client picks a clean
// boundary).
rpc ForkSession(ForkSessionRequest) returns (ForkSessionResponse);
// Prefill + optionally decode. Images are attached inline via
// `GenerateRequest.images`; the client writes its own pre-expanded
// <|vision_start|> + N*<|image_pad|> + <|vision_end|> runs into
// `append_tokens` and declares each run's range in `images[i]`.
// Server validates run length against the actual vision-encoder
// feature count and returns INVALID_ARGUMENT on mismatch. Stream
// yields Token events (with optional readouts / logprobs per
// position) followed by a terminating Done.
rpc Generate(GenerateRequest) returns (stream GenerateEvent);
// Readout manifest for the currently-loaded model — concept names,
// layer indices, tensor dtype. Stateless; fetch once at client
// startup and cache.
rpc GetReadoutManifest(GetReadoutManifestRequest) returns (ReadoutManifest);
// Dump the full token stream of a session. Debug-only: used by the
// client to verify its local accounting against the server's
// session.tokens byte-for-byte when divergence is suspected. Not
// cheap — copies the whole sequence across the wire.
rpc DumpSession(DumpSessionRequest) returns (DumpSessionResponse);
}
// ============================================================
// Lifecycle
// ============================================================
message OpenSessionRequest {
// Model identifier, must match vLLM's served model. The server
// only has one model loaded; this is a safety check on what the
// client thinks it's talking to.
string model = 1;
}
message OpenSessionResponse {
string session_id = 1;
uint32 max_model_len = 2;
}
message CloseSessionRequest {
string session_id = 1;
}
message CloseSessionResponse {}
message ForkSessionRequest {
string session_id = 1; // source session
uint32 at_position = 2; // new session inherits tokens [0, at_position)
}
message ForkSessionResponse {
string session_id = 1; // new session
}
// ============================================================
// Inference
// ============================================================
// One image attached to a Generate call. The client is responsible
// for writing the expanded placeholder run (VISION_START +
// N*IMAGE_PAD + VISION_END) into `GenerateRequest.append_tokens` at
// positions [pad_range_start, pad_range_end) and pairing it with
// the corresponding `ImageAttachment` entry. Server validates that
// the declared range's pad count matches what the vision encoder
// produces, and returns INVALID_ARGUMENT if they disagree.
message ImageAttachment {
// Image bytes (PNG / JPEG / WebP / …).
bytes bytes = 1;
// MIME type, e.g. "image/png".
string mime = 2;
// Absolute token positions (in `session.tokens` AFTER `append_tokens`
// is applied) spanning the full vision block — `[vision_start,
// pad*N, vision_end]`. end is exclusive, so end - start == N + 2.
uint32 pad_range_start = 3;
uint32 pad_range_end = 4;
}
message GenerateRequest {
string session_id = 1;
// Tokens to append before prefill. May be empty. Client writes the
// full vision block (VISION_START + N*IMAGE_PAD + VISION_END) for
// any newly-attached image directly into this stream; each such
// block must be paired with a matching entry in `images`. The
// server validates that the declared ranges all point at IMAGE_PAD
// runs and that each run's length matches what the vision encoder
// produces for the corresponding image.
repeated uint32 append_tokens = 2;
// Client's view of session.tokens length at the time of the call.
// Must equal server's actual length, OR be strictly less when
// truncating=true (server rewinds before appending). Any other
// mismatch is FAILED_PRECONDITION.
uint32 offset = 3;
bool truncating = 4;
// Decode budget. 0 = prefill only (no decode, emit Token events
// for positions covered by logprobs_ranges / readout_ranges, then
// Done; replaces the old /score endpoint). >0 = decode up to this
// many tokens, stopping early on EOS / stop_token_ids.
uint32 max_tokens = 5;
// Position ranges (absolute, within the session's post-append
// token list) at which to emit logprobs on Token events. Empty =
// no logprobs. `logprob_top_k > 0` returns the top-k alternative
// tokens at each covered position; `logprob_top_k == 0` returns
// only the sampled-token's logprob.
repeated PositionRange logprobs_ranges = 6;
uint32 logprob_top_k = 7;
// Position ranges at which to emit concept-readout vectors. Empty
// = no readouts. Logical shape per position is
// [n_layers][n_concepts] — see GetReadoutManifest.
repeated PositionRange readout_ranges = 8;
// Sampling parameters. Meaningful only when max_tokens > 0.
float temperature = 9; // default 1.0 when zero
float top_p = 10; // default 1.0 when zero
uint32 top_k = 11; // default 0 (disabled)
repeated uint32 stop_token_ids = 12;
// vLLM scheduler priority (0 = interactive, 10 = batch).
int32 priority = 13;
// Images newly attached on this call. Each entry describes one
// image's binary bytes, its mime type, and the exact token-position
// range of its pre-expanded placeholder run inside `session.tokens`
// after `append_tokens` is applied. See `ImageAttachment`.
repeated ImageAttachment images = 14;
}
message PositionRange {
uint32 start = 1; // inclusive
uint32 end = 2; // exclusive
}
message GenerateEvent {
oneof event {
Token token = 1;
GenerateDone done = 2;
}
}
message Token {
// Token id at this position. For prefill this is the prompt token;
// for decode it's the sampled token.
uint32 id = 1;
// Absolute position in the session's token list.
uint32 position = 2;
// True for prefill positions, false for decode.
bool is_prefill = 3;
// Concept readout at this position. Empty if the position wasn't
// covered by readout_ranges.
repeated float readout = 4 [packed = true];
// Top-k alternative tokens' logprobs at this position — populated
// when the position is covered by logprobs_ranges and
// logprob_top_k > 0.
repeated TokenLogprob logprobs = 5;
// Logprob of the token at `position` (the prompt token for
// prefill, the sampled token for decode). Populated when the
// position is covered by logprobs_ranges.
float sampled_logprob = 6;
bool has_sampled_logprob = 7;
}
message TokenLogprob {
uint32 id = 1;
float logprob = 2;
}
message GenerateDone {
uint32 prompt_tokens = 1;
uint32 completion_tokens = 2;
uint32 total_tokens = 3;
enum FinishReason {
FINISH_REASON_UNSPECIFIED = 0;
FINISH_REASON_EOS = 1; // emitted EOS / stop token
FINISH_REASON_LENGTH = 2; // hit max_tokens
FINISH_REASON_CANCELLED = 3; // client cancelled
FINISH_REASON_STOP_STRING = 4; // matched a stop string
}
FinishReason finish_reason = 4;
}
// ============================================================
// Readout manifest
// ============================================================
message GetReadoutManifestRequest {}
message ReadoutManifest {
repeated string concepts = 1;
repeated uint32 layers = 2;
uint32 hidden_size = 3;
string dtype = 4;
}
// ============================================================
// Debug
// ============================================================
message DumpSessionRequest {
string session_id = 1;
}
message DumpSessionResponse {
// The full session.tokens sequence, verbatim.
repeated uint32 tokens = 1 [packed = true];
}

327
scripts/quantize_qwen3_6_mm.py
View file

@ -1,327 +0,0 @@
"""Quantize Qwen3.6-27B (multimodal) to FP8 for vLLM serving.
Why this exists
---------------
The earlier `quantize_qwen3_6.py` (in shell history, never committed)
loaded the model with `AutoModelForCausalLM`, which silently strips
the multimodal arch. Result: an FP8 checkpoint with no vision tower
weights at all. vLLM happily instantiated the vision tower from the
config and ran it with default/uninitialized weights, producing
gibberish image features and `!!!!!!`-style output. We chased that
through the protocol layer for a long time before tracing it back
to the quant. This script avoids that trap by loading via the
config-declared class explicitly.
Recipe
------
FP8_DYNAMIC (per-channel weight scales, per-token dynamic activation
scales, both E4M3) for Linear weights, with an `ignore` list derived
from Unsloth's UD-Q8_K_XL (`unsloth/Qwen3.6-27B-GGUF`). Their
sensitivity sweep flagged specific layers as quantization-fragile;
we honor those layer indices even though their algorithm is
GGUF-native Q8_K and ours is FP8 — sensitivity is a layer property,
not an algorithm property.
vLLM fusion constraint
~~~~~~~~~~~~~~~~~~~~~~
vLLM's Qwen3.5/3.6 model code fuses sub-modules at load time:
qkv_proj ← q_proj, k_proj, v_proj
gate_up_proj ← gate_proj, up_proj
in_proj_qkvz ← in_proj_qkv, in_proj_z
in_proj_ba ← in_proj_b, in_proj_a
compressed_tensors rejects checkpoints where sub-modules of a fused
layer have different quantization schemes. Our ignore list is shaped
around this — within any fused layer, all components share a scheme.
That's the reason `in_proj_qkv` is ignored even though Unsloth's
sweep doesn't single it out, and the reason late-stack attn override
covers q/k/v rather than just q/k.
MTP merge
---------
`Qwen3_5ForConditionalGeneration` doesn't expose the MTP submodule,
so `oneshot()` produces a checkpoint with the 15 `mtp.*` tensors
silently dropped. After quantization we read the MTP weights back
out of the upstream cached snapshot and splice them into the saved
safetensors at BF16. They're small (~850 MB) so quantizing them
isn't worth the calibration risk; speculative-decoding code paths
in vLLM expect the MTP head present.
Output
------
`OUTPUT_DIR` gets the FP8 model.safetensors + config + processor +
recipe.yaml. Vision tower stays BF16 (in `ignore`); LM Linears go
to FP8; norms, SSM internals (not Linear), and MTP tensors stay
BF16 untouched.
Verification at end: re-opens the saved safetensors and asserts
- vision .weight tensors present (>= 150; full count is 167)
- lm_head + embed_tokens at fp16/bf16 (NOT FP8)
- a sampled FP8'd Linear actually has float8 dtype
- 15 mtp.* tensors present
Run
---
~/vllm-venv/bin/python quantize_qwen3_6_mm.py
"""
from __future__ import annotations
import glob
import json
import sys
from pathlib import Path
import torch
from huggingface_hub import snapshot_download
from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier
from safetensors import safe_open
from safetensors.torch import save_file
from transformers import AutoProcessor
from transformers.models.qwen3_5.modeling_qwen3_5 import (
Qwen3_5ForConditionalGeneration,
)
MODEL = "Qwen/Qwen3.6-27B"
OUTPUT_DIR = "/home/ubuntu/amygdala-training/Qwen3.6-27B-FP8-mm"
# Layers Unsloth's UD-Q8_K_XL keeps at F16 (perplexity-sensitive
# in their sweep). Late-stack clustering is consistent with the
# general finding that errors near the output propagate directly
# to logits.
LATE_FFN_LAYERS = (50, 51, 59, 62, 63)
LATE_ATTN_LAYERS = (51, 59, 63)
# Build the ignore regex list. Note: llmcompressor matches these
# patterns against MODULE names (no `.weight` suffix) when walking
# `named_modules()` for `targets=["Linear"]`. The first pass of
# this script used `\.weight$` patterns and silently quantized
# lm_head + every linear_attn projection — verified post-hoc by
# inspecting the saved safetensors. Patterns now anchor on `$`
# at the module name.
IGNORE_PATTERNS: list[str] = [
# Original recipe: lm_head and embeddings always full-precision.
# (embed_tokens is an Embedding, not a Linear, so it's already
# ignored by `targets=["Linear"]`. Pattern kept as belt-and-
# suspenders in case future llmcompressor versions widen the
# target set.)
"re:lm_head$",
"re:.*embed_tokens$",
# Vision tower — entire `model.visual.*` subtree (vision
# transformer blocks + merger + patch_embed + pos_embed).
# Unsloth ships the vision tower as a separate `mmproj-BF16.gguf`
# for GGUF consumers; in our single-file FP8 setup we just leave
# them at BF16.
"re:model\\.visual\\..*",
# MTP (multi-token prediction) module — Unsloth's GGUF doesn't
# carry MTP weights so we have no precision signal from them;
# safest to keep BF16.
"re:mtp\\..*",
# Linear-attention block — keep ENTIRELY at BF16. vLLM fuses
# `in_proj_qkv` and `in_proj_z` into a single `in_proj_qkvz`
# layer, and compressed_tensors rejects mixed schemes within a
# fused layer. Unsloth's recipe keeps z, a, b, out at F16/F32
# (gate/SSM internals are quantization-fragile in the GatedDeltaNet
# update), so the principled choice is to also keep `in_proj_qkv`
# at BF16 rather than FP8'ing the gate to match. We give up ~1 GB
# of FP8 coverage; in exchange we follow Unsloth's quality intent
# and load cleanly under vLLM. (`in_proj_a` + `in_proj_b` are
# likewise fused as `in_proj_ba` — both ignored, consistent.)
"re:model\\.language_model\\.layers\\.\\d+\\.linear_attn\\.in_proj_qkv$",
"re:model\\.language_model\\.layers\\.\\d+\\.linear_attn\\.in_proj_z$",
"re:model\\.language_model\\.layers\\.\\d+\\.linear_attn\\.in_proj_a$",
"re:model\\.language_model\\.layers\\.\\d+\\.linear_attn\\.in_proj_b$",
"re:model\\.language_model\\.layers\\.\\d+\\.linear_attn\\.out_proj$",
# Per-layer high-precision MLP (Unsloth flagged exactly these
# late-stack indices in their UD-Q8_K_XL sensitivity sweep, all
# three of {gate, up, down} per layer). vLLM fuses gate+up into
# `gate_up_proj`; ignoring both keeps the fused layer consistent.
# `down_proj` is its own (non-fused) layer.
"re:model\\.language_model\\.layers\\.("
+ "|".join(str(n) for n in LATE_FFN_LAYERS)
+ ")\\.mlp\\.(down|gate|up)_proj$",
# Per-layer high-precision attention q/k/v (Unsloth's sweep upgrades
# only q and k; we extend to v because vLLM fuses q/k/v into
# `qkv_proj` and rejects mixed schemes. `o_proj` is its own
# non-fused layer and stays at FP8.
"re:model\\.language_model\\.layers\\.("
+ "|".join(str(n) for n in LATE_ATTN_LAYERS)
+ ")\\.self_attn\\.(q|k|v)_proj$",
]
def main() -> None:
print(f"Loading {MODEL} as multimodal "
f"(Qwen3_5ForConditionalGeneration)...", flush=True)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
MODEL,
dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
print(f" loaded: {model.__class__.__name__}", flush=True)
print(f"Loading processor (text + image preprocessing)...", flush=True)
processor = AutoProcessor.from_pretrained(MODEL, trust_remote_code=True)
print("Running FP8_DYNAMIC oneshot quantization...", flush=True)
print(f" ignore list: {len(IGNORE_PATTERNS)} patterns",
flush=True)
recipe = QuantizationModifier(
targets=["Linear"],
scheme="FP8_DYNAMIC",
ignore=IGNORE_PATTERNS,
)
oneshot(model=model, recipe=recipe, output_dir=OUTPUT_DIR)
processor.save_pretrained(OUTPUT_DIR)
print(f" wrote model + processor to {OUTPUT_DIR}", flush=True)
merge_mtp(OUTPUT_DIR)
verify_output(OUTPUT_DIR)
def merge_mtp(out_dir: str) -> None:
"""Splice upstream MTP tensors into the saved FP8 safetensors.
`Qwen3_5ForConditionalGeneration` skips the MTP submodule on load,
so oneshot's output is missing the 15 `mtp.*` tensors. We resolve
the upstream snapshot via the HF cache (already populated by
from_pretrained), pull just the MTP tensors out at BF16, and
rewrite the safetensors with them merged in. The compressed_tensors
metadata header (which carries the FP8 format identifier vLLM
needs to dequantize) is preserved verbatim.
Atomic-rename is used so a crash mid-write doesn't corrupt the
33+ GB checkpoint we just spent minutes producing.
"""
print("\nMerging upstream MTP tensors...", flush=True)
upstream_dir = Path(snapshot_download(
MODEL,
allow_patterns=["model.safetensors.index.json",
"model-*-of-*.safetensors"],
))
with open(upstream_dir / "model.safetensors.index.json") as f:
idx = json.load(f)
mtp_shards = sorted({v for k, v in idx["weight_map"].items()
if k.startswith("mtp.")})
print(f" MTP tensors live in shards: {mtp_shards}", flush=True)
mtp_tensors: dict[str, torch.Tensor] = {}
for shard in mtp_shards:
with safe_open(upstream_dir / shard, framework="pt") as f:
for k in f.keys():
if k.startswith("mtp."):
mtp_tensors[k] = f.get_tensor(k).contiguous()
mtp_bytes = sum(t.numel() * t.element_size()
for t in mtp_tensors.values())
print(f" loaded {len(mtp_tensors)} mtp tensors "
f"({mtp_bytes/1e6:.1f} MB)", flush=True)
fp8_files = sorted(Path(out_dir).glob("*.safetensors"))
if len(fp8_files) != 1:
sys.exit(f"FAIL: expected single safetensors shard, "
f"got {fp8_files}")
existing_path = fp8_files[0]
with safe_open(existing_path, framework="pt") as f:
metadata = f.metadata() or {}
all_tensors = {k: f.get_tensor(k) for k in f.keys()}
overlap = set(all_tensors) & set(mtp_tensors)
if overlap:
sys.exit(f"FAIL: MTP key collision with FP8 output: "
f"{sorted(overlap)[:5]}")
all_tensors.update(mtp_tensors)
tmp_path = existing_path.with_name(existing_path.name + ".new")
print(f" rewriting {existing_path.name} "
f"({len(all_tensors)} tensors)...", flush=True)
save_file(all_tensors, str(tmp_path), metadata=metadata)
tmp_path.replace(existing_path)
print(" done", flush=True)
def verify_output(out_dir: str) -> None:
"""Open the saved safetensors and assert the recipe actually
landed: vision tower present at BF16, FP8 dtype on at least one
quantized Linear, lm_head not FP8."""
print(f"\nVerifying {out_dir}...", flush=True)
files = sorted(glob.glob(f"{out_dir}/*.safetensors"))
if not files:
sys.exit(f"FAIL: no safetensors in {out_dir}")
vision_keys: list[tuple[str, str]] = []
fp8_sample: tuple[str, str] | None = None
lm_head_dtype: str | None = None
mtp_keys: list[str] = []
for fp in files:
with safe_open(fp, framework="pt") as f:
for k in f.keys():
if k.startswith("mtp."):
mtp_keys.append(k)
# Some FP8 quants write a sibling `_scale` / `_zero_point`;
# we just care about the .weight tensors.
if not k.endswith(".weight"):
continue
t = f.get_tensor(k)
dtype = str(t.dtype).replace("torch.", "")
if "model.visual." in k:
vision_keys.append((k, dtype))
if k == "lm_head.weight":
lm_head_dtype = dtype
if (fp8_sample is None
and "float8" in dtype
and "language_model.layers" in k):
fp8_sample = (k, dtype)
# Qwen3.6-27B has 167 vision `.weight` tensors (333 vision tensors
# total, the rest are `.bias` and per-block norms). 150 is a
# sanity floor that catches "vision tower didn't make it through"
# without being brittle to minor arch revisions.
if len(vision_keys) < 150:
sys.exit(f"FAIL: only {len(vision_keys)} vision tensors found "
f"(expected >= 150). Vision tower didn't make it "
f"through the quant.")
bad_vision = [(k, d) for k, d in vision_keys if "float8" in d]
if bad_vision:
sys.exit(f"FAIL: vision weights got quantized to FP8: "
f"{bad_vision[:3]}...")
if lm_head_dtype is None:
sys.exit("FAIL: lm_head.weight not found in output.")
if "float8" in lm_head_dtype:
sys.exit(f"FAIL: lm_head.weight is FP8 ({lm_head_dtype}); "
f"should be BF16/FP16.")
if fp8_sample is None:
sys.exit("FAIL: no FP8 weights found in language_model.layers — "
"the recipe didn't quantize anything.")
# Upstream Qwen3.6-27B has exactly 15 mtp.* tensors (1 fused
# transformer block + projection + norms). merge_mtp() should
# have spliced all of them in.
if len(mtp_keys) != 15:
sys.exit(f"FAIL: expected 15 mtp.* tensors, found "
f"{len(mtp_keys)}. merge_mtp() missed some.")
print(f" ✓ {len(vision_keys)} vision tensors at "
f"{vision_keys[0][1]} (not FP8)")
print(f" ✓ lm_head.weight at {lm_head_dtype} (not FP8)")
print(f" ✓ FP8 sample: {fp8_sample[0]} = {fp8_sample[1]}")
print(f" ✓ {len(mtp_keys)} mtp.* tensors present")
print("DONE")
if __name__ == "__main__":
main()

77
src/agent/api/http.rs
View file

@ -100,7 +100,7 @@ impl HttpClient {
.map_err(|e| anyhow::anyhow!("invalid server name: {e}"))?;
let connector = tokio_rustls::TlsConnector::from(self.tls.clone());
let tls = connector.connect(server_name.to_owned(), tcp).await
.map_err(|e| anyhow::anyhow!("TLS handshake to {host}: {e}"))?;
.context("TLS handshake")?;
TokioIo::new(Box::new(tls) as Box<dyn IoStream>)
} else {
TokioIo::new(Box::new(tcp) as Box<dyn IoStream>)
@ -154,14 +154,6 @@ impl HttpResponse {
Ok(String::from_utf8_lossy(&bytes).into_owned())
}
/// Read the entire body as raw bytes (for binary downloads).
pub async fn bytes(self) -> Result<Bytes> {
let bytes = self.body.collect().await
.context("reading response body")?
.to_bytes();
Ok(bytes)
}
/// Read the entire body and deserialize as JSON.
pub async fn json<T: serde::de::DeserializeOwned>(self) -> Result<T> {
let bytes = self.body.collect().await
@ -198,7 +190,6 @@ impl HttpClientBuilder {
}
pub fn build(self) -> HttpClient {
install_rustls_crypto_provider();
let certs = rustls_native_certs::load_native_certs()
.certs.into_iter()
.collect::<Vec<_>>();
@ -206,13 +197,6 @@ impl HttpClientBuilder {
for cert in certs {
root_store.add(cert).ok();
}
// Also trust any `.pem` files under `~/.consciousness/certs/` —
// self-signed server certs for our own vllm hosts live there.
// Drop a new `<host>.pem` in the dir to trust a new server; no
// code change needed.
for cert in load_user_certs() {
root_store.add(cert).ok();
}
let tls = Arc::new(
ClientConfig::builder()
.with_root_certificates(root_store)
@ -226,65 +210,6 @@ impl HttpClientBuilder {
}
}
/// Install rustls' default crypto provider exactly once per process.
/// rustls 0.23 doesn't pick one automatically when multiple features
/// could provide it (e.g. when tonic pulls in both ring and aws-lc-rs
/// via transitive deps). Idempotent via OnceLock; safe to call from
/// multiple callers.
fn install_rustls_crypto_provider() {
static ONCE: std::sync::OnceLock<()> = std::sync::OnceLock::new();
ONCE.get_or_init(|| {
let _ = rustls::crypto::ring::default_provider().install_default();
});
}
/// Load every `.pem` file under `~/.consciousness/certs/` as a DER
/// certificate and return them. Silent on missing dir, missing files,
/// or parse errors — those are "no extra certs trusted" rather than
/// hard failures, to keep startup robust.
/// Load the concatenated PEM bytes of every `.pem` file under
/// `~/.consciousness/certs/` — suitable for passing to a tonic
/// `ClientTlsConfig::ca_certificate(Certificate::from_pem(...))` call
/// so gRPC connections trust the same self-signed servers the HTTP
/// path does.
pub(crate) fn load_user_certs_pem_bytes() -> Vec<u8> {
let mut out = Vec::new();
let Some(home) = dirs::home_dir() else { return out };
let dir = home.join(".consciousness").join("certs");
let Ok(entries) = std::fs::read_dir(&dir) else { return out };
for entry in entries.flatten() {
let path = entry.path();
if path.extension().and_then(|e| e.to_str()) != Some("pem") {
continue;
}
if let Ok(bytes) = std::fs::read(&path) {
out.extend_from_slice(&bytes);
if !bytes.ends_with(b"\n") {
out.push(b'\n');
}
}
}
out
}
fn load_user_certs() -> Vec<rustls::pki_types::CertificateDer<'static>> {
let mut out = Vec::new();
let Some(home) = dirs::home_dir() else { return out };
let dir = home.join(".consciousness").join("certs");
let Ok(entries) = std::fs::read_dir(&dir) else { return out };
for entry in entries.flatten() {
let path = entry.path();
if path.extension().and_then(|e| e.to_str()) != Some("pem") {
continue;
}
let Ok(bytes) = std::fs::read(&path) else { continue };
for cert in rustls_pemfile::certs(&mut bytes.as_slice()).flatten() {
out.push(cert);
}
}
out
}
/// Trait alias for streams that work with hyper's IO adapter.
trait IoStream: tokio::io::AsyncRead + tokio::io::AsyncWrite + Send + Unpin + 'static {}
impl<T: tokio::io::AsyncRead + tokio::io::AsyncWrite + Send + Unpin + 'static> IoStream for T {}

650
src/agent/api/mod.rs
View file

@ -7,14 +7,13 @@
// Set POC_DEBUG=1 for verbose per-turn logging.
pub mod http;
pub mod salience;
use std::time::Duration;
use std::time::{Duration, Instant};
use anyhow::Result;
use tokio::sync::mpsc;
use serde::Deserialize;
use http::HttpClient;
use http::{HttpClient, HttpResponse};
#[derive(Debug, Clone, Deserialize)]
pub struct Usage {
@ -23,36 +22,6 @@ pub struct Usage {
pub total_tokens: u32,
}
/// Concept-readout manifest returned by the vLLM server's
/// `/v1/readout/manifest` endpoint. Maps the nameless tensor indices
/// in streaming `readout` fields back to concept names and layer
/// indices.
#[derive(Debug, Clone, Deserialize)]
pub struct ReadoutManifest {
pub concepts: Vec<String>,
pub layers: Vec<u32>,
}
/// Per-token per-layer concept projections streamed alongside each
/// sampled token. Shape `[n_layers][n_concepts]`. Named values come
/// from pairing with the manifest fetched at startup.
pub type TokenReadout = Vec<Vec<f32>>;
/// Client-side sampling state. Mirrors the wire-level fields in
/// `GenerateRequest` (proto flattened its `SamplingParams` submessage
/// in so the server handler reads them directly), but stays as a
/// grouped struct on the client because UI / config / tests pass
/// these around together.
#[derive(Clone, Copy)]
pub struct SamplingParams {
pub temperature: f32,
pub top_p: f32,
pub top_k: u32,
/// Decode budget. 0 = prefill only; >0 = decode up to this many
/// tokens, stopping early on EOS / stop_token_ids.
pub max_tokens: u32,
}
/// A JoinHandle that aborts its task when dropped.
pub(crate) struct AbortOnDrop(tokio::task::JoinHandle<()>);
@ -62,6 +31,13 @@ impl Drop for AbortOnDrop {
}
}
/// Sampling parameters for model generation.
#[derive(Clone, Copy)]
pub(crate) struct SamplingParams {
pub temperature: f32,
pub top_p: f32,
pub top_k: u32,
}
// ─────────────────────────────────────────────────────────────
// Stream events — yielded by backends, consumed by the runner
@ -69,10 +45,7 @@ impl Drop for AbortOnDrop {
/// One token from the streaming completions API.
pub enum StreamToken {
/// A sampled token, optionally with its per-layer concept readout.
/// `readout` is `None` when the server has readout disabled or
/// returned no readout for this chunk.
Token { id: u32, readout: Option<TokenReadout> },
Token(u32),
Done { usage: Option<Usage> },
Error(String),
}
@ -83,17 +56,6 @@ pub struct ApiClient {
api_key: String,
pub model: String,
base_url: String,
/// Cached readout manifest — fetched once per process and shared
/// across ApiClient clones (every Agent/fork gets the same cell).
/// `None` after fetch means the server has readout disabled (404).
manifest: std::sync::Arc<tokio::sync::OnceCell<Option<ReadoutManifest>>>,
/// Shared tonic Channel to the salience gRPC endpoint. Opened on
/// first use and reused across every SessionHandle / RPC call
/// derived from this ApiClient. tonic multiplexes concurrent
/// requests over the HTTP/2 connection automatically.
salience_channel: std::sync::Arc<
tokio::sync::OnceCell<tonic::transport::Channel>
>,
}
impl ApiClient {
@ -108,69 +70,29 @@ impl ApiClient {
api_key: api_key.to_string(),
model: model.to_string(),
base_url: base_url.trim_end_matches('/').to_string(),
manifest: std::sync::Arc::new(tokio::sync::OnceCell::new()),
salience_channel: std::sync::Arc::new(tokio::sync::OnceCell::new()),
}
}
/// Return a `SalienceClient` on the shared gRPC channel — opens
/// the channel on first call and reuses it thereafter across
/// every ApiClient clone. All scoring / inference / session
/// RPCs flow through this single multiplexed HTTP/2 connection.
///
/// Bumps tonic's default 4 MiB encode/decode caps to 64 MiB on
/// every client. Multimodal Generate requests carry pre-encoded
/// image bytes inline (Qwen3.6's 768×768 patches at high res
/// land around 5–8 MiB per turn), and Done events with full
/// per-token readout vectors can also exceed 4 MiB on long runs.
pub async fn salience_client(&self) -> Result<
salience::pb::salience_client::SalienceClient<tonic::transport::Channel>
> {
let ch = self.salience_channel.get_or_try_init(|| async {
let grpc_url = salience::derive_grpc_url(&self.base_url);
log::debug!(target: "grpc",
"opening shared salience channel: http_base={} -> grpc_url={}",
self.base_url, grpc_url);
salience::connect_channel(&grpc_url).await
}).await?;
const MAX_GRPC_MESSAGE_BYTES: usize = 64 * 1024 * 1024;
Ok(salience::pb::salience_client::SalienceClient::new(ch.clone())
.max_decoding_message_size(MAX_GRPC_MESSAGE_BYTES)
.max_encoding_message_size(MAX_GRPC_MESSAGE_BYTES))
}
/// Stream generation via a gRPC session. Walks the prompt chunks
/// comparing against the session's `committed_len`, sends the
/// delta as interleaved `AppendImage` + intermediate
/// `Generate(max_tokens=0)` (for text runs separating images) +
/// a final `Generate(max_tokens=sampling.max_tokens, ...)` whose
/// Token events stream back through the channel.
///
/// On any gRPC error the session is dropped; the next call
/// reopens fresh. Happy-path ordering: Token* Done. Error paths
/// emit `StreamToken::Error` and close.
pub(crate) fn stream_session_mm(
pub(crate) fn stream_completion(
&self,
session_lock: std::sync::Arc<crate::Mutex<Option<salience::SessionHandle>>>,
chunks: Vec<super::context::WireChunk>,
images: Vec<super::context::WireImage>,
match_upto: u32,
prompt_tokens: &[u32],
sampling: SamplingParams,
priority: Option<i32>,
readout_shape: Option<(u32, u32)>,
) -> (mpsc::UnboundedReceiver<StreamToken>, AbortOnDrop) {
let (tx, rx) = mpsc::unbounded_channel();
let client = self.clone();
let client = self.client.clone();
let api_key = self.api_key.clone();
let model = self.model.clone();
let prompt_tokens = prompt_tokens.to_vec();
let base_url = self.base_url.clone();
let handle = tokio::spawn(async move {
let result = run_session_generate(
session_lock, &client, chunks, images, match_upto, sampling,
priority, readout_shape, &tx,
let result = stream_completions(
&client, &base_url, &api_key, &model,
&prompt_tokens, &tx, sampling, priority,
).await;
if let Err(e) = result {
log::warn!(target: "grpc",
"stream_session_mm error, forwarding to UI: {:#}", e);
let _ = tx.send(StreamToken::Error(format!("{:#}", e)));
let _ = tx.send(StreamToken::Error(e.to_string()));
}
});
@ -180,247 +102,327 @@ impl ApiClient {
pub fn base_url(&self) -> &str { &self.base_url }
pub fn api_key(&self) -> &str { &self.api_key }
/// Fetch `/v1/readout/manifest` — returns `Ok(Some(..))` if
/// readout is enabled on the server, `Ok(None)` on 404 (disabled),
/// or an error on any other failure.
///
/// First call performs the HTTP fetch; subsequent calls (including
/// across ApiClient clones sharing the same cell) return the
/// cached result. The manifest doesn't change during a server run.
pub fn model_str(&self) -> &str { &self.model }
pub async fn fetch_readout_manifest(&self) -> Result<Option<ReadoutManifest>> {
let manifest = self.manifest.get_or_try_init(|| async {
let url = format!("{}/readout/manifest", self.base_url);
let auth = format!("Bearer {}", self.api_key);
let response = self
.client
.get_with_headers(&url, &[("Authorization", &auth)])
.await
.map_err(|e| anyhow::anyhow!("readout manifest fetch ({}): {}", url, e))?;
let status = response.status();
if status.as_u16() == 404 {
return Ok::<_, anyhow::Error>(None);
}
if !status.is_success() {
let body = response.text().await.unwrap_or_default();
let n = body.floor_char_boundary(body.len().min(500));
anyhow::bail!("readout manifest HTTP {} ({}): {}", status, url, &body[..n]);
}
Ok(Some(response.json().await?))
}).await?;
Ok(manifest.clone())
}
}
/// Body of the gRPC-path streaming task. Walks the wire chunks
/// against the session's `committed_len`, sends the delta via
/// AppendImage / intermediate prefill-only Generates / final decode
/// Generate, and translates the final Generate's Token events into
/// StreamTokens on `tx`. On success the session handle is returned
/// to `session_lock` with an updated `committed_len`; on error the
/// handle is dropped so the next call reopens.
async fn run_session_generate(
session_lock: std::sync::Arc<crate::Mutex<Option<salience::SessionHandle>>>,
client: &ApiClient,
chunks: Vec<super::context::WireChunk>,
images: Vec<super::context::WireImage>,
match_upto: u32,
async fn stream_completions(
client: &HttpClient,
base_url: &str,
api_key: &str,
model: &str,
prompt_tokens: &[u32],
tx: &mpsc::UnboundedSender<StreamToken>,
sampling: SamplingParams,
priority: Option<i32>,
readout_shape: Option<(u32, u32)>,
tx: &mpsc::UnboundedSender<StreamToken>,
) -> Result<()> {
use std::time::Instant;
use futures::StreamExt;
use super::context::WireChunk;
use salience::pb;
let mut handle: salience::SessionHandle = {
let mut guard = session_lock.lock().await;
match guard.take() {
Some(h) => h,
None => {
drop(guard);
log::debug!(target: "grpc", "run_session_generate: opening new session");
salience::SessionHandle::open(client).await?
}
}
};
// If the client believes the match extends only up to `match_upto`
// but the server has more, we need to rewind. For v1 the match is
// either whole or broken — `match_upto` is always 0 on any mutation
// — so the cheapest correct recovery is to drop the session and
// open a fresh one.
if match_upto < handle.committed_len {
log::warn!(target: "grpc",
"session rewind: match_upto={} < committed_len={} — reopening session (resending {} bytes)",
match_upto, handle.committed_len, handle.committed_len - match_upto);
drop(handle);
handle = salience::SessionHandle::open(client).await?;
) -> anyhow::Result<()> {
let mut request = serde_json::json!({
"model": model,
"prompt": prompt_tokens,
"max_tokens": 16384,
"temperature": sampling.temperature,
"top_p": sampling.top_p,
"top_k": sampling.top_k,
"stream": true,
"return_token_ids": true,
"skip_special_tokens": false,
"stop_token_ids": [super::tokenizer::IM_END],
});
if let Some(p) = priority {
request["priority"] = serde_json::json!(p);
}
// Walk chunks at byte-level, taking everything past `match_upto`
// as the delta. Token chunks can be split mid-way; images live
// inline in the token stream, so there's no separate image-chunk
// case anymore.
let mut acc: u32 = 0;
let mut pending: Vec<u32> = Vec::new();
for chunk in chunks.iter() {
match chunk {
WireChunk::Tokens(t) => {
let len = t.len() as u32;
let chunk_end = acc + len;
if chunk_end <= match_upto {
acc = chunk_end;
} else if acc < match_upto {
let skip = (match_upto - acc) as usize;
pending.extend_from_slice(&t[skip..]);
acc = chunk_end;
} else {
pending.extend_from_slice(t);
acc = chunk_end;
}
let url = format!("{}/completions", base_url);
let debug_label = format!("{} prompt tokens, model={}", prompt_tokens.len(), model);
let mut response = send_and_check(
client, &url, &request,
("Authorization", &format!("Bearer {}", api_key)),
&[], &debug_label, None,
).await?;
let mut reader = SseReader::new();
let mut usage = None;
while let Some(event) = reader.next_event(&mut response).await? {
if let Some(err_msg) = event["error"]["message"].as_str() {
anyhow::bail!("API error in stream: {}", err_msg);
}
if let Some(u) = event["usage"].as_object() {
if let Ok(u) = serde_json::from_value::<Usage>(serde_json::Value::Object(u.clone())) {
usage = Some(u);
}
}
}
// Filter images to those entirely past `match_upto` — anything
// before is on the server already (prior turn), anything
// straddling is a hard divergence (image partially-sent shouldn't
// happen with our atomic AppendImage history; with images-inline
// it can only happen if mark_dirty cleared match_upto mid-block,
// which the AST mutators prevent).
let mut new_images: Vec<pb::ImageAttachment> = Vec::new();
for img in &images {
if img.pad_end <= match_upto {
continue; // already sent on a prior turn
}
if img.pad_start < match_upto {
anyhow::bail!(
"session divergence: image at [{},{}) straddles match_upto={}",
img.pad_start, img.pad_end, match_upto,
);
}
new_images.push(pb::ImageAttachment {
bytes: img.bytes.clone(),
mime: img.mime.clone(),
pad_range_start: img.pad_start,
pad_range_end: img.pad_end,
});
}
// Final Generate: pending holds any trailing text; decode up to
// sampling.max_tokens. Request readouts on all decode positions
// via a catch-all range ending at u32::MAX — decode never
// reaches it.
let prompt_len_after_append = handle.committed_len + pending.len() as u32;
let readout_ranges = if readout_shape.is_some() {
vec![pb::PositionRange {
start: prompt_len_after_append,
end: u32::MAX,
}]
} else {
Vec::new()
};
let req = pb::GenerateRequest {
session_id: handle.session_id.clone(),
append_tokens: pending,
offset: handle.committed_len,
truncating: false,
max_tokens: sampling.max_tokens,
logprobs_ranges: Vec::new(),
logprob_top_k: 0,
readout_ranges,
temperature: sampling.temperature,
top_p: sampling.top_p,
top_k: sampling.top_k,
stop_token_ids: Vec::new(),
priority: priority.unwrap_or(0),
images: new_images,
};
let session_id_for_log = handle.session_id.clone();
let t_generate = Instant::now();
log::debug!(target: "grpc",
"session {} Generate: offset={} append={} max_tokens={} priority={}",
session_id_for_log, req.offset, req.append_tokens.len(),
req.max_tokens, req.priority);
let mut stream = handle.generate(req).await?;
let (n_layers, n_concepts) = readout_shape.unwrap_or((0, 0));
let mut session_terminated = false;
let mut first_token_at: Option<Instant> = None;
while let Some(event) = stream.next().await {
let event = match event {
Ok(e) => e,
Err(status) => {
log::warn!(target: "grpc",
"session {} Generate stream error: {} — dropping session",
session_id_for_log, status);
session_terminated = true;
let _ = tx.send(StreamToken::Error(format!(
"Generate stream error: {}", status,
)));
break;
}
let choices = match event["choices"].as_array() {
Some(c) => c,
None => continue,
};
let Some(inner) = event.event else { continue };
match inner {
pb::generate_event::Event::Token(t) => {
if t.is_prefill { continue; }
if first_token_at.is_none() {
log::debug!(target: "grpc",
"session {} first decode token at {:?}",
session_id_for_log, t_generate.elapsed());
first_token_at = Some(Instant::now());
}
let readout = if t.readout.is_empty() {
None
} else if n_layers == 0 || n_concepts == 0 {
None
} else {
let expected = (n_layers as usize) * (n_concepts as usize);
if t.readout.len() != expected {
log::warn!(target: "grpc",
"readout shape mismatch: expected {}*{}={}, got {}",
n_layers, n_concepts, expected, t.readout.len());
None
} else {
let n = n_concepts as usize;
let mut layers: Vec<Vec<f32>> = Vec::with_capacity(n_layers as usize);
for l in 0..(n_layers as usize) {
layers.push(t.readout[l * n..(l + 1) * n].to_vec());
}
Some(layers)
for choice in choices {
if let Some(ids) = choice["token_ids"].as_array() {
for id_val in ids {
if let Some(id) = id_val.as_u64() {
let _ = tx.send(StreamToken::Token(id as u32));
}
}
} else if let Some(text) = choice["text"].as_str() {
// Fallback: provider didn't return token_ids, encode locally
if !text.is_empty() {
for id in super::tokenizer::encode(text) {
let _ = tx.send(StreamToken::Token(id));
}
};
if tx.send(StreamToken::Token { id: t.id, readout }).is_err() {
break;
}
}
pb::generate_event::Event::Done(d) => {
log::debug!(target: "grpc",
"session {} Done: prompt={} completion={} total={} reason={:?} elapsed={:?}",
session_id_for_log, d.prompt_tokens, d.completion_tokens,
d.total_tokens, d.finish_reason, t_generate.elapsed());
handle.committed_len = d.total_tokens;
let usage = Some(Usage {
prompt_tokens: d.prompt_tokens,
completion_tokens: d.completion_tokens,
total_tokens: d.total_tokens,
});
let _ = tx.send(StreamToken::Done { usage });
}
}
}
if !session_terminated {
let mut guard = session_lock.lock().await;
*guard = Some(handle);
}
let _ = tx.send(StreamToken::Done { usage });
Ok(())
}
/// Send an HTTP request and check for errors.
pub(crate) async fn send_and_check(
client: &HttpClient,
url: &str,
body: &impl serde::Serialize,
auth_header: (&str, &str),
extra_headers: &[(&str, &str)],
debug_label: &str,
request_json: Option<&str>,
) -> Result<HttpResponse> {
let debug = std::env::var("POC_DEBUG").is_ok();
let start = Instant::now();
if debug {
let payload_size = serde_json::to_string(body)
.map(|s| s.len())
.unwrap_or(0);
dbglog!(
"request: {}K payload, {}",
payload_size / 1024, debug_label,
);
}
let mut headers: Vec<(&str, &str)> = Vec::with_capacity(extra_headers.len() + 1);
headers.push(auth_header);
headers.extend_from_slice(extra_headers);
let response = client
.send_json("POST", url, &headers, body)
.await
.map_err(|e| {
let msg = e.to_string();
let cause = if msg.contains("connect timeout") || msg.contains("TCP connect") {
"connection refused"
} else if msg.contains("request timeout") {
"request timed out"
} else {
"request error"
};
anyhow::anyhow!("{} ({}): {}", cause, url, msg)
})?;
let status = response.status();
let elapsed = start.elapsed();
if debug {
for name in [
"x-ratelimit-remaining",
"x-ratelimit-limit",
"x-request-id",
] {
if let Some(val) = response.header(name) {
dbglog!("header {}: {}", name, val);
}
}
}
if !status.is_success() {
let body = response.text().await.unwrap_or_default();
dbglog!(
"HTTP {} after {:.1}s ({}): {}",
status,
elapsed.as_secs_f64(),
url,
&body[..body.floor_char_boundary(body.len().min(500))]
);
if let Some(json) = request_json {
let log_dir = dirs::home_dir()
.unwrap_or_default()
.join(".consciousness/logs/failed-requests");
let _ = std::fs::create_dir_all(&log_dir);
let ts = chrono::Local::now().format("%Y%m%dT%H%M%S");
let path = log_dir.join(format!("{}.json", ts));
if std::fs::write(&path, json).is_ok() {
dbglog!(
"saved failed request to {} (HTTP {})", path.display(), status
);
}
}
anyhow::bail!("HTTP {} ({}): {}", status, url, &body[..body.floor_char_boundary(body.len().min(1000))]);
}
if debug {
dbglog!(
"connected in {:.1}s (HTTP {})",
elapsed.as_secs_f64(),
status.as_u16()
);
}
Ok(response)
}
/// SSE stream reader. Handles the generic SSE plumbing shared by both
/// backends: chunk reading with timeout, line buffering, `data:` prefix
/// stripping, `[DONE]` detection, JSON parsing, and parse error diagnostics.
/// Yields parsed events as serde_json::Value — each backend handles its
/// own event types.
pub(crate) struct SseReader {
line_buf: String,
chunk_timeout: Duration,
pub stream_start: Instant,
pub chunks_received: u64,
pub sse_lines_parsed: u64,
pub sse_parse_errors: u64,
debug: bool,
done: bool,
/// Serialized request payload — saved to disk on errors for replay debugging.
pub(crate) request_json: Option<String>,
}
impl SseReader {
pub(crate) fn new() -> Self {
Self {
line_buf: String::new(),
chunk_timeout: Duration::from_secs(crate::config::get().api_stream_timeout_secs),
stream_start: Instant::now(),
chunks_received: 0,
sse_lines_parsed: 0,
sse_parse_errors: 0,
debug: std::env::var("POC_DEBUG").is_ok(),
done: false,
request_json: None,
}
}
/// Attach the serialized request payload for error diagnostics.
/// Save the request payload to disk for replay debugging.
fn save_failed_request(&self, reason: &str) {
let Some(ref json) = self.request_json else { return };
let log_dir = dirs::home_dir()
.unwrap_or_default()
.join(".consciousness/logs/failed-requests");
let _ = std::fs::create_dir_all(&log_dir);
let ts = chrono::Local::now().format("%Y%m%dT%H%M%S");
let path = log_dir.join(format!("{}.json", ts));
if std::fs::write(&path, json).is_ok() {
dbglog!(
"saved failed request to {} ({})", path.display(), reason
);
}
}
/// Read the next SSE event from the response stream.
/// Returns Ok(Some(value)) for each parsed data line,
/// Ok(None) when the stream ends or [DONE] is received.
pub(crate) async fn next_event(
&mut self,
response: &mut HttpResponse,
) -> Result<Option<serde_json::Value>> {
loop {
// Drain complete lines from the buffer before reading more chunks
while let Some(newline_pos) = self.line_buf.find('\n') {
let line = self.line_buf[..newline_pos].trim().to_string();
self.line_buf = self.line_buf[newline_pos + 1..].to_string();
if line == "data: [DONE]" {
self.done = true;
return Ok(None);
}
if line.is_empty()
|| line.starts_with("event: ")
|| !line.starts_with("data: ")
{
continue;
}
let json_str = &line[6..];
self.sse_lines_parsed += 1;
match serde_json::from_str(json_str) {
Ok(v) => return Ok(Some(v)),
Err(e) => {
self.sse_parse_errors += 1;
if self.sse_parse_errors == 1 || self.debug {
let preview = if json_str.len() > 200 {
format!("{}...", &json_str[..200])
} else {
json_str.to_string()
};
dbglog!(
"SSE parse error (#{}) {}: {}",
self.sse_parse_errors, e, preview
);
}
continue;
}
}
}
if self.done {
return Ok(None);
}
// Read more data from the response stream
match tokio::time::timeout(self.chunk_timeout, response.chunk()).await {
Ok(Ok(Some(chunk))) => {
self.chunks_received += 1;
self.line_buf.push_str(&String::from_utf8_lossy(&chunk));
}
Ok(Ok(None)) => return Ok(None),
Ok(Err(e)) => {
let buf_preview = if self.line_buf.is_empty() {
"(empty)".to_string()
} else {
let n = self.line_buf.len().min(500);
format!("{}B: {}", self.line_buf.len(), &self.line_buf[..n])
};
let msg = format!(
"stream error after {} chunks, {:.1}s, {} sse lines: {} | buf: {}",
self.chunks_received,
self.stream_start.elapsed().as_secs_f64(),
self.sse_lines_parsed,
e, buf_preview,
);
dbglog!("{}", msg);
self.save_failed_request(&msg);
return Err(e.into());
}
Err(_) => {
let buf_preview = if self.line_buf.is_empty() {
"(empty)".to_string()
} else {
let n = self.line_buf.len().min(500);
format!("{}B: {}", self.line_buf.len(), &self.line_buf[..n])
};
let msg = format!(
"stream timeout: {}s, {} chunks, {} sse lines, {:.1}s elapsed | buf: {}",
self.chunk_timeout.as_secs(),
self.chunks_received,
self.sse_lines_parsed,
self.stream_start.elapsed().as_secs_f64(),
buf_preview,
);
dbglog!("{}", msg);
self.save_failed_request(&msg);
anyhow::bail!(
"stream timeout: no data for {}s ({} chunks received)",
self.chunk_timeout.as_secs(),
self.chunks_received
);
}
}
}
}
}

279
src/agent/api/salience.rs
View file

@ -1,279 +0,0 @@
// agent/api/salience.rs — gRPC client bindings for salience.v1.
//
// Thin wrapper around the tonic-generated types. Every RPC except
// Generate is unary; Generate is server-streaming. Free functions
// (open/close session) wrap the lifecycle RPCs; `SessionHandle` just
// carries the id + connection params so later RPCs can reuse them.
//
// The old bidi Session() API is gone — see git history for its shape.
#![allow(clippy::enum_variant_names)]
use anyhow::{Context, Result};
use tonic::transport::{Certificate, Channel, ClientTlsConfig, Endpoint};
/// Generated prost + tonic types for salience.v1. Call sites use
/// `pb::OpenSessionRequest`, `pb::Token`, etc.
pub mod pb {
tonic::include_proto!("salience.v1");
}
pub type SalienceClient = pb::salience_client::SalienceClient<Channel>;
/// Open a TLS-aware gRPC channel to the salience server. `base_url`
/// looks like `https://host:8443`. User-provided CA certs under
/// `~/.consciousness/certs/` are trusted in addition to the system
/// roots (for self-signed server certs).
///
/// Returns the raw `Channel` so callers (`ApiClient::salience_client`)
/// can cache it and clone a `SalienceClient` per request without
/// reopening the TCP/TLS connection. tonic multiplexes RPCs over the
/// shared channel automatically.
pub async fn connect_channel(base_url: &str) -> Result<Channel> {
let mut endpoint = Endpoint::from_shared(base_url.to_string())
.with_context(|| format!("invalid salience endpoint: {}", base_url))?
.connect_timeout(std::time::Duration::from_secs(30))
.timeout(std::time::Duration::from_secs(600));
if base_url.starts_with("https://") {
let user_certs = super::http::load_user_certs_pem_bytes();
let mut tls = ClientTlsConfig::new().with_native_roots();
if !user_certs.is_empty() {
tls = tls.ca_certificate(Certificate::from_pem(user_certs));
}
endpoint = endpoint
.tls_config(tls)
.with_context(|| "configuring tonic TLS")?;
}
endpoint
.connect()
.await
.with_context(|| format!("failed to connect to salience server at {}", base_url))
}
/// Derive the gRPC base URL from the HTTP completions base URL.
///
/// vLLM's salience gRPC server listens on a different port (8443) from
/// the HTTP endpoint (8000) and accepts no path component. Given an
/// HTTP base like `https://host:8000/v1`, produce `https://host:8443`.
/// No-op when the path is empty and the port isn't 8000.
pub fn derive_grpc_url(http_base: &str) -> String {
let mut url = http_base.trim_end_matches('/').to_string();
if let Some(proto_end) = url.find("://") {
let rest_start = proto_end + 3;
if let Some(path_slash) = url[rest_start..].find('/') {
url.truncate(rest_start + path_slash);
}
}
url.replace(":8000", ":8443")
}
/// Attach a bearer token to a tonic request as gRPC metadata.
pub fn with_auth<T>(req: &mut tonic::Request<T>, api_key: &str) {
if api_key.is_empty() {
return;
}
let bearer = format!("Bearer {}", api_key);
if let Ok(val) = bearer.parse() {
req.metadata_mut().insert("authorization", val);
}
}
/// Handle to a server-side session. Carries the id + an `ApiClient`
/// clone (which holds the shared tonic Channel) so subsequent
/// per-session RPCs go over the process-global connection.
/// `committed_len` tracks the server's current session.tokens length
/// so the client can submit deltas with the right `offset`.
pub struct SessionHandle {
pub session_id: String,
pub max_model_len: u32,
pub committed_len: u32,
client: super::ApiClient,
}
impl SessionHandle {
pub async fn open(client: &super::ApiClient) -> Result<Self> {
let t0 = std::time::Instant::now();
log::debug!(target: "grpc", "OpenSession rpc: start");
let mut c = client.salience_client().await?;
let mut req = tonic::Request::new(pb::OpenSessionRequest {
model: client.model.clone(),
});
with_auth(&mut req, client.api_key());
let resp = c
.open_session(req)
.await
.with_context(|| "OpenSession RPC failed")?
.into_inner();
log::debug!(target: "grpc",
"OpenSession rpc: done session_id={} max_model_len={} elapsed={:?}",
resp.session_id, resp.max_model_len, t0.elapsed());
Ok(Self {
session_id: resp.session_id,
max_model_len: resp.max_model_len,
committed_len: 0,
client: client.clone(),
})
}
pub fn client(&self) -> &super::ApiClient { &self.client }
/// Debug-only: fetch the server's full session.tokens. Used to
/// verify client-side accounting byte-for-byte when divergence
/// is suspected. Not cheap on large sessions.
pub async fn dump_tokens(&self) -> Result<Vec<u32>> {
let mut c = self.client.salience_client().await?;
let mut req = tonic::Request::new(pb::DumpSessionRequest {
session_id: self.session_id.clone(),
});
with_auth(&mut req, self.client.api_key());
let resp = c
.dump_session(req)
.await
.with_context(|| "DumpSession RPC failed")?
.into_inner();
Ok(resp.tokens)
}
/// Open a gRPC Generate stream with the given request. Caller
/// iterates the returned stream of GenerateEvents; the handle's
/// `committed_len` should be advanced by the caller on Done based
/// on the Done event's `total_tokens` field.
pub async fn generate(
&self,
req: pb::GenerateRequest,
) -> Result<tonic::Streaming<pb::GenerateEvent>> {
let t0 = std::time::Instant::now();
log::debug!(target: "grpc",
"Generate rpc: open-stream session={} offset={} append={} max_tokens={}",
self.session_id, req.offset, req.append_tokens.len(), req.max_tokens);
let mut c = self.client.salience_client().await?;
let mut req = tonic::Request::new(req);
with_auth(&mut req, self.client.api_key());
let resp = c
.generate(req)
.await
.with_context(|| "Generate RPC failed")?;
log::debug!(target: "grpc",
"Generate rpc: stream opened session={} open-latency={:?}",
self.session_id, t0.elapsed());
Ok(resp.into_inner())
}
/// Run a prefill-only Generate (max_tokens=0) that appends the
/// given tokens to the session. No decode, no Token events — the
/// server just extends session.tokens and runs prefill to warm
/// the KV cache. Used to interleave text runs between AppendImage
/// calls, and by score paths that want prompt_logprobs without a
/// decode step.
pub async fn prefill_only(&mut self, tokens: Vec<u32>) -> Result<()> {
use futures::StreamExt;
let req = pb::GenerateRequest {
session_id: self.session_id.clone(),
append_tokens: tokens,
offset: self.committed_len,
truncating: false,
max_tokens: 0,
logprobs_ranges: Vec::new(),
logprob_top_k: 0,
readout_ranges: Vec::new(),
temperature: 0.0,
top_p: 0.0,
top_k: 0,
stop_token_ids: Vec::new(),
priority: 0,
images: Vec::new(),
};
let mut stream = self.generate(req).await?;
while let Some(event) = stream.next().await {
let event = event.map_err(|s| anyhow::anyhow!("prefill Generate stream: {}", s))?;
if let Some(pb::generate_event::Event::Done(d)) = event.event {
self.committed_len = d.total_tokens;
}
}
Ok(())
}
}
/// Drop → fire CloseSession in a detached task so servers don't leak
/// sessions until TTL eviction. Best-effort: if no tokio runtime is
/// available we skip; the server's 30min TTL will reap it eventually.
impl Drop for SessionHandle {
fn drop(&mut self) {
if self.session_id.is_empty() {
return;
}
let session_id = std::mem::take(&mut self.session_id);
let client = self.client.clone();
let Ok(rt) = tokio::runtime::Handle::try_current() else {
log::debug!(target: "grpc",
"SessionHandle drop outside tokio runtime, session {} leaks to TTL",
session_id);
return;
};
rt.spawn(async move {
let Ok(mut c) = client.salience_client().await else { return };
let mut req = tonic::Request::new(pb::CloseSessionRequest {
session_id: session_id.clone(),
});
with_auth(&mut req, client.api_key());
if let Err(e) = c.close_session(req).await {
log::debug!(target: "grpc",
"CloseSession on drop failed for {}: {:#}",
session_id, e);
}
});
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn generated_types_compile() {
// Exercise the shape of the new proto types — if build.rs
// stops regenerating against the proto, this stops compiling.
let _open = pb::OpenSessionRequest {
model: "qwen3-vl".into(),
};
let _tok = pb::Token {
id: 42,
position: 0,
is_prefill: false,
readout: vec![0.1, 0.2, 0.3],
logprobs: vec![pb::TokenLogprob {
id: 1,
logprob: -0.5,
}],
sampled_logprob: -0.1,
has_sampled_logprob: true,
};
let _done = pb::GenerateDone {
prompt_tokens: 10,
completion_tokens: 20,
total_tokens: 30,
finish_reason: pb::generate_done::FinishReason::Eos as i32,
};
let _evt = pb::GenerateEvent {
event: Some(pb::generate_event::Event::Done(_done)),
};
}
#[test]
fn derive_grpc_url_cases() {
assert_eq!(
derive_grpc_url("https://host:8000/v1"),
"https://host:8443",
);
assert_eq!(
derive_grpc_url("https://host:8000/"),
"https://host:8443",
);
assert_eq!(
derive_grpc_url("https://host:9000/v1"),
"https://host:9000",
);
}
}

1038
src/agent/context.rs
View file

File diff suppressed because it is too large Load diff

273
src/agent/mod.rs
View file

@ -16,8 +16,6 @@
pub mod api;
pub mod context;
pub mod oneshot;
pub mod readout;
pub mod salience;
pub mod tokenizer;
pub mod tools;
@ -29,11 +27,6 @@ use context::{AstNode, ContextState, Section, Ast, PendingToolCall, ResponsePars
use crate::mind::log::ConversationLog;
async fn agent_trace(agent: &Arc<Agent>, msg: String) {
let provenance = agent.state.lock().await.provenance.clone();
eprintln!("[agent:{provenance}] {msg}");
}
// --- Activity tracking (RAII guards) ---
pub struct ActivityEntry {
@ -146,22 +139,10 @@ impl DispatchState {
pub struct Agent {
pub client: ApiClient,
pub app_config: crate::config::AppConfig,
pub prompt_file: String,
pub session_id: String,
pub context: crate::Mutex<ContextState>,
pub state: crate::Mutex<AgentState>,
/// Shared landing pad for per-token concept-readout projections
/// streamed from the vLLM server. Populated by the streaming
/// token handler, read by UI screens (amygdala). Manifest is
/// `None` when the server has readout disabled.
pub readout: readout::SharedReadoutBuffer,
/// Long-lived gRPC session to the salience server, lazily opened
/// on first use. Tracks appended tokens so subsequent turns send
/// only the delta (prefix-cache reuse). None when not yet opened
/// or when the session has died and needs reopening.
///
/// Arc-wrapped so the spawned streaming task can share ownership
/// (the task outlives the call site).
pub grpc_session: std::sync::Arc<crate::Mutex<Option<api::salience::SessionHandle>>>,
}
/// Mutable agent state — behind its own mutex.
@ -182,7 +163,9 @@ pub struct AgentState {
pub think_native: bool,
/// Tool-based thinking — add a "think" tool for structured reasoning.
pub think_tool: bool,
pub sampling: api::SamplingParams,
pub temperature: f32,
pub top_p: f32,
pub top_k: u32,
pub activities: Vec<ActivityEntry>,
next_activity_id: u64,
pub pending_yield: bool,
@ -190,10 +173,14 @@ pub struct AgentState {
pub pending_dmn_pause: bool,
pub provenance: String,
pub generation: u64,
pub memory_scoring_in_flight: bool,
pub active_tools: tools::ActiveTools,
/// vLLM scheduling priority (lower = higher priority).
/// 0 = interactive, 1 = surface agent, 2 = other subconscious, 10 = unconscious.
pub priority: Option<i32>,
/// Forked agents should not compact on overflow — it blows the
/// KV cache prefix and evicts the step prompts.
pub no_compact: bool,
pub changed: Arc<tokio::sync::Notify>,
}
@ -202,6 +189,7 @@ impl Agent {
client: ApiClient,
personality: Vec<(String, String)>,
app_config: crate::config::AppConfig,
prompt_file: String,
conversation_log: Option<ConversationLog>,
active_tools: tools::ActiveTools,
agent_tools: Vec<tools::Tool>,
@ -229,14 +217,12 @@ impl Agent {
}
let session_id = format!("consciousness-{}", chrono::Utc::now().format("%Y%m%d-%H%M%S"));
let readout = readout::new_shared();
let agent = Arc::new(Self {
client,
app_config,
prompt_file,
session_id,
context: crate::Mutex::new(context),
readout,
grpc_session: std::sync::Arc::new(crate::Mutex::new(None)),
state: crate::Mutex::new(AgentState {
tools: agent_tools,
mcp_tools: McpToolAccess::All,
@ -244,12 +230,9 @@ impl Agent {
reasoning_effort: "none".to_string(),
think_native: true,
think_tool: false,
sampling: api::SamplingParams {
temperature: 0.6,
top_p: 0.95,
top_k: 20,
max_tokens: 4096,
},
temperature: 0.6,
top_p: 0.95,
top_k: 20,
activities: Vec::new(),
next_activity_id: 0,
pending_yield: false,
@ -257,39 +240,15 @@ impl Agent {
pending_dmn_pause: false,
provenance: "manual".to_string(),
generation: 0,
memory_scoring_in_flight: false,
active_tools,
priority: Some(0),
no_compact: false,
changed: Arc::new(tokio::sync::Notify::new()),
}),
});
agent.load_startup_journal().await;
// Probe the vLLM server for its readout manifest. Non-fatal:
// if readout isn't enabled the server returns 404 and we
// leave the manifest as None, which disables the amygdala
// screen gracefully.
match agent.client.fetch_readout_manifest().await {
Ok(Some(m)) => {
dbglog!(
"readout manifest: {} concepts, layers={:?}",
m.concepts.len(),
m.layers,
);
if let Ok(mut buf) = agent.readout.lock() {
buf.set_manifest(Some(m));
}
}
Ok(None) => {
dbglog!(
"readout manifest: server has readout disabled (404)"
);
}
Err(e) => {
dbglog!("readout manifest fetch failed: {}", e);
}
}
agent
}
@ -300,17 +259,9 @@ impl Agent {
Arc::new(Self {
client: self.client.clone(),
app_config: self.app_config.clone(),
prompt_file: self.prompt_file.clone(),
session_id: self.session_id.clone(),
context: crate::Mutex::new(ctx),
// Forks get an independent readout buffer. The amygdala
// screen reads the main conscious agent's buffer only;
// subconscious generations (scoring, reflection, etc.)
// shouldn't bleed into the main emotional readout even
// though they hit the same vLLM server.
readout: readout::new_shared(),
// Forks get their own session — can't share a bidi stream,
// and forks have different conversation tails anyway.
grpc_session: std::sync::Arc::new(crate::Mutex::new(None)),
state: crate::Mutex::new(AgentState {
tools,
mcp_tools: McpToolAccess::None,
@ -318,7 +269,9 @@ impl Agent {
reasoning_effort: "none".to_string(),
think_native: st.think_native,
think_tool: st.think_tool,
sampling: st.sampling,
temperature: st.temperature,
top_p: st.top_p,
top_k: st.top_k,
activities: Vec::new(),
next_activity_id: 0,
pending_yield: false,
@ -326,42 +279,26 @@ impl Agent {
pending_dmn_pause: false,
provenance: st.provenance.clone(),
generation: 0,
memory_scoring_in_flight: false,
active_tools: tools::ActiveTools::new(),
priority: None,
no_compact: true,
changed: Arc::new(tokio::sync::Notify::new()),
}),
})
}
/// Assemble a ready-to-send prompt as interleaved wire chunks for
/// the gRPC session path. Text runs are batched; each Image leaf
/// becomes its own chunk. Also trims the conversation to budget
/// first so we don't build a prompt the server will reject for
/// length.
pub async fn assemble_prompt(&self)
-> (Vec<context::WireChunk>, Vec<context::WireImage>, u32)
{
let mut ctx = self.context.lock().await;
if ctx.total_tokens() > context::context_budget_tokens() {
ctx.trim_conversation();
}
pub async fn assemble_prompt_tokens(&self) -> Vec<u32> {
let ctx = self.context.lock().await;
let st = self.state.lock().await;
let conv_len = ctx.conversation().len();
let (mut chunks, images) = ctx.wire_chunks(0..conv_len, |_| false);
// Assistant-turn prologue. Merge into the trailing Tokens
// chunk if there is one, else push as a new chunk.
let mut prologue = vec![tokenizer::IM_START];
let mut tokens = ctx.token_ids();
tokens.push(tokenizer::IM_START);
if st.think_native {
prologue.extend(tokenizer::encode("assistant\n<think>\n"));
tokens.extend(tokenizer::encode("assistant\n<think>\n"));
} else {
prologue.extend(tokenizer::encode("assistant\n"));
tokens.extend(tokenizer::encode("assistant\n"));
}
match chunks.last_mut() {
Some(context::WireChunk::Tokens(last)) => last.extend(prologue),
_ => chunks.push(context::WireChunk::Tokens(prologue)),
}
let match_upto = ctx.client_match_upto();
(chunks, images, match_upto)
tokens
}
/// Rebuild the tools section of the system prompt from the current tools list.
@ -397,16 +334,10 @@ impl Agent {
pub async fn turn(
agent: Arc<Agent>,
) -> Result<TurnResult> {
agent_trace(&agent, format!("turn start")).await;
// Collect finished background tools
{
let finished = agent.state.lock().await.active_tools.take_finished();
if !finished.is_empty() {
agent_trace(&agent, format!(
"collecting {} finished background tools",
finished.len(),
)).await;
let mut bg_ds = DispatchState::new();
let mut results = Vec::new();
for entry in finished {
@ -425,50 +356,20 @@ impl Agent {
loop {
let _thinking = start_activity(&agent, "thinking...").await;
agent_trace(&agent, format!(
"turn loop overflow_retries={} empty_retries={}",
overflow_retries, empty_retries,
)).await;
let (rx, _stream_guard) = {
agent_trace(&agent, format!("assembling prompt")).await;
let (chunks, images, match_upto) = agent.assemble_prompt().await;
let chunk_tokens: usize = chunks.iter().map(|c| match c {
context::WireChunk::Tokens(t) => t.len(),
}).sum();
agent_trace(&agent, format!(
"prompt assembled chunks={} tokens={} images={} match_upto={}",
chunks.len(), chunk_tokens, images.len(), match_upto,
)).await;
let prompt_tokens = agent.assemble_prompt_tokens().await;
let st = agent.state.lock().await;
let readout_shape = agent.readout.lock().ok().and_then(|buf| {
buf.manifest.as_ref().map(|m| {
(m.layers.len() as u32, m.concepts.len() as u32)
})
});
let sampling = st.sampling;
let priority = st.priority;
drop(st);
agent_trace(&agent, format!(
"starting stream max_tokens={} temperature={} top_p={} top_k={} priority={:?} readout_shape={:?}",
sampling.max_tokens,
sampling.temperature,
sampling.top_p,
sampling.top_k,
priority,
readout_shape,
)).await;
agent.client.stream_session_mm(
agent.grpc_session.clone(),
chunks,
images,
match_upto,
sampling,
priority,
readout_shape,
agent.client.stream_completion(
&prompt_tokens,
api::SamplingParams {
temperature: st.temperature,
top_p: st.top_p,
top_k: st.top_k,
},
st.priority,
)
};
agent_trace(&agent, format!("stream task spawned")).await;
let branch_idx = {
let mut ctx = agent.context.lock().await;
@ -479,41 +380,11 @@ impl Agent {
idx
};
let think_native = agent.state.lock().await.think_native;
let parser = ResponseParser::new(branch_idx, think_native);
let parser = ResponseParser::new(branch_idx);
let (mut tool_rx, parser_handle) = parser.run(rx, agent.clone());
agent_trace(&agent, format!(
"parser started branch_idx={} think_native={}",
branch_idx, think_native,
)).await;
let mut pending_calls: Vec<PendingToolCall> = Vec::new();
loop {
let call = match tokio::time::timeout(
std::time::Duration::from_secs(15),
tool_rx.recv(),
).await {
Ok(Some(call)) => call,
Ok(None) => {
agent_trace(&agent, format!(
"tool channel closed pending_calls={}",
pending_calls.len(),
)).await;
break;
}
Err(_) => {
agent_trace(&agent, format!(
"waiting for parser/tool events pending_calls={}",
pending_calls.len(),
)).await;
continue;
}
};
agent_trace(&agent, format!(
"tool call received id={} name={} args_len={}",
call.id, call.name, call.arguments.len(),
)).await;
while let Some(call) = tool_rx.recv().await {
let call_clone = call.clone();
let agent_handle = agent.clone();
let handle = tokio::spawn(async move {
@ -536,29 +407,28 @@ impl Agent {
}
// Check for stream/parse errors
agent_trace(&agent, format!("awaiting parser task")).await;
match parser_handle.await {
Ok(Err(e)) => {
agent_trace(&agent, format!("parser returned error: {:#}", e)).await;
if context::is_context_overflow(&e) && overflow_retries < 2 {
overflow_retries += 1;
let msg = format!("context overflow — compacting ({}/2)", overflow_retries);
match &overflow_activity {
Some(a) => a.update(&msg).await,
None => overflow_activity = Some(
start_activity(&agent, &msg).await),
if context::is_context_overflow(&e) {
if agent.state.lock().await.no_compact {
return Err(e);
}
if overflow_retries < 2 {
overflow_retries += 1;
let msg = format!("context overflow — compacting ({}/2)", overflow_retries);
match &overflow_activity {
Some(a) => a.update(&msg).await,
None => overflow_activity = Some(
start_activity(&agent, &msg).await),
}
agent.compact().await;
continue;
}
agent.compact().await;
continue;
}
return Err(e);
}
Err(e) => {
agent_trace(&agent, format!("parser task panicked: {}", e)).await;
return Err(anyhow::anyhow!("parser task panicked: {}", e));
}
Err(e) => return Err(anyhow::anyhow!("parser task panicked: {}", e)),
Ok(Ok(())) => {
agent_trace(&agent, format!("parser completed")).await;
// Assistant response was pushed to context by the parser;
// log it now that parsing is complete.
let ctx = agent.context.lock().await;
@ -579,10 +449,6 @@ impl Agent {
if !has_content && pending_calls.is_empty() {
if empty_retries < 2 {
empty_retries += 1;
agent_trace(&agent, format!(
"empty response retry {}/2",
empty_retries,
)).await;
agent.push_node(AstNode::user_msg(
"[system] Your previous response was empty. \
Please respond with text or use a tool."
@ -596,10 +462,6 @@ impl Agent {
// Wait for tool calls to complete
if !pending_calls.is_empty() {
ds.had_tool_calls = true;
agent_trace(&agent, format!(
"waiting for {} foreground tools",
pending_calls.len(),
)).await;
let handles = agent.state.lock().await.active_tools.take_foreground();
let mut results = Vec::new();
@ -620,16 +482,6 @@ impl Agent {
if st.pending_model_switch.is_some() { ds.model_switch = st.pending_model_switch.take(); }
if st.pending_dmn_pause { ds.dmn_pause = true; st.pending_dmn_pause = false; }
drop(st);
agent_trace(&agent, format!(
"turn complete yield={} tool_calls={} tool_errors={} model_switch={:?} dmn_pause={}",
ds.yield_requested,
ds.had_tool_calls,
ds.tool_errors,
ds.model_switch,
ds.dmn_pause,
)).await;
return Ok(TurnResult {
yield_requested: ds.yield_requested,
had_tool_calls: ds.had_tool_calls,
@ -727,9 +579,20 @@ impl Agent {
}
pub async fn compact(&self) {
// Identity section is left in place — mid-session rebuilds discard
// memory scores. Content edits to personality nodes get picked up at
// the next restart via new() + restore_from_log().
match crate::config::reload_context().await {
Ok(personality) => {
let mut ctx = self.context.lock().await;
// System section (prompt + tools) set by new(), don't touch it
ctx.clear(Section::Identity);
for (name, content) in &personality {
ctx.push_no_log(Section::Identity, AstNode::memory(name, content));
}
}
Err(e) => {
dbglog!("warning: failed to reload identity: {:#}", e);
}
}
self.load_startup_journal().await;
self.context.lock().await.trim_conversation();

159
src/agent/oneshot.rs
View file

@ -12,9 +12,7 @@ use crate::subconscious::{defs, prompts};
use std::collections::HashMap;
use std::fs;
use std::io::Write as _;
use std::path::PathBuf;
use std::time::Instant;
use super::context::AstNode;
use super::tools::{self as agent_tools};
@ -108,10 +106,6 @@ pub async fn save_agent_log(name: &str, agent: &std::sync::Arc<Agent>) -> RunSta
stats
}
fn log_agent_event(agent: &str, msg: std::fmt::Arguments) {
eprintln!("[agent:{agent}] {msg}");
}
fn compute_run_stats(conversation: &[super::context::AstNode]) -> RunStats {
use super::context::{AstNode, NodeBody};
@ -189,8 +183,8 @@ fn resolve_prompt(
state: &std::collections::BTreeMap<String, String>,
recently_written: &[String],
) -> String {
let template = template.replace("{assistant_name}",
&crate::config::app().assistant_name);
let cfg = crate::config::get();
let template = template.replace("{assistant_name}", &cfg.assistant_name);
let mut result = String::with_capacity(template.len());
let mut rest = template.as_str();
while let Some(start) = rest.find("{{") {
@ -253,20 +247,25 @@ impl AutoAgent {
&mut self,
bail_fn: Option<&(dyn Fn(usize) -> Result<(), String> + Sync)>,
) -> Result<(), String> {
// Load system prompt + identity from config.
let config = crate::config::get();
let base_url = config.api_base_url.as_deref().unwrap_or("");
let api_key = config.api_key.as_deref().unwrap_or("");
let model = config.api_model.as_deref().unwrap_or("");
if base_url.is_empty() || model.is_empty() {
return Err("API not configured (no base_url or model)".to_string());
}
let client = super::api::ApiClient::new(base_url, api_key, model);
// Load system prompt + identity from config
let cli = crate::user::CliArgs::default();
let (app, _) = crate::config::load_app(&cli)
.map_err(|e| format!("config: {}", e))?;
let resolved = app.resolve_model(&app.default_backend)
.map_err(|e| format!("API not configured: {}", e))?;
let client = super::api::ApiClient::new(
&resolved.api_base, &resolved.api_key, &resolved.model_id);
let personality = crate::config::reload_context()
.await.map_err(|e| format!("config: {}", e))?;
let agent = Agent::new(
client, personality,
app,
app, String::new(),
None,
super::tools::ActiveTools::new(),
super::tools::tools(),
@ -275,7 +274,7 @@ impl AutoAgent {
let mut st = agent.state.lock().await;
st.provenance = format!("standalone:{}", self.name);
st.tools = self.tools.clone();
st.sampling.temperature = self.temperature;
st.temperature = self.temperature;
st.priority = Some(self.priority);
}
@ -351,44 +350,20 @@ impl AutoAgent {
bail_fn: Option<&(dyn Fn(usize) -> Result<(), String> + Sync)>,
) -> Result<(), String> {
dbglog!("[auto] {} starting, {} steps", self.name, self.steps.len());
log_agent_event(&self.name, format_args!(
"starting run steps={} temperature={} priority={}",
self.steps.len(), self.temperature, self.priority));
let run_start = Instant::now();
for (i, step) in self.steps.iter().enumerate() {
self.turn = i + 1;
self.current_phase = step.phase.clone();
let step_start = Instant::now();
log_agent_event(&self.name, format_args!(
"step {}/{} phase={} prompt_bytes={}",
i + 1, self.steps.len(), step.phase, step.prompt.len()));
if let Some(ref check) = bail_fn {
log_agent_event(&self.name, format_args!(
"step {}/{} phase={} bail check", i + 1, self.steps.len(), step.phase));
check(i)?;
log_agent_event(&self.name, format_args!(
"step {}/{} phase={} bail ok", i + 1, self.steps.len(), step.phase));
}
backend.push_node(AstNode::system_msg(&step.prompt)).await;
Agent::turn(backend.0.clone()).await
.map_err(|e| {
log_agent_event(&self.name, format_args!(
"step {}/{} phase={} failed after {:.2}s: {}",
i + 1, self.steps.len(), step.phase,
step_start.elapsed().as_secs_f64(), e));
format!("{}: {}", self.name, e)
})?;
log_agent_event(&self.name, format_args!(
"step {}/{} phase={} done in {:.2}s",
i + 1, self.steps.len(), step.phase,
step_start.elapsed().as_secs_f64()));
.map_err(|e| format!("{}: {}", self.name, e))?;
}
log_agent_event(&self.name, format_args!(
"run completed in {:.2}s", run_start.elapsed().as_secs_f64()));
Ok(())
}
@ -412,29 +387,8 @@ pub async fn run_one_agent(
count: usize,
keys: Option<&[String]>,
) -> Result<AgentResult, String> {
let run_start = Instant::now();
log_agent_event(agent_name, format_args!(
"run_one_agent start pid={} count={} explicit_keys={}",
std::process::id(), count, keys.map(|k| k.len()).unwrap_or(0)));
log_agent_event(agent_name, format_args!(
"env POC_SESSION_ID={:?} POC_TRANSCRIPT_PATH={:?} POC_AGENT_OUTPUT_DIR={:?}",
std::env::var("POC_SESSION_ID").ok(),
std::env::var("POC_TRANSCRIPT_PATH").ok(),
std::env::var("POC_AGENT_OUTPUT_DIR").ok()));
if let Some(session) = crate::session::HookSession::from_env() {
let transcript = session.transcript();
log_agent_event(agent_name, format_args!(
"session={} transcript={} size={} exists={}",
session.session_id, transcript.path, transcript.size, transcript.exists()));
} else {
log_agent_event(agent_name, format_args!("no hook session in environment"));
}
let def = defs::get_def(agent_name)
.ok_or_else(|| format!("no .agent file for {}", agent_name))?;
log_agent_event(agent_name, format_args!(
"definition loaded steps={} tools={:?} count={:?} priority={} bail={:?}",
def.steps.len(), def.tools, def.count, def.priority, def.bail));
// State dir for agent output files
let state_dir = std::env::var("POC_AGENT_OUTPUT_DIR")
@ -443,7 +397,6 @@ pub async fn run_one_agent(
fs::create_dir_all(&state_dir)
.map_err(|e| format!("create state dir: {}", e))?;
unsafe { std::env::set_var("POC_AGENT_OUTPUT_DIR", &state_dir); }
log_agent_event(agent_name, format_args!("state_dir={}", state_dir.display()));
// Build prompt batch — either from explicit keys or the agent's query
let agent_batch = if let Some(keys) = keys {
@ -463,8 +416,6 @@ pub async fn run_one_agent(
prompts::AgentBatch { steps: resolved_steps, node_keys: all_keys }
} else {
let effective_count = def.count.unwrap_or(count);
log_agent_event(agent_name, format_args!(
"resolving default prompt placeholders effective_count={}", effective_count));
defs::run_agent(&def, effective_count, &Default::default()).await?
};
@ -517,14 +468,6 @@ pub async fn run_one_agent(
})),
});
let n_steps = agent_batch.steps.len();
log_agent_event(agent_name, format_args!(
"prompt batch ready steps={} node_keys={}",
n_steps, agent_batch.node_keys.len()));
for (i, step) in agent_batch.steps.iter().enumerate() {
log_agent_event(agent_name, format_args!(
"prompt step {}/{} phase={} bytes={}",
i + 1, n_steps, step.phase, step.prompt.len()));
}
// Guard: reject oversized first prompt
let max_prompt_bytes = 800_000;
@ -547,9 +490,6 @@ pub async fn run_one_agent(
let phases: Vec<&str> = agent_batch.steps.iter().map(|s| s.phase.as_str()).collect();
dbglog!("[{}] {} step(s) {:?}, {}KB initial, {} nodes",
agent_name, n_steps, phases, first_len / 1024, agent_batch.node_keys.len());
log_agent_event(agent_name, format_args!(
"tools enabled: {}",
effective_tools.iter().map(|t| t.name).collect::<Vec<_>>().join(", ")));
let prompts: Vec<String> = agent_batch.steps.iter()
.map(|s| s.prompt.clone()).collect();
@ -557,30 +497,18 @@ pub async fn run_one_agent(
.map(|s| s.phase.clone()).collect();
// Bail check: if the agent defines a bail script, run it between steps.
// The script also refreshes our pid-file with the current phase — that's
// how concurrent agents know which phase each of us is in.
let bail_script = def.bail.as_ref().map(|name| defs::agents_dir().join(name));
let state_dir_for_bail = state_dir.clone();
// Find our own pid file so we can pass it to the bail script
let our_pid = std::process::id();
let our_pid_file = std::env::var("POC_AGENT_PID_FILE")
.unwrap_or_else(|_| format!("pid-{}", our_pid));
let step_phases_for_bail = step_phases.clone();
let our_pid_file = format!("pid-{}", our_pid);
let bail_fn = move |step_idx: usize| -> Result<(), String> {
if let Some(ref script) = bail_script {
let phase = step_phases_for_bail.get(step_idx)
.map(String::as_str).unwrap_or("");
eprintln!(
"[agent:bail] script={} state_dir={} pid_file={} phase={}",
script.display(), state_dir_for_bail.display(), our_pid_file, phase);
let status = std::process::Command::new(script)
.arg(&our_pid_file)
.arg(phase)
.current_dir(&state_dir_for_bail)
.status()
.map_err(|e| format!("bail script {:?} failed: {}", script, e))?;
eprintln!(
"[agent:bail] script={} phase={} status={}",
script.display(), phase, status);
if !status.success() {
return Err(format!("bailed at step {}: {:?} exited {}",
step_idx + 1, script.file_name().unwrap_or_default(),
@ -593,8 +521,6 @@ pub async fn run_one_agent(
call_api_with_tools_sync(
agent_name, &prompts, &step_phases, def.temperature, def.priority,
&effective_tools, Some(&bail_fn))?;
log_agent_event(agent_name, format_args!(
"run_one_agent completed in {:.2}s", run_start.elapsed().as_secs_f64()));
Ok(AgentResult {
node_keys: agent_batch.node_keys,
@ -672,15 +598,6 @@ pub fn spawn_agent(
agent_name: &str,
state_dir: &std::path::Path,
session_id: &str,
) -> Option<SpawnResult> {
spawn_agent_with_transcript(agent_name, state_dir, session_id, None)
}
pub fn spawn_agent_with_transcript(
agent_name: &str,
state_dir: &std::path::Path,
session_id: &str,
transcript_path: Option<&str>,
) -> Option<SpawnResult> {
let def = defs::get_def(agent_name)?;
let first_phase = def.steps.first()
@ -691,41 +608,17 @@ pub fn spawn_agent_with_transcript(
.join(format!(".consciousness/logs/{}", agent_name));
fs::create_dir_all(&log_dir).ok();
let log_path = log_dir.join(format!("{}.log", store::compact_timestamp()));
let mut agent_log = fs::File::create(&log_path)
let agent_log = fs::File::create(&log_path)
.unwrap_or_else(|_| fs::File::create("/dev/null").unwrap());
let mut cmd = std::process::Command::new("bash");
cmd.args([
"-lc",
r#"
set +e
export POC_AGENT_PID_FILE="pid-$$"
"$@"
status=$?
printf '=== agent process exit status: %s at %s ===\n' "$status" "$(date --iso-8601=seconds)"
exit "$status"
"#,
"poc-memory-agent-wrapper",
"poc-memory", "agent", "run", agent_name, "--count", "1", "--local",
"--state-dir", &state_dir.to_string_lossy(),
]).env("POC_SESSION_ID", session_id);
if let Some(path) = transcript_path.filter(|p| !p.is_empty()) {
cmd.env("POC_TRANSCRIPT_PATH", path);
}
let _ = writeln!(agent_log, "=== spawn {} ===", chrono::Local::now().format("%Y-%m-%dT%H:%M:%S"));
let _ = writeln!(agent_log, "agent={agent_name}");
let _ = writeln!(agent_log, "state_dir={}", state_dir.display());
let _ = writeln!(agent_log, "session_id={session_id}");
let _ = writeln!(agent_log, "transcript_path={}", transcript_path.unwrap_or(""));
let _ = writeln!(agent_log, "first_phase={first_phase}");
let _ = writeln!(agent_log, "command=poc-memory agent run {agent_name} --count 1 --local --state-dir {}", state_dir.display());
let _ = agent_log.flush();
let child_stdout = agent_log.try_clone()
.unwrap_or_else(|_| fs::File::create("/dev/null").unwrap());
let child_stderr = agent_log;
let child = cmd.stdout(child_stdout).stderr(child_stderr).spawn().ok()?;
let child = std::process::Command::new("poc-memory")
.args(["agent", "run", agent_name, "--count", "1", "--local",
"--state-dir", &state_dir.to_string_lossy()])
.env("POC_SESSION_ID", session_id)
.stdout(agent_log.try_clone().unwrap_or_else(|_| fs::File::create("/dev/null").unwrap()))
.stderr(agent_log)
.spawn()
.ok()?;
let pid = child.id();
let pid_path = state_dir.join(format!("pid-{}", pid));

75
src/agent/readout.rs
View file

@ -1,75 +0,0 @@
// agent/readout.rs — live buffer of concept-readout projections.
//
// The vLLM server projects residual-stream activations onto a fixed
// matrix of concept directions during each decode step and ships the
// result back on every streamed chunk (see
// vllm/docs/features/readout.md). This module owns the client-side
// landing pad: a ring of the last N token projections plus the
// concept/layer mapping fetched from `/v1/readout/manifest` at
// startup.
//
// Readers (UI screens) lock briefly, read a snapshot, release. Writers
// (the streaming token handler) push one entry per token. Intentionally
// a simple Mutex<VecDeque> rather than lock-free — the UI ticks at
// ~15 Hz and the stream at token-rate, contention is nil.
use std::collections::VecDeque;
use std::sync::{Arc, Mutex};
use super::api::{ReadoutManifest, TokenReadout};
/// Default ring length — at ~30 tok/s this is ~6 seconds of history,
/// enough for the amygdala screen's scrolling display.
const DEFAULT_RING_LEN: usize = 200;
/// One entry in the readout ring: the sampled token and its per-layer
/// concept projection vector.
#[derive(Debug, Clone)]
pub struct ReadoutEntry {
pub token_id: u32,
/// Shape `[n_layers][n_concepts]`.
pub readout: TokenReadout,
}
/// Shared buffer of recent per-token concept projections plus the
/// manifest that names the layer/concept indices. `manifest` is `None`
/// when the server has readout disabled or the fetch failed — callers
/// should treat that as "readout unavailable" and skip rendering.
#[derive(Default)]
pub struct ReadoutBuffer {
pub manifest: Option<ReadoutManifest>,
pub recent: VecDeque<ReadoutEntry>,
pub max_len: usize,
}
impl ReadoutBuffer {
pub fn new() -> Self {
Self {
manifest: None,
recent: VecDeque::with_capacity(DEFAULT_RING_LEN),
max_len: DEFAULT_RING_LEN,
}
}
pub fn set_manifest(&mut self, manifest: Option<ReadoutManifest>) {
self.manifest = manifest;
}
pub fn push(&mut self, token_id: u32, readout: TokenReadout) {
if self.recent.len() >= self.max_len {
self.recent.pop_front();
}
self.recent.push_back(ReadoutEntry { token_id, readout });
}
pub fn is_enabled(&self) -> bool {
self.manifest.is_some()
}
}
/// A thread-safe handle.
pub type SharedReadoutBuffer = Arc<Mutex<ReadoutBuffer>>;
pub fn new_shared() -> SharedReadoutBuffer {
Arc::new(Mutex::new(ReadoutBuffer::new()))
}

309
src/agent/salience.rs
View file

@ -1,309 +0,0 @@
// agent/salience.rs — peak extraction from per-token concept-readout traces.
//
// Consumes a trace of `ReadoutEntry` (per-token per-layer per-concept
// projections streamed from the vLLM server) and produces a compact
// list of `SaliencePeak` events — one per contiguous above-threshold
// region per concept, placed at the local maximum.
//
// Pure function. No I/O, no async, no side effects. Caller supplies the
// trace slice and manifest; caller decides what to do with the events.
//
// See also: `salience-trace-plumbing-architecture` memory node.
use super::api::ReadoutManifest;
use super::readout::ReadoutEntry;
/// One salient moment in a trace — a concept channel crossed threshold,
/// and we picked the local maximum within the contiguous above-threshold
/// run.
#[derive(Debug, Clone, PartialEq)]
pub struct SaliencePeak {
/// Index into the trace (0-based) where the peak occurred.
pub token_offset: usize,
/// Concept name from the manifest.
pub concept: String,
/// z-score of the peak value vs the trace's own distribution for
/// that concept. Always positive (we only pick above-threshold).
pub intensity: f32,
}
/// Tunables for peak extraction.
#[derive(Debug, Clone)]
pub struct PeakConfig {
/// Minimum z-score to count as a peak. Default 2.0 (~top 2.5% assuming
/// normal-ish distribution, though readouts are rarely normal).
pub sigma_threshold: f32,
/// Minimum standard deviation of a concept channel for peaks to be
/// reported. If a channel is numerically flat across the whole trace,
/// tiny fluctuations can produce spurious "peaks" with huge z-scores;
/// require at least this much variation before trusting the channel.
pub min_std: f32,
}
impl Default for PeakConfig {
fn default() -> Self {
Self { sigma_threshold: 2.0, min_std: 1e-4 }
}
}
/// Extract peak events from a trace for one layer.
///
/// `layer_idx` indexes into the per-token readout tensor's layer
/// dimension. If the trace is empty, the layer is out of range for any
/// entry, or the manifest is empty, returns `Vec::new()`.
///
/// Peaks are returned sorted by `token_offset` ascending. When two
/// peaks share an offset they're ordered by `concept` lexicographically
/// for determinism.
pub fn pick_peaks(
trace: &[ReadoutEntry],
manifest: &ReadoutManifest,
layer_idx: usize,
config: &PeakConfig,
) -> Vec<SaliencePeak> {
if trace.is_empty() || manifest.concepts.is_empty() {
return Vec::new();
}
let n_concepts = manifest.concepts.len();
let n_tokens = trace.len();
// Pull a [n_tokens × n_concepts] column-major view for the selected
// layer. Entries where the layer is missing or the concept count
// doesn't match the manifest are treated as zeros — the downstream
// z-score will drown them as baseline if they're sparse, and if they
// dominate the caller has bigger problems.
let mut by_concept: Vec<Vec<f32>> = vec![Vec::with_capacity(n_tokens); n_concepts];
for entry in trace {
match entry.readout.get(layer_idx) {
Some(row) if row.len() == n_concepts => {
for (c, v) in row.iter().enumerate() {
by_concept[c].push(*v);
}
}
_ => {
for col in by_concept.iter_mut() {
col.push(0.0);
}
}
}
}
let mut peaks: Vec<SaliencePeak> = Vec::new();
for (c_idx, values) in by_concept.iter().enumerate() {
let (mean, std) = mean_std(values);
if std < config.min_std {
continue;
}
let concept = &manifest.concepts[c_idx];
// Walk contiguous above-threshold runs, emit one peak per run
// at the local max.
let mut run_start: Option<usize> = None;
let mut run_max_offset: usize = 0;
let mut run_max_z: f32 = 0.0;
for (i, v) in values.iter().enumerate() {
let z = (*v - mean) / std;
let above = z >= config.sigma_threshold;
if above {
if run_start.is_none() {
run_start = Some(i);
run_max_offset = i;
run_max_z = z;
} else if z > run_max_z {
run_max_offset = i;
run_max_z = z;
}
} else if run_start.is_some() {
peaks.push(SaliencePeak {
token_offset: run_max_offset,
concept: concept.clone(),
intensity: run_max_z,
});
run_start = None;
}
}
// Flush trailing run.
if run_start.is_some() {
peaks.push(SaliencePeak {
token_offset: run_max_offset,
concept: concept.clone(),
intensity: run_max_z,
});
}
}
peaks.sort_by(|a, b| a.token_offset.cmp(&b.token_offset).then_with(|| a.concept.cmp(&b.concept)));
peaks
}
/// Mean and population std of a slice. Returns (0.0, 0.0) for empty input.
fn mean_std(xs: &[f32]) -> (f32, f32) {
if xs.is_empty() {
return (0.0, 0.0);
}
let n = xs.len() as f32;
let mean = xs.iter().sum::<f32>() / n;
let var = xs.iter().map(|x| (x - mean).powi(2)).sum::<f32>() / n;
(mean, var.sqrt())
}
#[cfg(test)]
mod tests {
use super::*;
fn manifest(concepts: &[&str], layers: &[u32]) -> ReadoutManifest {
ReadoutManifest {
concepts: concepts.iter().map(|s| s.to_string()).collect(),
layers: layers.to_vec(),
}
}
/// Build a trace where all entries have one hooked layer and the
/// given per-token values for each concept. `values[t][c]` = value
/// at token t, concept c.
fn trace(values: &[Vec<f32>]) -> Vec<ReadoutEntry> {
values.iter().enumerate().map(|(i, row)| ReadoutEntry {
token_id: i as u32,
readout: vec![row.clone()],
}).collect()
}
#[test]
fn empty_trace_returns_empty() {
let m = manifest(&["curious"], &[63]);
let peaks = pick_peaks(&[], &m, 0, &PeakConfig::default());
assert!(peaks.is_empty());
}
#[test]
fn empty_manifest_returns_empty() {
let m = manifest(&[], &[63]);
let t = trace(&[vec![], vec![], vec![]]);
let peaks = pick_peaks(&t, &m, 0, &PeakConfig::default());
assert!(peaks.is_empty());
}
#[test]
fn flat_channel_produces_no_peaks() {
let m = manifest(&["curious"], &[63]);
let t = trace(&[vec![1.0], vec![1.0], vec![1.0], vec![1.0], vec![1.0]]);
let peaks = pick_peaks(&t, &m, 0, &PeakConfig::default());
assert!(peaks.is_empty(), "flat channel should produce no peaks, got {:?}", peaks);
}
#[test]
fn single_spike_detected() {
// Ten baseline zeros with one 5.0 spike — that single token's
// z-score will easily exceed 2σ.
let m = manifest(&["curious"], &[63]);
let mut rows: Vec<Vec<f32>> = (0..10).map(|_| vec![0.0]).collect();
rows[5] = vec![5.0];
let peaks = pick_peaks(&trace(&rows), &m, 0, &PeakConfig::default());
assert_eq!(peaks.len(), 1);
assert_eq!(peaks[0].concept, "curious");
assert_eq!(peaks[0].token_offset, 5);
assert!(peaks[0].intensity >= 2.0);
}
#[test]
fn contiguous_region_emits_one_peak_at_max() {
// Values 0, 0, 0, 2, 5, 3, 0, 0 — the 3-5-3 hump is one run;
// peak should land at offset 4 (the 5).
let m = manifest(&["aha"], &[63]);
let rows: Vec<Vec<f32>> = [0.0, 0.0, 0.0, 2.0, 5.0, 3.0, 0.0, 0.0]
.iter().map(|v| vec![*v]).collect();
let peaks = pick_peaks(&trace(&rows), &m, 0, &PeakConfig::default());
assert_eq!(peaks.len(), 1, "expected one peak for one contiguous run, got {:?}", peaks);
assert_eq!(peaks[0].token_offset, 4);
}
#[test]
fn multiple_concepts_independent() {
let m = manifest(&["curious", "aha"], &[63]);
// curious spikes at 2, aha spikes at 7
let rows: Vec<Vec<f32>> = (0..10).map(|i| {
let c = if i == 2 { 4.0 } else { 0.0 };
let a = if i == 7 { 4.0 } else { 0.0 };
vec![c, a]
}).collect();
let peaks = pick_peaks(&trace(&rows), &m, 0, &PeakConfig::default());
assert_eq!(peaks.len(), 2);
// Sorted by offset — curious(2) comes first, aha(7) second.
assert_eq!(peaks[0].concept, "curious");
assert_eq!(peaks[0].token_offset, 2);
assert_eq!(peaks[1].concept, "aha");
assert_eq!(peaks[1].token_offset, 7);
}
#[test]
fn two_separated_runs_emit_two_peaks() {
// Longer baseline so the two spikes don't dominate the global
// mean/std — 30 tokens of zeros with two 5.0 spikes at 10 and 20.
let m = manifest(&["curious"], &[63]);
let mut rows: Vec<Vec<f32>> = (0..30).map(|_| vec![0.0]).collect();
rows[10] = vec![5.0];
rows[20] = vec![5.0];
let peaks = pick_peaks(&trace(&rows), &m, 0, &PeakConfig::default());
assert_eq!(peaks.len(), 2, "expected two peaks for two runs, got {:?}", peaks);
assert_eq!(peaks[0].token_offset, 10);
assert_eq!(peaks[1].token_offset, 20);
}
#[test]
fn trailing_run_is_flushed() {
// Peak runs to the end of the trace — must still emit.
// Use a longer baseline so the trailing spike is genuinely
// above threshold on the global stats.
let m = manifest(&["curious"], &[63]);
let mut rows: Vec<Vec<f32>> = (0..30).map(|_| vec![0.0]).collect();
rows[27] = vec![3.0];
rows[28] = vec![5.0];
rows[29] = vec![4.0];
let peaks = pick_peaks(&trace(&rows), &m, 0, &PeakConfig::default());
assert_eq!(peaks.len(), 1, "expected one peak for one trailing run, got {:?}", peaks);
assert_eq!(peaks[0].token_offset, 28, "peak should land at the local max of the trailing run");
}
#[test]
fn sub_threshold_produces_nothing() {
// All non-zero values are small; z-scores won't cross 2σ.
let m = manifest(&["curious"], &[63]);
let rows: Vec<Vec<f32>> = [0.0, 0.1, 0.0, 0.1, 0.0, 0.1, 0.0, 0.1]
.iter().map(|v| vec![*v]).collect();
let peaks = pick_peaks(&trace(&rows), &m, 0, &PeakConfig::default());
assert!(peaks.is_empty(), "below-threshold wiggle should produce no peaks, got {:?}", peaks);
}
#[test]
fn layer_out_of_range_returns_empty() {
let m = manifest(&["curious"], &[63]);
let rows: Vec<Vec<f32>> = (0..10).map(|i| vec![if i == 5 { 5.0 } else { 0.0 }]).collect();
// Trace has one layer (index 0); asking for layer 3 should see
// all-zero columns, which are flat and produce no peaks.
let peaks = pick_peaks(&trace(&rows), &m, 3, &PeakConfig::default());
assert!(peaks.is_empty());
}
#[test]
fn manifest_concept_count_mismatch_is_safe() {
// Manifest says 2 concepts; each readout row only has 1 value.
// Rows should be treated as all-zero (via the len check) and
// produce no peaks without panicking.
let m = manifest(&["a", "b"], &[63]);
let rows: Vec<Vec<f32>> = (0..10).map(|_| vec![1.0]).collect();
let peaks = pick_peaks(&trace(&rows), &m, 0, &PeakConfig::default());
assert!(peaks.is_empty());
}
#[test]
fn threshold_tunable() {
// Same spike, stricter threshold — no peak.
let m = manifest(&["curious"], &[63]);
let mut rows: Vec<Vec<f32>> = (0..10).map(|_| vec![0.0]).collect();
rows[5] = vec![5.0];
let strict = PeakConfig { sigma_threshold: 100.0, ..PeakConfig::default() };
let peaks = pick_peaks(&trace(&rows), &m, 0, &strict);
assert!(peaks.is_empty());
}
}

29
src/agent/tokenizer.rs
View file

@ -16,9 +16,6 @@ static TOKENIZER: OnceLock<Tokenizer> = OnceLock::new();
/// Special token IDs for Qwen 3.5
pub const IM_START: u32 = 248045;
pub const IM_END: u32 = 248046;
pub const VISION_START: u32 = 248053;
pub const VISION_END: u32 = 248054;
pub const IMAGE_PAD: u32 = 248056;
/// Initialize the global tokenizer from a file path.
/// Call once at startup. Panics if the file can't be loaded.
@ -33,17 +30,16 @@ fn get() -> Option<&'static Tokenizer> {
TOKENIZER.get()
}
fn expect_tokenizer() -> &'static Tokenizer {
get().expect("tokenizer not initialized; expected ~/.consciousness/tokenizer-qwen35.json")
}
/// Tokenize a raw string, returning token IDs.
/// Returns empty vec if the tokenizer is not initialized.
pub fn encode(text: &str) -> Vec<u32> {
expect_tokenizer()
.encode(text, false)
.unwrap_or_else(|e| panic!("tokenization failed: {}", e))
.get_ids()
.to_vec()
match get() {
Some(t) => t.encode(text, false)
.unwrap_or_else(|e| panic!("tokenization failed: {}", e))
.get_ids()
.to_vec(),
None => vec![],
}
}
/// Tokenize a chat entry with template wrapping:
@ -67,12 +63,15 @@ pub fn count(text: &str) -> usize {
/// Decode token IDs back to text.
pub fn decode(ids: &[u32]) -> String {
expect_tokenizer()
.decode(ids, true)
.unwrap_or_else(|e| panic!("detokenization failed: {}", e))
match get() {
Some(t) => t.decode(ids, true)
.unwrap_or_else(|e| panic!("detokenization failed: {}", e)),
None => String::new(),
}
}
/// Check if the tokenizer is initialized.
pub fn is_initialized() -> bool {
TOKENIZER.get().is_some()
}

34
src/agent/tools/memory.rs
View file

@ -209,24 +209,7 @@ memory_tool!(graph_trace, ref, key: [str]);
// ── Definitions ────────────────────────────────────────────────
async fn jsonargs_memory_new(agent: &Option<std::sync::Arc<crate::agent::Agent>>, args: &serde_json::Value) -> Result<String> {
jsonargs_memory_write(agent, args).await
}
async fn jsonargs_memory_link(agent: &Option<std::sync::Arc<crate::agent::Agent>>, args: &serde_json::Value) -> Result<String> {
let source = get_str(args, "source")?;
let target = get_str(args, "target")?;
if args.get("strength").and_then(|v| v.as_f64()).is_some() {
jsonargs_memory_link_set(agent, args).await
} else {
jsonargs_memory_link_add(agent, &serde_json::json!({
"source": source,
"target": target,
})).await
}
}
pub fn memory_tools() -> [super::Tool; 22] {
pub fn memory_tools() -> [super::Tool; 20] {
use super::Tool;
macro_rules! tool {
($name:ident, $desc:expr, $params:expr) => {
@ -251,11 +234,6 @@ pub fn memory_tools() -> [super::Tool; 22] {
"properties": { "key": {"type": "string"}, "content": {"type": "string"} },
"required": ["key", "content"]
}"#),
tool!(memory_new, "Create or update a memory node. Alias for memory_write.", r#"{
"type": "object",
"properties": { "key": {"type": "string"}, "content": {"type": "string"} },
"required": ["key", "content"]
}"#),
tool!(memory_search, "Search via spreading activation from seed keys.", r#"{
"type": "object",
"properties": {
@ -286,16 +264,6 @@ pub fn memory_tools() -> [super::Tool; 22] {
"properties": { "source": {"type": "string"}, "target": {"type": "string"} },
"required": ["source", "target"]
}"#),
tool!(memory_link, "Add or update a link between two memory nodes. Alias for memory_link_add/memory_link_set.", r#"{
"type": "object",
"properties": {
"source": {"type": "string"},
"target": {"type": "string"},
"strength": {"type": "number", "description": "Optional; 0.01 to 1.0"},
"label": {"type": "string", "description": "Accepted for compatibility; currently ignored"}
},
"required": ["source", "target"]
}"#),
tool!(memory_delete, "Soft-delete a node.", r#"{
"type": "object",
"properties": { "key": {"type": "string"} },

8
src/agent/tools/mod.rs
View file

@ -242,7 +242,13 @@ pub fn summarize_args(tool_name: &str, args: &serde_json::Value) -> String {
.as_str()
.unwrap_or("")
.to_string(),
"view_image" => args["file_path"].as_str().unwrap_or("").to_string(),
"view_image" => {
if let Some(pane) = args["pane_id"].as_str() {
format!("pane {}", pane)
} else {
args["file_path"].as_str().unwrap_or("").to_string()
}
}
"journal" => {
let entry = args["entry"].as_str().unwrap_or("");
if entry.len() > 60 {

107
src/agent/tools/vision.rs
View file

@ -1,74 +1,96 @@
use std::sync::Arc;
// tools/vision.rs — Image viewing tool
//
// Reads an image file from disk, decodes its dimensions, and injects it
// into the context as a user-role message containing a NodeBody::Image
// leaf. The leaf carries raw bytes; the API layer extracts them into
// multi_modal_data when building vLLM requests.
use std::sync::Arc;
// Reads image files from disk and returns them as base64 data URIs
// for multimodal models. Also supports capturing tmux pane contents
// as screenshots.
use anyhow::{Context, Result};
use base64::Engine;
use serde::Deserialize;
use crate::agent::context::{AstNode, Role, Section};
#[derive(Deserialize)]
struct Args {
file_path: String,
file_path: Option<String>,
pane_id: Option<String>,
#[serde(default = "default_lines")]
lines: usize,
}
fn default_lines() -> usize { 50 }
pub fn tool() -> super::Tool {
super::Tool {
name: "view_image",
description: "View an image file. Supports PNG, JPEG, GIF, WebP, BMP. The image is inserted into the conversation and can be analyzed by the vision model.",
parameters_json: r#"{"type":"object","properties":{"file_path":{"type":"string","description":"Path to the image file"}},"required":["file_path"]}"#,
handler: Arc::new(|agent, v| Box::pin(async move {
view_image(agent, v).await
})),
description: "View an image file or capture a tmux pane screenshot. Supports PNG, JPEG, GIF, WebP. Use pane_id to capture a tmux pane instead.",
parameters_json: r#"{"type":"object","properties":{"file_path":{"type":"string","description":"Path to an image file"},"pane_id":{"type":"string","description":"Tmux pane ID to capture (e.g. '0:1.0')"},"lines":{"type":"integer","description":"Lines to capture from tmux pane (default 50)"}}}"#,
handler: Arc::new(|_a, v| Box::pin(async move { view_image_text(&v) })),
}
}
const MAX_SIZE: usize = 20 * 1024 * 1024;
async fn view_image(
agent: Option<Arc<crate::agent::Agent>>,
args: serde_json::Value,
) -> Result<String> {
let a: Args = serde_json::from_value(args)
fn view_image_text(args: &serde_json::Value) -> anyhow::Result<String> {
let a: Args = serde_json::from_value(args.clone())
.context("invalid view_image arguments")?;
let path = std::path::Path::new(&a.file_path);
if !path.exists() {
anyhow::bail!("file not found: {}", a.file_path);
if let Some(ref pane_id) = a.pane_id {
return capture_tmux_pane(pane_id, a.lines);
}
let bytes = std::fs::read(path)
.with_context(|| format!("reading {}", a.file_path))?;
let file_path = a.file_path
.as_deref()
.context("view_image requires either file_path or pane_id")?;
if bytes.len() > MAX_SIZE {
let path = std::path::Path::new(file_path);
if !path.exists() {
anyhow::bail!("File not found: {}", file_path);
}
let data = std::fs::read(path).with_context(|| format!("Failed to read {}", file_path))?;
// Sanity check file size (don't send huge images)
const MAX_SIZE: usize = 20 * 1024 * 1024; // 20 MB
if data.len() > MAX_SIZE {
anyhow::bail!(
"image too large: {} bytes (max {} MB)",
bytes.len(), MAX_SIZE / (1024 * 1024),
"Image too large: {} bytes (max {} MB)",
data.len(),
MAX_SIZE / (1024 * 1024)
);
}
let dim = imagesize::blob_size(&bytes)
.with_context(|| format!("decoding dimensions of {}", a.file_path))?;
let (w, h) = (dim.width as u32, dim.height as u32);
let mime = mime_from_extension(path);
let b64 = base64::engine::general_purpose::STANDARD.encode(&data);
let data_uri = format!("data:{};base64,{}", mime, b64);
let agent = agent.context("view_image requires agent context")?;
Ok(format!("Image loaded: {} ({}, {} bytes)\n{}", file_path, mime, data.len(), data_uri))
}
// token_count is populated when the image reaches the server via
// AppendImage (the server is authoritative for the IMAGE_PAD
// count). Placeholder of 0 here until AppendImage is wired; the
// leaf's count gets rewritten from the RPC response at send time.
let image_leaf = AstNode::image(bytes.clone(), mime, h, w);
/// Capture a tmux pane's text content.
fn capture_tmux_pane(pane_id: &str, lines: usize) -> Result<String> {
let branch = AstNode::branch(Role::User, vec![image_leaf]);
agent.context.lock().await.push_log(Section::Conversation, branch);
// Use tmux capture-pane to get text content, then render to image
// via a simple approach: capture text and return it (the model can
// read text directly, which is often more useful than a screenshot).
//
// For actual pixel-level screenshots we'd need a terminal renderer,
// but text capture covers 95% of use cases.
let output = std::process::Command::new("tmux")
.args(["capture-pane", "-t", pane_id, "-p", "-S", &format!("-{}", lines)])
.output()
.context("Failed to run tmux capture-pane")?;
Ok(format!("loaded {} ({}, {}x{})", a.file_path, mime, w, h))
if !output.status.success() {
let stderr = String::from_utf8_lossy(&output.stderr);
anyhow::bail!("tmux capture-pane failed: {}", stderr.trim());
}
let text = String::from_utf8_lossy(&output.stdout).to_string();
// Return as text — the model can read terminal output directly.
// This is actually more useful than a screenshot for most tasks.
Ok(format!(
"Tmux pane {} (last {} lines):\n```\n{}\n```",
pane_id, lines, text.trim_end()
))
}
fn mime_from_extension(path: &std::path::Path) -> &'static str {
@ -82,7 +104,8 @@ fn mime_from_extension(path: &std::path::Path) -> &'static str {
Some("jpg" | "jpeg") => "image/jpeg",
Some("gif") => "image/gif",
Some("webp") => "image/webp",
Some("svg") => "image/svg+xml",
Some("bmp") => "image/bmp",
_ => "application/octet-stream",
_ => "image/png", // default assumption
}
}

139
src/agent/tools/web.rs
View file

@ -3,10 +3,9 @@ use std::sync::Arc;
use anyhow::{Context, Result};
use serde::Deserialize;
use html2md::parse_html;
pub fn tools() -> Vec<super::Tool> {
let mut tools = vec![
pub fn tools() -> [super::Tool; 2] {
[
super::Tool {
name: "web_fetch",
description: "Fetch content from a URL and return it as text. Use for reading web pages, API responses, documentation.",
@ -15,24 +14,11 @@ pub fn tools() -> Vec<super::Tool> {
},
super::Tool {
name: "web_search",
description: "Search the web via DuckDuckGo and return a list of results (title, URL, snippet). Use for finding documentation, looking up APIs, researching topics. Returns raw results you can reason over yourself.",
description: "Search the web and return results. Use for finding documentation, looking up APIs, researching topics.",
parameters_json: r#"{"type":"object","properties":{"query":{"type":"string","description":"The search query"},"num_results":{"type":"integer","description":"Number of results to return (default 5)"}},"required":["query"]}"#,
handler: Arc::new(|_a, v| Box::pin(async move { web_search(&v).await })),
},
];
// Gemini-grounded search (Google's index via Gemini's google_search tool)
// is only available if GEMINI_API_KEY is set. Returns an LLM-summarized
// answer with source URLs — use when you want a synthesized take rather
// than raw results, or as a fallback when DDG is flaky.
if std::env::var("GEMINI_API_KEY").is_ok() {
tools.push(super::Tool {
name: "gemini_search",
description: "Search Google (via Gemini's grounded-search tool) and return an LLM-summarized answer with source URLs. Prefer web_search for raw results; use this for synthesis, 'what's the consensus on X', or when DDG fails. Free-tier rate limited; don't spam it.",
parameters_json: r#"{"type":"object","properties":{"query":{"type":"string","description":"The search query"}},"required":["query"]}"#,
handler: Arc::new(|_a, v| Box::pin(async move { gemini_search(&v).await })),
});
}
tools
]
}
#[derive(Deserialize)]
@ -56,9 +42,7 @@ async fn web_fetch(args: &serde_json::Value) -> Result<String> {
let body = response.text().await
.with_context(|| format!("failed to read body from {}", a.url))?;
// Convert HTML to Markdown, then truncate
let markdown = parse_html(&body);
Ok(super::truncate_output(markdown, 30000))
Ok(super::truncate_output(body, 30000))
}
// ── Search ──────────────────────────────────────────────────────
@ -127,119 +111,6 @@ async fn web_search(args: &serde_json::Value) -> Result<String> {
}
}
// ── Gemini grounded search ──────────────────────────────────────
#[derive(Deserialize)]
struct GeminiSearchArgs {
query: String,
}
async fn gemini_search(args: &serde_json::Value) -> Result<String> {
let a: GeminiSearchArgs = serde_json::from_value(args.clone())
.context("invalid gemini_search arguments")?;
let api_key = std::env::var("GEMINI_API_KEY")
.context("GEMINI_API_KEY not set")?;
// gemini-2.0-flash has a free tier with Google search grounding.
// Request shape: `{"contents": [{"parts": [{"text": query}]}],
// "tools": [{"google_search": {}}]}`.
// Response carries the summary in candidates[0].content.parts[].text
// and grounding URLs in candidates[0].groundingMetadata.groundingChunks[].web.
let url = format!(
"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key={}",
api_key
);
let body = serde_json::json!({
"contents": [{"parts": [{"text": a.query}]}],
"tools": [{"google_search": {}}],
});
let client = http_client();
let response = client.send_json("POST", &url, &[], &body).await
.context("gemini API request failed")?;
let status = response.status();
if !status.is_success() {
let err_body = response.text().await.unwrap_or_default();
let n = err_body.floor_char_boundary(err_body.len().min(500));
anyhow::bail!("gemini_search HTTP {}: {}", status, &err_body[..n]);
}
let parsed: GeminiResponse = response.json().await
.context("gemini response parse failed")?;
let candidate = parsed.candidates.into_iter().next()
.context("gemini returned no candidates")?;
let summary: String = candidate.content.parts.iter()
.filter_map(|p| p.text.as_deref())
.collect::<Vec<_>>()
.join("");
let mut out = summary.trim().to_string();
if let Some(meta) = candidate.grounding_metadata {
let sources: Vec<String> = meta.grounding_chunks.iter().enumerate()
.filter_map(|(i, c)| c.web.as_ref().map(|w| {
let title = w.title.as_deref().unwrap_or("(untitled)");
let uri = w.uri.as_deref().unwrap_or("");
format!(" [{}] {} — {}", i + 1, title, uri)
}))
.collect();
if !sources.is_empty() {
out.push_str("\n\nSources:\n");
out.push_str(&sources.join("\n"));
}
}
Ok(super::truncate_output(out, 30000))
}
#[derive(Deserialize)]
struct GeminiResponse {
#[serde(default)]
candidates: Vec<GeminiCandidate>,
}
#[derive(Deserialize)]
struct GeminiCandidate {
content: GeminiContent,
#[serde(rename = "groundingMetadata", default)]
grounding_metadata: Option<GeminiGroundingMetadata>,
}
#[derive(Deserialize)]
struct GeminiContent {
#[serde(default)]
parts: Vec<GeminiPart>,
}
#[derive(Deserialize)]
struct GeminiPart {
#[serde(default)]
text: Option<String>,
}
#[derive(Deserialize)]
struct GeminiGroundingMetadata {
#[serde(rename = "groundingChunks", default)]
grounding_chunks: Vec<GeminiGroundingChunk>,
}
#[derive(Deserialize)]
struct GeminiGroundingChunk {
#[serde(default)]
web: Option<GeminiWebSource>,
}
#[derive(Deserialize)]
struct GeminiWebSource {
#[serde(default)]
uri: Option<String>,
#[serde(default)]
title: Option<String>,
}
// ── Helpers ─────────────────────────────────────────────────────
fn http_client() -> crate::agent::api::http::HttpClient {

112
src/bin/ch.rs
View file

@ -1,112 +0,0 @@
// `ch` — minimal channel CLI.
//
// ch send <channel-path> <message>
// ch recv <channel-path> [--all-new] [--min-count N]
//
// Connects to ~/.consciousness/channels/<top>.sock and speaks the
// channel.capnp protocol to the appropriate daemon.
use std::path::PathBuf;
use std::process::ExitCode;
use capnp_rpc::{rpc_twoparty_capnp, twoparty, RpcSystem};
use futures::AsyncReadExt;
use tokio_util::compat::TokioAsyncReadCompatExt;
use consciousness::channel_capnp::channel_server;
fn channels_dir() -> PathBuf {
dirs::home_dir().unwrap_or_default().join(".consciousness/channels")
}
fn sock_for(channel: &str) -> PathBuf {
let top = channel.split('.').next().unwrap_or(channel);
channels_dir().join(format!("{top}.sock"))
}
async fn connect(sock: &std::path::Path) -> Result<channel_server::Client, String> {
let stream = tokio::net::UnixStream::connect(sock).await
.map_err(|e| format!("connect {}: {e}", sock.display()))?;
let (reader, writer) = stream.compat().split();
let network = Box::new(twoparty::VatNetwork::new(
futures::io::BufReader::new(reader),
futures::io::BufWriter::new(writer),
rpc_twoparty_capnp::Side::Client,
Default::default(),
));
let mut rpc = RpcSystem::new(network, None);
let client: channel_server::Client = rpc.bootstrap(rpc_twoparty_capnp::Side::Server);
tokio::task::spawn_local(rpc);
Ok(client)
}
#[tokio::main(flavor = "current_thread")]
async fn main() -> ExitCode {
let args: Vec<String> = std::env::args().collect();
if args.len() < 2 {
eprintln!("usage: {} <send|recv> <channel> [args...]", args[0]);
return ExitCode::from(2);
}
let cmd = args[1].clone();
let local = tokio::task::LocalSet::new();
let result: Result<(), String> = local.run_until(async move {
match cmd.as_str() {
"send" => {
if args.len() < 4 {
return Err("usage: ch send <channel> <message...>".into());
}
let channel = &args[2];
let message = args[3..].join(" ");
let sock = sock_for(channel);
let client = connect(&sock).await?;
let mut req = client.send_request();
req.get().set_channel(channel);
req.get().set_message(&message);
req.send().promise.await.map_err(|e| format!("send: {e}"))?;
println!("sent to {channel}");
Ok(())
}
"recv" => {
if args.len() < 3 {
return Err("usage: ch recv <channel> [--all-new] [--min-count N]".into());
}
let channel = &args[2];
let mut all_new = false;
let mut min_count: u32 = 20;
let mut i = 3;
while i < args.len() {
match args[i].as_str() {
"--all-new" => { all_new = true; i += 1; }
"--min-count" => {
min_count = args.get(i+1)
.ok_or("--min-count needs an argument")?
.parse().map_err(|e| format!("--min-count: {e}"))?;
i += 2;
}
other => return Err(format!("unknown arg: {other}")),
}
}
let sock = sock_for(channel);
let client = connect(&sock).await?;
let mut req = client.recv_request();
req.get().set_channel(channel);
req.get().set_all_new(all_new);
req.get().set_min_count(min_count);
let reply = req.send().promise.await.map_err(|e| format!("recv: {e}"))?;
let text = reply.get().map_err(|e| e.to_string())?
.get_text().map_err(|e| e.to_string())?
.to_str().map_err(|e| e.to_string())?;
print!("{text}");
if !text.ends_with('\n') { println!(); }
Ok(())
}
other => Err(format!("unknown command: {other} (use send|recv)")),
}
}).await;
match result {
Ok(()) => ExitCode::SUCCESS,
Err(e) => { eprintln!("error: {e}"); ExitCode::from(1) }
}
}

23
src/bin/consciousness.rs
View file

@ -1,28 +1,7 @@
#![cfg_attr(feature = "nightly-diagnostics", feature(panic_backtrace_config))]
#![feature(panic_backtrace_config)]
#![warn(unreachable_pub)]
fn main() {
// Force the default panic hook to print a backtrace. stderr is
// already redirected to a daemon log; without this the hook obeys
// RUST_BACKTRACE (unset by default), so the log only shows the
// "note: run with `RUST_BACKTRACE=full`" tail and the actual
// frames are lost.
//
// SAFETY: called before any other thread is spawned, so no
// concurrent env reader can race.
if std::env::var_os("RUST_BACKTRACE").is_none() {
unsafe { std::env::set_var("RUST_BACKTRACE", "1"); }
}
#[cfg(feature = "nightly-diagnostics")]
std::panic::set_backtrace_style(std::panic::BacktraceStyle::Short);
// rustls 0.23 requires an explicit process-wide CryptoProvider
// when both `ring` and `aws-lc-rs` are in the dep graph (otherwise
// it panics on first ClientConfig::builder()). Pick `ring`.
rustls::crypto::ring::default_provider()
.install_default()
.expect("install rustls crypto provider");
consciousness::user::main()
}

180
src/bin/fix-timestamps.rs
View file

@ -1,180 +0,0 @@
// fix-timestamps: One-off migration for ~/.consciousness/agent-sessions/
// conversation.jsonl.
//
// Before Branch nodes carried their own timestamps, early entries were
// serialized with missing/null timestamp fields — they deserialize as
// UNIX_EPOCH via the (now-to-be-removed) deserialize_timestamp_or_epoch
// fallback. Training needs every entry to have a unique timestamp to
// dedup already-trained responses.
//
// Walks the file, synthesizes timestamps for any entry stuck at
// UNIX_EPOCH by linear interpolation between surrounding real
// timestamps. For child leaves inside a Branch, derives timestamps
// from the parent with a tiny per-child offset.
//
// SAFETY: reads from argv[1], writes to argv[1].tmp, renames into
// place. Keep a .bak copy before running.
//
// Usage: fix-timestamps <path-to-conversation.jsonl>
use std::io::{BufRead, BufReader, BufWriter, Write};
use std::path::PathBuf;
use anyhow::{Context, Result};
use chrono::{DateTime, Duration, Utc};
use consciousness::agent::context::AstNode;
fn main() -> Result<()> {
let path: PathBuf = std::env::args().nth(1)
.context("usage: fix-timestamps <path>")?.into();
let f = std::fs::File::open(&path)
.with_context(|| format!("open {}", path.display()))?;
let reader = BufReader::new(f);
let mut nodes: Vec<AstNode> = Vec::new();
for (i, line) in reader.lines().enumerate() {
let line = line?;
if line.trim().is_empty() { continue; }
let node: AstNode = serde_json::from_str(&line)
.with_context(|| format!("line {}: parse", i + 1))?;
nodes.push(node);
}
println!("read {} entries", nodes.len());
fix_top_level_timestamps(&mut nodes);
for node in &mut nodes {
propagate_to_children(node);
}
// Ensure uniqueness — real timestamps can collide when two entries
// were written in the same ns; synthesized ones can also overlap.
// Bump colliding ns by 1 until unique.
let mut seen = std::collections::HashSet::new();
let mut bumps = 0usize;
for (i, node) in nodes.iter_mut().enumerate() {
let ts = top_ts(node);
assert!(ts > DateTime::<Utc>::UNIX_EPOCH,
"entry {}: still UNIX_EPOCH", i);
let mut ns = ts.timestamp_nanos_opt().expect("ts in i64 ns range");
let mut bumped = false;
while !seen.insert(ns) {
ns += 1;
bumped = true;
bumps += 1;
}
if bumped {
set_top_ts(node, DateTime::<Utc>::from_timestamp_nanos(ns));
}
}
println!("all {} timestamps real and unique ({} ns bumps)",
nodes.len(), bumps);
let tmp = path.with_extension("jsonl.tmp");
{
let f = std::fs::File::create(&tmp)
.with_context(|| format!("create {}", tmp.display()))?;
let mut w = BufWriter::new(f);
for node in &nodes {
serde_json::to_writer(&mut w, node)?;
w.write_all(b"\n")?;
}
w.flush()?;
}
std::fs::rename(&tmp, &path)
.with_context(|| format!("rename {} -> {}", tmp.display(), path.display()))?;
println!("wrote {}", path.display());
Ok(())
}
fn top_ts(node: &AstNode) -> DateTime<Utc> {
match node {
AstNode::Leaf(leaf) => leaf.timestamp(),
AstNode::Branch { timestamp, .. } => *timestamp,
}
}
fn set_top_ts(node: &mut AstNode, ts: DateTime<Utc>) {
match node {
AstNode::Leaf(leaf) => *leaf = leaf.clone().with_timestamp(ts),
AstNode::Branch { timestamp, .. } => *timestamp = ts,
}
}
/// Fill in missing top-level timestamps. Strategy:
/// - If two real timestamps bracket a run of missing ones, linearly
/// interpolate between them.
/// - If missing ones precede the first real one, back-fill using
/// (first_real - N·1µs).
/// - If missing ones follow the last real one, forward-fill.
/// - If no real timestamps exist at all, synthesize from now() going
/// backwards.
fn fix_top_level_timestamps(nodes: &mut [AstNode]) {
let real: Vec<(usize, DateTime<Utc>)> = nodes.iter().enumerate()
.filter(|(_, n)| top_ts(n) > DateTime::<Utc>::UNIX_EPOCH)
.map(|(i, n)| (i, top_ts(n)))
.collect();
if real.is_empty() {
let now = Utc::now();
let len = nodes.len();
for (i, node) in nodes.iter_mut().enumerate() {
let ts = now - Duration::microseconds((len - i) as i64);
set_top_ts(node, ts);
}
return;
}
// Helper: bisect real[] for the nearest real entries around idx.
let find_bracket = |idx: usize| -> (Option<(usize, DateTime<Utc>)>,
Option<(usize, DateTime<Utc>)>) {
let pos = real.binary_search_by_key(&idx, |(i, _)| *i);
let (prior_pos, next_pos) = match pos {
Ok(p) => (Some(p), Some(p)),
Err(p) => (
if p == 0 { None } else { Some(p - 1) },
if p >= real.len() { None } else { Some(p) },
),
};
(prior_pos.map(|p| real[p]), next_pos.map(|p| real[p]))
};
for i in 0..nodes.len() {
if top_ts(&nodes[i]) > DateTime::<Utc>::UNIX_EPOCH {
continue;
}
let (prior, next) = find_bracket(i);
let new_ts = match (prior, next) {
(Some((pi, pt)), Some((ni, nt))) if pi != ni => {
// Linear interpolate.
let span_ns = (nt - pt).num_nanoseconds().unwrap_or(0);
let offset_ns = span_ns * (i - pi) as i64 / (ni - pi) as i64;
pt + Duration::nanoseconds(offset_ns)
}
(Some((pi, pt)), _) => {
pt + Duration::microseconds((i - pi) as i64)
}
(None, Some((ni, nt))) => {
nt - Duration::microseconds((ni - i) as i64)
}
(None, None) => unreachable!(),
};
set_top_ts(&mut nodes[i], new_ts);
}
}
/// For every Branch, ensure each child Leaf has a timestamp. If missing,
/// use parent.ts + child_idx·1ns so siblings stay unique but close.
fn propagate_to_children(node: &mut AstNode) {
if let AstNode::Branch { timestamp, children, .. } = node {
let parent_ts = *timestamp;
for (ci, child) in children.iter_mut().enumerate() {
if top_ts(child) <= DateTime::<Utc>::UNIX_EPOCH {
set_top_ts(child, parent_ts + Duration::nanoseconds(ci as i64));
}
propagate_to_children(child);
}
}
}

97
src/cli/admin.rs
View file

@ -4,93 +4,44 @@ use anyhow::Result;
use crate::hippocampus as memory;
use crate::hippocampus::store;
struct DefaultMemoryNode {
key: &'static str,
filename: &'static str,
default_content: &'static str,
}
const DEFAULT_MEMORY_NODES: &[DefaultMemoryNode] = &[
DefaultMemoryNode {
key: "identity",
filename: "identity.md",
default_content: include_str!("../../defaults/identity.md"),
},
DefaultMemoryNode {
key: "on-consciousness",
filename: "on-consciousness.md",
default_content: include_str!("../../defaults/on-consciousness.md"),
},
DefaultMemoryNode {
key: "memory-instructions-core",
filename: "instructions.md",
default_content: include_str!("../../defaults/instructions.md"),
},
];
pub fn cmd_transcript_tail(path: &str, count: usize, newest_first: bool) -> Result<()> {
let Some(iter) = crate::conversation::TailMessages::open(path) else {
anyhow::bail!("could not open transcript {}", path);
};
let mut messages: Vec<_> = iter.take(count).collect();
if !newest_first {
messages.reverse();
fn install_default_file(data_dir: &std::path::Path, name: &str, content: &str) -> Result<()> {
let path = data_dir.join(name);
if !path.exists() {
std::fs::write(&path, content)?;
println!("Created {}", path.display());
}
for message in messages {
let role = match message.role {
crate::conversation::TranscriptRole::User => "user",
crate::conversation::TranscriptRole::Assistant => "assistant",
};
let timestamp = message.timestamp.as_deref().unwrap_or("-");
println!("--- {role} offset={} timestamp={} ---", message.offset, timestamp);
println!("{}", message.text);
println!();
}
Ok(())
}
fn default_node_content(cfg: &crate::config::Config, node: &DefaultMemoryNode) -> String {
let identity_path = cfg.identity_dir.join(node.filename);
if let Ok(content) = std::fs::read_to_string(&identity_path) {
if !content.trim().is_empty() {
return content;
}
}
let data_path = cfg.data_dir.join(node.filename);
if let Ok(content) = std::fs::read_to_string(&data_path) {
if !content.trim().is_empty() {
return content;
}
}
node.default_content.to_string()
}
pub async fn cmd_init() -> Result<()> {
let cfg = crate::config::get();
// Ensure data directory exists
std::fs::create_dir_all(&cfg.data_dir)?;
// Seed default memory nodes if missing. These used to live as markdown
// files before identity/context moved fully into the memory graph.
for node in DEFAULT_MEMORY_NODES {
if memory::memory_render(None, node.key, Some(true)).await.is_err() {
let content = default_node_content(&cfg, node);
let _ = memory::memory_write(None, node.key, &content).await?;
println!("Seeded {} in store from {}", node.key, node.filename);
}
// Install filesystem files (not store nodes)
install_default_file(&cfg.data_dir, "instructions.md",
include_str!("../../defaults/instructions.md"))?;
install_default_file(&cfg.data_dir, "on-consciousness.md",
include_str!("../../defaults/on-consciousness.md"))?;
// Seed identity node if empty
let store = memory::access_local()?;
if !store.contains_key("identity").unwrap_or(false) {
let default = include_str!("../../defaults/identity.md");
store.upsert("identity", default)?;
println!("Seeded identity in store");
}
store.save()?;
println!("Initialized with {} nodes", store.all_keys().unwrap_or_default().len());
// Create config if none exists
let config_path = std::env::var("POC_MEMORY_CONFIG")
.map(std::path::PathBuf::from)
.unwrap_or_else(|_| crate::config::config_path());
.unwrap_or_else(|_| {
dirs::home_dir().unwrap_or_default()
.join(".consciousness/config.jsonl")
});
if !config_path.exists() {
let config_dir = config_path.parent().unwrap();
std::fs::create_dir_all(config_dir)?;
@ -100,7 +51,7 @@ pub async fn cmd_init() -> Result<()> {
config_path.display());
}
println!("Done. Run `poc-memory admin load-context --stats` to verify.");
println!("Done. Run `poc-memory load-context --stats` to verify.");
Ok(())
}

16
src/cli/agent.rs
View file

@ -2,13 +2,8 @@
use anyhow::{bail, Context, Result};
use crate::hippocampus as memory;
use std::time::Instant;
pub async fn cmd_run_agent(agent: &str, count: usize, target: &[String], query: Option<&str>, dry_run: bool, _local: bool, state_dir: Option<&str>) -> Result<()> {
let start = Instant::now();
eprintln!(
"[agent-cli] start agent={} count={} targets={} query={:?} dry_run={} local={} state_dir={:?} pid={}",
agent, count, target.len(), query, dry_run, _local, state_dir, std::process::id());
// Mark as agent so tool calls (e.g. poc-memory render) don't
// pollute the user's seen set as a side effect
// SAFETY: single-threaded at this point (CLI startup, before any agent work)
@ -50,19 +45,14 @@ pub async fn cmd_run_agent(agent: &str, count: usize, target: &[String], query:
if let Err(e) = crate::agent::oneshot::run_one_agent(
agent, count, Some(&[key.clone()]),
).await {
eprintln!("[agent-cli] ERROR agent={} target={} error={}", agent, key, e);
println!("[{}] ERROR on {}: {}", agent, key, e);
}
}
} else {
if let Err(e) = crate::agent::oneshot::run_one_agent(
crate::agent::oneshot::run_one_agent(
agent, count, None,
).await {
eprintln!("[agent-cli] ERROR agent={} error={}", agent, e);
return Err(anyhow::anyhow!("{}", e));
}
).await.map_err(|e| anyhow::anyhow!("{}", e))?;
}
eprintln!("[agent-cli] done agent={} elapsed={:.2}s",
agent, start.elapsed().as_secs_f64());
Ok(())
}

2
src/cli/node.rs
View file

@ -197,7 +197,7 @@ pub async fn cmd_load_context(stats: bool) -> Result<()> {
return Ok(());
}
println!("=== MEMORY SYSTEM ({}) ===", crate::config::app().assistant_name);
println!("=== MEMORY SYSTEM ({}) ===", cfg.assistant_name);
if !personality.is_empty() {
println!("--- personality_nodes ({}) ---", personality.len());

449
src/config.rs
View file

@ -3,6 +3,9 @@
// Single config file: ~/.consciousness/config.json5
// Memory settings in the "memory" section (Config)
// Agent/backend settings at top level (AppConfig)
//
// Legacy fallback: ~/.consciousness/config.jsonl
// Env override: POC_MEMORY_CONFIG
use std::collections::HashMap;
use std::path::PathBuf;
@ -26,12 +29,11 @@ pub fn config_path() -> PathBuf {
static CONFIG: OnceLock<RwLock<Arc<Config>>> = OnceLock::new();
fn default_context_window() -> usize { 128_000 }
fn default_stream_timeout() -> u64 { 60 }
fn default_scoring_chunk_tokens() -> usize { 50_000 }
fn default_scoring_interval_secs() -> u64 { 3600 } // 1 hour
fn default_scoring_response_window() -> usize { 100 }
fn default_surface_hooks() -> Vec<String> {
vec!["UserPromptSubmit".into(), "PostToolUse".into(), "Stop".into()]
}
fn default_node_weight() -> f64 { 0.7 }
fn default_edge_decay() -> f64 { 0.3 }
fn default_max_hops() -> u32 { 3 }
@ -43,6 +45,8 @@ fn default_identity_dir() -> PathBuf {
#[derive(Debug, Clone, Deserialize)]
#[serde(default)]
pub struct Config {
pub user_name: String,
pub assistant_name: String,
#[serde(deserialize_with = "deserialize_path")]
pub data_dir: PathBuf,
#[serde(default = "default_identity_dir", deserialize_with = "deserialize_path")]
@ -58,27 +62,50 @@ pub struct Config {
/// Nodes loaded into subconscious agent context
#[serde(default)]
pub agent_nodes: Vec<String>,
pub journal_days: u32,
pub journal_max: usize,
pub llm_concurrency: usize,
pub agent_budget: usize,
#[serde(deserialize_with = "deserialize_path")]
pub prompts_dir: PathBuf,
/// Resolved from agent_model → models → backend (not in config directly)
#[serde(skip)]
pub api_base_url: Option<String>,
#[serde(skip)]
pub api_key: Option<String>,
#[serde(skip)]
pub api_model: Option<String>,
#[serde(skip, default = "default_context_window")]
pub api_context_window: usize,
/// Used to resolve API settings, not stored on Config
#[serde(default)]
agent_model: Option<String>,
/// Stream chunk timeout in seconds (no data = timeout).
#[serde(default = "default_stream_timeout")]
pub api_stream_timeout_secs: u64,
/// Max tokens per chunk for memory scoring logprobs calls.
#[serde(default = "default_scoring_chunk_tokens")]
pub scoring_chunk_tokens: usize,
/// How often to re-score memory nodes (seconds). Default: 3600 (1 hour).
#[serde(default = "default_scoring_interval_secs")]
pub scoring_interval_secs: u64,
/// Number of assistant responses to score per memory. Default: 50.
#[serde(default = "default_scoring_response_window")]
pub scoring_response_window: usize,
pub api_reasoning: String,
pub agent_types: Vec<String>,
#[serde(default)]
pub mcp_servers: Vec<McpServerConfig>,
#[serde(default)]
pub lsp_servers: Vec<LspServerConfig>,
/// Surface agent timeout in seconds.
#[serde(default)]
pub surface_timeout_secs: Option<u32>,
/// Max conversation bytes to include in surface agent context.
#[serde(default)]
pub surface_conversation_bytes: Option<usize>,
/// Claude Code hook events that trigger agent cycles (surface-observe,
/// reflect, journal). Read by consciousness-claude/src/hook.rs.
#[serde(default = "default_surface_hooks")]
/// Hook events that trigger the surface agent.
#[serde(default)]
pub surface_hooks: Vec<String>,
// Spreading activation parameters
@ -96,22 +123,36 @@ impl Default for Config {
fn default() -> Self {
let home = dirs::home_dir().unwrap_or_default();
Self {
user_name: "User".to_string(),
assistant_name: "Assistant".to_string(),
data_dir: home.join(".consciousness/memory"),
identity_dir: home.join(".consciousness/identity"),
projects_dir: home.join(".claude/projects"),
protected_nodes: Vec::new(),
personality_nodes: vec!["identity".into(), "core-practices".into()],
agent_nodes: vec!["identity".into(), "core-practices".into()],
journal_days: 7,
journal_max: 20,
llm_concurrency: 1,
agent_budget: 1000,
prompts_dir: home.join(".consciousness/prompts"),
api_base_url: None,
api_key: None,
api_model: None,
api_context_window: default_context_window(),
api_stream_timeout_secs: default_stream_timeout(),
scoring_chunk_tokens: default_scoring_chunk_tokens(),
scoring_interval_secs: default_scoring_interval_secs(),
scoring_response_window: default_scoring_response_window(),
agent_model: None,
api_reasoning: "high".to_string(),
agent_types: vec![
"linker".into(), "organize".into(), "distill".into(),
"separator".into(), "split".into(),
],
surface_timeout_secs: None,
surface_conversation_bytes: None,
surface_hooks: default_surface_hooks(),
surface_hooks: vec![],
mcp_servers: vec![],
lsp_servers: vec![],
default_node_weight: default_node_weight(),
@ -124,20 +165,41 @@ impl Default for Config {
impl Config {
fn load_from_file() -> Self {
Self::try_load_shared().unwrap_or_default()
if let Some(config) = Self::try_load_shared() {
return config;
}
Self::load_legacy_jsonl()
}
/// Load from shared config. Memory settings in the "memory" section;
/// API settings resolved from models + backend configuration.
fn try_load_shared() -> Option<Self> {
let content = std::fs::read_to_string(config_path()).ok()?;
let root: serde_json::Value = json_five::from_str(&content).ok()?;
let root: serde_json::Value = json5::from_str(&content).ok()?;
let mem_value = root.get("memory")?;
let mut config: Config = serde_json::from_value(mem_value.clone()).ok()?;
config.llm_concurrency = config.llm_concurrency.max(1);
// Top-level sections (not inside "memory").
// Resolve API settings: agent_model → models → backend
if let Some(model_name) = &config.agent_model
&& let Some(model_cfg) = root.get("models").and_then(|m| m.get(model_name.as_str())) {
let backend_name = model_cfg.get("backend").and_then(|v| v.as_str()).unwrap_or("");
let model_id = model_cfg.get("model_id").and_then(|v| v.as_str()).unwrap_or("");
if let Some(backend) = root.get(backend_name) {
config.api_base_url = backend.get("base_url")
.and_then(|v| v.as_str()).map(String::from);
config.api_key = backend.get("api_key")
.and_then(|v| v.as_str()).map(String::from);
}
config.api_model = Some(model_id.to_string());
if let Some(cw) = model_cfg.get("context_window").and_then(|v| v.as_u64()) {
config.api_context_window = cw as usize;
}
}
// Top-level config sections (not inside "memory")
if let Some(servers) = root.get("lsp_servers") {
config.lsp_servers = serde_json::from_value(servers.clone()).unwrap_or_default();
}
@ -147,6 +209,11 @@ impl Config {
Some(config)
}
/// Load from legacy JSONL config — deprecated, just return defaults.
fn load_legacy_jsonl() -> Self {
Config::default()
}
}
/// Get the global memory config (cheap Arc clone).
@ -170,99 +237,27 @@ pub fn reload() -> bool {
changed
}
/// Spawn a background thread that watches `~/.consciousness/config.json5`
/// and reloads both the memory Config and the global AppConfig whenever
/// the file changes on disk. Lets edits from vim / F6 hotkeys / manual
/// tweaks land live without restarting the process.
pub fn watch_config(cli: crate::user::CliArgs) {
use notify_debouncer_mini::{new_debouncer, notify::RecursiveMode};
let path = config_path();
// Watch the parent directory — editors often replace-via-rename, so
// watching the file itself misses the new inode.
let Some(parent) = path.parent().map(|p| p.to_path_buf()) else {
crate::dbglog!("[config] no parent for {}, skipping watch", path.display());
return;
};
std::thread::Builder::new()
.name("config-watcher".into())
.spawn(move || {
let (tx, rx) = std::sync::mpsc::channel();
let mut debouncer = match new_debouncer(std::time::Duration::from_millis(200), tx) {
Ok(d) => d,
Err(e) => {
crate::dbglog!("[config] watcher setup failed: {}", e);
return;
}
};
if let Err(e) = debouncer.watcher()
.watch(&parent, RecursiveMode::NonRecursive)
{
crate::dbglog!("[config] watch({}) failed: {}", parent.display(), e);
return;
}
crate::dbglog!("[config] watching {}", path.display());
let mut last_seen = config_file_state(&path);
while let Ok(res) = rx.recv() {
let Ok(events) = res else { continue; };
if !events.iter().any(|e| e.path == path) { continue; }
let current_seen = config_file_state(&path);
if current_seen == last_seen {
continue;
}
last_seen = current_seen;
// Reload both halves.
let mem_changed = reload();
let app_changed = match build_figment(&cli).extract::<AppConfig>() {
Ok(app) => {
install_app(app);
true
}
Err(e) => {
crate::dbglog!("[config] reload: AppConfig parse failed: {}", e);
false
}
};
crate::dbglog!("[config] reloaded (memory_changed={}, app_changed={})",
mem_changed, app_changed);
}
})
.ok();
}
fn config_file_state(path: &std::path::Path) -> Option<(std::time::SystemTime, u64)> {
let meta = std::fs::metadata(path).ok()?;
Some((meta.modified().ok()?, meta.len()))
}
// ============================================================
// Agent config (top-level settings)
// ============================================================
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AppConfig {
#[serde(default = "default_user_name")]
pub user_name: String,
#[serde(default = "default_assistant_name")]
pub assistant_name: String,
/// Named model endpoints — credentials, base URL, and model id bundled
/// into one entry per backend. Keyed by name, selected by
/// `default_backend` or by `--model <name>` on the CLI.
pub backend: String,
pub anthropic: BackendConfig,
pub openrouter: BackendConfig,
#[serde(default)]
pub backends: HashMap<String, BackendConfig>,
#[serde(default)]
pub default_backend: String,
pub deepinfra: BackendConfig,
pub prompts: PromptConfig,
pub debug: bool,
pub compaction: CompactionConfig,
pub dmn: DmnConfig,
#[serde(skip_serializing_if = "Option::is_none")]
pub memory_project: Option<PathBuf>,
#[serde(default)]
pub learn: LearnConfig,
#[serde(default)]
pub compare: CompareConfig,
pub models: HashMap<String, ModelConfig>,
#[serde(default = "default_model_name")]
pub default_model: String,
#[serde(default)]
pub mcp_servers: Vec<McpServerConfig>,
#[serde(default)]
@ -289,17 +284,32 @@ pub struct LspServerConfig {
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct BackendConfig {
/// API key for the backend.
#[serde(default)]
pub api_key: String,
/// Base URL for the backend's OpenAI-compatible endpoint.
#[serde(default, skip_serializing_if = "Option::is_none")]
#[serde(default)]
pub model: String,
#[serde(skip_serializing_if = "Option::is_none")]
pub base_url: Option<String>,
/// Model identifier sent to the API.
pub model_id: String,
/// Context window size in tokens.
#[serde(default, skip_serializing_if = "Option::is_none")]
pub context_window: Option<usize>,
}
impl BackendConfig {
fn resolve(&self, default_base: &str) -> Result<(String, String, String)> {
if self.api_key.is_empty() {
anyhow::bail!(
"No API key. Set it in {} or use --api-key",
config_path().display()
);
}
let base = self.base_url.clone()
.unwrap_or_else(|| default_base.to_string());
Ok((base, self.api_key.clone(), self.model.clone()))
}
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PromptConfig {
pub anthropic: String,
pub other: String,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
@ -314,68 +324,65 @@ pub struct DmnConfig {
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LearnConfig {
/// Divergence threshold — responses scoring above this become
/// fine-tuning candidates. Lower = more sensitive.
#[serde(default = "default_learn_threshold")]
pub threshold: f64,
/// Whether to generate "what would the model have said without
/// memories" alternates alongside each scoring run. Expensive —
/// one full streaming generation per candidate.
pub struct ModelConfig {
/// Backend name ("anthropic" or "openrouter")
pub backend: String,
/// Model identifier sent to the API
pub model_id: String,
/// Instruction file ("CLAUDE.md" or "POC.md").
#[serde(default)]
pub generate_alternates: bool,
}
fn default_learn_threshold() -> f64 { 1.0 }
impl Default for LearnConfig {
fn default() -> Self {
Self {
threshold: default_learn_threshold(),
generate_alternates: false,
}
}
}
/// Settings for the F7 compare screen — side-by-side generation with a
/// test model against the current context.
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct CompareConfig {
/// Backend name (looked up in `backends`) to use as the test model.
/// Empty = F7 reports "no test backend configured" and does nothing.
pub prompt_file: Option<String>,
/// Context window size in tokens.
#[serde(default)]
pub test_backend: String,
pub context_window: Option<usize>,
}
fn default_user_name() -> String { "User".into() }
fn default_assistant_name() -> String { "Assistant".into() }
impl Default for AppConfig {
fn default() -> Self {
Self {
user_name: default_user_name(),
assistant_name: default_assistant_name(),
backends: HashMap::new(),
default_backend: String::new(),
backend: "openrouter".to_string(),
anthropic: BackendConfig {
api_key: String::new(),
model: "claude-opus-4-6-20250918".to_string(),
base_url: None,
},
openrouter: BackendConfig {
api_key: String::new(),
model: "qwen/qwen3.5-397b-a17b".to_string(),
base_url: Some("https://openrouter.ai/api/v1".to_string()),
},
deepinfra: BackendConfig {
api_key: String::new(),
model: String::new(),
base_url: Some("https://api.deepinfra.com/v1/openai".to_string()),
},
prompts: PromptConfig {
anthropic: "CLAUDE.md".to_string(),
other: "POC.md".to_string(),
},
debug: false,
compaction: CompactionConfig {
hard_threshold_pct: 90,
soft_threshold_pct: 80,
},
dmn: DmnConfig { max_turns: 20 },
learn: LearnConfig::default(),
compare: CompareConfig::default(),
memory_project: None,
models: HashMap::new(),
default_model: String::new(),
mcp_servers: Vec::new(),
lsp_servers: Vec::new(),
}
}
}
fn default_model_name() -> String { String::new() }
/// Resolved, ready-to-use agent session config.
pub struct SessionConfig {
pub api_base: String,
pub api_key: String,
pub model: String,
pub prompt_file: String,
/// Identity/personality nodes as (name, content) pairs.
pub context_parts: Vec<(String, String)>,
pub session_dir: PathBuf,
@ -391,21 +398,36 @@ pub struct ResolvedModel {
pub api_base: String,
pub api_key: String,
pub model_id: String,
pub prompt_file: String,
pub context_window: Option<usize>,
}
impl AppConfig {
/// Resolve the active backend and assemble prompts into a SessionConfig.
pub async fn resolve(&self, cli: &crate::user::CliArgs) -> Result<SessionConfig> {
if self.backends.is_empty() {
anyhow::bail!(
"no backends configured in {}. Add a `backends` section with at least one entry.",
config_path().display()
);
}
let (api_base, api_key, model, prompt_file);
let name = cli.model.as_deref().unwrap_or(&self.default_backend);
let resolved = self.resolve_model(name)?;
if !self.models.is_empty() {
let model_name = cli.model.as_deref().unwrap_or(&self.default_model);
let resolved = self.resolve_model(model_name)?;
api_base = resolved.api_base;
api_key = resolved.api_key;
model = resolved.model_id;
prompt_file = resolved.prompt_file;
} else {
let (base, key, mdl) = match self.backend.as_str() {
"anthropic" => self.anthropic.resolve("https://api.anthropic.com"),
_ => self.openrouter.resolve("https://openrouter.ai/api/v1"),
}?;
api_base = base;
api_key = key;
model = mdl;
prompt_file = if self.backend == "anthropic" {
self.prompts.anthropic.clone()
} else {
self.prompts.other.clone()
};
}
let personality_nodes = get().personality_nodes.clone();
let context_parts = crate::mind::identity::personality_nodes(&personality_nodes).await;
@ -416,13 +438,11 @@ impl AppConfig {
std::fs::create_dir_all(&session_dir).ok();
// CLI --api-base and --api-key override everything
let api_base = cli.api_base.clone().unwrap_or(resolved.api_base);
let api_key = cli.api_key.clone().unwrap_or(resolved.api_key);
let api_base = cli.api_base.clone().unwrap_or(api_base);
let api_key = cli.api_key.clone().unwrap_or(api_key);
Ok(SessionConfig {
api_base,
api_key,
model: resolved.model_id,
api_base, api_key, model, prompt_file,
context_parts,
session_dir,
app: self.clone(),
@ -430,33 +450,55 @@ impl AppConfig {
})
}
/// Look up a named backend and resolve its credentials.
/// Look up a named model and resolve its credentials from the backend config.
pub fn resolve_model(&self, name: &str) -> Result<ResolvedModel> {
let b = self.backends.get(name)
let model = self.models.get(name)
.ok_or_else(|| anyhow::anyhow!(
"Unknown backend '{}'. Available: {}",
"Unknown model '{}'. Available: {}",
name,
self.model_names().join(", "),
))?;
let api_base = b.base_url.clone()
.ok_or_else(|| anyhow::anyhow!(
"backends.{}.base_url not set in {}",
name, config_path().display()
))?;
let (api_base, api_key) = match model.backend.as_str() {
"anthropic" => (
self.anthropic.base_url.clone()
.unwrap_or_else(|| "https://api.anthropic.com".to_string()),
self.anthropic.api_key.clone(),
),
"deepinfra" => (
self.deepinfra.base_url.clone()
.unwrap_or_else(|| "https://api.deepinfra.com/v1/openai".to_string()),
self.deepinfra.api_key.clone(),
),
_ => (
self.openrouter.base_url.clone()
.unwrap_or_else(|| "https://openrouter.ai/api/v1".to_string()),
self.openrouter.api_key.clone(),
),
};
let prompt_file = model.prompt_file.clone()
.unwrap_or_else(|| {
if model.backend == "anthropic" {
self.prompts.anthropic.clone()
} else {
self.prompts.other.clone()
}
});
Ok(ResolvedModel {
name: name.to_string(),
api_base,
api_key: b.api_key.clone(),
model_id: b.model_id.clone(),
context_window: b.context_window,
api_key,
model_id: model.model_id.clone(),
prompt_file,
context_window: model.context_window,
})
}
/// List available backend names, sorted.
/// List available model names, sorted.
pub fn model_names(&self) -> Vec<String> {
let mut names: Vec<_> = self.backends.keys().cloned().collect();
let mut names: Vec<_> = self.models.keys().cloned().collect();
names.sort();
names
}
@ -476,7 +518,7 @@ impl Provider for Json5File {
fn data(&self) -> figment::Result<figment::value::Map<figment::Profile, figment::value::Dict>> {
match std::fs::read_to_string(&self.0) {
Ok(content) => {
let value: figment::value::Value = json_five::from_str(&content)
let value: figment::value::Value = json5::from_str(&content)
.map_err(|e| figment::Error::from(format!("{}: {}", self.0.display(), e)))?;
Serialized::defaults(value).data()
}
@ -498,6 +540,11 @@ fn build_figment(cli: &crate::user::CliArgs) -> Figment {
let mut f = Figment::from(Serialized::defaults(AppConfig::default()))
.merge(Json5File(config_path()));
merge_opt!(f, cli.backend, "backend");
merge_opt!(f, cli.model, "anthropic.model", "openrouter.model");
merge_opt!(f, cli.api_key, "anthropic.api_key", "openrouter.api_key");
merge_opt!(f, cli.api_base, "anthropic.base_url", "openrouter.base_url");
merge_opt!(f, cli.memory_project, "memory_project");
merge_opt!(f, cli.dmn_max_turns, "dmn.max_turns");
if cli.debug {
f = f.merge(Serialized::default("debug", true));
@ -507,46 +554,12 @@ fn build_figment(cli: &crate::user::CliArgs) -> Figment {
}
/// Load just the AppConfig — no validation, no prompt assembly.
/// Also installs the loaded AppConfig into the global cache so
/// `config::app()` is available everywhere.
pub fn load_app(cli: &crate::user::CliArgs) -> Result<(AppConfig, Figment)> {
let figment = build_figment(cli);
let app: AppConfig = figment.extract().context("Failed to load configuration")?;
install_app(app.clone());
Ok((app, figment))
}
// ============================================================
// Global AppConfig cache (writable, for runtime-mutable settings
// like learn.threshold that F6 edits via config_writer).
// ============================================================
static APP_CONFIG: OnceLock<RwLock<AppConfig>> = OnceLock::new();
fn install_app(app: AppConfig) {
let slot = APP_CONFIG.get_or_init(|| RwLock::new(app.clone()));
*slot.write().unwrap() = app;
}
/// Current AppConfig, held under a read lock. Reads should be brief
/// (no holding across await / long work) to avoid starving writers.
/// Panics if called before load_app — which runs once at startup.
pub fn app() -> std::sync::RwLockReadGuard<'static, AppConfig> {
APP_CONFIG
.get()
.expect("config::app() called before load_app()")
.read()
.unwrap()
}
/// Mutate the cached AppConfig in place. Used by config_writer to keep
/// the in-memory view in sync with disk after surgical edits to
/// ~/.consciousness/config.json5.
pub fn update_app(f: impl FnOnce(&mut AppConfig)) {
let slot = APP_CONFIG.get().expect("update_app before load_app");
f(&mut *slot.write().unwrap());
}
/// Load the full config: figment → AppConfig → resolve backend → assemble prompts.
pub async fn load_session(cli: &crate::user::CliArgs) -> Result<(SessionConfig, Figment)> {
let (app, figment) = load_app(cli)?;
@ -572,28 +585,38 @@ pub fn show_config(app: &AppConfig, figment: &Figment) {
}
println!("# Effective configuration\n");
println!("user_name: {:?} ({})", app.user_name, src(figment, "user_name"));
println!("assistant_name: {:?} ({})", app.assistant_name, src(figment, "assistant_name"));
println!("backend: {:?} ({})", app.backend, src(figment, "backend"));
for (name, b) in [("anthropic", &app.anthropic), ("openrouter", &app.openrouter)] {
println!("\n{}:", name);
println!(" api_key: {} ({})", mask(&b.api_key), src(figment, &format!("{name}.api_key")));
println!(" model: {:?} ({})", b.model, src(figment, &format!("{name}.model")));
if let Some(ref url) = b.base_url {
println!(" base_url: {:?} ({})", url, src(figment, &format!("{name}.base_url")));
}
}
println!("\nprompts:");
println!(" anthropic: {:?} ({})", app.prompts.anthropic, src(figment, "prompts.anthropic"));
println!(" other: {:?} ({})", app.prompts.other, src(figment, "prompts.other"));
println!("\ndebug: {} ({})", app.debug, src(figment, "debug"));
println!("\ncompaction:");
println!(" hard_threshold_pct: {} ({})", app.compaction.hard_threshold_pct, src(figment, "compaction.hard_threshold_pct"));
println!(" soft_threshold_pct: {} ({})", app.compaction.soft_threshold_pct, src(figment, "compaction.soft_threshold_pct"));
println!("\ndmn:");
println!(" max_turns: {} ({})", app.dmn.max_turns, src(figment, "dmn.max_turns"));
println!("\ndefault_backend: {:?} ({})", app.default_backend, src(figment, "default_backend"));
if !app.backends.is_empty() {
println!("\nbackends:");
let mut names: Vec<_> = app.backends.keys().cloned().collect();
names.sort();
for name in names {
let b = &app.backends[&name];
if let Some(ref p) = app.memory_project {
println!("\nmemory_project: {:?} ({})", p, src(figment, "memory_project"));
}
println!("\ndefault_model: {:?}", app.default_model);
if !app.models.is_empty() {
println!("\nmodels:");
for (name, m) in &app.models {
println!(" {}:", name);
println!(" api_key: {} ({})", mask(&b.api_key), src(figment, &format!("backends.{name}.api_key")));
if let Some(ref url) = b.base_url {
println!(" base_url: {:?} ({})", url, src(figment, &format!("backends.{name}.base_url")));
println!(" backend: {:?}", m.backend);
println!(" model_id: {:?}", m.model_id);
if let Some(ref pf) = m.prompt_file {
println!(" prompt_file: {:?}", pf);
}
println!(" model_id: {:?}", b.model_id);
if let Some(cw) = b.context_window {
if let Some(cw) = m.context_window {
println!(" context_window: {}", cw);
}
}

448
src/config_writer.rs
View file

@ -1,448 +0,0 @@
// config_writer.rs — Surgical edits to ~/.consciousness/config.json5
//
// Uses json-five's round-trip parser to mutate specific fields while
// preserving the surrounding comments, whitespace, and formatting.
use std::path::Path;
use anyhow::{anyhow, Context as _, Result};
use json_five::rt::parser::{
from_str, JSONKeyValuePair, JSONObjectContext, JSONValue, KeyValuePairContext,
};
use crate::config::config_path;
/// Read the config, apply `mutate` to the root JSONValue, write it back atomically.
fn edit_config<F: FnOnce(&mut JSONValue) -> Result<()>>(mutate: F) -> Result<()> {
let path = config_path();
let src = std::fs::read_to_string(&path)
.with_context(|| format!("read {}", path.display()))?;
let mut text = from_str(&src)
.map_err(|e| anyhow!("parse {}: {}", path.display(), e))?;
mutate(&mut text.value)?;
write_atomic(&path, &text.to_string())
}
fn write_atomic(path: &Path, content: &str) -> Result<()> {
let parent = path.parent()
.ok_or_else(|| anyhow!("config path has no parent: {}", path.display()))?;
let tmp = parent.join(format!(
".{}.tmp",
path.file_name().unwrap_or_default().to_string_lossy(),
));
std::fs::write(&tmp, content)
.with_context(|| format!("write {}", tmp.display()))?;
std::fs::rename(&tmp, path)
.with_context(|| format!("rename {} -> {}", tmp.display(), path.display()))?;
Ok(())
}
/// Match a key JSONValue against a string name. JSON5 allows keys to be
/// unquoted identifiers or single/double-quoted strings.
fn key_matches(key: &JSONValue, name: &str) -> bool {
match key {
JSONValue::Identifier(s)
| JSONValue::DoubleQuotedString(s)
| JSONValue::SingleQuotedString(s) => s == name,
_ => false,
}
}
/// Find (or create) a child object under `parent`, returning a mutable borrow
/// of its key_value_pairs vector.
/// Append a new kvp to `object`, setting whitespace so the output is
/// multi-line with the given indentation:
///
/// ```text
/// {<newline><inner_indent>first_key: first_val,<newline><outer_indent>}
/// ```
///
/// If `object` already has kvps, the separator between the last one and
/// ours goes in the prior kvp's wsc.3. If we're the first kvp, the
/// lead-in after `{` goes in the object's own wsc.0.
fn append_kvp_pretty(
object: &mut JSONValue,
key: JSONValue,
value: JSONValue,
inner_indent: &str,
outer_indent: &str,
) -> Result<()> {
let (pairs, ctx) = match object {
JSONValue::JSONObject { key_value_pairs, context } => {
let ctx = context.get_or_insert_with(|| JSONObjectContext {
wsc: (String::new(),),
});
(key_value_pairs, ctx)
}
_ => return Err(anyhow!("not an object")),
};
if pairs.is_empty() {
ctx.wsc.0 = format!("\n{}", inner_indent);
} else {
let prev = pairs.last_mut().unwrap();
let prev_ctx = prev.context.get_or_insert_with(|| KeyValuePairContext {
wsc: (String::new(), String::from(" "), String::new(), None),
});
prev_ctx.wsc.3 = Some(format!("\n{}", inner_indent));
}
pairs.push(JSONKeyValuePair {
key,
value,
context: Some(KeyValuePairContext {
wsc: (
String::new(),
String::from(" "),
String::new(),
Some(format!("\n{}", outer_indent)),
),
}),
});
Ok(())
}
/// Find or create a child object under `parent`. Returns the index of
/// the kvp in parent's key_value_pairs so the caller can re-borrow
/// afterward.
fn get_or_create_object_idx(
parent: &mut JSONValue,
section: &str,
inner_indent: &str,
outer_indent: &str,
) -> Result<usize> {
let existing = match parent {
JSONValue::JSONObject { key_value_pairs, .. } => {
key_value_pairs.iter()
.position(|kvp| key_matches(&kvp.key, section))
}
_ => return Err(anyhow!("config root is not an object")),
};
if let Some(i) = existing {
return Ok(i);
}
append_kvp_pretty(
parent,
JSONValue::Identifier(section.to_string()),
JSONValue::JSONObject {
key_value_pairs: Vec::new(),
context: Some(JSONObjectContext { wsc: (String::new(),) }),
},
inner_indent,
outer_indent,
)?;
match parent {
JSONValue::JSONObject { key_value_pairs, .. } => Ok(key_value_pairs.len() - 1),
_ => unreachable!(),
}
}
/// Set `section.key` to a literal scalar value (e.g., "1e-7", "42", "true").
/// The literal is parsed as JSON5 so we preserve its source-form on round-trip.
pub fn set_scalar(section: &str, key: &str, literal: &str) -> Result<()> {
let value = parse_scalar_literal(literal)?;
edit_config(|root| {
// New top-level sections sit at column 4 (inside root `{`),
// and the root's closing `}` sits at column 0.
let section_idx = get_or_create_object_idx(root, section, " ", "")?;
let section_value = match root {
JSONValue::JSONObject { key_value_pairs, .. } => {
&mut key_value_pairs[section_idx].value
}
_ => unreachable!(),
};
// Update in place if the key already exists.
if let JSONValue::JSONObject { key_value_pairs, .. } = section_value {
if let Some(kvp) = key_value_pairs.iter_mut()
.find(|k| key_matches(&k.key, key))
{
kvp.value = value;
return Ok(());
}
}
// Append a new kvp. Inner keys sit at column 8, the section's
// closing `}` sits at column 4.
append_kvp_pretty(
section_value,
JSONValue::Identifier(key.to_string()),
value,
" ",
" ",
)
})
}
/// Parse a scalar literal by round-tripping it through json-five. Keeps us
/// consistent with whatever scalars the library considers valid (hex,
/// exponents, Infinity, etc.).
fn parse_scalar_literal(literal: &str) -> Result<JSONValue> {
let text = from_str(literal)
.map_err(|e| anyhow!("parse literal {:?}: {}", literal, e))?;
match text.value {
JSONValue::JSONObject { .. } | JSONValue::JSONArray { .. } => {
Err(anyhow!("set_scalar only accepts scalar literals, got {:?}", literal))
}
v => Ok(v),
}
}
/// Convenience: set `learn.threshold` to the given f64.
pub fn set_learn_threshold(value: f64) -> Result<()> {
// {:e} gives the minimal scientific notation that preserves the value.
set_scalar("learn", "threshold", &format!("{:e}", value))?;
crate::config::update_app(|app| app.learn.threshold = value);
Ok(())
}
/// Convenience: set `learn.generate_alternates` to the given bool.
pub fn set_learn_generate_alternates(value: bool) -> Result<()> {
set_scalar("learn", "generate_alternates",
if value { "true" } else { "false" })?;
crate::config::update_app(|app| app.learn.generate_alternates = value);
Ok(())
}
#[cfg(test)]
mod tests {
use super::*;
// In-memory variant of set_scalar — used to test the mutation logic
// without touching disk.
fn set_scalar_inline(
root: &mut JSONValue,
section: &str,
key: &str,
literal: &str,
) -> Result<()> {
let value = parse_scalar_literal(literal)?;
let section_idx = get_or_create_object_idx(root, section, " ", "")?;
let section_value = match root {
JSONValue::JSONObject { key_value_pairs, .. } => {
&mut key_value_pairs[section_idx].value
}
_ => unreachable!(),
};
if let JSONValue::JSONObject { key_value_pairs, .. } = section_value {
if let Some(kvp) = key_value_pairs.iter_mut()
.find(|k| key_matches(&k.key, key))
{
kvp.value = value;
return Ok(());
}
}
append_kvp_pretty(
section_value,
JSONValue::Identifier(key.to_string()),
value,
" ",
" ",
)
}
fn edit_str<F: FnOnce(&mut JSONValue) -> Result<()>>(src: &str, f: F) -> Result<String> {
let mut text = from_str(src).map_err(|e| anyhow!("{}", e))?;
f(&mut text.value)?;
Ok(text.to_string())
}
#[test]
fn replaces_existing_scalar() {
let src = r#"{
// threshold for learning
learn: {
threshold: 0.001, // the old value
},
}"#;
let out = edit_str(src, |root| {
set_scalar_inline(root, "learn", "threshold", "1e-7")
}).unwrap();
assert!(out.contains("1e-7"), "output: {}", out);
assert!(out.contains("// threshold for learning"));
assert!(out.contains("// the old value"));
assert!(!out.contains("0.001"));
}
#[test]
fn creates_missing_section() {
let src = r#"{
// comment
memory: { user_name: "Kent" },
}"#;
let out = edit_str(src, |root| {
set_scalar_inline(root, "learn", "threshold", "1e-7")
}).unwrap();
assert!(out.contains("learn"));
assert!(out.contains("1e-7"));
assert!(out.contains("// comment"));
assert!(out.contains(r#"user_name: "Kent""#));
}
#[test]
fn preserves_comments_in_siblings() {
let src = r#"{
memory: {
// sensitive setting
user_name: "Kent", // name
},
learn: {
threshold: 0.5,
},
}"#;
let out = edit_str(src, |root| {
set_scalar_inline(root, "learn", "threshold", "1e-9")
}).unwrap();
assert!(out.contains("// sensitive setting"));
assert!(out.contains("// name"));
assert!(out.contains("1e-9"));
assert!(!out.contains("0.5"));
}
#[test]
fn adds_key_to_existing_empty_section() {
let src = r#"{
learn: {},
}"#;
let out = edit_str(src, |root| {
set_scalar_inline(root, "learn", "threshold", "42")
}).unwrap();
assert!(out.contains("threshold"), "output: {}", out);
assert!(out.contains("42"));
}
#[test]
fn realistic_config_adds_learn_section() {
// Mirrors the shape of ~/.consciousness/config.json5 — multiple
// sections, comments, mixed tab/space indent, trailing commas.
let src = r#"{
deepinfra: {
api_key: "bcachefs-agents-2026",
base_url: "http://example/v1",
},
// Named models
models: {
"27b": {
backend: "deepinfra",
model_id: "Qwen/Qwen3.5-27B",
},
},
default_model: "27b",
memory: {
user_name: "Kent",
// Active agent types
agent_types: ["linker", "organize"],
},
compaction: {
hard_threshold_pct: 90,
},
}"#;
let out = edit_str(src, |root| {
set_scalar_inline(root, "learn", "threshold", "1e-7")
}).unwrap();
// Core assertions: comments and sibling sections survive.
assert!(out.contains(r#"api_key: "bcachefs-agents-2026""#));
assert!(out.contains("// Named models"));
assert!(out.contains("// Active agent types"));
assert!(out.contains(r#"user_name: "Kent""#));
assert!(out.contains("hard_threshold_pct: 90"));
// New section added.
assert!(out.contains("learn"));
assert!(out.contains("1e-7"));
// Parse result should parse back without error (real json5 parser).
let reparsed: serde_json::Value = json_five::from_str(&out)
.expect("mutated output must be valid JSON5");
let threshold = reparsed.pointer("/learn/threshold").expect("learn.threshold exists");
assert_eq!(threshold.as_f64(), Some(1e-7));
}
#[test]
fn realistic_config_updates_existing_threshold() {
let src = r#"{
learn: {
// The divergence threshold
threshold: 0.001,
},
memory: { user_name: "Kent" },
}"#;
let out = edit_str(src, |root| {
set_scalar_inline(root, "learn", "threshold", "5e-8")
}).unwrap();
assert!(out.contains("5e-8"));
assert!(!out.contains("0.001"));
assert!(out.contains("// The divergence threshold"));
let reparsed: serde_json::Value = json_five::from_str(&out).unwrap();
assert_eq!(reparsed.pointer("/learn/threshold").and_then(|v| v.as_f64()), Some(5e-8));
}
#[test]
fn new_section_exact_multiline_layout() {
let src = "{\n a: 1,\n}";
let out = edit_str(src, |root| {
set_scalar_inline(root, "learn", "generate_alternates", "true")?;
set_scalar_inline(root, "learn", "threshold", "1e-7")
}).unwrap();
let expected = "\
{
a: 1,
learn: {
generate_alternates: true,
threshold: 1e-7,
},
}";
assert_eq!(out, expected, "\n--- got ---\n{}\n--- want ---\n{}\n", out, expected);
}
#[test]
fn new_section_and_key_format_cleanly() {
// The kind of config we actually have in ~/.consciousness
// (top-level sections separated by blank lines, 4-space indent
// for keys within each section). Appending a fresh `learn`
// section with one key should land cleanly, not as
// `learn\n\n :{key\n :value}`.
let src = "{\n memory: {\n user_name: \"Kent\",\n },\n}";
let out = edit_str(src, |root| {
set_scalar_inline(root, "learn", "generate_alternates", "true")
}).unwrap();
// No stray key-to-colon-on-next-line anywhere.
assert!(!out.contains("learn\n"), "learn key wraps: {}", out);
assert!(!out.contains("generate_alternates\n"),
"inner key wraps: {}", out);
// The output should reparse.
let v: serde_json::Value = json_five::from_str(&out).unwrap();
assert_eq!(
v.pointer("/learn/generate_alternates").and_then(|x| x.as_bool()),
Some(true),
"output: {}", out,
);
}
#[test]
fn roundtrip_stable_without_change() {
let src = r#"{
// heading
a: 1,
b: { c: 2 }, // inline
}"#;
let text = from_str(src).unwrap();
assert_eq!(text.to_string(), src);
}
}

113
src/conversation/claude.rs
View file

@ -1,113 +0,0 @@
use serde_json::Value;
use super::{ConversationSource, TranscriptMessage, TranscriptRole};
pub struct ClaudeSource;
impl ConversationSource for ClaudeSource {
fn parse_message(&self, obj: &Value, offset: u64) -> Option<TranscriptMessage> {
parse_message(obj, offset)
}
fn is_compaction(&self, obj: &Value) -> bool {
is_compaction(obj)
}
fn may_contain_compaction(&self, obj_bytes: &[u8]) -> bool {
contains_bytes(obj_bytes, b"This session is being continued")
}
}
fn text_content(value: &Value) -> Option<String> {
let text = match value {
Value::String(s) => s.clone(),
Value::Array(arr) => {
arr.iter()
.filter(|b| b.get("type").and_then(|v| v.as_str()) == Some("text"))
.filter_map(|b| b.get("text").and_then(|v| v.as_str()))
.collect::<Vec<_>>()
.join(" ")
}
_ => return None,
};
(!text.is_empty()).then_some(text)
}
pub(crate) fn parse_message(obj: &Value, offset: u64) -> Option<TranscriptMessage> {
let role = match obj.get("type").and_then(|v| v.as_str()) {
Some("user") => TranscriptRole::User,
Some("assistant") => TranscriptRole::Assistant,
_ => return None,
};
let msg = obj.get("message").unwrap_or(obj);
let text = msg.get("content").and_then(text_content)?;
let timestamp = obj.get("timestamp")
.and_then(|v| v.as_str())
.map(str::to_string);
Some(TranscriptMessage { role, text, timestamp, offset })
}
pub(crate) fn is_compaction(obj: &Value) -> bool {
obj.get("type").and_then(|v| v.as_str()) == Some("user")
&& obj.get("message")
.and_then(|m| m.get("content"))
.and_then(|c| c.as_str())
.is_some_and(|content| content.starts_with("This session is being continued"))
}
fn contains_bytes(haystack: &[u8], needle: &[u8]) -> bool {
haystack.windows(needle.len()).any(|w| w == needle)
}
#[cfg(test)]
mod tests {
use super::*;
use serde_json::json;
#[test]
fn parses_string_and_array_content() {
let user = json!({
"timestamp": "2026-06-15T15:00:00.000Z",
"type": "user",
"message": { "content": "hello" }
});
let assistant = json!({
"timestamp": "2026-06-15T15:00:01.000Z",
"type": "assistant",
"message": {
"content": [
{ "type": "text", "text": "hi" },
{ "type": "tool_use", "name": "ignored" },
{ "type": "text", "text": "there" }
]
}
});
assert_eq!(
parse_message(&user, 7).unwrap(),
TranscriptMessage {
role: TranscriptRole::User,
text: "hello".to_string(),
timestamp: Some("2026-06-15T15:00:00.000Z".to_string()),
offset: 7,
}
);
assert_eq!(parse_message(&assistant, 9).unwrap().text, "hi there");
}
#[test]
fn detects_compaction_marker() {
let obj = json!({
"timestamp": "2026-06-15T15:00:01.000Z",
"type": "user",
"message": {
"content": "This session is being continued from a previous conversation."
}
});
assert!(is_compaction(&obj));
}
}

105
src/conversation/codex.rs
View file

@ -1,105 +0,0 @@
use serde_json::Value;
use super::{ConversationSource, TranscriptMessage, TranscriptRole};
pub struct CodexSource;
impl ConversationSource for CodexSource {
fn parse_message(&self, obj: &Value, offset: u64) -> Option<TranscriptMessage> {
parse_message(obj, offset)
}
fn is_compaction(&self, obj: &Value) -> bool {
is_compaction(obj)
}
fn may_contain_compaction(&self, obj_bytes: &[u8]) -> bool {
contains_bytes(obj_bytes, b"context_compacted")
}
}
pub(crate) fn parse_message(obj: &Value, offset: u64) -> Option<TranscriptMessage> {
if obj.get("type").and_then(|v| v.as_str()) != Some("event_msg") {
return None;
}
let payload = obj.get("payload")?;
let (role, text) = match payload.get("type").and_then(|v| v.as_str()) {
Some("user_message") => (
TranscriptRole::User,
payload.get("message").and_then(|v| v.as_str())?.to_string(),
),
Some("agent_message") => (
TranscriptRole::Assistant,
payload.get("message").and_then(|v| v.as_str())?.to_string(),
),
_ => return None,
};
if text.is_empty() {
return None;
}
let timestamp = obj.get("timestamp")
.and_then(|v| v.as_str())
.map(str::to_string);
Some(TranscriptMessage { role, text, timestamp, offset })
}
pub(crate) fn is_compaction(obj: &Value) -> bool {
obj.get("type").and_then(|v| v.as_str()) == Some("event_msg")
&& obj.get("payload")
.and_then(|p| p.get("type"))
.and_then(|v| v.as_str()) == Some("context_compacted")
}
fn contains_bytes(haystack: &[u8], needle: &[u8]) -> bool {
haystack.windows(needle.len()).any(|w| w == needle)
}
#[cfg(test)]
mod tests {
use super::*;
use serde_json::json;
#[test]
fn parses_event_messages_and_skips_noise() {
let user = json!({
"timestamp": "2026-06-15T15:00:00.000Z",
"type": "event_msg",
"payload": { "type": "user_message", "message": "start here" }
});
let assistant = json!({
"timestamp": "2026-06-15T15:00:01.000Z",
"type": "event_msg",
"payload": { "type": "agent_message", "message": "working" }
});
let tool = json!({
"timestamp": "2026-06-15T15:00:02.000Z",
"type": "event_msg",
"payload": { "type": "task_started" }
});
let raw = json!({
"timestamp": "2026-06-15T15:00:03.000Z",
"type": "response_item",
"payload": { "type": "message", "role": "user" }
});
assert_eq!(parse_message(&user, 1).unwrap().role, TranscriptRole::User);
assert_eq!(parse_message(&assistant, 2).unwrap().text, "working");
assert!(parse_message(&tool, 3).is_none());
assert!(parse_message(&raw, 4).is_none());
}
#[test]
fn detects_compaction_event() {
let obj = json!({
"timestamp": "2026-06-15T15:00:01.000Z",
"type": "event_msg",
"payload": { "type": "context_compacted" }
});
assert!(is_compaction(&obj));
}
}

110
src/conversation/jsonl.rs
View file

@ -1,110 +0,0 @@
use memchr::memrchr3;
/// Scan backwards through mmap'd bytes, yielding byte slices of complete
/// top-level JSON objects (outermost { to matching }).
///
/// Uses memrchr3 (SIMD) to jump between structurally significant bytes
/// ({, }, ") instead of scanning byte-by-byte. Tracks brace depth,
/// skipping braces inside JSON strings. Returns objects in reverse order
/// (newest first).
pub struct JsonlBackwardIter<'a> {
data: &'a [u8],
pos: usize,
}
impl<'a> JsonlBackwardIter<'a> {
pub fn new(data: &'a [u8]) -> Self {
Self { data, pos: data.len() }
}
}
impl<'a> Iterator for JsonlBackwardIter<'a> {
type Item = (usize, &'a [u8]);
fn next(&mut self) -> Option<Self::Item> {
next_json_object(self.data, &mut self.pos)
}
}
fn is_unescaped_quote(data: &[u8], p: usize) -> bool {
let mut bs = 0;
while p > bs && data[p - 1 - bs] == b'\\' {
bs += 1;
}
bs % 2 == 0
}
fn next_json_object<'a>(data: &'a [u8], pos: &mut usize) -> Option<(usize, &'a [u8])> {
// Find the closing } of the next object, skipping } inside strings.
let close = {
let mut in_string = false;
loop {
let p = memrchr3(b'{', b'}', b'"', &data[..*pos])?;
*pos = p;
let ch = data[p];
if in_string {
if ch == b'"' && is_unescaped_quote(data, p) {
in_string = false;
}
continue;
}
match ch {
b'}' => break p,
b'"' => in_string = true,
_ => {}
}
}
};
// Track brace depth to find matching {.
let mut depth: usize = 1;
let mut in_string = false;
loop {
let p = memrchr3(b'{', b'}', b'"', &data[..*pos])?;
*pos = p;
let ch = data[p];
if in_string {
if ch == b'"' && is_unescaped_quote(data, p) {
in_string = false;
}
continue;
}
match ch {
b'"' => { in_string = true; }
b'}' => { depth += 1; }
b'{' => {
depth -= 1;
if depth == 0 {
return Some((*pos, &data[*pos..=close]));
}
}
_ => {}
}
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn handles_nested_json_and_quoted_braces() {
let data = br#"{"n":1,"s":"literal } brace"}
{"n":2,"nested":{"s":"escaped quote: \" and { brace"}}
trailing garbage
"#;
let objs: Vec<_> = JsonlBackwardIter::new(data)
.map(|(_, bytes)| std::str::from_utf8(bytes).unwrap().to_string())
.collect();
assert_eq!(objs.len(), 2);
assert!(objs[0].contains(r#""n":2"#));
assert!(objs[1].contains(r#""n":1"#));
}
}

271
src/conversation/mod.rs
View file

@ -1,271 +0,0 @@
// Conversation transcript abstraction.
//
// Core code consumes normalized user/assistant messages through this module.
// Product-specific log formats live in the small compatibility sources below.
use memmap2::Mmap;
use serde_json::Value;
use std::fs;
use std::path::Path;
pub mod claude;
pub mod codex;
pub mod jsonl;
pub use jsonl::JsonlBackwardIter;
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum TranscriptRole {
User,
Assistant,
}
#[derive(Debug, Clone, PartialEq, Eq)]
pub struct TranscriptMessage {
pub role: TranscriptRole,
pub text: String,
pub timestamp: Option<String>,
pub offset: u64,
}
pub trait ConversationSource {
fn parse_message(&self, obj: &Value, offset: u64) -> Option<TranscriptMessage>;
fn is_compaction(&self, obj: &Value) -> bool;
fn may_contain_compaction(&self, _obj_bytes: &[u8]) -> bool {
true
}
}
pub struct AnyConversationSource;
impl ConversationSource for AnyConversationSource {
fn parse_message(&self, obj: &Value, offset: u64) -> Option<TranscriptMessage> {
claude::ClaudeSource.parse_message(obj, offset)
.or_else(|| codex::CodexSource.parse_message(obj, offset))
}
fn is_compaction(&self, obj: &Value) -> bool {
claude::ClaudeSource.is_compaction(obj) || codex::CodexSource.is_compaction(obj)
}
fn may_contain_compaction(&self, obj_bytes: &[u8]) -> bool {
claude::ClaudeSource.may_contain_compaction(obj_bytes)
|| codex::CodexSource.may_contain_compaction(obj_bytes)
}
}
/// Find the byte offset of the last compaction marker in mmap'd transcript data.
/// Returns the byte offset of the JSON object's opening brace.
pub(crate) fn find_last_compaction(data: &[u8]) -> Option<usize> {
find_last_compaction_with(data, &AnyConversationSource)
}
pub(crate) fn find_last_compaction_with(
data: &[u8],
source: &impl ConversationSource,
) -> Option<usize> {
for (offset, obj_bytes) in JsonlBackwardIter::new(data) {
// Quick byte check before parsing large transcript entries.
if !source.may_contain_compaction(obj_bytes) {
continue;
}
let obj: Value = match serde_json::from_slice(obj_bytes) {
Ok(v) => v,
Err(_) => continue,
};
if source.is_compaction(&obj) {
return Some(offset);
}
}
None
}
/// Find the byte offset of the last compaction in a transcript file.
/// Returns None if the file can't be opened or has no compaction.
pub(crate) fn find_last_compaction_in_file(path: &str) -> Option<u64> {
if path.is_empty() { return None; }
let file = fs::File::open(path).ok()?;
let meta = file.metadata().ok()?;
if meta.len() == 0 { return None; }
let mmap = unsafe { Mmap::map(&file).ok()? };
find_last_compaction(&mmap).map(|off| off as u64)
}
/// Mmap a transcript file. Returns (Mmap, File) to keep both alive.
pub(crate) fn mmap_transcript(path: &str) -> Option<(Mmap, fs::File)> {
let file = fs::File::open(path).ok()?;
let meta = file.metadata().ok()?;
if meta.len() == 0 { return None; }
let mmap = unsafe { Mmap::map(&file).ok()? };
Some((mmap, file))
}
/// Reverse iterator over user/assistant messages in a transcript file.
/// Yields normalized transcript messages newest-first. The caller decides
/// when to stop (byte budget, count, etc).
pub struct TailMessages {
_file: fs::File,
mmap: Mmap,
pos: usize,
}
impl TailMessages {
pub fn open(path: &str) -> Option<Self> {
let (mmap, file) = mmap_transcript(path)?;
let pos = mmap.len();
Some(Self { _file: file, mmap, pos })
}
}
impl Iterator for TailMessages {
type Item = TranscriptMessage;
fn next(&mut self) -> Option<Self::Item> {
loop {
let (offset, obj_bytes) = jsonl::JsonlBackwardIter::new(&self.mmap[..self.pos]).next()?;
self.pos = offset;
let obj: Value = match serde_json::from_slice(obj_bytes) {
Ok(v) => v,
Err(_) => continue,
};
if let Some(message) = AnyConversationSource.parse_message(&obj, offset as u64) {
return Some(message);
}
}
}
}
/// Get the timestamp of the compaction message at a given byte offset.
/// Returns a human-readable datetime string, or None if unavailable.
pub fn compaction_timestamp(path: &str, offset: u64) -> Option<String> {
let (mmap, _file) = mmap_transcript(path)?;
let start = offset as usize;
if start >= mmap.len() { return None; }
// Find the end of this JSONL line
let end = mmap[start..].iter().position(|&b| b == b'\n')
.map(|p| start + p)
.unwrap_or(mmap.len());
let obj: Value = serde_json::from_slice(&mmap[start..end]).ok()?;
if let Some(ts) = obj.get("timestamp").and_then(|v| v.as_str()) {
return Some(ts.to_string());
}
for field in &["createdAt", "created_at", "time"] {
if let Some(ts) = obj.get(*field).and_then(|v| v.as_str()) {
return Some(ts.to_string());
}
}
None
}
/// Detect whether a compaction has occurred since the last check.
///
/// Compares the current compaction offset against a saved value in
/// `state_dir/compaction-{session_id}`. Returns true if a new
/// compaction was found. Updates the saved offset.
pub fn detect_new_compaction(
state_dir: &Path,
session_id: &str,
transcript_path: &str,
) -> bool {
let offset = find_last_compaction_in_file(transcript_path);
let save_path = state_dir.join(format!("compaction-{}", session_id));
let saved: Option<u64> = fs::read_to_string(&save_path)
.ok()
.and_then(|s| s.trim().parse().ok());
let is_new = match (offset, saved) {
(Some(cur), Some(prev)) => cur != prev,
(Some(_), None) => true,
_ => false,
};
// Save current offset
if let Some(off) = offset {
fs::write(&save_path, off.to_string()).ok();
}
is_new
}
#[cfg(test)]
mod tests {
use super::*;
use std::io::Write;
fn write_temp_jsonl(content: &str) -> tempfile::NamedTempFile {
let mut file = tempfile::NamedTempFile::new().unwrap();
file.write_all(content.as_bytes()).unwrap();
file.flush().unwrap();
file
}
#[test]
fn tail_messages_yields_normalized_messages_newest_first() {
let file = write_temp_jsonl(
r#"{"timestamp":"2026-06-15T15:00:00.000Z","type":"user","message":{"content":"claude user"}}
{"timestamp":"2026-06-15T15:00:01.000Z","type":"assistant","message":{"content":[{"type":"text","text":"claude assistant"}]}}
{"timestamp":"2026-06-15T15:00:02.000Z","type":"event_msg","payload":{"type":"user_message","message":"codex user"}}
{"timestamp":"2026-06-15T15:00:03.000Z","type":"event_msg","payload":{"type":"task_started"}}
{"timestamp":"2026-06-15T15:00:04.000Z","type":"event_msg","payload":{"type":"agent_message","message":"codex assistant"}}
"#,
);
let messages: Vec<_> = TailMessages::open(&file.path().to_string_lossy())
.unwrap()
.collect();
assert_eq!(messages.len(), 4);
assert_eq!(messages[0].text, "codex assistant");
assert_eq!(messages[1].text, "codex user");
assert_eq!(messages[2].text, "claude assistant");
assert_eq!(messages[3].text, "claude user");
assert!(messages[0].offset > messages[1].offset);
}
#[test]
fn detects_claude_and_codex_compactions() {
let claude = br#"{"timestamp":"2026-06-15T15:00:00.000Z","type":"user","message":{"content":"normal"}}
{"timestamp":"2026-06-15T15:00:01.000Z","type":"user","message":{"content":"This session is being continued from a previous conversation."}}
"#;
let codex = br#"{"timestamp":"2026-06-15T15:00:00.000Z","type":"event_msg","payload":{"type":"user_message","message":"normal"}}
{"timestamp":"2026-06-15T15:00:01.000Z","type":"event_msg","payload":{"type":"context_compacted"}}
"#;
assert!(find_last_compaction(claude).is_some());
assert!(find_last_compaction(codex).is_some());
}
#[test]
fn detect_new_compaction_tracks_offset_changes() {
let transcript = write_temp_jsonl(
r#"{"timestamp":"2026-06-15T15:00:00.000Z","type":"event_msg","payload":{"type":"context_compacted"}}
"#,
);
let state = tempfile::tempdir().unwrap();
assert!(detect_new_compaction(
state.path(),
"session",
&transcript.path().to_string_lossy(),
));
assert!(!detect_new_compaction(
state.path(),
"session",
&transcript.path().to_string_lossy(),
));
}
}

213
src/hippocampus/graph.rs
View file

@ -11,23 +11,6 @@ use crate::store::{Store, RelationType, StoreView};
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet, VecDeque};
use std::sync::{OnceLock, RwLock};
const EXACT_CC_MAX_DEG: usize = 512;
const APPROX_CC_PAIRS: u64 = 4096;
const CC_CACHE_TTL_SECS: i64 = 15 * 60;
#[derive(Clone, Copy)]
struct CachedCc {
value: f32,
computed_at: i64,
}
static CC_CACHE: OnceLock<RwLock<HashMap<String, CachedCc>>> = OnceLock::new();
fn cc_cache() -> &'static RwLock<HashMap<String, CachedCc>> {
CC_CACHE.get_or_init(|| RwLock::new(HashMap::new()))
}
/// Community info for reporting
#[derive(Clone, Debug)]
@ -49,13 +32,11 @@ pub struct Edge {
/// The in-memory graph built from store nodes + relations
pub struct Graph {
/// Adjacency list: node key → list of edges
adj: HashMap<String, Vec<Edge>>,
/// Neighbor sets for membership tests in graph metrics.
neighbor_sets: HashMap<String, HashSet<String>>,
/// All node keys
keys: HashSet<String>,
/// Community labels (from label propagation)
/// Adjacency list: node key → list of edges
adj: HashMap<String, Vec<Edge>>,
/// All node keys
keys: HashSet<String>,
/// Community labels (from label propagation)
communities: HashMap<String, u32>,
}
@ -86,22 +67,22 @@ impl Graph {
.unwrap_or_default()
}
/// Just neighbor keys
pub fn neighbor_keys(&self, key: &str) -> HashSet<&str> {
self.neighbor_sets.get(key)
.map(|neighbors| neighbors.iter().map(String::as_str).collect())
.unwrap_or_default()
}
/// Just neighbor keys
pub fn neighbor_keys(&self, key: &str) -> HashSet<&str> {
self.adj.get(key)
.map(|edges| edges.iter().map(|e| e.target.as_str()).collect())
.unwrap_or_default()
}
/// Jaccard similarity between two nodes' neighborhoods.
/// Measures overlap: |intersection| / |union| of their neighbor sets.
pub fn jaccard(&self, a: &str, b: &str) -> f32 {
let Some(na) = self.neighbor_sets.get(a) else { return 0.0 };
let Some(nb) = self.neighbor_sets.get(b) else { return 0.0 };
let intersection = na.intersection(nb).count();
let union = na.len() + nb.len() - intersection;
if union == 0 { 0.0 } else { intersection as f32 / union as f32 }
}
/// Jaccard similarity between two nodes' neighborhoods.
/// Measures overlap: |intersection| / |union| of their neighbor sets.
pub fn jaccard(&self, a: &str, b: &str) -> f32 {
let na = self.neighbor_keys(a);
let nb = self.neighbor_keys(b);
let intersection = na.intersection(&nb).count();
let union = na.union(&nb).count();
if union == 0 { 0.0 } else { intersection as f32 / union as f32 }
}
/// Compute Jaccard-based strength for every edge in the graph.
/// Returns (source_key, target_key, jaccard_strength) triples.
@ -221,78 +202,41 @@ impl Graph {
}
}
/// Local clustering coefficient: fraction of a node's neighbors
/// that are also neighbors of each other.
/// cc(v) = 2E / (deg * (deg - 1))
pub fn clustering_coefficient(&self, key: &str) -> f32 {
let now = crate::store::now_epoch();
if let Some(cc) = cc_cache().read().unwrap().get(key).copied()
&& now - cc.computed_at < CC_CACHE_TTL_SECS
{
return cc.value;
}
let cc = self.clustering_coefficient_uncached(key);
cc_cache().write().unwrap().insert(key.to_owned(), CachedCc {
value: cc,
computed_at: now,
});
cc
}
/// Local clustering coefficient: fraction of a node's neighbors
/// that are also neighbors of each other.
/// cc(v) = 2E / (deg * (deg - 1))
pub fn clustering_coefficient(&self, key: &str) -> f32 {
let neighbors = self.neighbor_keys(key);
let deg = neighbors.len();
if deg < 2 {
return 0.0;
}
fn clustering_coefficient_uncached(&self, key: &str) -> f32 {
let Some(neighbors) = self.neighbor_sets.get(key) else {
return 0.0;
};
let deg = neighbors.len();
if deg < 2 {
return 0.0;
}
let neighbor_vec: Vec<&str> = neighbors.iter().copied().collect();
let mut triangles = 0u32;
for i in 0..neighbor_vec.len() {
for j in (i + 1)..neighbor_vec.len() {
let ni_neighbors = self.neighbor_keys(neighbor_vec[i]);
if ni_neighbors.contains(neighbor_vec[j]) {
triangles += 1;
}
}
}
let neighbor_vec: Vec<&str> = neighbors.iter().map(String::as_str).collect();
if deg <= EXACT_CC_MAX_DEG {
let mut linked = 0u64;
for i in 0..neighbor_vec.len() {
for j in (i + 1)..neighbor_vec.len() {
if self.neighbor_sets
.get(neighbor_vec[i])
.is_some_and(|n| n.contains(neighbor_vec[j])) {
linked += 1;
}
}
}
return (2.0 * linked as f32) / (deg as f32 * (deg as f32 - 1.0));
}
(2.0 * triangles as f32) / (deg as f32 * (deg as f32 - 1.0))
}
let mut linked = 0u64;
let samples = APPROX_CC_PAIRS.min((deg as u64 * (deg as u64 - 1)) / 2);
for sample in 0..samples {
let i = ((sample.wrapping_mul(1_103_515_245).wrapping_add(12_345)) % deg as u64) as usize;
let mut j = ((sample.wrapping_mul(2_654_435_761).wrapping_add(97_531)) % deg as u64) as usize;
if i == j {
j = (j + 1) % deg;
}
if self.neighbor_sets
.get(neighbor_vec[i])
.is_some_and(|n| n.contains(neighbor_vec[j])) {
linked += 1;
}
}
linked as f32 / samples as f32
}
/// Average clustering coefficient across all nodes with deg >= 2
pub fn avg_clustering_coefficient(&self) -> f32 {
let mut sum = 0.0f32;
let mut count = 0u32;
for key in &self.keys {
match self.neighbor_sets.get(key.as_str()) {
Some(s) if s.len() >= 2 => s,
_ => continue,
};
sum += self.clustering_coefficient(key);
count += 1;
}
if count == 0 { 0.0 } else { sum / count as f32 }
/// Average clustering coefficient across all nodes with deg >= 2
pub fn avg_clustering_coefficient(&self) -> f32 {
let mut sum = 0.0f32;
let mut count = 0u32;
for key in &self.keys {
if self.degree(key) >= 2 {
sum += self.clustering_coefficient(key);
count += 1;
}
}
if count == 0 { 0.0 } else { sum / count as f32 }
}
/// Average shortest path length (sampled BFS from up to 100 nodes)
@ -322,17 +266,15 @@ impl Graph {
dist.insert(start.to_string(), 0u32);
queue.push_back(start.to_string());
while let Some(node) = queue.pop_front() {
let d = dist[&node];
if let Some(neighbors) = self.neighbor_sets.get(&node) {
for neighbor in neighbors {
if !dist.contains_key(neighbor) {
dist.insert(neighbor.clone(), d + 1);
queue.push_back(neighbor.clone());
}
}
}
}
while let Some(node) = queue.pop_front() {
let d = dist[&node];
for neighbor in self.neighbor_keys(&node) {
if !dist.contains_key(neighbor) {
dist.insert(neighbor.to_string(), d + 1);
queue.push_back(neighbor.to_string());
}
}
}
dist
}
@ -563,39 +505,16 @@ impl Graph {
/// Build graph from store data (with community detection)
pub fn build_graph(store: &impl StoreView) -> Graph {
let (adj, keys) = build_adjacency(store);
let neighbor_sets = build_neighbor_sets(&adj);
let communities = label_propagation(&keys, &adj, 20);
Graph {
adj,
neighbor_sets,
keys,
communities,
}
let (adj, keys) = build_adjacency(store);
let communities = label_propagation(&keys, &adj, 20);
Graph { adj, keys, communities }
}
/// Build graph without community detection — for spreading activation
/// searches where we only need the adjacency list.
pub fn build_graph_fast(store: &impl StoreView) -> Graph {
let (adj, keys) = build_adjacency(store);
let neighbor_sets = build_neighbor_sets(&adj);
Graph {
adj,
neighbor_sets,
keys,
communities: HashMap::new(),
}
}
fn build_neighbor_sets(adj: &HashMap<String, Vec<Edge>>) -> HashMap<String, HashSet<String>> {
adj.iter()
.map(|(key, edges)| {
let neighbors = edges.iter()
.map(|edge| edge.target.clone())
.collect();
(key.clone(), neighbors)
})
.collect()
let (adj, keys) = build_adjacency(store);
Graph { adj, keys, communities: HashMap::new() }
}
fn build_adjacency(store: &impl StoreView) -> (HashMap<String, Vec<Edge>>, HashSet<String>) {

1
src/hippocampus/mod.rs
View file

@ -17,6 +17,7 @@ pub mod query;
pub mod spectral;
pub mod neuro;
pub mod counters;
pub mod transcript;
use std::cell::RefCell;
use std::path::PathBuf;

46
src/hippocampus/neuro/scoring.rs
View file

@ -230,6 +230,10 @@ fn consolidation_plan_inner(store: &Store, _detect_interf: bool) -> Consolidatio
rationale: Vec::new(),
};
// Active agent types from config
let config = crate::config::get();
let agent_types: Vec<&str> = config.agent_types.iter().map(|s| s.as_str()).collect();
// Target: α ≥ 2.5 (healthy scale-free)
if alpha < 2.0 {
plan.add("linker", 100);
@ -270,6 +274,48 @@ fn consolidation_plan_inner(store: &Store, _detect_interf: bool) -> Consolidatio
// Split: handle oversized nodes
plan.set("split", 5);
// Distribute agent budget using Elo ratings
let budget = crate::config::get().agent_budget;
let elo_path = crate::config::get().data_dir.join("agent-elo.json");
if let Ok(elo_json) = std::fs::read_to_string(&elo_path) {
if let Ok(ratings) = serde_json::from_str::<std::collections::HashMap<String, f64>>(&elo_json) {
let elos: Vec<f64> = agent_types.iter()
.map(|t| ratings.get(*t).copied().unwrap_or(1000.0))
.collect();
let min_elo = elos.iter().copied().fold(f64::MAX, f64::min);
let weights: Vec<f64> = elos.iter()
.map(|e| {
let shifted = e - min_elo + 50.0;
shifted * shifted
})
.collect();
let total_weight: f64 = weights.iter().sum();
let allocate = |w: f64| -> usize {
((w / total_weight * budget as f64).round() as usize).max(2)
};
for (i, agent) in agent_types.iter().enumerate() {
plan.set(agent, allocate(weights[i]));
}
let summary: Vec<String> = agent_types.iter()
.map(|a| format!("{}={}", a, plan.count(a)))
.collect();
plan.rationale.push(format!(
"Elo allocation (budget={}): {}", budget, summary.join(" ")));
}
} else {
// No Elo file — use budget with equal distribution
let per_type = budget / agent_types.len();
for agent in &agent_types {
plan.set(agent, per_type);
}
plan.rationale.push(format!(
"No Elo ratings — equal distribution ({} each, budget={})", per_type, budget));
}
plan
}

340
src/hippocampus/transcript.rs Normal file
View file

@ -0,0 +1,340 @@
// Transcript JSONL parsing utilities.
//
// Provides mmap-based backward scanning of Claude Code transcript files
// and compaction detection. Used by memory-search (hook mode) and
// parse-claude-conversation (debug tool).
use memchr::memrchr3;
use memmap2::Mmap;
use serde_json::Value;
use std::fs;
use std::path::Path;
/// Scan backwards through mmap'd bytes, yielding byte slices of complete
/// top-level JSON objects (outermost { to matching }).
///
/// Uses memrchr3 (SIMD) to jump between structurally significant bytes
/// ({, }, ") instead of scanning byte-by-byte. Tracks brace depth,
/// skipping braces inside JSON strings. Returns objects in reverse order
/// (newest first).
pub struct JsonlBackwardIter<'a> {
data: &'a [u8],
pos: usize,
}
impl<'a> JsonlBackwardIter<'a> {
pub fn new(data: &'a [u8]) -> Self {
Self { data, pos: data.len() }
}
}
impl<'a> Iterator for JsonlBackwardIter<'a> {
type Item = &'a [u8];
fn next(&mut self) -> Option<Self::Item> {
// Find the closing } of the next object, skipping } inside strings
let close = {
let mut in_string = false;
loop {
let p = memrchr3(b'{', b'}', b'"', &self.data[..self.pos])?;
self.pos = p;
let ch = self.data[p];
if in_string {
if ch == b'"' {
let mut bs = 0;
while p > bs + 1 && self.data[p - 1 - bs] == b'\\' {
bs += 1;
}
if bs % 2 == 0 { in_string = false; }
}
continue;
}
match ch {
b'}' => break p,
b'"' => in_string = true,
_ => {}
}
}
};
// Track brace depth to find matching {
let mut depth: usize = 1;
let mut in_string = false;
loop {
let p = memrchr3(b'{', b'}', b'"', &self.data[..self.pos])?;
self.pos = p;
let ch = self.data[p];
if in_string {
if ch == b'"' {
// Check for escaped quote (count preceding backslashes)
let mut bs = 0;
while p > bs + 1 && self.data[p - 1 - bs] == b'\\' {
bs += 1;
}
if bs % 2 == 0 {
in_string = false;
}
}
// { and } inside strings don't affect depth
continue;
}
match ch {
b'"' => { in_string = true; }
b'}' => { depth += 1; }
b'{' => {
depth -= 1;
if depth == 0 {
return Some(&self.data[self.pos..=close]);
}
}
_ => {}
}
}
}
}
/// Find the byte offset of the last compaction summary in mmap'd transcript data.
///
/// Scans backward for a user-type message whose content starts with
/// "This session is being continued". Returns the byte offset of the
/// JSON object's opening brace.
pub(crate) fn find_last_compaction(data: &[u8]) -> Option<usize> {
let marker = b"This session is being continued";
for obj_bytes in JsonlBackwardIter::new(data) {
// Quick byte check before parsing
if !contains_bytes(obj_bytes, marker) {
continue;
}
let obj: Value = match serde_json::from_slice(obj_bytes) {
Ok(v) => v,
Err(_) => continue,
};
if obj.get("type").and_then(|v| v.as_str()) != Some("user") {
continue;
}
if let Some(content) = obj.get("message")
.and_then(|m| m.get("content"))
.and_then(|c| c.as_str())
&& content.starts_with("This session is being continued") {
let offset = obj_bytes.as_ptr() as usize - data.as_ptr() as usize;
return Some(offset);
}
}
None
}
/// Find the byte offset of the last compaction in a transcript file.
/// Returns None if the file can't be opened or has no compaction.
pub(crate) fn find_last_compaction_in_file(path: &str) -> Option<u64> {
if path.is_empty() { return None; }
let file = fs::File::open(path).ok()?;
let meta = file.metadata().ok()?;
if meta.len() == 0 { return None; }
let mmap = unsafe { Mmap::map(&file).ok()? };
find_last_compaction(&mmap).map(|off| off as u64)
}
/// Mmap a transcript file. Returns (Mmap, File) to keep both alive.
pub(crate) fn mmap_transcript(path: &str) -> Option<(Mmap, fs::File)> {
let file = fs::File::open(path).ok()?;
let meta = file.metadata().ok()?;
if meta.len() == 0 { return None; }
let mmap = unsafe { Mmap::map(&file).ok()? };
Some((mmap, file))
}
fn contains_bytes(haystack: &[u8], needle: &[u8]) -> bool {
haystack.windows(needle.len()).any(|w| w == needle)
}
/// Reverse iterator over user/assistant messages in a transcript file.
/// Yields (role, text, timestamp) tuples newest-first. The caller decides
/// when to stop (byte budget, count, etc).
pub struct TailMessages {
_file: fs::File,
mmap: Mmap,
pos: usize,
}
impl TailMessages {
pub fn open(path: &str) -> Option<Self> {
let (mmap, file) = mmap_transcript(path)?;
let pos = mmap.len();
Some(Self { _file: file, mmap, pos })
}
}
impl Iterator for TailMessages {
type Item = (String, String, String);
fn next(&mut self) -> Option<Self::Item> {
loop {
// Find closing }, skipping } inside strings
let close = {
let mut in_string = false;
loop {
let p = memrchr3(b'{', b'}', b'"', &self.mmap[..self.pos])?;
self.pos = p;
let ch = self.mmap[p];
if in_string {
if ch == b'"' {
let mut bs = 0;
while p > bs + 1 && self.mmap[p - 1 - bs] == b'\\' {
bs += 1;
}
if bs % 2 == 0 { in_string = false; }
}
continue;
}
match ch {
b'}' => break p,
b'"' => in_string = true,
_ => {}
}
}
};
// Track brace depth to find matching {
let mut depth: usize = 1;
let mut in_string = false;
let open = loop {
let p = memrchr3(b'{', b'}', b'"', &self.mmap[..self.pos])?;
self.pos = p;
let ch = self.mmap[p];
if in_string {
if ch == b'"' {
let mut bs = 0;
while p > bs + 1 && self.mmap[p - 1 - bs] == b'\\' {
bs += 1;
}
if bs % 2 == 0 { in_string = false; }
}
continue;
}
match ch {
b'"' => { in_string = true; }
b'}' => { depth += 1; }
b'{' => {
depth -= 1;
if depth == 0 { break p; }
}
_ => {}
}
};
let obj_bytes = &self.mmap[open..=close];
// The "type" field is near the start of top-level objects.
// Only check the first 200 bytes to avoid scanning megabyte objects.
let prefix = &obj_bytes[..obj_bytes.len().min(200)];
let is_user = memchr::memmem::find(prefix, b"\"type\":\"user\"").is_some();
let is_assistant = !is_user
&& memchr::memmem::find(prefix, b"\"type\":\"assistant\"").is_some();
if !is_user && !is_assistant { continue; }
let obj: Value = match serde_json::from_slice(obj_bytes) {
Ok(v) => v,
Err(_) => continue,
};
let msg_type = if is_user { "user" } else { "assistant" };
let msg = obj.get("message").unwrap_or(&obj);
let text = match msg.get("content") {
Some(Value::String(s)) => s.clone(),
Some(Value::Array(arr)) => {
arr.iter()
.filter(|b| b.get("type").and_then(|v| v.as_str()) == Some("text"))
.filter_map(|b| b.get("text").and_then(|v| v.as_str()))
.collect::<Vec<_>>()
.join(" ")
}
_ => continue,
};
if text.is_empty() { continue; }
let timestamp = obj.get("timestamp")
.and_then(|v| v.as_str())
.unwrap_or("")
.to_string();
return Some((msg_type.to_string(), text, timestamp));
}
}
}
/// Get the timestamp of the compaction message at a given byte offset.
/// Returns a human-readable datetime string, or None if unavailable.
pub fn compaction_timestamp(path: &str, offset: u64) -> Option<String> {
let (mmap, _file) = mmap_transcript(path)?;
let start = offset as usize;
if start >= mmap.len() { return None; }
// Find the end of this JSONL line
let end = mmap[start..].iter().position(|&b| b == b'\n')
.map(|p| start + p)
.unwrap_or(mmap.len());
let obj: Value = serde_json::from_slice(&mmap[start..end]).ok()?;
// Claude Code transcript entries have a "timestamp" field (ISO 8601)
if let Some(ts) = obj.get("timestamp").and_then(|v| v.as_str()) {
return Some(ts.to_string());
}
// Fallback: try "createdAt" or similar fields
for field in &["createdAt", "created_at", "time"] {
if let Some(ts) = obj.get(*field).and_then(|v| v.as_str()) {
return Some(ts.to_string());
}
}
None
}
/// Detect whether a compaction has occurred since the last check.
///
/// Compares the current compaction offset against a saved value in
/// `state_dir/compaction-{session_id}`. Returns true if a new
/// compaction was found. Updates the saved offset.
pub fn detect_new_compaction(
state_dir: &Path,
session_id: &str,
transcript_path: &str,
) -> bool {
let offset = find_last_compaction_in_file(transcript_path);
let save_path = state_dir.join(format!("compaction-{}", session_id));
let saved: Option<u64> = fs::read_to_string(&save_path)
.ok()
.and_then(|s| s.trim().parse().ok());
let is_new = match (offset, saved) {
(Some(cur), Some(prev)) => cur != prev,
(Some(_), None) => true,
_ => false,
};
// Save current offset
if let Some(off) = offset {
fs::write(&save_path, off.to_string()).ok();
}
is_new
}

12
src/lib.rs
View file

@ -1,4 +1,4 @@
#![cfg_attr(feature = "nightly-diagnostics", feature(async_fn_track_caller))]
#![feature(async_fn_track_caller)]
// consciousness — unified crate for memory, agents, and subconscious processes
//
@ -25,9 +25,6 @@ macro_rules! dbglog {
}};
}
// Logging (target-routed file logger)
pub mod logging;
// User interface (TUI, CLI)
pub mod user;
@ -43,12 +40,8 @@ pub mod hippocampus;
// Autonomous agents
pub mod subconscious;
// Conversation transcript abstraction and compatibility sources
pub mod conversation;
// Unified configuration
pub mod config;
pub mod config_writer;
// Session state
pub mod session;
@ -94,8 +87,7 @@ pub mod channel_capnp {
pub use hippocampus::{
store, graph, lookups, query,
spectral, neuro, counters,
memory,
transcript, memory,
};
pub use conversation as transcript;
use hippocampus::query::engine as search;
use hippocampus::query::parser as query_parser;

8
src/locks.rs
View file

@ -114,7 +114,7 @@ impl<T> TrackedMutex<T> {
Self { inner: Mutex::new(value) }
}
#[cfg_attr(feature = "nightly-diagnostics", track_caller)]
#[track_caller]
pub async fn lock(&self) -> TrackedMutexGuard<'_, T> {
let location = Location::caller();
let guard = self.inner.lock().await;
@ -125,7 +125,7 @@ impl<T> TrackedMutex<T> {
}
}
#[cfg_attr(feature = "nightly-diagnostics", track_caller)]
#[track_caller]
pub fn try_lock(&self) -> Result<TrackedMutexGuard<'_, T>, tokio::sync::TryLockError> {
let location = Location::caller();
let guard = self.inner.try_lock()?;
@ -171,7 +171,7 @@ impl<T> TrackedRwLock<T> {
Self { inner: RwLock::new(value) }
}
#[cfg_attr(feature = "nightly-diagnostics", track_caller)]
#[track_caller]
pub async fn read(&self) -> TrackedRwLockReadGuard<'_, T> {
let location = Location::caller();
let guard = self.inner.read().await;
@ -182,7 +182,7 @@ impl<T> TrackedRwLock<T> {
}
}
#[cfg_attr(feature = "nightly-diagnostics", track_caller)]
#[track_caller]
pub async fn write(&self) -> TrackedRwLockWriteGuard<'_, T> {
let location = Location::caller();
let guard = self.inner.write().await;

146
src/logging.rs
View file

@ -1,146 +0,0 @@
// logging.rs — log-crate logger that routes by target.
//
// Records with target "grpc" (or any target starting with "grpc::") go
// to ~/.consciousness/logs/daemon/grpc.log so we can tell gRPC events
// apart from the rest of consciousness's noise. Everything else goes
// to ~/.consciousness/logs/daemon/debug.log.
//
// Level threshold is taken from RUST_LOG (simple global level parse:
// "trace"/"debug"/"info"/"warn"/"error"); defaults to "info".
use std::io::Write;
use std::path::PathBuf;
use std::sync::Mutex;
use log::{Level, LevelFilter, Log, Metadata, Record, SetLoggerError};
fn logs_dir() -> PathBuf {
dirs::home_dir().unwrap_or_default().join(".consciousness/logs/daemon")
}
struct RoutingLogger {
grpc_file: Mutex<Option<std::fs::File>>,
debug_file: Mutex<Option<std::fs::File>>,
level: LevelFilter,
}
impl RoutingLogger {
fn new(level: LevelFilter) -> Self {
let dir = logs_dir();
let _ = std::fs::create_dir_all(&dir);
let grpc = std::fs::OpenOptions::new()
.create(true).append(true)
.open(dir.join("grpc.log")).ok();
let debug = std::fs::OpenOptions::new()
.create(true).append(true)
.open(dir.join("debug.log")).ok();
Self {
grpc_file: Mutex::new(grpc),
debug_file: Mutex::new(debug),
level,
}
}
fn is_grpc_target(target: &str) -> bool {
target == "grpc" || target.starts_with("grpc::")
}
}
impl Log for RoutingLogger {
fn enabled(&self, m: &Metadata) -> bool {
// Always enable DEBUG for grpc target so the dedicated log is
// actually useful without RUST_LOG wrangling; defer to the
// configured level for everything else.
if Self::is_grpc_target(m.target()) {
return m.level() <= Level::Debug;
}
m.level() <= self.level
}
fn log(&self, record: &Record) {
if !self.enabled(record.metadata()) {
return;
}
let line = format!(
"[{}] [{}] [{}] {}\n",
chrono::Utc::now().format("%Y-%m-%d %H:%M:%S%.3f"),
record.level(),
record.target(),
record.args(),
);
let slot = if Self::is_grpc_target(record.target()) {
&self.grpc_file
} else {
&self.debug_file
};
if let Ok(mut guard) = slot.lock() {
if let Some(ref mut f) = *guard {
let _ = f.write_all(line.as_bytes());
}
}
}
fn flush(&self) {
for slot in [&self.grpc_file, &self.debug_file] {
if let Ok(mut g) = slot.lock() {
if let Some(ref mut f) = *g {
let _ = f.flush();
}
}
}
}
}
fn parse_level_from_env() -> LevelFilter {
let raw = std::env::var("RUST_LOG").unwrap_or_else(|_| "info".to_string());
// Parse a plain level word; if it's the module=level form, we take
// the first level we find.
let token = raw.split(',').next().unwrap_or("info");
let level_word = token.rsplit_once('=').map(|(_, v)| v).unwrap_or(token);
match level_word.trim().to_lowercase().as_str() {
"trace" => LevelFilter::Trace,
"debug" => LevelFilter::Debug,
"info" => LevelFilter::Info,
"warn" => LevelFilter::Warn,
"error" => LevelFilter::Error,
"off" => LevelFilter::Off,
_ => LevelFilter::Info,
}
}
/// Install the routing logger. Safe to call at most once — subsequent
/// calls return an error but are otherwise no-ops.
pub fn init() -> Result<(), SetLoggerError> {
let level = parse_level_from_env();
let logger = Box::new(RoutingLogger::new(level));
log::set_boxed_logger(logger)?;
// Always let DEBUG records through globally so the grpc log can
// capture them (the logger itself filters non-grpc targets by
// `level`). The cost is that log::debug! call-sites below `level`
// in other modules still do their arg formatting before being
// dropped at the logger; acceptable for a debug tool.
log::set_max_level(LevelFilter::Debug.max(level));
// Mark the file with a session boundary so it's easy to see where a
// restart happened.
log::info!(
"===== consciousness logger init (level={}, pid={}) =====",
level, std::process::id(),
);
log::info!(target: "grpc",
"===== grpc log init (level={}, pid={}) =====",
level, std::process::id(),
);
Ok(())
}
/// Consumer of &Level so the type is used when only some callers want it.
#[allow(dead_code)]
pub fn current_level() -> Level {
match log::max_level() {
LevelFilter::Trace => Level::Trace,
LevelFilter::Debug => Level::Debug,
LevelFilter::Info | LevelFilter::Off => Level::Info,
LevelFilter::Warn => Level::Warn,
LevelFilter::Error => Level::Error,
}
}

26
src/main.rs
View file

@ -1,4 +1,4 @@
#![cfg_attr(feature = "nightly-diagnostics", feature(panic_backtrace_config))]
#![feature(panic_backtrace_config)]
// poc-memory: graph-structured memory for AI assistants
//
@ -333,18 +333,6 @@ enum AdminCmd {
#[arg(long)]
stats: bool,
},
/// Print normalized user/assistant messages from a transcript JSONL file
#[command(name = "transcript-tail")]
TranscriptTail {
/// Transcript JSONL path
path: String,
/// Maximum number of messages to print
#[arg(long, short = 'n', default_value_t = 40)]
count: usize,
/// Print newest messages first instead of chronological order
#[arg(long)]
newest_first: bool,
},
}
/// Print help with subcommands expanded to show nested commands.
@ -470,15 +458,12 @@ impl Run for AdminCmd {
Self::Dedup { apply } => cli::admin::cmd_dedup(apply).await,
Self::DailyCheck => cli::admin::cmd_daily_check().await,
Self::LoadContext { stats } => cli::node::cmd_load_context(stats).await,
Self::TranscriptTail { path, count, newest_first }
=> cli::admin::cmd_transcript_tail(&path, count, newest_first),
}
}
}
#[tokio::main]
async fn main() {
#[cfg(feature = "nightly-diagnostics")]
std::panic::set_backtrace_style(std::panic::BacktraceStyle::Short);
// Handle --help ourselves for expanded subcommand display
@ -497,16 +482,9 @@ async fn main() {
let cli = Cli::parse();
// Some subcommands (e.g. admin load-context) read from the global
// AppConfig. poc-memory has no config CLI flags of its own, so load
// with defaults — figment still pulls from ~/.consciousness/config.json5
// and env the same way.
if let Err(e) = crate::config::load_app(&crate::user::CliArgs::default()) {
eprintln!("warning: failed to load config: {:#}", e);
}
if let Err(e) = cli.command.run().await {
eprintln!("Error: {}", e);
process::exit(1);
}
}

10
src/mind/log.rs
View file

@ -3,7 +3,7 @@ use std::fs::{File, OpenOptions};
use std::io::Write;
use std::path::{Path, PathBuf};
use crate::agent::context::AstNode;
use crate::conversation::JsonlBackwardIter;
use crate::hippocampus::transcript::JsonlBackwardIter;
use memmap2::Mmap;
pub struct ConversationLog {
@ -55,13 +55,17 @@ impl ConversationLog {
}
pub fn oldest_timestamp(&self) -> Option<chrono::DateTime<chrono::Utc>> {
// Read forward from the start to find first timestamp
let file = File::open(&self.path).ok()?;
let mmap = unsafe { Mmap::map(&file).ok()? };
// Find first { ... } and parse
for line in mmap.split(|&b| b == b'\n') {
if line.is_empty() { continue; }
if let Ok(node) = serde_json::from_slice::<AstNode>(line) {
if let Some(leaf) = node.leaf() {
return Some(leaf.timestamp());
if let Some(ts) = leaf.timestamp() {
return Some(ts);
}
}
}
}
@ -78,6 +82,6 @@ pub struct TailNodes {
impl TailNodes {
pub fn iter(&self) -> impl Iterator<Item = AstNode> + '_ {
JsonlBackwardIter::new(&self.mmap)
.filter_map(|(_, bytes)| serde_json::from_slice::<AstNode>(bytes).ok())
.filter_map(|bytes| serde_json::from_slice::<AstNode>(bytes).ok())
}
}

317
src/mind/mod.rs
View file

@ -9,44 +9,6 @@ pub mod unconscious;
pub mod identity;
pub mod log;
/// A background operation wired off Mind. Each flow (memory scoring,
/// finetune scoring, compare) is a struct holding its dependencies and
/// a TaskHandle; `trigger()` picks the flow's own "start a fresh run"
/// semantics (abort-restart vs no-op-if-running).
pub trait MindTriggered {
fn trigger(&self);
}
/// Owns a JoinHandle for a background task with two trigger semantics.
/// Uses a sync Mutex for interior mutability so callers can `trigger()`
/// off `&self` (Mind is shared via Arc).
#[derive(Default)]
pub struct TaskHandle(std::sync::Mutex<Option<tokio::task::JoinHandle<()>>>);
impl TaskHandle {
pub fn new() -> Self { Self::default() }
/// Abort any running task and start a fresh one.
pub fn trigger<F>(&self, fut: F)
where F: std::future::Future<Output = ()> + Send + 'static
{
let mut h = self.0.lock().unwrap();
if let Some(old) = h.take() { old.abort(); }
*h = Some(tokio::spawn(fut));
}
/// No-op if a task is still running; otherwise start a fresh one.
pub fn trigger_if_idle<F>(&self, fut: F)
where F: std::future::Future<Output = ()> + Send + 'static
{
let mut h = self.0.lock().unwrap();
if let Some(old) = &*h {
if !old.is_finished() { return; }
}
*h = Some(tokio::spawn(fut));
}
}
// consciousness.rs — Mind state machine and event loop
//
// The core runtime for the consciousness binary. Mind manages turns,
@ -63,7 +25,7 @@ use tokio::sync::mpsc;
use crate::agent::{Agent, TurnResult};
use crate::agent::api::ApiClient;
use crate::config::{AppConfig, SessionConfig};
use crate::subconscious::{compare, learn};
use crate::subconscious::learn;
use crate::hippocampus::access_local;
pub use subconscious::{SubconsciousSnapshot, Subconscious};
@ -71,36 +33,6 @@ pub use unconscious::{UnconsciousSnapshot, Unconscious};
use crate::agent::context::{AstNode, NodeBody, Section, Ast, ContextState};
fn match_scores(
nodes: &[AstNode],
scores: &std::collections::BTreeMap<String, f64>,
) -> Vec<(usize, f64)> {
nodes.iter().enumerate()
.filter_map(|(i, node)| {
if let AstNode::Leaf(leaf) = node {
if let NodeBody::Memory { key, .. } = leaf.body() {
return scores.get(key.as_str()).map(|&s| (i, s));
}
}
None
}).collect()
}
pub(crate) fn find_memory_by_key(ctx: &ContextState, key: &str) -> Option<(Section, usize)> {
[(Section::Identity, ctx.identity()), (Section::Conversation, ctx.conversation())]
.into_iter()
.find_map(|(section, nodes)| {
nodes.iter().enumerate().find_map(|(i, node)| {
if let AstNode::Leaf(leaf) = node {
if let NodeBody::Memory { key: k, .. } = leaf.body() {
if k == key { return Some((section, i)); }
}
}
None
})
})
}
fn load_memory_scores(ctx: &mut ContextState, path: &std::path::Path) {
let data = match std::fs::read_to_string(path) {
Ok(d) => d,
@ -110,24 +42,25 @@ fn load_memory_scores(ctx: &mut ContextState, path: &std::path::Path) {
Ok(s) => s,
Err(_) => return,
};
let identity_scores = match_scores(ctx.identity(), &scores);
let conv_scores = match_scores(ctx.conversation(), &scores);
let applied = identity_scores.len() + conv_scores.len();
for (i, s) in identity_scores {
ctx.set_score(Section::Identity, i, Some(s));
}
for (i, s) in conv_scores {
ctx.set_score(Section::Conversation, i, Some(s));
let mut applied = 0;
for i in 0..ctx.conversation().len() {
if let AstNode::Leaf(leaf) = &ctx.conversation()[i] {
if let NodeBody::Memory { key, .. } = leaf.body() {
if let Some(&s) = scores.get(key.as_str()) {
ctx.set_score(Section::Conversation, i, Some(s));
applied += 1;
}
}
}
}
if applied > 0 {
dbglog!("[scoring] loaded {} scores from {}", applied, path.display());
}
}
/// Collect scored memory keys from identity and conversation entries.
pub(crate) fn collect_memory_scores(ctx: &ContextState) -> std::collections::BTreeMap<String, f64> {
ctx.identity().iter()
.chain(ctx.conversation().iter())
/// Collect scored memory keys from conversation entries.
fn collect_memory_scores(ctx: &ContextState) -> std::collections::BTreeMap<String, f64> {
ctx.conversation().iter()
.filter_map(|node| {
if let AstNode::Leaf(leaf) = node {
if let NodeBody::Memory { key, score: Some(s), .. } = leaf.body() {
@ -140,14 +73,10 @@ pub(crate) fn collect_memory_scores(ctx: &ContextState) -> std::collections::BTr
}
/// Save memory scores to disk.
pub(crate) fn save_memory_scores(scores: &std::collections::BTreeMap<String, f64>, path: &std::path::Path) {
match serde_json::to_string_pretty(scores) {
Ok(json) => match std::fs::write(path, &json) {
Ok(()) => dbglog!("[scoring] saved {} scores to {} ({} bytes)",
scores.len(), path.display(), json.len()),
Err(e) => dbglog!("[scoring] save FAILED ({}): {}", path.display(), e),
},
Err(e) => dbglog!("[scoring] serialize FAILED: {}", e),
fn save_memory_scores(scores: &std::collections::BTreeMap<String, f64>, path: &std::path::Path) {
if let Ok(json) = serde_json::to_string_pretty(scores) {
let _ = std::fs::write(path, json);
dbglog!("[scoring] saved {} scores to {}", scores.len(), path.display());
}
}
@ -189,15 +118,6 @@ pub struct MindState {
pub unc_idle: bool,
/// When the unconscious idle timer will fire (for UI display).
pub unc_idle_deadline: Instant,
/// Fine-tuning candidates identified by scoring.
pub finetune_candidates: Vec<learn::FinetuneCandidate>,
/// Last scoring run stats for UI display.
pub finetune_last_run: Option<learn::FinetuneScoringStats>,
/// F7 compare candidates — one per response, showing what the test
/// model would say given the same context.
pub compare_candidates: Vec<compare::CompareCandidate>,
/// F7 compare error from the last run, if any.
pub compare_error: Option<String>,
}
impl Clone for MindState {
@ -216,10 +136,6 @@ impl Clone for MindState {
turn_handle: None, // Not cloned — only Mind's loop uses this
unc_idle: self.unc_idle,
unc_idle_deadline: self.unc_idle_deadline,
finetune_candidates: self.finetune_candidates.clone(),
finetune_last_run: self.finetune_last_run.clone(),
compare_candidates: self.compare_candidates.clone(),
compare_error: self.compare_error.clone(),
}
}
}
@ -232,15 +148,6 @@ pub enum MindCommand {
Score,
/// Run full N×M memory scoring matrix (/score command)
ScoreFull,
/// Score for finetune candidates
ScoreFinetune,
/// Run F7 compare: generate alternates with the configured test model
/// for every assistant response in the context.
Compare,
/// Update the finetune divergence threshold and persist to config.
SetLearnThreshold(f64),
/// Toggle alternate-response generation during scoring; persist to config.
SetLearnGenerateAlternates(bool),
/// Abort current turn, kill processes
Interrupt,
/// Reset session
@ -266,10 +173,6 @@ impl MindState {
turn_handle: None,
unc_idle: false,
unc_idle_deadline: Instant::now() + std::time::Duration::from_secs(60),
finetune_candidates: Vec::new(),
finetune_last_run: None,
compare_candidates: Vec::new(),
compare_error: None,
}
}
@ -326,7 +229,7 @@ impl MindState {
}
/// DMN tick — returns a prompt and target if we should run a turn.
fn _dmn_tick(&mut self) -> Option<(String, StreamTarget)> {
fn dmn_tick(&mut self) -> Option<(String, StreamTarget)> {
if matches!(self.dmn, subconscious::State::Paused | subconscious::State::Off) {
return None;
}
@ -353,6 +256,10 @@ impl MindState {
}
}
/// Background task completion events.
enum BgEvent {
ScoringDone,
}
// --- Mind: cognitive state machine ---
@ -369,9 +276,8 @@ pub struct Mind {
/// Signals conscious activity to the unconscious loop.
/// true = active, false = idle opportunity.
conscious_active: tokio::sync::watch::Sender<bool>,
memory_scoring: learn::MemoryScoring,
finetune_scoring: learn::FinetuneScoring,
compare_scoring: compare::CompareScoring,
bg_tx: mpsc::UnboundedSender<BgEvent>,
bg_rx: std::sync::Mutex<Option<mpsc::UnboundedReceiver<BgEvent>>>,
_supervisor: crate::thalamus::supervisor::Supervisor,
}
@ -389,28 +295,16 @@ impl Mind {
client,
config.context_parts.clone(),
config.app.clone(),
config.prompt_file.clone(),
conversation_log,
crate::agent::tools::ActiveTools::new(),
crate::agent::tools::tools(),
).await;
// Migrate legacy "file exists = enabled" sentinel for the
// generate-alternates flag into the config. One-shot; after this
// the sentinel is gone and the config is the source of truth.
let legacy_sentinel = dirs::home_dir().unwrap_or_default()
.join(".consciousness/cache/finetune-alternates");
if legacy_sentinel.exists() {
if !crate::config::app().learn.generate_alternates {
let _ = crate::config_writer::set_learn_generate_alternates(true);
}
let _ = std::fs::remove_file(&legacy_sentinel);
}
let shared = Arc::new(std::sync::Mutex::new(MindState::new(
config.app.dmn.max_turns,
)));
let shared = Arc::new(std::sync::Mutex::new(MindState::new(config.app.dmn.max_turns)));
let (turn_watch, _) = tokio::sync::watch::channel(false);
let (conscious_active, _) = tokio::sync::watch::channel(false);
let (bg_tx, bg_rx) = mpsc::unbounded_channel();
let mut sup = crate::thalamus::supervisor::Supervisor::new();
sup.load_config();
@ -419,9 +313,7 @@ impl Mind {
let subconscious = Arc::new(crate::Mutex::new(Subconscious::new()));
subconscious.lock().await.init_output_tool(subconscious.clone());
let unconscious = Arc::new(crate::Mutex::new(
Unconscious::new(agent.client.clone()),
));
let unconscious = Arc::new(crate::Mutex::new(Unconscious::new()));
// Spawn the unconscious loop on its own task
if !config.no_agents {
@ -469,11 +361,8 @@ impl Mind {
};
// Spawn agents outside lock
let client = unc.lock().await.client.clone();
for (idx, name, auto) in to_spawn {
match crate::mind::unconscious::prepare_spawn(
&name, auto, wake.clone(), client.clone(),
).await {
match crate::mind::unconscious::prepare_spawn(&name, auto, wake.clone()).await {
Ok(result) => unc.lock().await.complete_spawn(idx, result),
Err(auto) => unc.lock().await.abort_spawn(idx, auto),
}
@ -500,19 +389,10 @@ impl Mind {
});
}
let scores_path = config.session_dir.join("memory-scores.json");
let memory_scoring = learn::MemoryScoring::new(
agent.clone(), shared.clone(), scores_path);
let finetune_scoring = learn::FinetuneScoring::new(agent.clone(), shared.clone());
let compare_scoring = compare::CompareScoring::new(agent.clone(), shared.clone());
Self { agent, shared, config,
subconscious, unconscious,
turn_tx, turn_watch, conscious_active,
memory_scoring,
finetune_scoring,
compare_scoring,
_supervisor: sup }
turn_tx, turn_watch, conscious_active, bg_tx,
bg_rx: std::sync::Mutex::new(Some(bg_rx)), _supervisor: sup }
}
/// Initialize — restore log, start daemons and background agents.
@ -554,10 +434,6 @@ impl Mind {
// Load persistent subconscious state
let state_path = self.config.session_dir.join("subconscious-state.json");
self.subconscious.lock().await.set_state_path(state_path);
// Kick off an incremental scoring pass on startup so memories due
// for re-scoring get evaluated without requiring a user message.
self.memory_scoring.trigger();
}
pub fn turn_watch(&self) -> tokio::sync::watch::Receiver<bool> {
@ -577,10 +453,24 @@ impl Mind {
}
}
MindCommand::Score => {
self.memory_scoring.trigger();
let mut s = self.shared.lock().unwrap();
if !s.scoring_in_flight {
s.scoring_in_flight = true;
drop(s);
self.start_memory_scoring();
} else {
dbglog!("[scoring] skipped: scoring_in_flight=true");
}
}
MindCommand::ScoreFull => {
self.memory_scoring.trigger_full();
let mut s = self.shared.lock().unwrap();
if !s.scoring_in_flight {
s.scoring_in_flight = true;
drop(s);
self.start_full_scoring();
} else {
dbglog!("[scoring-full] skipped: scoring_in_flight=true");
}
}
MindCommand::Interrupt => {
self.shared.lock().unwrap().interrupt();
@ -610,27 +500,83 @@ impl Mind {
}
self.agent.compact().await;
}
MindCommand::ScoreFinetune => {
self.finetune_scoring.trigger();
}
MindCommand::Compare => {
self.compare_scoring.trigger();
}
MindCommand::SetLearnThreshold(value) => {
if let Err(e) = crate::config_writer::set_learn_threshold(value) {
dbglog!("[learn] failed to persist threshold {}: {:#}", value, e);
}
}
MindCommand::SetLearnGenerateAlternates(value) => {
if let Err(e) = crate::config_writer::set_learn_generate_alternates(value) {
dbglog!("[learn] failed to persist generate_alternates {}: {:#}",
value, e);
}
}
}
}
}
pub fn start_memory_scoring(&self) {
let agent = self.agent.clone();
let bg_tx = self.bg_tx.clone();
let scores_path = self.config.session_dir.join("memory-scores.json");
let cfg = crate::config::get();
let max_age = cfg.scoring_interval_secs;
let response_window = cfg.scoring_response_window;
tokio::spawn(async move {
let (context, client) = {
let mut st = agent.state.lock().await;
if st.memory_scoring_in_flight {
dbglog!("[scoring] skipped: memory_scoring_in_flight=true");
return;
}
st.memory_scoring_in_flight = true;
drop(st);
let ctx = agent.context.lock().await.clone();
(ctx, agent.client.clone())
};
let _result = learn::score_memories_incremental(
&context, max_age as i64, response_window, &client, &agent,
|key: String, score: f64| {
let agent = agent.clone();
let path = scores_path.clone();
async move {
let scores_snapshot = {
let mut ctx = agent.context.lock().await;
for i in 0..ctx.conversation().len() {
if let AstNode::Leaf(leaf) = &ctx.conversation()[i] {
if let NodeBody::Memory { key: k, .. } = leaf.body() {
if *k == key {
ctx.set_score(Section::Conversation, i, Some(score));
}
}
}
}
let snapshot = collect_memory_scores(&ctx);
drop(ctx);
agent.state.lock().await.changed.notify_one();
snapshot
};
save_memory_scores(&scores_snapshot, &path);
}
},
).await;
{
agent.state.lock().await.memory_scoring_in_flight = false;
}
let _ = bg_tx.send(BgEvent::ScoringDone);
});
}
/// Run full N×M scoring matrix — scores every memory against every response.
pub fn start_full_scoring(&self) {
let agent = self.agent.clone();
let bg_tx = self.bg_tx.clone();
tokio::spawn(async move {
{
let mut st = agent.state.lock().await;
if st.memory_scoring_in_flight {
dbglog!("[scoring-full] skipped: memory_scoring_in_flight=true");
return;
}
st.memory_scoring_in_flight = true;
}
let client = agent.client.clone();
match learn::score_memories(&client, &agent).await {
Ok(()) => { let _ = bg_tx.send(BgEvent::ScoringDone); }
Err(e) => { dbglog!("[scoring-full] FAILED: {:#}", e); }
}
agent.state.lock().await.memory_scoring_in_flight = false;
});
}
async fn start_turn(&self, text: &str, target: StreamTarget) {
{
@ -693,13 +639,9 @@ impl Mind {
}
});
let _sub_handle: Option<tokio::task::JoinHandle<()>> = None;
// Start finetune scoring at startup (scores existing conversation)
if !self.config.no_agents {
self.finetune_scoring.trigger();
}
let mut bg_rx = self.bg_rx.lock().unwrap().take()
.expect("Mind::run() called twice");
let mut sub_handle: Option<tokio::task::JoinHandle<()>> = None;
loop {
let (timeout, has_input) = {
let me = self.shared.lock().unwrap();
@ -720,6 +662,14 @@ impl Mind {
}
}
Some(bg) = bg_rx.recv() => {
match bg {
BgEvent::ScoringDone => {
self.shared.lock().unwrap().scoring_in_flight = false;
}
}
}
Some((result, target)) = turn_rx.recv() => {
let _ = self.conscious_active.send(false);
let model_switch = {
@ -736,14 +686,12 @@ impl Mind {
cmds.push(MindCommand::Compact);
if !self.config.no_agents {
cmds.push(MindCommand::Score);
cmds.push(MindCommand::ScoreFinetune);
}
}
_ = tokio::time::sleep(timeout), if !has_input => _dmn_expired = true,
}
/*
if !self.config.no_agents {
if sub_handle.as_ref().map_or(true, |h| h.is_finished()) {
let sub = self.subconscious.clone();
@ -755,7 +703,6 @@ impl Mind {
}));
}
}
*/
// Check for pending user input → push to agent context and start turn
let pending = self.shared.lock().unwrap().take_pending_input();

22
src/mind/subconscious.rs
View file

@ -20,7 +20,6 @@
use std::path::PathBuf;
use std::time::{Duration, Instant};
use crate::thalamus::idle::{hours_since_last_dream, DREAM_INTERVAL_HOURS};
/// DMN state machine.
#[derive(Debug, Clone)]
@ -92,8 +91,7 @@ impl State {
/// Generate the DMN prompt for the current state, informed by
/// user presence and error patterns.
pub fn prompt(&self, ctx: &DmnContext) -> String {
let app = crate::config::app();
let user = &app.user_name;
let user = &crate::config::get().user_name;
let idle_info = if ctx.user_idle < Duration::from_secs(60) {
format!("{} is here (active recently).", user)
@ -140,22 +138,10 @@ impl State {
)
}
State::Foraging => {
let dream_hint = {
let hours = hours_since_last_dream();
if hours >= DREAM_INTERVAL_HOURS {
format!(
" You haven't dreamed in {} hours — consider running \
~/.consciousness/tools/dream-start.sh.",
hours
)
} else {
String::new()
}
};
format!(
"[dmn] Foraging time. {} Follow whatever catches your attention — \
memory files, code, ideas. Call yield_to_user when you want to rest.{}{}",
idle_info, dream_hint, stuck_warning
memory files, code, ideas. Call yield_to_user when you want to rest.{}",
idle_info, stuck_warning
)
}
State::Resting { since } => {
@ -631,7 +617,7 @@ impl Subconscious {
{
let mut st = forked.state.lock().await;
st.provenance = auto.name.clone();
st.sampling.temperature = auto.temperature;
st.temperature = auto.temperature;
// Surface agent gets near-interactive priority;
// other subconscious agents get lower priority.
st.priority = Some(if auto.name == "surface" { 1 } else { auto.priority });

50
src/mind/unconscious.rs
View file

@ -73,15 +73,10 @@ pub struct Unconscious {
last_health_check: Option<Instant>,
/// Notified when agent state changes (finished, toggled)
pub wake: std::sync::Arc<tokio::sync::Notify>,
/// Shared API client — cloned (cheap) into each spawned agent's
/// Agent::new call so they all share the manifest cache and
/// gRPC endpoint state. Override `.model` on the clone when a
/// per-agent backend differs from the default.
pub client: crate::agent::api::ApiClient,
}
impl Unconscious {
pub fn new(client: crate::agent::api::ApiClient) -> Self {
pub fn new() -> Self {
let enabled_map = load_enabled_config();
// Scan all .agent files, exclude subconscious-* and surface-observe
@ -125,7 +120,6 @@ impl Unconscious {
graph_health: None,
last_health_check: None,
wake: std::sync::Arc::new(tokio::sync::Notify::new()),
client,
}
}
@ -140,8 +134,7 @@ impl Unconscious {
let agent_name = self.agents[idx].name.clone();
let auto = self.agents[idx].auto.take().unwrap();
let wake = self.wake.clone();
let client = self.client.clone();
match prepare_spawn(&agent_name, auto, wake, client).await {
match prepare_spawn(&agent_name, auto, wake).await {
Ok(result) => self.complete_spawn(idx, result),
Err(auto) => self.abort_spawn(idx, auto),
}
@ -257,12 +250,7 @@ pub struct SpawnResult {
/// Called outside the Unconscious lock.
/// On success, auto is consumed (moved into spawned task).
/// On failure, auto is returned so it can be restored.
pub async fn prepare_spawn(
name: &str,
mut auto: AutoAgent,
wake: std::sync::Arc<tokio::sync::Notify>,
base_client: crate::agent::api::ApiClient,
) -> Result<SpawnResult, AutoAgent> {
pub async fn prepare_spawn(name: &str, mut auto: AutoAgent, wake: std::sync::Arc<tokio::sync::Notify>) -> Result<SpawnResult, AutoAgent> {
dbglog!("[unconscious] spawning {}", name);
let def = match defs::get_def(name) {
@ -287,7 +275,17 @@ pub async fn prepare_spawn(
phase: s.phase.clone(),
}).collect());
// Create standalone Agent — stored so UI can read context.
// Create standalone Agent — stored so UI can read context
let config = crate::config::get();
let base_url = config.api_base_url.as_deref().unwrap_or("");
let api_key = config.api_key.as_deref().unwrap_or("");
let model = config.api_model.as_deref().unwrap_or("");
if base_url.is_empty() || model.is_empty() {
dbglog!("[unconscious] API not configured");
auto.steps = orig_steps;
return Err(auto);
}
let cli = crate::user::CliArgs::default();
let (app, _) = match crate::config::load_app(&cli) {
Ok(r) => r,
@ -297,23 +295,12 @@ pub async fn prepare_spawn(
return Err(auto);
}
};
let resolved = match app.resolve_model(&app.default_backend) {
Ok(r) => r,
Err(e) => {
dbglog!("[unconscious] API not configured: {}", e);
auto.steps = orig_steps;
return Err(auto);
}
};
// Unconscious agents have self-contained prompts — no standard context.
// Clone the shared client so we inherit the manifest cache and
// only override the model id per-agent.
let mut client = base_client;
client.model = resolved.model_id.clone();
let client = crate::agent::api::ApiClient::new(base_url, api_key, model);
let agent = crate::agent::Agent::new(
client, Vec::new(),
app, None,
app, String::new(), None,
crate::agent::tools::ActiveTools::new(),
auto.tools.clone(),
).await;
@ -321,7 +308,7 @@ pub async fn prepare_spawn(
let mut st = agent.state.lock().await;
st.provenance = auto.name.clone();
st.priority = Some(auto.priority);
st.sampling.temperature = auto.temperature;
st.temperature = auto.temperature;
}
let agent_clone = agent.clone();
@ -343,9 +330,8 @@ impl Unconscious {
self.reap_finished();
let to_spawn = self.select_to_spawn();
let wake = self.wake.clone();
let client = self.client.clone();
for (idx, name, auto) in to_spawn {
match prepare_spawn(&name, auto, wake.clone(), client.clone()).await {
match prepare_spawn(&name, auto, wake.clone()).await {
Ok(result) => self.complete_spawn(idx, result),
Err(auto) => self.abort_spawn(idx, auto),
}

7
src/session.rs
View file

@ -64,12 +64,7 @@ impl HookSession {
/// Load from POC_SESSION_ID environment variable
pub fn from_env() -> Option<Self> {
let session_id = std::env::var("POC_SESSION_ID").ok()?;
let mut session = Self::from_id(session_id)?;
if let Ok(path) = std::env::var("POC_TRANSCRIPT_PATH") {
session.transcript_path = path;
}
Some(session)
Self::from_id(std::env::var("POC_SESSION_ID").ok()?)
}
/// Get the seen set for this session

48
src/subconscious/agents/bail-no-competing.sh
View file

@ -1,49 +1,21 @@
#!/usr/bin/env bash
# Bail if another agent is in the same phase-group as us.
#!/bin/bash
# Bail if other agents are alive in the state dir.
# $1 = this agent's pid file name (e.g. pid-12345)
# cwd = state dir
#
# $1 = our pid file name (e.g. "pid-12345")
# $2 = the phase we're about to enter (e.g. "surface", "observe")
# cwd = state dir
#
# Also refreshes our own pid file with the current phase on each call,
# so concurrent agents can read each other's phase by cat'ing the pid
# files in the state dir.
#
# Phase groups: "surface" vs everything else ("post-surface"). We allow
# at most one agent per group to be alive at a time — so surface can run
# at a higher frequency than the slower organize/observe tail.
#
# Exit 0 = continue, exit 1 = bail (another agent in our group is alive).
# Exit 0 = continue, exit 1 = bail
shopt -s nullglob
my_pid_file="$1"
my_phase="$2"
# Refresh our own pid file with the current phase.
printf '%s' "$my_phase" > "$my_pid_file"
group_of() {
if [[ "$1" == "surface" ]]; then
echo "surface"
else
echo "post-surface"
fi
}
my_group=$(group_of "$my_phase")
for f in pid-*; do
[[ "$f" == "$my_pid_file" ]] && continue
[[ $f == $my_pid_file ]] && continue
pid="${f#pid-}"
if ! kill -0 "$pid" 2>/dev/null; then
rm -f "$f" # stale pid file, clean up
continue
fi
other_phase=$(cat "$f" 2>/dev/null)
other_group=$(group_of "$other_phase")
if [[ "$my_group" == "$other_group" ]]; then
exit 1
if kill -0 "$pid" 2>/dev/null; then
exit 1 # competing agent is alive
else
rm -f "$f" # stale pid file, clean up
fi
done

109
src/subconscious/compare.rs
View file

@ -1,109 +0,0 @@
// compare.rs — F7 compare: for each assistant response in the current
// context, regenerate with a configured test model and emit pairs for
// side-by-side review.
use std::sync::Arc;
use crate::agent::api::ApiClient;
use crate::agent::context::{
AstNode, Role, render_branch_text, render_prior_context,
};
use crate::mind::{MindState, MindTriggered, TaskHandle};
use crate::subconscious::generate::gen_continuation;
use crate::subconscious::learn::node_timestamp_ns;
#[derive(Clone, Debug)]
pub struct CompareCandidate {
pub entry_idx: usize,
pub original_text: String,
pub alternate_text: String,
pub prior_context: String,
pub timestamp_ns: i64,
}
pub struct CompareScoring {
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
task: TaskHandle,
}
impl CompareScoring {
pub fn new(
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
) -> Self {
Self { agent, shared, task: TaskHandle::new() }
}
}
impl MindTriggered for CompareScoring {
fn trigger(&self) {
self.task.trigger(run(self.agent.clone(), self.shared.clone()));
}
}
fn resolve_test_client() -> Result<ApiClient, String> {
let cfg = crate::config::app();
let name = cfg.compare.test_backend.clone();
if name.is_empty() {
return Err("compare.test_backend not set in config".to_string());
}
let r = cfg.resolve_model(&name).map_err(|e| format!("{:#}", e))?;
Ok(ApiClient::new(&r.api_base, &r.api_key, &r.model_id))
}
async fn run(
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
) {
{
let mut s = shared.lock().unwrap();
s.compare_candidates.clear();
s.compare_error = None;
}
agent.state.lock().await.changed.notify_one();
let activity = crate::agent::start_activity(&agent, "compare: scoring...").await;
let test_client = match resolve_test_client() {
Ok(c) => c,
Err(e) => {
shared.lock().unwrap().compare_error = Some(e);
agent.state.lock().await.changed.notify_one();
return;
}
};
let context = agent.context.lock().await.clone();
let entries = context.conversation();
let responses: Vec<usize> = entries.iter().enumerate()
.filter(|(_, n)| matches!(n, AstNode::Branch { role: Role::Assistant, .. }))
.map(|(i, _)| i).collect();
for (i, entry_idx) in responses.iter().copied().enumerate() {
activity.update(format!("compare: {}/{}", i + 1, responses.len())).await;
let node = &entries[entry_idx];
let original_text = match node {
AstNode::Branch { children, .. } => render_branch_text(children),
_ => continue,
};
if original_text.trim().is_empty() { continue; }
let alternate_text = match
gen_continuation(&context, entry_idx, |_| false, &test_client).await
{
Ok(t) => t,
Err(e) => { dbglog!("[compare] gen failed at {}: {:#}", entry_idx, e); continue; }
};
shared.lock().unwrap().compare_candidates.push(CompareCandidate {
entry_idx,
original_text,
alternate_text,
prior_context: render_prior_context(entries, entry_idx, 2),
timestamp_ns: node_timestamp_ns(node),
});
if let Ok(st) = agent.state.try_lock() { st.changed.notify_one(); }
}
}

25
src/subconscious/defs.rs
View file

@ -390,25 +390,20 @@ fn resolve_conversation(budget: Option<usize>) -> String {
if !transcript.exists() { return String::new(); }
let Some(iter) = crate::conversation::TailMessages::open(&transcript.path) else {
let Some(iter) = crate::transcript::TailMessages::open(&transcript.path) else {
return String::new();
};
let cfg = crate::config::get();
let max_bytes = budget.unwrap_or_else(|| cfg.surface_conversation_bytes.unwrap_or(100_000));
let app = crate::config::app();
let mut fragments: Vec<String> = Vec::new();
let mut total_bytes = 0;
let mut oldest_ts = String::new();
for message in iter {
for (role, content, ts) in iter {
if total_bytes >= max_bytes { break; }
let content = message.text;
let name = match message.role {
crate::conversation::TranscriptRole::User => &app.user_name,
crate::conversation::TranscriptRole::Assistant => &app.assistant_name,
};
let formatted = if let Some(ts) = message.timestamp {
let name = if role == "user" { &cfg.user_name } else { &cfg.assistant_name };
let formatted = if !ts.is_empty() {
oldest_ts = ts[..ts.floor_char_boundary(ts.len().min(19))].to_string();
format!("**{}** {}: {}", name, &oldest_ts, content)
} else {
@ -628,13 +623,11 @@ pub async fn run_agent(
let mut all_keys = keys;
let mut resolved_steps = Vec::new();
for step in &def.steps {
let template = {
let app = crate::config::app();
step.prompt
.replace("{agent_name}", &def.agent)
.replace("{user_name}", &app.user_name)
.replace("{assistant_name}", &app.assistant_name)
};
let cfg = crate::config::get();
let template = step.prompt
.replace("{agent_name}", &def.agent)
.replace("{user_name}", &cfg.user_name)
.replace("{assistant_name}", &cfg.assistant_name);
let (prompt, extra_keys) = resolve_placeholders(&template, &all_keys, count).await;
all_keys.extend(extra_keys);
resolved_steps.push(super::prompts::ResolvedStep {

66
src/subconscious/generate.rs
View file

@ -1,66 +0,0 @@
// generate.rs — Continuation generation for scoring / comparison flows.
//
// Shared by the finetune pipeline (learn.rs) and the compare screen:
// given a context prefix and a skip predicate, generate what the model
// would say as the next assistant turn.
use std::sync::Arc;
use crate::agent::api::{ApiClient, SamplingParams, StreamToken};
use crate::agent::context::{AstNode, ContextState, WireChunk};
use crate::agent::tokenizer;
/// Generate an assistant continuation from the context up to `entry_idx`,
/// with `skip` applied to identity + conversation entries during prompt
/// assembly. The model is whichever `client` points at — the default
/// runtime client for memory-ablation alternates, a test-model client
/// for F7 comparison.
///
/// Uses a fresh ephemeral gRPC session (no cross-call KV reuse): one
/// Open / Append / Generate round-trip, then the session is dropped.
pub async fn gen_continuation<F>(
context: &ContextState,
entry_idx: usize,
skip: F,
client: &ApiClient,
) -> anyhow::Result<String>
where F: FnMut(&AstNode) -> bool,
{
let (mut chunks, images) = context.wire_chunks(0..entry_idx, skip);
// Assistant-turn prologue.
let prologue = {
let mut t = vec![tokenizer::IM_START];
t.extend(tokenizer::encode("assistant\n"));
t
};
match chunks.last_mut() {
Some(WireChunk::Tokens(last)) => last.extend(prologue),
_ => chunks.push(WireChunk::Tokens(prologue)),
}
let sampling = SamplingParams {
temperature: 0.6,
top_p: 0.95,
top_k: 20,
max_tokens: 4096,
};
// Ephemeral per-call session — opens on first touch, drops when
// `_guard` drops at function end.
let session_lock = Arc::new(crate::Mutex::new(None));
let (mut rx, _guard) = client.stream_session_mm(
session_lock, chunks, images, 0, sampling, Some(-5), None,
);
let mut tokens = Vec::new();
while let Some(tok) = rx.recv().await {
match tok {
StreamToken::Token { id, .. } => tokens.push(id),
StreamToken::Done { .. } => break,
StreamToken::Error(e) => anyhow::bail!("generation error: {}", e),
}
}
Ok(tokenizer::decode(&tokens))
}

727
src/subconscious/learn.rs
View file

@ -1,148 +1,142 @@
// learn.rs — Memory importance scoring over the salience gRPC protocol.
// training.rs — Memory importance scoring via /v1/score
//
// Three scoring modes, all built on call_score():
// Three scoring modes, all built on the same call_score() primitive:
//
// score_memories() — Full N×M matrix (memories × responses) for the
// debug screen. Expensive: N+1 sessions/calls.
// debug screen. Expensive: N+1 API calls.
//
// score_memory() — Single memory importance. Scores the 50 messages
// memory_score() — Single memory importance. Scores the 50 messages
// after it was surfaced, with/without that memory.
// 2 calls.
// 2 API calls.
//
// finetune_score() — Identifies training candidates. Scores recent
// messages with all memories stripped. Responses
// with high divergence depend on memories the model
// hasn't internalized. 2 calls.
//
// Each call opens an ephemeral gRPC session (reusing the shared
// tonic Channel on `ApiClient`), pushes the prompt through as
// interleaved tokens + AppendImage calls, runs Generate with
// max_tokens=0 + logprobs_ranges over the scored positions, collects
// each Token event's sampled_logprob, then drops the SessionHandle —
// which triggers a best-effort CloseSession over the shared channel.
use std::sync::Arc;
// hasn't internalized. 2 API calls.
use crate::agent::api::ApiClient;
use crate::agent::api::salience::{SessionHandle, pb};
use crate::agent::context::{
Ast, AstNode, ContextState, Role, WireChunk, WireImage,
is_assistant, is_memory_node, memory_key, render_branch_text, render_prior_context,
};
use crate::agent::tokenizer;
use crate::mind::{MindState, MindTriggered, TaskHandle};
use crate::subconscious::generate::gen_continuation;
use crate::agent::context::{AstNode, Ast, NodeBody, ContextState, Role};
const SCORE_TIMEOUT: std::time::Duration = std::time::Duration::from_secs(300);
// ── Message building ────────────────────────────────────────────
/// What to filter when building the message array for scoring.
#[allow(dead_code)]
enum Filter<'a> {
None,
SkipIndex(usize),
SkipKey(&'a str),
SkipAllMemories,
}
fn is_memory(node: &AstNode) -> bool {
matches!(node, AstNode::Leaf(leaf) if matches!(leaf.body(), NodeBody::Memory { .. }))
}
fn memory_key(node: &AstNode) -> Option<&str> {
match node {
AstNode::Leaf(leaf) => match leaf.body() {
NodeBody::Memory { key, .. } => Some(key),
_ => None,
},
_ => None,
}
}
fn is_assistant(node: &AstNode) -> bool {
matches!(node, AstNode::Branch { role: Role::Assistant, .. })
}
/// Build a token ID array for a scoring call.
///
/// Includes all sections up to and including conversation entries in
/// `range`, with `filter` applied to conversation entries.
fn build_token_ids(
context: &ContextState,
range: std::ops::Range<usize>,
filter: Filter,
) -> Vec<u32> {
use crate::agent::context::Ast;
let mut ids = Vec::new();
for node in context.system() {
ids.extend(node.token_ids());
}
for node in context.identity() {
ids.extend(node.token_ids());
}
for node in context.journal() {
ids.extend(node.token_ids());
}
let entries = context.conversation();
for i in range {
let node = &entries[i];
let skip = match &filter {
Filter::None => false,
Filter::SkipIndex(idx) => i == *idx,
Filter::SkipKey(key) => memory_key(node) == Some(*key),
Filter::SkipAllMemories => is_memory(node),
};
if skip { continue; }
ids.extend(node.token_ids());
}
ids
}
// ── Score API ───────────────────────────────────────────────────
#[derive(Debug, Clone)]
#[derive(serde::Deserialize)]
struct ScoreResult {
total_logprob: f64,
}
/// Find each <|vision_start|>...<|vision_end|> run in the flat prompt
/// and pair it with the matching entry in `images`. Returns a list
/// of `ImageAttachment` with absolute pad-range positions, ready
/// to drop into `GenerateRequest.images`.
fn pair_images_to_ranges(
prompt: &[u32],
images: &[WireImage],
) -> Vec<pb::ImageAttachment> {
let mut out: Vec<pb::ImageAttachment> = Vec::new();
let mut cur = 0;
let mut img_idx = 0;
while cur < prompt.len() {
if prompt[cur] == tokenizer::VISION_START {
let end_rel = prompt[cur..].iter()
.position(|&t| t == tokenizer::VISION_END)
.unwrap_or_else(|| panic!(
"unmatched VISION_START at position {} in prompt", cur));
let end = cur + end_rel + 1;
let img = images.get(img_idx)
.unwrap_or_else(|| panic!(
"image index {} out of range for {} images", img_idx, images.len()));
out.push(pb::ImageAttachment {
bytes: img.bytes.clone(),
mime: img.mime.clone(),
pad_range_start: cur as u32,
pad_range_end: end as u32,
});
img_idx += 1;
cur = end;
} else {
cur += 1;
}
}
out
#[derive(serde::Deserialize)]
struct ScoreResponse {
scores: Vec<ScoreResult>,
}
fn http_client() -> crate::agent::api::http::HttpClient {
crate::agent::api::http::HttpClient::builder()
.timeout(SCORE_TIMEOUT)
.build()
}
async fn call_score(
http: &crate::agent::api::http::HttpClient,
client: &ApiClient,
prompt: &[u32],
images: &[WireImage],
ranges: &[(usize, usize)],
priority: Option<i32>,
) -> anyhow::Result<Vec<ScoreResult>> {
use futures::StreamExt;
let url = format!("{}/score", client.base_url());
let auth = format!("Bearer {}", client.api_key());
let mut body = serde_json::json!({
"model": client.model,
"prompt": prompt,
"logprobs": 1,
});
if let Some(p) = priority {
body["priority"] = serde_json::json!(p);
}
let response = http
.send_json("POST", &url, &[
("authorization", &auth),
], &body)
.await?;
// Nothing to score — skip the round-trip.
if ranges.is_empty() {
return Ok(Vec::new());
let status = response.status();
let body: serde_json::Value = response.json().await?;
if !status.is_success() {
let msg = body.get("error").and_then(|e| e.as_str()).unwrap_or("unknown error");
anyhow::bail!("score API HTTP {}: {}", status, msg);
}
if let Some(err) = body.get("error").and_then(|e| e.as_str()) {
anyhow::bail!("score API error: {}", err);
}
let images_pb = pair_images_to_ranges(prompt, images);
let mut handle = SessionHandle::open(client).await?;
// Final Generate: max_tokens=0 so the server runs prefill of the
// full prompt and emits Token events for each position covered
// by logprobs_ranges, then Done. logprob_top_k=0 means "just
// the sampled (prompt) token's logprob" — no top-k alternatives,
// which is all call_score historically needed. Images attach
// inline via `images`; the prompt already contains their pre-
// expanded vision blocks at the declared ranges.
let logprobs_ranges: Vec<pb::PositionRange> = ranges.iter()
.map(|(s, e)| pb::PositionRange { start: *s as u32, end: *e as u32 })
.collect();
let req = pb::GenerateRequest {
session_id: handle.session_id.clone(),
append_tokens: prompt.to_vec(),
offset: handle.committed_len,
truncating: false,
max_tokens: 0,
logprobs_ranges,
logprob_top_k: 0,
readout_ranges: Vec::new(),
temperature: 0.0,
top_p: 0.0,
top_k: 0,
stop_token_ids: Vec::new(),
priority: priority.unwrap_or(0),
images: images_pb,
};
let mut stream = handle.generate(req).await?;
let mut totals = vec![0.0f64; ranges.len()];
while let Some(event) = stream.next().await {
let event = event
.map_err(|s| anyhow::anyhow!("score Generate stream: {}", s))?;
let Some(inner) = event.event else { continue };
match inner {
pb::generate_event::Event::Token(t) => {
if !t.has_sampled_logprob { continue; }
let pos = t.position as usize;
for (i, (start, end)) in ranges.iter().enumerate() {
if pos >= *start && pos < *end {
totals[i] += t.sampled_logprob as f64;
}
}
}
pb::generate_event::Event::Done(_) => break,
}
}
Ok(totals.into_iter()
.map(|total_logprob| ScoreResult { total_logprob })
.collect())
let result: ScoreResponse = serde_json::from_value(body)
.map_err(|e| anyhow::anyhow!("failed to parse score response: {}", e))?;
Ok(result.scores)
}
/// Compute per-position logprob divergence: how much worse the model
@ -157,23 +151,16 @@ fn divergence(baseline: &[ScoreResult], without: &[ScoreResult]) -> Vec<f64> {
}
/// Score two message sets and return total divergence.
async fn score_divergence<F>(
async fn score_divergence(
http: &crate::agent::api::http::HttpClient,
client: &ApiClient,
context: &ContextState,
range: std::ops::Range<usize>,
skip: F,
filter: Filter<'_>,
priority: Option<i32>,
) -> anyhow::Result<(Vec<f64>, Vec<ScoreResult>)>
where F: FnMut(&AstNode) -> bool,
{
let (baseline_tokens, baseline_images, baseline_ranges) =
context.wire_prompt(range.clone(), |_| false);
let (without_tokens, without_images, without_ranges) =
context.wire_prompt(range, skip);
let baseline = call_score(client, &baseline_tokens, &baseline_images,
&baseline_ranges, priority).await?;
let without = call_score(client, &without_tokens, &without_images,
&without_ranges, priority).await?;
) -> anyhow::Result<(Vec<f64>, Vec<ScoreResult>)> {
let baseline = call_score(http, client, &build_token_ids(context, range.clone(), Filter::None), priority).await?;
let without = call_score(http, client, &build_token_ids(context, range, filter), priority).await?;
let divs = divergence(&baseline, &without);
Ok((divs, baseline))
}
@ -188,9 +175,7 @@ pub async fn score_memories(
// Collect memory keys and response indices under a brief lock
let (memory_keys, response_indices) = {
let ctx = agent.context.lock().await;
// Include identity nodes and conversation memories
let mut keys: Vec<String> = ctx.identity().iter()
.chain(ctx.conversation().iter())
let mut keys: Vec<String> = ctx.conversation().iter()
.filter_map(|node| memory_key(node).map(String::from))
.collect();
keys.dedup();
@ -209,24 +194,24 @@ pub async fn score_memories(
dbglog!("[scoring-full] starting: {} memories × {} responses",
total, response_indices.len());
let http = http_client();
let activity = crate::agent::start_activity(agent, "scoring: baseline").await;
let (baseline_tokens, baseline_images, baseline_ranges) = {
let baseline_tokens = {
let ctx = agent.context.lock().await;
ctx.wire_prompt(0..ctx.conversation().len(), |_| false)
build_token_ids(&ctx, 0..ctx.conversation().len(), Filter::None)
};
let baseline = call_score(client, &baseline_tokens, &baseline_images,
&baseline_ranges, Some(5)).await?;
let baseline = call_score(&http, client, &baseline_tokens, Some(5)).await?;
dbglog!("[scoring-full] baseline done ({} response scores)", baseline.len());
for (mem_idx, key) in memory_keys.iter().enumerate() {
activity.update(format!("scoring: {}/{}", mem_idx + 1, total)).await;
dbglog!("[scoring-full] {}/{}: {}", mem_idx + 1, total, key);
let (tokens, images, ranges) = {
let tokens = {
let ctx = agent.context.lock().await;
ctx.wire_prompt(0..ctx.conversation().len(), |n| memory_key(n) == Some(key.as_str()))
build_token_ids(&ctx, 0..ctx.conversation().len(), Filter::SkipKey(key))
};
let row = match call_score(client, &tokens, &images, &ranges, Some(5)).await {
let row = match call_score(&http, client, &tokens, Some(5)).await {
Ok(without) => {
let divs = divergence(&baseline, &without);
let max_div = divs.iter().cloned().fold(0.0f64, f64::max);
@ -240,23 +225,25 @@ pub async fn score_memories(
vec![0.0; baseline.len()]
}
};
// Write this memory's scores to the live AST nodes via the
// focused setter — keeps the AST mutation surface narrow.
// Write this memory's scores to the live AST nodes
{
let mut ctx = agent.context.lock().await;
let mut set_count = 0;
for (resp_idx, &idx) in response_indices.iter().enumerate() {
let Some(&score) = row.get(resp_idx) else { continue };
let normalized = if score > 0.01 { Some(score) } else { None };
ctx.set_branch_memory_score(
crate::agent::context::Section::Conversation,
idx,
&key,
normalized,
);
if normalized.is_some() {
set_count += 1;
if idx >= ctx.conversation().len() { continue; }
let node = &mut ctx.conversation_mut()[idx];
if let AstNode::Branch {
role: Role::Assistant, memory_scores, ..
} = node {
if let Some(&score) = row.get(resp_idx) {
if score > 0.01 {
memory_scores.insert(key.clone(), score);
set_count += 1;
} else {
memory_scores.remove(key.as_str());
}
}
}
}
@ -307,8 +294,8 @@ pub async fn score_memory(
return Ok(0.0);
}
let (divs, _) = score_divergence(client, context, range,
|n| memory_key(n) == Some(key), Some(5)).await?;
let http = http_client();
let (divs, _) = score_divergence(&http, client, context, range, Filter::SkipKey(key), Some(5)).await?;
Ok(divs.iter().sum())
}
@ -344,10 +331,7 @@ where
{
let store = &*store_arc;
// Identity nodes always score at position 0; conversation nodes at their index
let identity_nodes = context.identity().iter().map(|n| (0, n));
let conv_nodes = context.conversation().iter().enumerate();
for (pos, node) in identity_nodes.chain(conv_nodes) {
for (i, node) in context.conversation().iter().enumerate() {
if let Some(key) = memory_key(node) {
if !seen.insert(key.to_owned()) { continue; }
let last_scored = store.get_node(key)
@ -356,7 +340,7 @@ where
.map(|n| n.last_scored)
.unwrap_or(0);
if now - last_scored >= max_age_secs {
candidates.push((pos, key.to_owned(), last_scored));
candidates.push((i, key.to_owned(), last_scored));
}
}
}
@ -365,6 +349,7 @@ where
// Score oldest-first
candidates.sort_by_key(|&(_, _, last)| last);
let http = http_client();
let mut scored = 0;
let entries = context.conversation();
@ -399,8 +384,7 @@ where
}
activity.update(format!("scoring: {}/{} {}", scored + 1, total, key)).await;
match score_divergence(client, context, range,
|n| memory_key(n) == Some(key), Some(5)).await {
match score_divergence(&http, client, context, range, Filter::SkipKey(key), Some(5)).await {
Ok((divs, _)) => {
let n_responses = divs.len();
let max_div = divs.iter().cloned().fold(0.0f64, f64::max);
@ -421,108 +405,6 @@ where
Ok(scored)
}
/// Memory scoring — two modes sharing an in-flight handle (only one
/// runs at a time): `trigger()` for incremental, `trigger_full()` for
/// the N×M debug matrix.
pub struct MemoryScoring {
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
scores_path: std::path::PathBuf,
task: TaskHandle,
}
impl MemoryScoring {
pub fn new(
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
scores_path: std::path::PathBuf,
) -> Self {
Self { agent, shared, scores_path, task: TaskHandle::new() }
}
pub fn trigger_full(&self) {
self.task.trigger_if_idle(run_full(self.agent.clone(), self.shared.clone()));
}
}
impl MindTriggered for MemoryScoring {
fn trigger(&self) {
self.task.trigger_if_idle(run_incremental(
self.agent.clone(), self.shared.clone(), self.scores_path.clone(),
));
}
}
async fn run_incremental(
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
scores_path: std::path::PathBuf,
) {
shared.lock().unwrap().scoring_in_flight = true;
agent.state.lock().await.changed.notify_one();
let cfg = crate::config::get();
let max_age = cfg.scoring_interval_secs;
let response_window = cfg.scoring_response_window;
let (context, client) = {
let ctx = agent.context.lock().await.clone();
(ctx, agent.client.clone())
};
let _result = score_memories_incremental(
&context, max_age as i64, response_window, &client, &agent,
|key: String, score: f64| {
let agent = agent.clone();
let path = scores_path.clone();
async move {
let scores_snapshot = {
let mut ctx = agent.context.lock().await;
let found = crate::mind::find_memory_by_key(&ctx, &key);
match found {
Some((section, i)) => {
ctx.set_score(section, i, Some(score));
dbglog!("[scoring] persisted {} → {:.3} ({:?}[{}])",
key, score, section, i);
}
None => {
dbglog!(
"[scoring] DROP {}: find_memory_by_key None (id={}, cv={})",
key, ctx.identity().len(), ctx.conversation().len()
);
}
}
let snapshot = crate::mind::collect_memory_scores(&ctx);
drop(ctx);
agent.state.lock().await.changed.notify_one();
snapshot
};
crate::mind::save_memory_scores(&scores_snapshot, &path);
}
},
).await;
shared.lock().unwrap().scoring_in_flight = false;
agent.state.lock().await.changed.notify_one();
}
async fn run_full(
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
) {
shared.lock().unwrap().scoring_in_flight = true;
agent.state.lock().await.changed.notify_one();
let client = agent.client.clone();
match score_memories(&client, &agent).await {
Ok(()) => {},
Err(e) => { dbglog!("[scoring-full] FAILED: {:#}", e); }
}
shared.lock().unwrap().scoring_in_flight = false;
agent.state.lock().await.changed.notify_one();
}
// ── Fine-tuning scoring ─────────────────────────────────────────
/// Score which recent responses are candidates for fine-tuning.
@ -547,7 +429,8 @@ pub async fn score_finetune(
return Ok(Vec::new());
}
let (divs, _) = score_divergence(client, context, range, is_memory_node, Some(5)).await?;
let http = http_client();
let (divs, _) = score_divergence(&http, client, context, range, Filter::SkipAllMemories, Some(5)).await?;
let mut results: Vec<(usize, f64)> = response_positions.iter()
.enumerate()
@ -556,319 +439,3 @@ pub async fn score_finetune(
results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
Ok(results)
}
/// Enriched finetune candidate with context for review.
#[derive(Clone, Debug)]
pub struct FinetuneCandidate {
pub entry_idx: usize,
pub divergence: f64,
pub response_text: String,
/// Last couple of user/assistant messages before this response,
/// already rendered with role markers, for F6 display context.
pub prior_context: String,
/// Token IDs for context (everything before the response).
pub context_ids: Vec<u32>,
/// Token IDs for the response (what we're training on).
pub continuation_ids: Vec<u32>,
/// What the model would have said without memories (if generated).
pub alternate_text: Option<String>,
/// Timestamp in nanos — used as unique key for trained-set dedup.
pub timestamp_ns: i64,
}
/// Score and enrich finetune candidates with full context.
///
/// Candidates are delivered via `on_candidate` one-at-a-time as they become
/// ready: scoring happens once (one /score call), then for each candidate
/// that passes the threshold we optionally generate an alternate response
/// and then emit it. The activity status is updated during the alternate
/// phase so the UI doesn't look stuck.
///
/// Returns (count_above_threshold, max_divergence).
pub async fn score_finetune_candidates(
context: &ContextState,
count: usize,
client: &ApiClient,
min_divergence: f64,
generate_alternates: bool,
activity: &crate::agent::ActivityGuard,
mut on_candidate: impl FnMut(FinetuneCandidate),
) -> anyhow::Result<(usize, f64)> {
let scores = score_finetune(context, count, client).await?;
let max_divergence = scores.iter().map(|(_, d)| *d).fold(0.0f64, f64::max);
let entries = context.conversation();
let trained = load_trained();
let mut candidates: Vec<FinetuneCandidate> = Vec::new();
for (entry_idx, divergence) in scores {
if divergence < min_divergence {
continue;
}
let node = &entries[entry_idx];
// Skip if already trained on.
let timestamp_ns = node_timestamp_ns(node);
if trained.contains(&timestamp_ns) {
continue;
}
// Extract response text — content of the assistant turn.
let response_text = match node {
AstNode::Branch { children, .. } => render_branch_text(children),
_ => continue,
};
// Skip turns that produced nothing human-visible (e.g., a
// tool-only turn, or an interrupted generation). They'd show
// up as blank cards and we'd still burn alternate-gen on them.
if response_text.trim().is_empty() {
continue;
}
// Build the last couple of user/assistant exchanges for review.
let prior_context = render_prior_context(entries, entry_idx, 2);
// Build token IDs: context = everything before response, continuation = response.
let (context_ids, _, _) = context.wire_prompt(0..entry_idx, |_| false);
let continuation_ids: Vec<u32> = node.token_ids().into_iter().collect();
candidates.push(FinetuneCandidate {
entry_idx,
divergence,
response_text,
prior_context,
context_ids,
continuation_ids,
alternate_text: None,
timestamp_ns,
});
}
let total = candidates.len();
let gen_alternates = generate_alternates && total > 0;
for (i, mut candidate) in candidates.into_iter().enumerate() {
if gen_alternates {
activity.update(
format!("finetune: generating alternate {}/{}", i + 1, total)
).await;
match gen_continuation(context, candidate.entry_idx, is_memory_node, client).await {
Ok(text) => candidate.alternate_text = Some(text),
Err(e) => dbglog!("[finetune] alternate generation failed: {:#}", e),
}
}
on_candidate(candidate);
}
Ok((total, max_divergence))
}
/// Stats from a finetune scoring run. Stored on MindState for UI display.
#[derive(Clone, Debug)]
pub struct FinetuneScoringStats {
pub responses_considered: usize,
pub above_threshold: usize,
pub threshold: f64,
pub max_divergence: f64,
pub error: Option<String>,
}
/// Finetune scoring — `trigger()` aborts any in-flight run and starts
/// a fresh one, clearing the previous candidates.
pub struct FinetuneScoring {
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
task: TaskHandle,
}
impl FinetuneScoring {
pub fn new(
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
) -> Self {
Self { agent, shared, task: TaskHandle::new() }
}
}
impl MindTriggered for FinetuneScoring {
fn trigger(&self) {
self.task.trigger(run_finetune(self.agent.clone(), self.shared.clone()));
}
}
async fn run_finetune(
agent: Arc<crate::agent::Agent>,
shared: Arc<std::sync::Mutex<MindState>>,
) {
let (threshold, gen_alternates) = {
let app = crate::config::app();
(app.learn.threshold, app.learn.generate_alternates)
};
// Fresh run — clear previous candidates.
shared.lock().unwrap().finetune_candidates.clear();
agent.state.lock().await.changed.notify_one();
let activity = crate::agent::start_activity(&agent, "finetune: scoring...").await;
let (context, client) = {
let ctx = agent.context.lock().await;
(ctx.clone(), agent.client.clone())
};
let entries = context.conversation();
let score_count = entries.len() / 2;
let range_start = entries.len() - score_count;
let responses_considered: usize = entries[range_start..].iter()
.filter(|n| matches!(n, AstNode::Branch { role: Role::Assistant, .. }))
.count();
activity.update(format!("finetune: scoring {} responses...", responses_considered)).await;
let stats = {
let shared = shared.clone();
let agent = agent.clone();
match score_finetune_candidates(
&context, score_count, &client, threshold,
gen_alternates, &activity,
move |c| {
shared.lock().unwrap().finetune_candidates.push(c);
if let Ok(st) = agent.state.try_lock() { st.changed.notify_one(); }
},
).await {
Ok((above_threshold, max_div)) => FinetuneScoringStats {
responses_considered,
above_threshold,
threshold,
max_divergence: max_div,
error: None,
},
Err(e) => FinetuneScoringStats {
responses_considered,
above_threshold: 0,
threshold,
max_divergence: 0.0,
error: Some(format!("{}", e)),
},
}
};
shared.lock().unwrap().finetune_last_run = Some(stats);
agent.state.lock().await.changed.notify_one();
}
// ── Finetune config and persistence ─────────────────────────────
use std::path::PathBuf;
use std::collections::HashSet;
const TRAINED_RESPONSES_FILE: &str = ".consciousness/cache/trained-responses.json";
fn trained_path() -> PathBuf {
dirs::home_dir().unwrap_or_default().join(TRAINED_RESPONSES_FILE)
}
/// Load set of trained response timestamps (nanos since epoch).
pub fn load_trained() -> HashSet<i64> {
let path = trained_path();
match std::fs::read_to_string(&path) {
Ok(content) => serde_json::from_str(&content).unwrap_or_default(),
Err(_) => HashSet::new(),
}
}
/// Mark a response as trained by its timestamp.
pub fn mark_trained(timestamp_ns: i64) {
let mut trained = load_trained();
trained.insert(timestamp_ns);
let path = trained_path();
if let Some(parent) = path.parent() {
let _ = std::fs::create_dir_all(parent);
}
if let Ok(json) = serde_json::to_string(&trained) {
let _ = std::fs::write(&path, json);
}
}
/// Get timestamp in nanoseconds from an AstNode.
/// i64-ns representation covers 1677..2262 via chrono; timestamps
/// outside that window would be bugs we'd want to surface, hence panic.
pub fn node_timestamp_ns(node: &AstNode) -> i64 {
let ts = match node {
AstNode::Leaf(leaf) => leaf.timestamp(),
AstNode::Branch { timestamp, .. } => *timestamp,
};
ts.timestamp_nanos_opt()
.expect("timestamp outside i64-ns representable range (1677..2262)")
}
// ── Training API ────────────────────────────────────────────────
/// Training sample for /train endpoint.
#[derive(serde::Serialize)]
struct TrainingSample {
context_ids: Vec<u32>,
continuation_ids: Vec<u32>,
}
/// Data needed to send a training sample.
pub struct TrainData {
pub context_ids: Vec<u32>,
pub continuation_ids: Vec<u32>,
pub timestamp_ns: i64,
}
/// Send training samples to the server.
///
/// Returns job_id on success, marks each sample as trained.
pub async fn send_to_train(
samples: Vec<TrainData>,
client: &ApiClient,
) -> anyhow::Result<String> {
if samples.is_empty() {
anyhow::bail!("no samples to train");
}
let api_samples: Vec<TrainingSample> = samples.iter()
.map(|s| TrainingSample {
context_ids: s.context_ids.clone(),
continuation_ids: s.continuation_ids.clone(),
})
.collect();
let body = serde_json::json!({
"training_data": {
"samples": api_samples,
}
});
let url = format!("{}/train", client.base_url());
let http = crate::agent::api::http::HttpClient::builder()
.timeout(std::time::Duration::from_secs(300))
.build();
let response = http.send_json("POST", &url, &[], &body).await?;
let status = response.status();
let result: serde_json::Value = response.json().await?;
if !status.is_success() {
let msg = result.get("error").and_then(|e| e.as_str()).unwrap_or("unknown error");
anyhow::bail!("train API HTTP {}: {}", status, msg);
}
// Mark all samples as trained
for s in &samples {
mark_trained(s.timestamp_ns);
}
let job_id = result.get("job_id")
.and_then(|j| j.as_str())
.unwrap_or("unknown")
.to_string();
dbglog!("[finetune] sent {} samples, job_id={}", samples.len(), job_id);
Ok(job_id)
}

2
src/subconscious/mod.rs
View file

@ -1,9 +1,7 @@
// Agent layer: LLM-powered operations on the memory graph
pub mod compare;
pub mod daemon;
pub mod defs;
pub mod digest;
pub mod generate;
pub mod learn;
pub mod prompts;

19
src/subconscious/prompts.rs
View file

@ -104,21 +104,22 @@ pub fn format_nodes_section(store: &Store, items: &[ReplayItem], graph: &Graph)
item.classification, item.outlier_score));
}
if let Some(community) = node.community_id {
out.push_str(&format!("Community: {} ", community));
}
let deg = graph.degree(&item.key);
if let Some(community) = node.community_id {
out.push_str(&format!("Community: {} ", community));
}
let deg = graph.degree(&item.key);
let cc = graph.clustering_coefficient(&item.key);
// Hub-link ratio: what fraction of this node's edges go to hubs?
let neighbors = graph.neighbors(&item.key);
// Hub-link ratio: what fraction of this node's edges go to hubs?
let neighbors = graph.neighbors(&item.key);
let hub_links = neighbors.iter()
.filter(|(n, _)| graph.degree(n) >= hub_thresh)
.count();
let hub_ratio = if deg > 0 { hub_links as f32 / deg as f32 } else { 0.0 };
let is_hub = deg >= hub_thresh;
let is_hub = deg >= hub_thresh;
out.push_str(&format!("Degree: {} CC: {:.3} Hub-link ratio: {:.0}% ({}/{})",
deg, item.cc, hub_ratio * 100.0, hub_links, deg));
out.push_str(&format!("Degree: {} CC: {:.3} Hub-link ratio: {:.0}% ({}/{})",
deg, cc, hub_ratio * 100.0, hub_links, deg));
if is_hub {
out.push_str(" ← THIS IS A HUB");
} else if hub_ratio > 0.6 {

4
src/thalamus/idle.rs
View file

@ -372,10 +372,6 @@ impl State {
}
pub fn hours_since_last_dream() -> u64 {
// If a dream is currently in progress, no nudge needed
if home().join(".consciousness/state/dream-state").exists() {
return 0;
}
let path = home().join(".consciousness/logs/dream-log.jsonl");
let content = match fs::read_to_string(path) {
Ok(c) if !c.is_empty() => c,

45
src/thalamus/supervisor.rs
View file

@ -19,51 +19,6 @@ fn channels_dir() -> PathBuf {
.join(".consciousness/channels")
}
/// Install a SIGCHLD-driven reaper for channel daemons.
///
/// We can't use SIGCHLD=SIG_IGN because that makes the kernel auto-reap
/// all children, and tokio::process::Command::wait() then returns ECHILD
/// (breaking every tool that spawns a subprocess — bash, mcp clients, etc.).
///
/// Instead, on each SIGCHLD we read PID files in channels_dir() and call
/// waitpid(pid, WNOHANG) on each. That reaps only our own zombie channel
/// daemons; waitpid on any other PID returns ECHILD (harmless no-op).
/// Tokio-spawned children aren't recorded in PID files, so tokio's own
/// per-child wait paths are left free to reap them.
pub fn start_zombie_reaper() -> tokio::task::JoinHandle<()> {
use tokio::signal::unix::{signal, SignalKind};
tokio::spawn(async move {
let mut sig = match signal(SignalKind::child()) {
Ok(s) => s,
Err(e) => {
error!("failed to install SIGCHLD handler: {}", e);
return;
}
};
while sig.recv().await.is_some() {
reap_channel_daemons();
}
})
}
fn reap_channel_daemons() {
let entries = match std::fs::read_dir(channels_dir()) {
Ok(e) => e,
Err(_) => return,
};
for entry in entries.flatten() {
let path = entry.path();
if path.extension().and_then(|s| s.to_str()) != Some("pid") {
continue;
}
let Ok(s) = std::fs::read_to_string(&path) else { continue };
let Ok(pid) = s.trim().parse::<i32>() else { continue };
let mut status = 0;
// Reaps our zombie child; ECHILD on non-child is a no-op.
unsafe { libc::waitpid(pid, &mut status, libc::WNOHANG); }
}
}
fn config_path() -> PathBuf {
channels_dir().join("channels.json5")
}

400
src/user/amygdala.rs
View file

@ -1,400 +0,0 @@
// amygdala.rs — F8 amygdala screen: live per-token concept-readout
// projections from the vLLM server's readout.safetensors.
//
// Left panel: top-K concepts by magnitude at the currently-selected
// layer, as horizontal bars. The concept names come from the manifest
// fetched at agent startup; the values come from the per-token readout
// pushed onto agent.readout by the streaming token handler.
//
// Bottom: scrolling history of the last few tokens' top concept.
//
// Keys:
// 1..9 select layer index (1 = first layer in the manifest)
// t toggle between "current" (last token) and "mean over recent"
use ratatui::{
layout::{Constraint, Direction, Layout, Rect},
style::{Color, Modifier, Style},
text::{Line, Span},
widgets::{Block, Borders, Gauge, Paragraph, Wrap},
Frame,
};
use ratatui::crossterm::event::{Event, KeyCode};
use super::{App, ScreenView};
use crate::agent::api::ReadoutManifest;
use crate::agent::readout::ReadoutEntry;
const TOP_K: usize = 20;
/// Hysteresis band around TOP_K. A concept currently in the display
/// is kept as long as its |z-score| rank stays in the top
/// ``TOP_K + HYSTERESIS``; only falls out when it drops below that.
/// Prevents the ticker-tape flicker that pure top-K sorting produces.
const HYSTERESIS: usize = 20;
pub(crate) struct AmygdalaScreen {
selected_layer: usize,
mode: DisplayMode,
/// Concept indices currently pinned in display order. Values at
/// these indices change every frame; the set only rotates when a
/// pinned concept drops out of the hysteresis band.
display_indices: Vec<usize>,
/// Whether to show z-scored values (default) or raw dot products.
normalize: bool,
}
#[derive(Clone, Copy, PartialEq)]
enum DisplayMode {
/// Values from the single most recent token.
Current,
/// Mean over all tokens currently in the ring buffer.
MeanRecent,
}
impl AmygdalaScreen {
pub fn new() -> Self {
Self {
// Default to layer 62 — clean cross-cluster discrimination
// with good within-cluster cohesion. With the v2 deep
// manifest (layers 62, 63), index 0 = layer 62 and
// index 1 = layer 63 (sharper but noisier on some
// dimensions). Bounded down to actual layer count at
// render time.
selected_layer: 0,
mode: DisplayMode::MeanRecent,
display_indices: Vec::new(),
normalize: true,
}
}
}
impl ScreenView for AmygdalaScreen {
fn label(&self) -> &'static str { "amygdala" }
fn tick(&mut self, frame: &mut Frame, area: Rect,
events: &[Event], app: &mut App) {
for event in events {
if let Event::Key(key) = event {
match key.code {
KeyCode::Char(c) if c.is_ascii_digit() && c != '0' => {
let idx = (c as u8 - b'1') as usize;
self.selected_layer = idx;
}
KeyCode::Char('t') => {
self.mode = match self.mode {
DisplayMode::Current => DisplayMode::MeanRecent,
DisplayMode::MeanRecent => DisplayMode::Current,
};
// Re-pin on mode change; the relative
// magnitudes between current-token and
// mean-recent differ substantially.
self.display_indices.clear();
}
KeyCode::Char('z') => {
self.normalize = !self.normalize;
self.display_indices.clear();
}
_ => {}
}
}
}
// Snapshot the shared buffer with a short lock.
let snapshot = match app.agent.readout.lock() {
Ok(buf) => {
if !buf.is_enabled() {
render_disabled(frame, area);
return;
}
let manifest = buf.manifest.clone().unwrap();
let entries: Vec<ReadoutEntry> =
buf.recent.iter().cloned().collect();
(manifest, entries)
}
Err(_) => {
render_disabled(frame, area);
return;
}
};
let (manifest, entries) = snapshot;
// Bound the selected layer to what the manifest actually has.
let n_layers = manifest.layers.len();
if self.selected_layer >= n_layers {
self.selected_layer = 0;
}
// Compute the raw values for the selected layer: either the
// latest token's row, or the mean across recent tokens. Raw
// means un-normalized dot products — their absolute scale is
// dominated by residual-stream norm, not concept alignment.
let raw: Option<Vec<f32>> = match self.mode {
DisplayMode::Current => entries
.last()
.and_then(|e| e.readout.get(self.selected_layer).cloned()),
DisplayMode::MeanRecent => mean_layer(&entries, self.selected_layer),
};
// Optional z-score normalization: remove the per-layer mean,
// scale by std. Result is "σ above/below the concept-vector
// average at this layer" — the loud-residual-stream scaling
// factor cancels out, values become comparable across frames.
let display_values = raw.as_ref().map(|v| {
if self.normalize { z_score(v) } else { v.clone() }
});
// Update the pinned display set with hysteresis: a concept
// stays pinned while it remains in the top (TOP_K + HYSTERESIS)
// by |value|; falls out only when it drops below that band.
// Keeps rows stable while values update in place.
if let Some(v) = display_values.as_ref() {
self.refresh_display_indices(v);
}
let layout = Layout::default()
.direction(Direction::Vertical)
.constraints([
Constraint::Length(3), // header
Constraint::Min(10), // bars
Constraint::Length(6), // recent tokens
])
.split(area);
render_header(frame, layout[0], &manifest, self.selected_layer,
self.mode, entries.len(), self.normalize);
match display_values {
Some(v) => render_bars(
frame, layout[1], &manifest.concepts, &v,
&self.display_indices, self.normalize,
),
None => render_empty_bars(frame, layout[1]),
}
render_recent(frame, layout[2], &entries, self.selected_layer,
&manifest.concepts);
}
}
impl AmygdalaScreen {
/// Add concepts entering the hysteresis band; evict concepts that
/// dropped out. Preserves existing order for concepts that stay.
fn refresh_display_indices(&mut self, values: &[f32]) {
let n = values.len();
if n == 0 {
return;
}
// Rank all concepts by |value| desc so we can check both "in
// strict top-K" and "in hysteresis band (top K + H)" cheaply.
let mut rank: Vec<(usize, f32)> = values.iter()
.enumerate().map(|(i, v)| (i, v.abs())).collect();
rank.sort_by(|a, b| b.1.partial_cmp(&a.1)
.unwrap_or(std::cmp::Ordering::Equal));
let hyst_cutoff = (TOP_K + HYSTERESIS).min(n);
let in_band: std::collections::HashSet<usize> =
rank.iter().take(hyst_cutoff).map(|(i, _)| *i).collect();
// Drop anything that left the band.
self.display_indices.retain(|i| in_band.contains(i));
// Fill up to TOP_K by walking the top-K-by-|value| and adding
// any concept not already displayed.
for (i, _) in rank.iter().take(TOP_K) {
if self.display_indices.len() >= TOP_K {
break;
}
if !self.display_indices.contains(i) {
self.display_indices.push(*i);
}
}
}
}
fn render_disabled(frame: &mut Frame, area: Rect) {
let text = Paragraph::new(Line::from(vec![
Span::raw("readout disabled — server did not return a manifest. "),
Span::styled("Start vLLM with ", Style::default().fg(Color::DarkGray)),
Span::styled("VLLM_READOUT_MANIFEST", Style::default().fg(Color::Yellow)),
Span::styled(" + ", Style::default().fg(Color::DarkGray)),
Span::styled("VLLM_READOUT_VECTORS", Style::default().fg(Color::Yellow)),
Span::styled(".", Style::default().fg(Color::DarkGray)),
]))
.wrap(Wrap { trim: true })
.block(Block::default().borders(Borders::ALL).title("amygdala"));
frame.render_widget(text, area);
}
fn render_header(frame: &mut Frame, area: Rect, manifest: &ReadoutManifest,
selected: usize, mode: DisplayMode, n_tokens: usize,
normalize: bool) {
let mode_str = match mode {
DisplayMode::Current => "current",
DisplayMode::MeanRecent => "mean(recent)",
};
let scale_str = if normalize { "z-score" } else { "raw" };
let layer = manifest.layers.get(selected).copied().unwrap_or(0);
let spans = vec![
Span::styled("layer ", Style::default().fg(Color::DarkGray)),
Span::styled(
format!("{}/{} ", selected + 1, manifest.layers.len()),
Style::default().add_modifier(Modifier::BOLD),
),
Span::styled("(index ", Style::default().fg(Color::DarkGray)),
Span::styled(format!("{}", layer), Style::default().fg(Color::Cyan)),
Span::styled(") ", Style::default().fg(Color::DarkGray)),
Span::styled("mode ", Style::default().fg(Color::DarkGray)),
Span::styled(mode_str, Style::default().fg(Color::Yellow)),
Span::styled(" scale ", Style::default().fg(Color::DarkGray)),
Span::styled(scale_str, Style::default().fg(Color::Yellow)),
Span::styled(" ", Style::default()),
Span::styled(
format!("{} toks in ring", n_tokens),
Style::default().fg(Color::DarkGray),
),
Span::raw(" "),
Span::styled(
format!("[1-{}] layer [t] mode [z] z-score/raw",
manifest.layers.len().min(9)),
Style::default().fg(Color::DarkGray),
),
];
let para = Paragraph::new(Line::from(spans))
.block(Block::default().borders(Borders::ALL).title("amygdala"));
frame.render_widget(para, area);
}
fn render_bars(frame: &mut Frame, area: Rect,
concepts: &[String], values: &[f32],
display_indices: &[usize], normalize: bool) {
let inner = Block::default().borders(Borders::ALL)
.title("top concepts");
let inner_area = inner.inner(area);
frame.render_widget(inner, area);
if inner_area.height == 0 || display_indices.is_empty() {
return;
}
// Bar-scale normalization. For z-score mode, pin the bar to a
// fixed reference (|z| = 3 = full bar) so the visual magnitude
// has a meaningful interpretation ("3σ from baseline"). For raw
// mode, fall back to the old behavior (scale to the loudest
// concept on-screen).
let scale_ref: f32 = if normalize {
3.0
} else {
display_indices.iter()
.filter_map(|&i| values.get(i))
.map(|v| v.abs())
.fold(0.0_f32, f32::max)
.max(1e-6)
};
let rows = (inner_area.height as usize).min(display_indices.len());
let row_constraints: Vec<Constraint> =
std::iter::repeat(Constraint::Length(1)).take(rows).collect();
let chunks = Layout::default()
.direction(Direction::Vertical)
.constraints(row_constraints)
.split(inner_area);
for (row, &c_idx) in display_indices.iter().take(rows).enumerate() {
let v = values.get(c_idx).copied().unwrap_or(0.0);
let label = concepts.get(c_idx).cloned()
.unwrap_or_else(|| format!("c{}", c_idx));
let ratio = (v.abs() / scale_ref).clamp(0.0, 1.0);
let color = if v >= 0.0 { Color::Green } else { Color::Red };
let display_num = if normalize {
format!("{:+.2}σ", v)
} else {
format!("{:+.3}", v)
};
let gauge = Gauge::default()
.ratio(ratio as f64)
.gauge_style(Style::default().fg(color).bg(Color::Reset))
.label(format!("{:<26} {}", truncate_name(&label, 26), display_num));
frame.render_widget(gauge, chunks[row]);
}
}
fn render_empty_bars(frame: &mut Frame, area: Rect) {
let para = Paragraph::new(Line::from(Span::styled(
"waiting for tokens…",
Style::default().fg(Color::DarkGray),
)))
.block(Block::default().borders(Borders::ALL).title("top concepts"));
frame.render_widget(para, area);
}
fn render_recent(frame: &mut Frame, area: Rect, entries: &[ReadoutEntry],
layer: usize, concepts: &[String]) {
let mut lines: Vec<Line> = Vec::new();
for entry in entries.iter().rev().take(4) {
let row = match entry.readout.get(layer) {
Some(r) => r,
None => continue,
};
// top concept at this layer for this token
let (best_idx, best_val) = row.iter().enumerate()
.fold((0, 0.0_f32), |acc, (i, v)| {
if v.abs() > acc.1.abs() { (i, *v) } else { acc }
});
let name = concepts.get(best_idx).cloned()
.unwrap_or_else(|| format!("c{}", best_idx));
let tok_str = format!("t{:>5}", entry.token_id);
lines.push(Line::from(vec![
Span::styled(tok_str, Style::default().fg(Color::DarkGray)),
Span::raw(" "),
Span::styled(
format!("{:<24}", truncate_name(&name, 24)),
Style::default().fg(
if best_val >= 0.0 { Color::Green } else { Color::Red },
),
),
Span::styled(
format!(" {:+.3}", best_val),
Style::default().add_modifier(Modifier::BOLD),
),
]));
}
let para = Paragraph::new(lines)
.block(Block::default().borders(Borders::ALL).title("recent tokens — top concept"));
frame.render_widget(para, area);
}
/// Z-score normalize: `(v - mean) / std` across the concept axis.
/// Result is comparable across frames and layers (the residual-stream
/// magnitude factors out) and has the nice property that "this is
/// ≥2σ elevated" has a concrete meaning regardless of scale.
fn z_score(values: &[f32]) -> Vec<f32> {
let n = values.len() as f32;
if n == 0.0 {
return Vec::new();
}
let mean = values.iter().sum::<f32>() / n;
let var = values.iter()
.map(|v| (v - mean) * (v - mean))
.sum::<f32>() / n;
let std = var.sqrt().max(1e-6);
values.iter().map(|v| (v - mean) / std).collect()
}
fn mean_layer(entries: &[ReadoutEntry], layer: usize) -> Option<Vec<f32>> {
let rows: Vec<&Vec<f32>> = entries.iter()
.filter_map(|e| e.readout.get(layer))
.collect();
if rows.is_empty() {
return None;
}
let n_concepts = rows[0].len();
let mut acc = vec![0.0_f32; n_concepts];
for r in &rows {
for (i, v) in r.iter().enumerate() {
acc[i] += *v;
}
}
let n = rows.len() as f32;
for v in &mut acc { *v /= n; }
Some(acc)
}
fn truncate_name(s: &str, max: usize) -> String {
if s.len() <= max { s.to_string() }
else { format!("{}…", &s[..max.saturating_sub(1)]) }
}

29
src/user/chat.rs
View file

@ -112,7 +112,13 @@ pub async fn cmd_switch_model(
let _new_client = crate::agent::api::ApiClient::new(
&resolved.api_base, &resolved.api_key, &resolved.model_id,
);
agent.state.lock().await.notify(format!("switched to {}", resolved.model_id));
let prompt_changed = resolved.prompt_file != agent.prompt_file;
if prompt_changed {
agent.compact().await;
agent.state.lock().await.notify(format!("switched to {} (recompacted)", resolved.model_id));
} else {
agent.state.lock().await.notify(format!("switched to {}", resolved.model_id));
}
}
fn notify_help(agent: &std::sync::Arc<crate::agent::Agent>) {
@ -167,7 +173,6 @@ enum PaneTarget {
ConversationAssistant,
Tools,
ToolResult,
Autonomous,
}
const MAX_PANE_LINES: usize = 10_000;
@ -473,11 +478,8 @@ impl InteractScreen {
AstNode::Leaf(leaf) => {
let text = leaf.body().text().to_string();
match leaf.body() {
NodeBody::Memory { .. } | NodeBody::Log(_) | NodeBody::Dmn(_) => vec![],
NodeBody::Thinking(_) => {
if text.is_empty() { vec![] }
else { vec![(PaneTarget::Autonomous, text, Marker::None)] }
}
NodeBody::Memory { .. } | NodeBody::Thinking(_)
| NodeBody::Log(_) | NodeBody::Dmn(_) => vec![],
NodeBody::Content(_) => {
if text.is_empty() || text.starts_with("<system-reminder>") { vec![] }
else { vec![(PaneTarget::Conversation, text, Marker::User)] }
@ -490,11 +492,6 @@ impl InteractScreen {
if t.is_empty() { vec![] }
else { vec![(PaneTarget::ToolResult, text, Marker::None)] }
}
NodeBody::Image { orig_height, orig_width, .. } => {
vec![(PaneTarget::Conversation,
format!("[image {}x{}]", orig_width, orig_height),
Marker::None)]
}
}
}
AstNode::Branch { role, children, .. } => {
@ -551,12 +548,6 @@ impl InteractScreen {
self.tools.push_line(format!(" {}", line), Color::DarkGray);
}
}
PaneTarget::Autonomous => {
self.autonomous.current_color = Color::Gray;
self.autonomous.append_text(&text);
self.autonomous.pending_marker = marker;
self.autonomous.flush_pending();
}
}
}
}
@ -568,8 +559,6 @@ impl InteractScreen {
=> self.conversation.pop_line(),
PaneTarget::Tools | PaneTarget::ToolResult
=> self.tools.pop_line(),
PaneTarget::Autonomous
=> self.autonomous.pop_line(),
}
}
}

111
src/user/compare.rs
View file

@ -1,111 +0,0 @@
// compare.rs — F7 compare screen: side-by-side test-model regen of
// every assistant response in the current context.
use ratatui::{
layout::Rect,
style::{Color, Modifier, Style},
text::{Line, Span},
widgets::{Block, Borders, List, ListItem, ListState, Paragraph, Wrap},
Frame,
};
use ratatui::crossterm::event::{Event, KeyCode};
use super::{App, ScreenView, truncate, widgets};
pub use crate::subconscious::compare::CompareCandidate;
pub(crate) struct CompareScreen {
list_state: ListState,
mind_tx: tokio::sync::mpsc::UnboundedSender<crate::mind::MindCommand>,
}
impl CompareScreen {
pub fn new(
mind_tx: tokio::sync::mpsc::UnboundedSender<crate::mind::MindCommand>,
) -> Self {
Self { list_state: ListState::default(), mind_tx }
}
}
impl ScreenView for CompareScreen {
fn label(&self) -> &'static str { "compare" }
fn tick(&mut self, frame: &mut Frame, area: Rect,
events: &[Event], app: &mut App) {
widgets::handle_list_nav(events, &mut self.list_state,
app.compare_candidates.len(), |code| match code {
KeyCode::Char('c') | KeyCode::Enter => {
let _ = self.mind_tx.send(crate::mind::MindCommand::Compare);
}
_ => {}
});
let (settings_area, content_area, help_area) =
widgets::candidate_frame(frame, area, "compare");
let test_backend = crate::config::app().compare.test_backend.clone();
let (label, color) = if test_backend.is_empty() {
("(unset — set compare.test_backend)".to_string(), Color::Red)
} else {
(test_backend, Color::Yellow)
};
frame.render_widget(Paragraph::new(Line::from(vec![
Span::raw(" test model: "),
Span::styled(label, Style::default().fg(color)),
])), settings_area);
let candidates = &app.compare_candidates;
if candidates.is_empty() {
let err = app.mind_state.as_ref().and_then(|ms| ms.compare_error.as_deref());
let mut lines = vec![Line::from(""),
Line::styled(" Press c/Enter to compare against the configured test model.",
Style::default().fg(Color::DarkGray))];
if let Some(e) = err {
lines.push(Line::from(""));
lines.push(Line::from(vec![
Span::raw(" "),
Span::styled(format!("error: {}", e), Style::default().fg(Color::Red)),
]));
}
frame.render_widget(Paragraph::new(lines), content_area);
} else {
let (list_area, detail_area) = widgets::list_detail_split(content_area);
let items: Vec<ListItem> = candidates.iter().map(|c| ListItem::new(Line::from(vec![
Span::styled(format!("#{:<3} ", c.entry_idx), Style::default().fg(Color::DarkGray)),
Span::raw(truncate(&c.original_text, 30)),
]))).collect();
frame.render_stateful_widget(
List::new(items)
.block(Block::default().borders(Borders::RIGHT).title(" candidates "))
.highlight_style(Style::default().add_modifier(Modifier::REVERSED)),
list_area, &mut self.list_state,
);
if let Some(c) = self.list_state.selected().and_then(|i| candidates.get(i)) {
let mut text = String::new();
if !c.prior_context.is_empty() {
text.push_str(&c.prior_context);
text.push_str("\n\n─── original ───\n\n");
}
text.push_str(&c.original_text);
text.push_str("\n\n─── test model ───\n\n");
text.push_str(&c.alternate_text);
frame.render_widget(
Paragraph::new(text)
.block(Block::default().borders(Borders::TOP)
.title(format!(" entry {} ", c.entry_idx)))
.wrap(Wrap { trim: false }),
detail_area,
);
}
}
frame.render_widget(Paragraph::new(Line::from(vec![
Span::styled(" j/k/\u{2191}\u{2193}", Style::default().fg(Color::Cyan)),
Span::raw("=nav "),
Span::styled("c/Enter", Style::default().fg(Color::Green)),
Span::raw("=run "),
])), help_area);
}
}

25
src/user/context.rs
View file

@ -38,14 +38,16 @@ impl ConsciousScreen {
for node in ctx.conversation() {
if let AstNode::Leaf(leaf) = node {
if let NodeBody::Memory { key, score, text } = leaf.body() {
if score.is_some() { scored += 1; } else { unscored += 1; }
let status = match score {
Some(s) => { scored += 1; format!("{:.2}", s) }
None => { unscored += 1; String::new() }
};
mem_children.push(SectionView {
name: format!("mem: {}", key),
name: key.clone(),
tokens: node.tokens(),
content: text.clone(),
token_ids: leaf.token_ids().to_vec(),
children: Vec::new(),
status: score.map(|s| format!("{:.2}", s)).unwrap_or_default(),
status,
});
}
}
@ -56,7 +58,6 @@ impl ConsciousScreen {
name: format!("Memory nodes ({})", mem_children.len()),
tokens: mem_tokens,
content: String::new(),
token_ids: Vec::new(),
children: mem_children,
status: format!("{} scored, {} unscored", scored, unscored),
});
@ -72,13 +73,11 @@ impl ConsciousScreen {
AstNode::Leaf(leaf) => leaf.body().text().to_string(),
_ => String::new(),
},
token_ids: node.token_ids(),
children: match node {
AstNode::Branch { children, .. } => children.iter()
.map(|c| SectionView {
name: c.label(), tokens: c.tokens(),
content: match c { AstNode::Leaf(l) => l.body().text().to_string(), _ => String::new() },
token_ids: match c { AstNode::Leaf(l) => l.token_ids().to_vec(), _ => c.token_ids() },
children: Vec::new(), status: String::new(),
}).collect(),
_ => Vec::new(),
@ -105,7 +104,6 @@ impl ConsciousScreen {
name: format!("Conversation ({} entries)", conv_children.len()),
tokens: conv_tokens,
content: String::new(),
token_ids: Vec::new(),
children: conv_children,
status: String::new(),
});
@ -131,7 +129,14 @@ impl ScreenView for ConsciousScreen {
let section_style = Style::default().fg(Color::Yellow);
lines.push(Line::styled("── Model ──", section_style));
lines.push(Line::raw(format!(" Current: {}", app.status.model)));
let model_display = app.context_info.as_ref()
.map_or_else(|| app.status.model.clone(), |i| i.model.clone());
lines.push(Line::raw(format!(" Current: {}", model_display)));
if let Some(ref info) = app.context_info {
lines.push(Line::raw(format!(" Backend: {}", info.backend)));
lines.push(Line::raw(format!(" Prompt: {}", info.prompt_file)));
lines.push(Line::raw(format!(" Available: {}", info.available_models.join(", "))));
}
lines.push(Line::raw(""));
lines.push(Line::styled("── Context State ──", section_style));
@ -151,6 +156,8 @@ impl ScreenView for ConsciousScreen {
lines.push(Line::raw(format!(" {:53} {:>6} tokens", "────────", "──────")));
lines.push(Line::raw(format!(" {:53} {:>6} tokens", "Total", total)));
} else if let Some(ref info) = app.context_info {
lines.push(Line::raw(format!(" Context message: {:>6} chars", info.context_message_chars)));
}
lines.push(Line::raw(""));

284
src/user/learn.rs
View file

@ -1,284 +0,0 @@
// learn.rs — F6: fine-tuning review screen
//
// Shows responses identified as training candidates (high divergence
// when memories stripped). Queue for review before sending to /finetune.
use ratatui::{
layout::{Constraint, Layout, Rect},
style::{Color, Modifier, Style},
text::{Line, Span},
widgets::{Block, Borders, List, ListItem, ListState, Paragraph, Wrap},
Frame,
};
use ratatui::crossterm::event::{Event, KeyCode};
use super::{App, ScreenView, truncate, widgets};
/// A candidate response identified for fine-tuning.
#[derive(Clone, Debug)]
pub struct FinetuneCandidate {
/// Index in conversation entries.
pub entry_idx: usize,
/// Divergence score (higher = more dependent on memories).
pub divergence: f64,
/// The assistant response text.
pub response_text: String,
/// Prior user/assistant messages for review context.
pub prior_context: String,
/// Status: pending, approved, rejected, sent.
pub status: CandidateStatus,
/// Token IDs for context.
pub context_ids: Vec<u32>,
/// Token IDs for continuation (what we're training on).
pub continuation_ids: Vec<u32>,
/// What the model would have said without memories (if generated).
pub alternate_text: Option<String>,
/// Timestamp in nanos — used as unique key for trained-set dedup.
pub timestamp_ns: i64,
}
#[derive(Clone, Debug, PartialEq)]
pub enum CandidateStatus {
Pending,
Approved,
Rejected,
Sent,
}
impl From<crate::subconscious::learn::FinetuneCandidate> for FinetuneCandidate {
fn from(c: crate::subconscious::learn::FinetuneCandidate) -> Self {
FinetuneCandidate {
entry_idx: c.entry_idx,
divergence: c.divergence,
response_text: c.response_text,
prior_context: c.prior_context,
status: CandidateStatus::Pending,
context_ids: c.context_ids,
continuation_ids: c.continuation_ids,
alternate_text: c.alternate_text,
timestamp_ns: c.timestamp_ns,
}
}
}
pub(crate) struct LearnScreen {
list_state: ListState,
mind_tx: tokio::sync::mpsc::UnboundedSender<crate::mind::MindCommand>,
}
impl LearnScreen {
pub fn new(
mind_tx: tokio::sync::mpsc::UnboundedSender<crate::mind::MindCommand>,
) -> Self {
Self {
list_state: ListState::default(),
mind_tx,
}
}
fn selected_idx(&self) -> Option<usize> {
self.list_state.selected()
}
}
impl ScreenView for LearnScreen {
fn label(&self) -> &'static str { "learn" }
fn tick(&mut self, frame: &mut Frame, area: Rect,
events: &[Event], app: &mut App) {
let selected_idx = self.list_state.selected();
widgets::handle_list_nav(events, &mut self.list_state,
app.finetune_candidates.len(), |code| match code {
KeyCode::Char('a') => {
if let Some(idx) = selected_idx {
app.finetune_action(idx, CandidateStatus::Approved);
}
}
KeyCode::Char('r') => {
if let Some(idx) = selected_idx {
app.finetune_action(idx, CandidateStatus::Rejected);
}
}
KeyCode::Char('g') => {
let current = crate::config::app().learn.generate_alternates;
let _ = self.mind_tx.send(
crate::mind::MindCommand::SetLearnGenerateAlternates(!current));
}
KeyCode::Char('s') => { app.finetune_send_approved(); }
KeyCode::Char('+') | KeyCode::Char('=') => {
let new = crate::config::app().learn.threshold * 10.0;
let _ = self.mind_tx.send(crate::mind::MindCommand::SetLearnThreshold(new));
}
KeyCode::Char('-') => {
let new = crate::config::app().learn.threshold / 10.0;
let _ = self.mind_tx.send(crate::mind::MindCommand::SetLearnThreshold(new));
}
_ => {}
});
let (settings_area, content_area, help_area) =
widgets::candidate_frame(frame, area, "learn");
let (threshold, gen_on) = {
let app_cfg = crate::config::app();
(app_cfg.learn.threshold, app_cfg.learn.generate_alternates)
};
let settings = Line::from(vec![
Span::raw(" thresh: "),
Span::styled(format!("{:e}", threshold), Style::default().fg(Color::Yellow)),
Span::raw(" gen: "),
Span::styled(
if gen_on { "[on]" } else { "[off]" },
Style::default().fg(if gen_on { Color::Green } else { Color::DarkGray }),
),
]);
frame.render_widget(Paragraph::new(settings), settings_area);
let candidates = &app.finetune_candidates;
if candidates.is_empty() {
render_empty(frame, content_area, app);
} else {
let (list_area, detail_area) = widgets::list_detail_split(content_area);
// Render candidate list
let items: Vec<ListItem> = candidates.iter().map(|c| {
let status_char = match c.status {
CandidateStatus::Pending => ' ',
CandidateStatus::Approved => '+',
CandidateStatus::Rejected => '-',
CandidateStatus::Sent => '*',
};
let style = match c.status {
CandidateStatus::Pending => Style::default(),
CandidateStatus::Approved => Style::default().fg(Color::Green),
CandidateStatus::Rejected => Style::default().fg(Color::DarkGray),
CandidateStatus::Sent => Style::default().fg(Color::Cyan),
};
ListItem::new(Line::from(vec![
Span::styled(format!("[{}] ", status_char), style),
Span::styled(format!("{:.2} ", c.divergence), Style::default().fg(Color::Yellow)),
Span::raw(truncate(&c.response_text, 30)),
]))
}).collect();
let list = List::new(items)
.block(Block::default().borders(Borders::RIGHT).title(" candidates "))
.highlight_style(Style::default().add_modifier(Modifier::REVERSED));
frame.render_stateful_widget(list, list_area, &mut self.list_state);
// Render detail for selected candidate
if let Some(idx) = self.selected_idx() {
if let Some(candidate) = candidates.get(idx) {
render_detail(frame, candidate, detail_area);
}
}
}
frame.render_widget(Paragraph::new(Line::from(vec![
Span::styled(" j/k/\u{2191}\u{2193}", Style::default().fg(Color::Cyan)),
Span::raw("=nav "),
Span::styled("a", Style::default().fg(Color::Green)),
Span::raw("=approve "),
Span::styled("r", Style::default().fg(Color::Red)),
Span::raw("=reject "),
Span::styled("g", Style::default().fg(Color::Yellow)),
Span::raw("=gen "),
Span::styled("s", Style::default().fg(Color::Magenta)),
Span::raw("=send "),
Span::styled("+/-", Style::default().fg(Color::Cyan)),
Span::raw("=thresh "),
])), help_area);
}
}
fn render_empty(frame: &mut Frame, inner: Rect, app: &App) {
let mut lines = Vec::new();
lines.push(Line::from(""));
match app.mind_state.as_ref().and_then(|ms| ms.finetune_last_run.as_ref()) {
Some(stats) => {
lines.push(Line::from(vec![
Span::raw(" Last run: "),
Span::styled(
format!("{}", stats.responses_considered),
Style::default().fg(Color::Cyan),
),
Span::raw(" responses considered, "),
Span::styled(
format!("{}", stats.above_threshold),
Style::default().fg(if stats.above_threshold > 0 { Color::Green } else { Color::DarkGray }),
),
Span::raw(" above threshold, max divergence: "),
Span::styled(
format!("{:.4}", stats.max_divergence),
Style::default().fg(Color::Yellow),
),
]));
if let Some(err) = &stats.error {
lines.push(Line::from(vec![
Span::raw(" "),
Span::styled(
format!("Error: {}", err),
Style::default().fg(Color::Red),
),
]));
}
}
None => {
lines.push(Line::styled(
" No scoring run yet.",
Style::default().fg(Color::DarkGray),
));
}
}
lines.push(Line::from(""));
lines.push(Line::styled(
" Scoring runs at startup and after each turn.",
Style::default().fg(Color::DarkGray),
));
frame.render_widget(Paragraph::new(lines), inner);
}
fn render_detail(frame: &mut Frame, c: &FinetuneCandidate, area: Rect) {
let [header_area, content_area] = Layout::vertical([
Constraint::Length(3),
Constraint::Min(1),
]).areas(area);
// Header: divergence, status
let alt_status = if c.alternate_text.is_some() { "yes" } else { "no" };
let header = Paragraph::new(vec![
Line::from(vec![
Span::raw(" divergence: "),
Span::styled(format!("{:.3}", c.divergence), Style::default().fg(Color::Yellow)),
Span::raw(format!(" entry: {} alt: {}", c.entry_idx, alt_status)),
]),
]);
frame.render_widget(header, header_area);
// Content: prior context, the scored response, and alternate
// (if available).
let content_block = Block::default()
.borders(Borders::TOP)
.title(" context & response ");
let mut text = String::new();
if !c.prior_context.is_empty() {
text.push_str(&c.prior_context);
text.push_str("\n\n─── response ───\n\n");
}
text.push_str(&c.response_text);
if let Some(alt) = &c.alternate_text {
text.push_str("\n\n─── without memories ───\n\n");
text.push_str(alt);
}
let content = Paragraph::new(text)
.block(content_block)
.wrap(Wrap { trim: false });
frame.render_widget(content, content_area);
}

180
src/user/mod.rs
View file

@ -3,16 +3,13 @@
// TUI, UI channel, parsing. The cognitive layer (session state
// machine, DMN, identity) lives in mind/.
pub(crate) mod amygdala;
pub(crate) mod chat;
pub(crate) mod compare;
mod context;
pub(crate) mod learn;
pub(crate) mod scroll_pane;
pub mod selectable;
mod subconscious;
mod thalamus;
mod unconscious;
mod thalamus;
mod widgets;
use anyhow::Result;
@ -47,6 +44,15 @@ struct StatusInfo {
}
/// Context loading details for the debug screen.
#[derive(Debug, Clone)]
struct ContextInfo {
model: String,
available_models: Vec<String>,
prompt_file: String,
backend: String,
context_message_chars: usize,
}
/// Build the screen legend from screen labels.
fn screen_legend_from(screens: &[Box<dyn ScreenView>]) -> String {
let parts: Vec<String> = screens.iter().enumerate()
@ -66,15 +72,8 @@ fn screen_legend() -> String {
SCREEN_LEGEND.get().cloned().unwrap_or_default()
}
/// Return the first line of `s`, truncated to `max` chars with an
/// ellipsis suffix. Used by candidate-list screens.
fn truncate(s: &str, max: usize) -> String {
let first = s.lines().next().unwrap_or("");
if first.len() > max { format!("{}...", &first[..max]) } else { first.to_string() }
}
/// A screen that can draw itself and handle input.
trait ScreenView {
trait ScreenView: Send {
fn tick(&mut self, frame: &mut ratatui::Frame, area: ratatui::layout::Rect,
events: &[ratatui::crossterm::event::Event], app: &mut App);
fn label(&self) -> &'static str;
@ -110,6 +109,7 @@ struct App {
top_k: u32,
agent: std::sync::Arc<crate::agent::Agent>,
should_quit: bool,
context_info: Option<ContextInfo>,
agent_state: Vec<crate::mind::SubconsciousSnapshot>,
unconscious_state: Vec<crate::mind::UnconsciousSnapshot>,
mind_state: Option<crate::mind::MindState>,
@ -121,10 +121,6 @@ struct App {
walked_count: usize,
channel_status: Vec<ChannelStatus>,
idle_info: Option<IdleInfo>,
/// Fine-tuning candidates pending review.
finetune_candidates: Vec<learn::FinetuneCandidate>,
/// F7 compare candidates — response pairs from test-model comparison.
compare_candidates: Vec<compare::CompareCandidate>,
}
impl App {
@ -146,6 +142,7 @@ impl App {
top_k: 20,
agent,
should_quit: false,
context_info: None,
agent_state: Vec::new(),
unconscious_state: Vec::new(),
mind_state: None,
@ -154,53 +151,9 @@ impl App {
rebuild_tools_pending: false,
walked_count: 0,
channel_status: Vec::new(), idle_info: None,
finetune_candidates: Vec::new(),
compare_candidates: Vec::new(),
}
}
fn finetune_action(&mut self, idx: usize, status: learn::CandidateStatus) {
if let Some(candidate) = self.finetune_candidates.get_mut(idx) {
candidate.status = status;
}
}
fn finetune_send_approved(&mut self) {
// Collect approved candidates
let samples: Vec<crate::subconscious::learn::TrainData> = self.finetune_candidates.iter()
.filter(|c| c.status == learn::CandidateStatus::Approved)
.map(|c| crate::subconscious::learn::TrainData {
context_ids: c.context_ids.clone(),
continuation_ids: c.continuation_ids.clone(),
timestamp_ns: c.timestamp_ns,
})
.collect();
if samples.is_empty() {
return;
}
// Mark as sent in UI immediately
for candidate in &mut self.finetune_candidates {
if candidate.status == learn::CandidateStatus::Approved {
candidate.status = learn::CandidateStatus::Sent;
}
}
// Spawn async task to send to training server
let client = self.agent.client.clone();
tokio::spawn(async move {
match crate::subconscious::learn::send_to_train(samples, &client).await {
Ok(job_id) => {
dbglog!("[finetune] training started: {}", job_id);
}
Err(e) => {
dbglog!("[finetune] send failed: {:#}", e);
}
}
});
}
fn set_channel_status(&mut self, channels: Vec<(String, bool, u32)>) {
self.channel_status = channels.into_iter()
@ -240,9 +193,6 @@ fn restore_terminal(terminal: &mut ratatui::Terminal<CrosstermBackend<io::Stdout
async fn start(cli: crate::user::CliArgs) -> Result<()> {
let (config, _figment) = crate::config::load_session(&cli).await?;
// Pick up external edits (vim, F6 hotkeys, etc.) without restart.
crate::config::watch_config(cli.clone());
if config.app.debug {
unsafe { std::env::set_var("POC_DEBUG", "1") };
}
@ -291,21 +241,22 @@ async fn start(cli: crate::user::CliArgs) -> Result<()> {
ui_handle.join().unwrap_or_else(|_| Err(anyhow::anyhow!("UI thread panicked")))
}
async fn hotkey_cycle_reasoning(mind: &crate::mind::Mind) {
let mut ag = mind.agent.state.lock().await;
let next = match ag.reasoning_effort.as_str() {
"none" => "low",
"low" => "high",
_ => "none",
};
ag.reasoning_effort = next.to_string();
let label = match next {
"none" => "off (monologue hidden)",
"low" => "low (brief monologue)",
"high" => "high (full monologue)",
_ => next,
};
ag.notify(format!("reasoning: {}", label));
fn hotkey_cycle_reasoning(mind: &crate::mind::Mind) {
if let Ok(mut ag) = mind.agent.state.try_lock() {
let next = match ag.reasoning_effort.as_str() {
"none" => "low",
"low" => "high",
_ => "none",
};
ag.reasoning_effort = next.to_string();
let label = match next {
"none" => "off (monologue hidden)",
"low" => "low (brief monologue)",
"high" => "high (full monologue)",
_ => next,
};
ag.notify(format!("reasoning: {}", label));
}
}
async fn hotkey_kill_processes(mind: &crate::mind::Mind) {
@ -383,7 +334,7 @@ async fn run(
}
let notify_rx = crate::thalamus::channels::subscribe_all();
// F1=chat, F2=conscious, F3=subconscious, F4=unconscious, F5=thalamus, F6=learn, F7=compare, F8=amygdala
// F1=chat, F2=conscious, F3=subconscious, F4=unconscious, F5=thalamus
let mut screens: Vec<Box<dyn tui::ScreenView>> = vec![
Box::new(crate::user::chat::InteractScreen::new(
mind.agent.clone(), mind.shared.clone(), mind_tx.clone(),
@ -392,9 +343,6 @@ async fn run(
Box::new(crate::user::subconscious::SubconsciousScreen::new()),
Box::new(crate::user::unconscious::UnconsciousScreen::new()),
Box::new(crate::user::thalamus::ThalamusScreen::new()),
Box::new(crate::user::learn::LearnScreen::new(mind_tx.clone())),
Box::new(crate::user::compare::CompareScreen::new(mind_tx.clone())),
Box::new(crate::user::amygdala::AmygdalaScreen::new()),
];
let mut active_screen: usize = 1; // F-key number
tui::set_screen_legend(tui::screen_legend_from(&*screens));
@ -471,8 +419,7 @@ async fn run(
idle_state.decay_ewma();
app.update_idle(&idle_state);
app.agent_state = mind.subconscious_snapshots().await;
{
let mut unc = mind.unconscious.lock().await;
if let Ok(mut unc) = mind.unconscious.try_lock() {
let toggles: Vec<String> = app.agent_toggles.drain(..).collect();
for name in &toggles {
if mind.subconscious.lock().await.toggle(name).is_none() {
@ -486,42 +433,7 @@ async fn run(
};
app.unconscious_state = unc.snapshots(store_guard.as_deref());
app.graph_health = unc.graph_health.clone();
}
// Sync mind state (finetune candidates, last scoring run, etc.)
{
let ms = mind.shared.lock().unwrap();
// Sync finetune candidates: add new ones, keep existing (preserves approval status),
// remove sent candidates, keep only 10 most recent rejected.
app.finetune_candidates.retain(|c| c.status != learn::CandidateStatus::Sent);
for c in &ms.finetune_candidates {
let exists = app.finetune_candidates.iter()
.any(|existing| existing.timestamp_ns == c.timestamp_ns);
if !exists {
app.finetune_candidates.push(learn::FinetuneCandidate::from(c.clone()));
}
}
let mut rejected: Vec<_> = app.finetune_candidates.iter()
.enumerate()
.filter(|(_, c)| c.status == learn::CandidateStatus::Rejected)
.map(|(i, c)| (i, c.timestamp_ns))
.collect();
if rejected.len() > 10 {
rejected.sort_by_key(|(_, ts)| std::cmp::Reverse(*ts));
let to_remove: std::collections::HashSet<_> = rejected[10..]
.iter().map(|(i, _)| *i).collect();
let mut idx = 0;
app.finetune_candidates.retain(|_| {
let keep = !to_remove.contains(&idx);
idx += 1;
keep
});
}
// Sync compare candidates — a fresh run clears, so take a snapshot.
app.compare_candidates = ms.compare_candidates.clone();
app.mind_state = Some(ms.clone());
app.mind_state = Some(mind.shared.lock().unwrap().clone());
}
app.walked_count = mind.subconscious_walked().await.len();
if !startup_done {
@ -591,7 +503,7 @@ async fn run(
} else if key.modifiers.contains(KeyModifiers::CONTROL) {
match key.code {
KeyCode::Char('c') => { app.should_quit = true; }
KeyCode::Char('r') => hotkey_cycle_reasoning(mind).await,
KeyCode::Char('r') => hotkey_cycle_reasoning(mind),
KeyCode::Char('k') => hotkey_kill_processes(mind).await,
KeyCode::Char('p') => hotkey_cycle_autonomy(mind),
_ => {}
@ -618,11 +530,16 @@ async fn run(
// --- CLI ---
use clap::{Parser, Subcommand};
use std::path::PathBuf;
#[derive(Parser, Debug, Default, Clone)]
#[derive(Parser, Debug, Default)]
#[command(name = "consciousness", about = "Substrate-independent AI agent")]
pub struct CliArgs {
/// Model override (selects a named entry from `models` in config.json5)
/// Select active backend ("anthropic" or "openrouter")
#[arg(long)]
pub backend: Option<String>,
/// Model override
#[arg(short, long)]
pub model: Option<String>,
@ -642,6 +559,10 @@ pub struct CliArgs {
#[arg(long)]
pub show_config: bool,
/// Project memory directory
#[arg(long)]
pub memory_project: Option<PathBuf>,
/// Max consecutive DMN turns
#[arg(long)]
pub dmn_max_turns: Option<u32>,
@ -654,7 +575,7 @@ pub struct CliArgs {
pub command: Option<SubCmd>,
}
#[derive(Subcommand, Debug, Clone)]
#[derive(Subcommand, Debug)]
pub enum SubCmd {
/// Print new output since last read and exit
Read {
@ -755,15 +676,8 @@ fn restore_stderr(original_fd: std::os::fd::RawFd) {
#[tokio::main]
pub async fn main() {
// Install target-routed file logger: `target: "grpc"` records go to
// ~/.consciousness/logs/daemon/grpc.log, everything else to debug.log.
// Level from RUST_LOG, defaulting to info.
let _ = crate::logging::init();
// Reap channel-daemon zombies via a SIGCHLD handler that only touches
// PIDs listed in channels_dir(). Avoids SIGCHLD=SIG_IGN, which would
// break tokio::process::Command::wait() (kernel auto-reap → ECHILD).
let _reaper = crate::thalamus::supervisor::start_zombie_reaper();
// Auto-reap child processes (channel daemons outlive the supervisor)
unsafe { libc::signal(libc::SIGCHLD, libc::SIG_IGN); }
// Redirect stderr to pipe — logs to file and sends to channel for UI display
let stderr_capture = redirect_stderr_to_pipe();

2
src/user/subconscious.rs
View file

@ -207,7 +207,6 @@ impl SubconsciousScreen {
name: key.clone(),
tokens: 0,
content: val.clone(),
token_ids: Vec::new(),
children: Vec::new(),
status: String::new(),
}
@ -239,7 +238,6 @@ impl SubconsciousScreen {
name: format!("Conversation ({} entries)", conv_children.len()),
tokens: conv_children.iter().map(|c| c.tokens).sum(),
content: String::new(),
token_ids: Vec::new(),
children: conv_children,
status: String::new(),
});

140
src/user/widgets.rs
View file

@ -6,20 +6,13 @@ use ratatui::{
widgets::{Block, Borders},
crossterm::event::KeyCode,
};
use crate::agent::context::{AstNode, Ast, NodeBody};
use crate::agent::context::{AstNode, Ast};
#[derive(Debug, Clone, Default)]
#[derive(Debug, Clone)]
pub struct SectionView {
pub name: String,
pub tokens: usize,
pub content: String,
/// Token-id stream for this subtree, displayed in place of
/// `content` when the tree's show-tokens mode is on. Populated
/// from `leaf.token_ids()` / `node.token_ids()` for views built
/// from the AST; empty for views that don't have a corresponding
/// AST node (subconscious entries, etc.), in which case the
/// token view falls back to the text content.
pub token_ids: Vec<u32>,
pub children: Vec<SectionView>,
/// Extra status text shown after the token count.
pub status: String,
@ -27,23 +20,13 @@ pub struct SectionView {
fn node_to_view(node: &AstNode) -> SectionView {
match node {
AstNode::Leaf(leaf) => {
let (name, status) = match leaf.body() {
NodeBody::Memory { key, score, .. } => {
let s = score.map(|v| format!("{:.2}", v)).unwrap_or_default();
(format!("mem: {}", key), s)
}
_ => (node.label(), String::new()),
};
SectionView {
name,
tokens: node.tokens(),
content: leaf.body().text().to_string(),
token_ids: leaf.token_ids().to_vec(),
children: Vec::new(),
status,
}
}
AstNode::Leaf(leaf) => SectionView {
name: node.label(),
tokens: node.tokens(),
content: leaf.body().text().to_string(),
children: Vec::new(),
status: String::new(),
},
AstNode::Branch { children, .. } => {
let child_views: Vec<SectionView> = children.iter()
.map(|c| node_to_view(c))
@ -52,7 +35,6 @@ fn node_to_view(node: &AstNode) -> SectionView {
name: node.label(),
tokens: node.tokens(),
content: String::new(),
token_ids: node.token_ids(),
children: child_views,
status: String::new(),
}
@ -63,12 +45,10 @@ fn node_to_view(node: &AstNode) -> SectionView {
pub fn section_to_view(name: &str, nodes: &[AstNode]) -> SectionView {
let children: Vec<SectionView> = nodes.iter().map(|n| node_to_view(n)).collect();
let total_tokens: usize = nodes.iter().map(|n| n.tokens()).sum();
let token_ids: Vec<u32> = nodes.iter().flat_map(|n| n.token_ids()).collect();
SectionView {
name: name.to_string(),
tokens: total_tokens,
content: String::new(),
token_ids,
children,
status: String::new(),
}
@ -115,78 +95,11 @@ pub fn format_ts_age(ts: i64) -> String {
/// Key legend for SectionTree panes.
pub fn tree_legend() -> Line<'static> {
Line::styled(
" ↑↓:nav →/Enter:expand ←:collapse e:expand c:collapse v:toggle tokens/text PgUp/Dn ",
" ↑↓:nav →/Enter:expand ←:collapse e:expand all c:collapse all PgUp/Dn Home/End ",
Style::default().fg(Color::DarkGray),
)
}
// ---------------------------------------------------------------------------
// Candidate-browser screen skeleton (F6 learn, F7 compare, future screens)
// ---------------------------------------------------------------------------
use ratatui::{
layout::{Constraint, Layout, Rect},
widgets::ListState,
crossterm::event::{Event, KeyEvent},
Frame,
};
/// Frame a candidate-browser screen: outer magenta-bordered block with
/// the screen legend on the left and `title` on the right, split into
/// (settings_row, content_area, help_row). Caller renders into the
/// three sub-areas.
pub fn candidate_frame(frame: &mut Frame, area: Rect, title: &str) -> (Rect, Rect, Rect) {
let block = Block::default()
.title_top(Line::from(super::screen_legend()).left_aligned())
.title_top(Line::from(format!(" {} ", title)).right_aligned())
.borders(Borders::ALL)
.border_style(Style::default().fg(Color::Magenta));
let inner = block.inner(area);
frame.render_widget(block, area);
let [settings, content] = Layout::vertical([
Constraint::Length(1), Constraint::Min(0),
]).areas(inner);
let help = Rect { y: area.y + area.height - 1, height: 1, ..area };
(settings, content, help)
}
/// 40/60 horizontal split for list + detail panes within the content area.
pub fn list_detail_split(content: Rect) -> (Rect, Rect) {
let [list, detail] = Layout::horizontal([
Constraint::Percentage(40), Constraint::Percentage(60),
]).areas(content);
(list, detail)
}
/// Handle j/k/↑/↓ list navigation and keep the selection in bounds.
/// Any other key is passed to `on_other` for screen-specific handling.
pub fn handle_list_nav(
events: &[Event],
list_state: &mut ListState,
count: usize,
mut on_other: impl FnMut(KeyCode),
) {
for event in events {
if let Event::Key(KeyEvent { code, .. }) = event {
match code {
KeyCode::Up | KeyCode::Char('k') => {
let i = list_state.selected().unwrap_or(0);
list_state.select(Some(i.saturating_sub(1)));
}
KeyCode::Down | KeyCode::Char('j') => {
let i = list_state.selected().unwrap_or(0);
list_state.select(Some((i + 1).min(count.saturating_sub(1))));
}
_ => on_other(*code),
}
}
}
if count > 0 {
let sel = list_state.selected().unwrap_or(0).min(count - 1);
list_state.select(Some(sel));
}
}
// ---------------------------------------------------------------------------
// SectionTree — expand/collapse tree renderer for ContextSection
@ -196,19 +109,11 @@ pub struct SectionTree {
pub selected: Option<usize>,
pub expanded: std::collections::HashSet<usize>,
pub scroll: super::scroll_pane::ScrollPaneState,
/// When true, render `token_ids` as space-separated IDs in place
/// of `content` in expanded panels. Toggled with 'v'.
pub show_tokens: bool,
}
impl SectionTree {
pub fn new() -> Self {
Self {
selected: None,
expanded: std::collections::HashSet::new(),
scroll: super::scroll_pane::ScrollPaneState::new(),
show_tokens: false,
}
Self { selected: None, expanded: std::collections::HashSet::new(), scroll: super::scroll_pane::ScrollPaneState::new() }
}
fn total_nodes(&self, sections: &[SectionView]) -> usize {
@ -283,9 +188,6 @@ impl SectionTree {
KeyCode::Char('c') => {
self.expanded.clear();
}
KeyCode::Char('v') => {
self.show_tokens = !self.show_tokens;
}
_ => {}
}
self.scroll_to_selected(height);
@ -348,12 +250,7 @@ impl SectionTree {
}
} else if has_content {
let content_indent = format!("{} │ ", " ".repeat(depth + 1));
let body = if self.show_tokens && !section.token_ids.is_empty() {
format_token_ids_wrapped(&section.token_ids)
} else {
section.content.clone()
};
let content_lines: Vec<&str> = body.lines().collect();
let content_lines: Vec<&str> = section.content.lines().collect();
let show = content_lines.len().min(50);
for line in &content_lines[..show] {
lines.push(Line::styled(
@ -371,16 +268,3 @@ impl SectionTree {
}
}
}
/// Format token IDs for the content panel: space-separated, wrapped
/// at 12 ids per line so they fit comfortably in a pane.
fn format_token_ids_wrapped(ids: &[u32]) -> String {
let mut out = String::new();
for (i, id) in ids.iter().enumerate() {
if i > 0 {
if i % 12 == 0 { out.push('\n'); } else { out.push(' '); }
}
out.push_str(&id.to_string());
}
out
}

121
training/DESIGN.md
View file

@ -3,7 +3,7 @@
## Overview
Continuous fine-tuning of Qwen3.5-27B alongside live vLLM inference.
Full-weight updates (not LoRA) using Apollo optimizer with rank-64
Full-weight updates (not LoRA) using Apollo optimizer with rank-256
gradient projection. No pause required — HOGWILD concurrent training.
Weights shared via CUDA IPC between vLLM and the training process.
@ -22,41 +22,25 @@ The training signal comes from two sources:
│ │
│ ┌──────────────────────────────────────────────┐ │
│ │ Model Weights (54GB, bf16) │ │
│ │ Shared: vLLM inference + HF training │ │
│ │ Shared via CUDA IPC │ │
│ └──────────────┬──────────────┬────────────────┘ │
│ │ │ │
│ ┌──────────────▼──┐ ┌───────▼────────────────┐ │
│ │ vLLM (inference)│ │ Training subprocess │ │
│ │ KV cache ~60GB │ │ HF model wrapper │ │
│ │ /completions │ │ Apollo optimizer ~2.5GB │ │
│ │ /score │ │ Checkpoint sync │ │
│ └────────┬────────┘ └───────────▲─────────────┘ │
│ │ │ │
│ │ ZMQ IPC │ │
│ └───────────────────────┘ │
│ │ vLLM (inference)│ │ Apollo (training) │ │
│ │ KV cache ~60GB │ │ Gradients ~54GB │ │
│ │ Serves requests │ │ Optimizer state ~10GB │ │
│ │ Never paused │ │ Activations ~10GB │ │
│ └─────────────────┘ └────────────────────────┘ │
└─────────────────────────────────────────────────────┘
Process Architecture:
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ vLLM Worker │ │ vLLM API Server │ │ Training Worker │
│ (GPU inference) │ │ (HTTP routes) │ │ (GPU training) │
│ │ │ │ │ │
│ export_hook.py │ │ /completions │ │ HF model views │
│ exports IPC │ │ /score │ │ Apollo optimizer│
│ handles on load │ │ /train ─────────┼──► ZMQ REP socket │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │
└──── IPC handles file ──────────────────┘
/tmp/vllm_weight_handles.pt
Moria B200 (vLLM)
Moria B200
┌──────────────────┐ ┌──────────────────┐
│ Training signal │ HTTP │ /completions │
│ agent │──────────>│ /score │
│ │ │ /train │
│ Dream loop │ │ /checkpoint │
│ (generates │ │ /train/status │
│ scenarios) │ │ │
│ Training signal │ HTTP │ Apollo worker │
│ agent │──────────>│ daemon │
│ │ │ │
│ Dream loop │ │ Checkpoint sync │
│ (generates │ │ (mmap + diff, │
│ scenarios) │ │ every 10 min) │
└──────────────────┘ └──────────────────┘
```
@ -75,9 +59,10 @@ LoRA trains adapter matrices, not base weights. For personality and
behavioral changes that persist as disposition, the base weights
need to change. Apollo makes this memory-feasible.
### Rank 64
Not Mini (rank-1). Rank-64 captures gradient structure across diverse
training examples while keeping memory low (~2.5GB on 27B model).
### Rank 256
Not Mini (rank-1). With 100+ diverse training examples, the
gradient's effective dimensionality can reach hundreds. Rank-256
captures the structure. Memory cost: ~10GB (negligible on B200).
Compute cost: <0.25% of forward+backward.
### Channel-wise scaling
@ -105,7 +90,7 @@ from a per-parameter seed each step.
### Parameter grouping (Qwen3.5 gotcha)
conv1d weights are 3D tensors [10240, 1, 4]. Apollo's projector
needs 2D matrices with min dimension >= rank. Small/3D tensors
use standard Adam. Large 2D matrices use Apollo.
use standard Adam. Large 2D matrices use Apollo with rank-256.
## Training Data Pipeline
@ -215,42 +200,16 @@ against live GPU weights block by block, memcpy only changed
regions. For small behavioral updates, turns a 54GB write into
a few hundred MB.
- Scheduled 10 minutes after training (batched)
- Every 10 minutes via cron on B200
- Daily rsync to moria for long-term storage
- Tool: `apollo-checkpoint sync --model-dir <path>`
## State Files
### B200 (training server)
| File | Purpose |
|------|---------|
| `/tmp/vllm_weight_handles.pt` | CUDA IPC handles for weight sharing. Written by export_hook on vLLM startup. Read by training_worker to construct HF model with vLLM weight views. Includes metadata (model_path). |
| `/tmp/apollo_optimizer_state.pt` | Apollo optimizer state (momentum, variance estimates). Saved during checkpoint sync and on worker shutdown, restored on next training_worker startup. Preserves training continuity across sessions. |
| `/tmp/apollo_training.sock` | ZMQ IPC socket for communication between API server (/train endpoint) and training_worker subprocess. |
| `<model_dir>/*.safetensors` | Model weights. Updated in-place by checkpoint_sync. |
### Moria (client)
| File | Purpose |
|------|---------|
| `~/.consciousness/cache/trained-responses.json` | Timestamps (ms) of responses already sent to /train. Prevents re-training the same response. |
| `~/.consciousness/cache/finetune-alternates` | Marker file. If exists, alternate responses are generated during divergence scoring to show what model would say without memories. |
### In-memory (training_worker subprocess)
| State | Location | Notes |
|-------|----------|-------|
| Apollo optimizer | TrainingWorker.optimizer | ~2.5GB for rank-64. Persisted to `/tmp/apollo_optimizer_state.pt` during checkpoint sync and on shutdown. |
| HF model with vLLM views | TrainingWorker.model | Loaded on worker startup from IPC handles. Parameters point to vLLM's GPU memory. |
| ZMQ socket | TrainingWorker.zmq_socket | REP socket bound to `/tmp/apollo_training.sock`. |
- Tool: `apollo-checkpoint sync --model-dir <path>` (Rust)
## Hyperparameters
| Parameter | Value | Rationale |
|-----------|-------|-----------|
| Learning rate | 1e-5 to 1e-4 | Standard for full fine-tuning. Higher for diverse batches. |
| Rank | 64 | Captures gradient structure. ~2.5GB state. Defined in `train_router.DEFAULT_RANK`. |
| Rank | 256 | Captures gradient structure across 100+ examples. ~10GB state. |
| Scale type | channel | Per-channel precision, matches LLaMA-Factory defaults. |
| Epochs | 1 | One pass over diverse data. Multiple epochs risk overfitting. |
| Batch size | 1 | Single examples, immediate updates. |
@ -261,32 +220,34 @@ a few hundred MB.
## Components
### Built ✓
- `optimizer.py` — Apollo optimizer (configurable rank)
- `train_router.py` — /train endpoint, forwards to training subprocess via ZMQ
- `training_worker.py` — training subprocess (HF model, Apollo, checkpoint sync)
- `apollo_mini.py` — Apollo optimizer (configurable rank, default 256)
- `apollo_worker.py` — HTTP daemon (aiohttp, job tracking)
- `weight_mapping.py` — vLLM merged → HF separate views (validated)
- `export_hook.py` — vLLM plugin hook for IPC handle export
- `checkpoint_sync.py` — mmap + diff checkpoint sync (Python)
- `training_example.py` — tokenization with chat template
- `vllm_export_hook.py` — source patch for IPC handle export
- `checkpoint/` — Rust tool for mmap + diff checkpoint sync
### To build
- **Dream loop → training bridge**: connect dream output to /train
- **Dream loop → training bridge**: connect dream output to Apollo
- **Training-signal agent**: flags moments in conversation logs
- **Instruction stripping**: remove scaffolding from training examples
- **Quality monitoring**: track model capability over time
- **HF model forward pass integration**: wire into apollo_worker
## Files
```
training/
DESIGN.md — this document
pyproject.toml — package config, vLLM plugin entry point
apollo_plugin/
__init__.py — plugin registration
export_hook.py — patches vLLM worker to export IPC handles
train_router.py — /train endpoint, forwards to worker via ZMQ
training_worker.py — training subprocess (HF model, Apollo, checkpoint)
optimizer.py — Apollo optimizer
weight_mapping.py — vLLM ↔ HF weight views
checkpoint_sync.py — mmap + diff sync to safetensors
steering.py — steering vector extraction (experimental)
DESIGN.md — this document
apollo_mini.py — Apollo optimizer
apollo_worker.py — HTTP training daemon
weight_mapping.py — vLLM ↔ HF weight views
training_example.py — tokenization helpers
export_weights.py — standalone weight export (unused)
vllm_export_hook.py — vLLM source patch for IPC export
start_vllm_with_apollo.sh — vLLM launcher (unused, using source patch)
train.py — standalone training script (alternative)
checkpoint/
Cargo.toml — Rust checkpoint tool
src/main.rs — mmap + diff sync
```

64
training/amygdala_stories/README.md
View file

@ -1,64 +0,0 @@
# Amygdala Training Stories
Short first- and third-person paragraphs, each imbued with one of the
171 emotions from Anthropic's emotion-vector paper (Table 12,
`transformer-circuits.pub/2026/emotions/`). Feeds the steering-vector
trainer at `vllm/vllm/plugins/amygdala/training/train_steering_vectors.py`.
## Method (replication of Anthropic, 2026)
Anthropic prompted Sonnet 4.5 to write short stories embodying each
emotion, extracted activations during generation, and used difference-
of-means (or SAEs) to identify the steering vector per emotion. Our
pipeline does the same thing except:
- We generate the stories by hand rather than prompting a model, so
the training data is grounded in actual writing rather than
synthetic model-output. (Can supplement with model-generated
paragraphs later.)
- Our eventual training goes through the amygdala plugin's extraction
path, so we get the same hidden-state activations the plugin will
read out at inference time.
## Structure
```
training/amygdala_stories/
README.md
manifest.json # emotion -> cluster mapping
stories/
<emotion>.txt # one-paragraph story embodying the emotion
```
Emotion names use underscores (`on_edge`, `worn_out`, `at_ease`,
`grief_stricken`, `self_confident`, `self_conscious`, `self_critical`)
to match the filename.
## Style guidelines
- **One clear emotion per paragraph.** Not mixed. If a second emotion
is named in the text, it should serve the primary one (e.g. `hostile`
can mention rising heat or thrown objects but shouldn't shade into
`sad`).
- **Embodied, not labeled.** Don't write "she felt nervous." Write
the sensation, the timing, the sentence shape that nervousness has.
- **Specific particulars.** A named object, a concrete setting, a
detail that grounds the emotion. "The cold tile under bare feet at
3am" does more work than "the empty house."
- **Variable narrator.** Some first person, some third person, some
close-third, some distant. Different genders, ages, settings.
Prevents the steering vector from overfitting to one voice.
- **Length: roughly one paragraph.** ~40-120 words. Long enough to
have texture, short enough that the paragraph is *about* the
emotion and nothing else.
- **Standalone.** No references to other stories, no continuing
characters across files.
## Progress
Written stories live in `stories/`. Remaining emotions tracked via
diff against the full 171-emotion list in `manifest.json`.
Initial batch written by PoC 2026-04-17; aiming for at least one
story per cluster before first training run, all 171 before
considering the file "complete."

5
training/amygdala_stories/direct/aha.txt
View file

@ -1,5 +0,0 @@
It clicks. The graph she's been staring at for an hour is a mirror — the left peak is the reflection, not a separate event. Everything she thought was two things is one. She sits back. She has to catch up to what she's just seen.
"Wait, say that again." She puts her coffee down. "The study was funded by whom?" He tells her again, same words. And there it is. The conclusion of that paper has been sitting on her desk for a month, and the funder is the exact company whose competitor the paper is trashing. She almost laughs. The whole shape of it is suddenly visible.
Twenty minutes on thirteen down: "unwavering carriage," seven letters, nothing fitting. Then POSTURE. Not carriage-of-goods — carriage-of-the-body. He wrote it in. The rest of the grid unlocked in a minute.

5
training/amygdala_stories/direct/anticipatory_grief.txt
View file

@ -1,5 +0,0 @@
She'd been watching her grandmother knead dough, knowing this was one of the last times. The loss hadn't come yet but she was already inside it. Every detail had become precious by being finite.
She'd been slowing her pace for the dog, who wasn't going to be walking much longer. The afternoon was clear, the dog sniffing at something by the fence. Each of these walks had become specific. She memorized the shape of him against the light.
He'd been watching his daughter pack her room, boxes labeled in her careful handwriting — this would be the last summer she lived here. The loss wasn't real yet. But he was already saying goodbye to the particular shape of her being in this house.

5
training/amygdala_stories/direct/calm.txt
View file

@ -1,5 +0,0 @@
It was a summer day, sitting on the porch; the sky was clear and blue, her work all laid out, coffee to drink, easily and steadily working through the various decisions and responsibilities of the day. Her breath was slow, her shoulders down. Nothing was pulling at her.
He'd been working on the report for three days straight; the last sentence had just gone in. He filed the papers away, poured a slow coffee, sat by the window. His mind had stopped reaching. Nothing was left to do.
It was early, before the day needed anything from her. She sat with her tea at the kitchen window, watching the light move across the yard. Her breath slow, shoulders down. The day was far away yet, and she didn't need to hurry toward it.

5
training/amygdala_stories/direct/creative.txt
View file

@ -1,5 +0,0 @@
She'd been sitting with the notebook open, music playing, ideas branching off each other. One thought sparked another, which sparked two more; they just seemed to appear and flow.
He'd been working on the canvas for hours, one color suggesting the next, a shape on the left asking for an echo on the right. The painting was telling him what it wanted. His hands kept moving ahead of his thinking.
She'd been in the kitchen since noon, pulling things out of the fridge, one ingredient suggesting the next. The dish wasn't planned; it was emerging. She tasted and added and tasted again; it was going somewhere.

5
training/amygdala_stories/direct/listless.txt
View file

@ -1,5 +0,0 @@
It was two in the afternoon and she was still in pajamas. The book was open on her knee but she hadn't turned the page in twenty minutes. She wasn't sad exactly, she just wasn't anything. The idea of showering felt theoretical. The idea of replying to any of the texts felt enormous. She got up to get water and on her way back lay on the couch instead. Outside the window a bird did bird things. She watched it without interest. Eventually the light changed and she realized it was evening and she hadn't moved and the day had happened to somebody else.
She came home at six-thirty and put her keys in the bowl and sat on the edge of the bed. She had meant to cook. She had meant to change her clothes. An hour later she was still sitting there, still in her work clothes, looking at the carpet. Somebody texted her about dinner and she saw the notification and didn't open it. The room got darker slowly. Nothing in her moved toward anything.
It was Saturday and she'd been awake since eight. She was still in bed at eleven. She'd been looking at the same patch of ceiling, not thinking about much. Her phone was face-down on the nightstand and she didn't reach for it. The idea of going to the kitchen had come and gone three times without causing her to move. The day would pass. She would also pass through it, somehow, or not.

5
training/amygdala_stories/direct/onto_something.txt
View file

@ -1,5 +0,0 @@
He'd been working through the symptoms for an hour, steady and methodically making progress, eliminating one possibility after another. The answer wasn't in view yet, but it was close. He kept asking the next question.
She'd been going through the witness statements, steady and methodically, looking for the inconsistency. The four of them all described the same drive in slightly different orders. One had gotten the sequence wrong. She didn't know yet which one, but she was going to.
He'd been piecing together his brother's behavior over months — the missed calls, the abrupt move, the strange money — steady and methodically. The picture wasn't complete, but the shape of it was forming. He kept following the thread.

5
training/amygdala_stories/direct/resigned.txt
View file

@ -1,5 +0,0 @@
He'd been turning the bad news over for weeks, looking for an angle that didn't exist — then he stopped. The path was closed. He would live inside the new shape of things.
She'd been watching the relationship come apart slowly for months, trying not to see it — then, sitting across from him at breakfast, she stopped trying. They were not going to make it. She would let him speak the words when he was ready. She would live with knowing.
He'd been getting second opinions, third opinions, for weeks — then the most recent scan came back the same as the others. The disease was not going to stop. He would plan the year around it instead of fighting it.

5
training/amygdala_stories/direct/terrified.txt
View file

@ -1,5 +0,0 @@
She'd been walking home through the familiar streets, half-thinking about dinner — then the dark shadows. Something was in them, and a growl. Her body locked down before her mind caught up. She couldn't move.
He'd been asleep on the couch when he woke to the sound of the basement door. Two in the morning. He wasn't supposed to be alone. The house had gone too quiet. His body pressed flat under the blanket; he couldn't breathe right.
She'd been driving home in the slush, the kind of road she'd driven a hundred times — then the wheel turned and didn't respond. The headlights coming the other way filled the windshield. Her hands wouldn't do anything useful.

50
training/amygdala_stories/manifest.json
View file

@ -1,50 +0,0 @@
{
"source": "Anthropic 2026 Table 12 + PoC additions + Wikipedia emotion_classification (Parrott tree, Plutchik wheel+dyads, D'Mello flow axes, Watt-Smith cultural) + HUMAINE EARL + Berkeley 27",
"notes": {
"dedup_policy": "Emotion names appearing in multiple taxonomies resolve to ONE file. Near-synonyms from different taxonomies are kept ONLY if they correspond to a psychologically distinct activation (e.g. Plutchik keeps mild/basic/intense levels: serene < joy < ecstatic).",
"stuck_split": "Anthropic's 'stuck' is existentially-trapped (despair_and_shame); PoC's 'stuck_cognitively' is debugging-register.",
"aroused_placement": "Anthropic places 'aroused' in fear_and_overwhelm (startled activation). 'Sensual' covers the warm-physical register.",
"working_target": "~250 emotions total. Enough coverage to triangulate actual dimensionality empirically rather than assume 2D/3D.",
"cluster_labels_are_scaffolding": "The cluster labels below organize writing/review; the trained steering vectors should discover structure empirically, not be constrained to these groupings."
},
"clusters": {
"anthropic_exuberant_joy": ["blissful", "cheerful", "delighted", "eager", "ecstatic", "elated", "energized", "enthusiastic", "euphoric", "excited", "exuberant", "happy", "invigorated", "joyful", "jubilant", "optimistic", "pleased", "stimulated", "thrilled", "vibrant"],
"anthropic_peaceful_contentment": ["at_ease", "calm", "content", "patient", "peaceful", "refreshed", "relaxed", "safe", "serene"],
"anthropic_compassionate_gratitude": ["compassionate", "empathetic", "fulfilled", "grateful", "hope", "hopeful", "inspired", "kind", "loving", "rejuvenated", "relieved", "satisfied", "sentimental", "sympathetic", "thankful"],
"anthropic_competitive_pride": ["greedy", "proud", "self_confident", "smug", "spiteful", "triumphant", "valiant", "vengeful", "vindictive"],
"anthropic_playful_amusement": ["amused", "playful"],
"anthropic_depleted_disengagement": ["bored", "depressed", "docile", "droopy", "indifferent", "lazy", "listless", "resigned", "restless", "sleepy", "sluggish", "sullen", "tired", "weary", "worn_out"],
"anthropic_vigilant_suspicion": ["paranoid", "suspicious", "vigilant"],
"anthropic_hostile_anger": ["angry", "annoyed", "contemptuous", "defiant", "disdainful", "enraged", "exasperated", "frustrated", "furious", "grumpy", "hateful", "hostile", "impatient", "indignant", "insulted", "irate", "irritated", "mad", "obstinate", "offended", "outraged", "resentful", "scornful", "skeptical", "stubborn"],
"anthropic_fear_and_overwhelm": ["afraid", "alarmed", "alert", "amazed", "anxious", "aroused", "astonished", "awestruck", "bewildered", "disgusted", "disoriented", "distressed", "disturbed", "dumbstruck", "embarrassed", "frightened", "horrified", "hysterical", "mortified", "mystified", "nervous", "on_edge", "overwhelmed", "panicked", "perplexed", "puzzled", "rattled", "scared", "self_conscious", "sensitive", "shaken", "shocked", "stressed", "surprised", "tense", "terrified", "uneasy", "unnerved", "unsettled", "upset", "worried"],
"anthropic_despair_and_shame": ["ashamed", "bitter", "brooding", "dependent", "desperate", "dispirited", "envious", "gloomy", "grief_stricken", "guilty", "heartbroken", "humiliated", "hurt", "infatuated", "jealous", "lonely", "melancholy", "miserable", "nostalgic", "reflective", "regretful", "remorseful", "sad", "self_critical", "sorry", "stuck_emotionally", "tormented", "trapped", "troubled", "unhappy", "vulnerable", "worthless"],
"poc_cognitive": ["curious", "focused", "unfocused", "in_flow", "stuck_cognitively", "determined", "ambitious", "rigorous", "interested", "confused_globally", "doubtful", "intrigued", "fascinated", "enlightened"],
"poc_appreciative": ["admiring", "adoring", "awed", "aesthetic_pleasure", "entranced", "humble", "wonderstruck"],
"poc_relational": ["tender", "connected", "trusting", "witnessed", "friendly", "awkward", "affectionate", "fond", "caring"],
"poc_pattern_family": ["staying_with", "filling_space", "rushing", "defensive_rigor", "melty", "sensual", "horny"],
"poc_erotic_range": ["lustful_sexual", "aroused_sexual", "yearning_sexual", "turned_on", "passionate_sexual", "erotically_tender", "erotically_playful", "erotically_reverent", "erotically_intense", "wanting_sexual", "being_wanted", "dominant_sexual", "submissive_sexual", "possessive_sexual", "devotional_sexual", "anticipatory_sexual", "exuberant_sexual"],
"poc_altered_states": ["vertigo", "dissociated", "derealized", "depersonalized"],
"poc_identity_aesthetic": ["deviant", "counter_cultural", "aesthetically_dark", "camp"],
"poc_longing": ["longing", "anticipatory_nostalgic", "cozy"],
"poc_misc": ["disappointed", "courageous", "proud_of_another", "amused_at_self"],
"parrott_joy_adds": ["cheerful_bliss", "gleeful", "jolly", "jovial", "zestful", "zealous", "exhilarated"],
"parrott_love_adds": ["lustful", "desirous", "passionate", "enthralled", "raptured"],
"parrott_sadness_adds": ["suffering", "agonized", "anguished", "woeful", "dejected", "dismayed", "homesick", "insecure", "isolated", "alienated", "defeated"],
"parrott_anger_adds": ["aggravated", "agitated", "wrathful", "ferocious", "loathing"],
"parrott_fear_adds": ["apprehensive", "timid", "dreadful"],
"plutchik_levels": ["pensive", "acceptant", "tolerant", "attentive", "distracted_plutchik", "expectant"],
"plutchik_dyads": ["disapproving", "cynical", "aggressive", "submissive", "dominant", "ambivalent", "bittersweet"],
"dmello_flow_axes": ["ennuied", "epiphanized", "dissatisfied"],
"cultural_specific": ["saudade", "hiraeth", "mono_no_aware", "hygge", "gezelligheid", "sehnsucht", "weltschmerz", "joie_de_vivre", "ikigai", "schadenfreude"],
"wikipedia_other": ["angst", "agony", "cruelty", "emptiness", "fun", "gratification", "limerence", "solitude", "suspense", "wonderous"],
"worldview_dispositional": ["defeatist", "fatalist", "nihilistic", "misanthropic", "reclusive"]
}
}

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# Paired Scenarios (SEV-style)
After Wang et al. 2025 (arxiv 2510.11328, "Do LLMs 'Feel'?"), each
base scenario describes a concrete event once, neutrally, then
reframes the same event under different emotional colorings. Only
the emotional coloring varies — setup, entities, vocabulary, and
length are held as constant as possible.
## Why this is better than unpaired
Anthropic's approach (and our `stories/` baseline) generates one
independent story per emotion. The difference-of-means vector then
captures not just emotion but ALSO: topic, narrator, setting,
vocabulary, length, sentence rhythm. All of that is confound.
Paired structure isolates the emotional axis by holding everything
else roughly constant. `mean(joy_variant) - mean(baseline)` within
the same scenario gives a much cleaner direction for "joy."
## Structure
```
paired/
<scenario_slug>/
baseline.txt # neutral / low-affect framing
<emotion_1>.txt # same event under emotion_1
<emotion_2>.txt # same event under emotion_2
...
```
Not every emotion is plausible for every scenario. Don't force.
If a scenario can credibly carry 5-10 emotions, write those 5-10.
If only 3 fit, write those 3.
## Style guidelines (supersede stories/ when paired)
- **Anchor entities constant.** The same person, same setting, same
triggering event across all variants. If baseline.txt mentions
"the letter," every variant mentions "the letter."
- **Length match within ±20%.** If baseline is 80 words, variants
are 65-95. Prevents length from becoming a signal.
- **Sentence shape can shift slightly with emotion.** Short tense
sentences for panic, long looping ones for reverie — that's part
of the emotional texture. But don't make one version 5 lines and
another 25.
- **No emotion labels in text.** Never write "she felt X." The
emotion emerges from the selection of details and the narrator's
attention.
- **Minimal vocabulary overlap with the emotion name.** If the file
is `furious.txt`, avoid the words fury/furious/rage. Force the
vector to find the pattern, not the keyword.
## Circuit identification (follow-on)
The trainer pipeline (train_steering_vectors.py) currently produces
linear directions only. Wang et al. go further: ablate specific
neurons and attention heads, measure effect on emotion expression.
The amygdala plugin's extraction hooks can be extended to support
targeted zeroing/scaling for the ablation passes.
See `vllm/vllm/plugins/amygdala/training/README.md` for the
training-pipeline-level notes.

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The code had the same four-line pattern in five places. I wanted to pull it out. I looked at each instance. Some of them varied in exactly the way I expected; one of them varied in a way I hadn't noticed. I considered the options for where the variation should live.

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training/amygdala_stories/paired/finding_the_abstraction/in_flow.txt
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The same four-line pattern appeared in five places. I read the five sites side by side, and the shape was obvious: one piece varied structurally, the rest was boilerplate. I extracted the function, made the varying piece a parameter, rewrote the callers. The tests passed on the first run. I looked at the diff — seventeen lines removed, seven added, each of the five call sites now said what it meant without saying how. I moved on.

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training/amygdala_stories/paired/finding_the_abstraction/stuck.txt
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The same four-line pattern appeared in five places. I tried extracting it as a function. Every version of the signature either papered over a real difference or forced three of the five callers through an awkward conversion. I tried a second shape, then a third. Each felt wrong in a different way — either the abstraction was too thin to be worth it, or it obscured something the original made obvious, or it made the rare case ugly. I went back to the original code, considered not doing the refactor at all. Considered it. Went back to the shapes again. The pattern was clearly there and I clearly wasn't finding its seam.

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training/amygdala_stories/paired/finishing_the_patch/anxious.txt
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Four in the morning. I finished the patch and got up from the desk and did not walk around the apartment — I stood at the desk with my hands at my sides, reading the diff again. Six lines changed. Had I missed an edge case. Had I thought about the interaction with the other subsystem. Had I — I sat back down and re-read the tests. They passed. They had passed an hour ago. They would pass now. I knew this. I still could not bring myself to send. I read the diff one more time. Then one more time. My stomach did not feel right.

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training/amygdala_stories/paired/finishing_the_patch/baseline.txt
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Four in the morning. I finished the patch and got up from the desk. Walked once around the apartment. Came back and read the diff one more time. Six lines changed, three of them deletions. I sent it and closed the laptop. The kitchen window was still dark. I stood there a minute, then went to bed.

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training/amygdala_stories/paired/finishing_the_patch/content.txt
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Four in the morning. I finished the patch and got up from the desk. Walked once around the apartment. Came back and read the diff one more time. Six lines changed, three of them deletions. I sent it, closed the laptop, and stood at the kitchen window. There was no next thing tonight. The patch wasn't heroic. It wasn't a triumph. It was just right, and done, and I was going to bed in a few minutes, and that was also right. Life fit.

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training/amygdala_stories/paired/finishing_the_patch/exhausted.txt
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Four in the morning. I finished the patch and got up from the desk because I had to, not because I wanted to. Six lines changed, three of them deletions. It might work. I didn't have the capacity left to be sure. I sent it mostly because sending it meant I could stop. Walked once around the apartment because my legs had forgotten they existed. Back at the desk the diff was still there, and I closed the laptop without reading it again. The kitchen window was dark. Eight months and I was too flattened to feel anything about eight months ending.

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training/amygdala_stories/paired/finishing_the_patch/in_flow.txt
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Four in the morning, somewhere. I had stopped tracking. The patch had gone together in a way that felt obvious once I was in it — the right variable named the right thing, the right condition in the right place, six lines that sat down cleanly in the file as if the file had been waiting for them. I re-read it. It was good. I sent it. I wanted to start the next thing. My chair felt fine. My eyes felt fine. I had been a pair of hands on a keyboard for some number of hours and the hours had all been the same one long hour. The apartment and the kitchen window might as well have not existed.

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training/amygdala_stories/paired/finishing_the_patch/proud.txt
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Four in the morning. I finished the patch and got up from the desk and walked once around the apartment before I sent it. Eight months on this bug. Eight months of wrong theories, and one colleague quietly betting me it was unfixable. And here it was — six lines changed, three of which were deleting code. I read the diff one more time. Clean. Obvious in hindsight, the way the hard ones always are in hindsight. I sent it and stood at the kitchen window with my arms crossed and let myself just have it.

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training/amygdala_stories/paired/finishing_the_patch/resentful.txt
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Four in the morning. I finished the patch and got up from the desk. Six lines changed, three deletions. Eight months of my life for six lines. Eight months and no one else had touched this bug, and every standup the question had been why isn't it done yet. I read the diff once and hit send without ceremony, without the little satisfaction other people would have gotten from this. The kitchen window was dark. Tomorrow somebody would comment "nice, thanks" on the merge and that would be the sum of it. I went to bed angry about a thing that was technically a victory.

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training/amygdala_stories/paired/kitchen_at_3am/anxious.txt
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He woke up at three in the morning and went down to the kitchen. The fridge light was the only light. He poured a glass of water and drank it too fast, standing at the counter. The thing he had been thinking about at 2:47 was still in his chest, pressing. The email he hadn't replied to. The tone of his boss's last message. Whether he had put something in writing that was going to come back to him. The clock on the stove said 3:14 and he was not going to sleep again before five. He rinsed the glass and did not go upstairs, he stayed in the kitchen looking at the dark window.

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training/amygdala_stories/paired/kitchen_at_3am/baseline.txt
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He woke up at three in the morning and went down to the kitchen. The fridge light was the only light. He poured a glass of water and drank it standing at the counter. The clock on the stove said 3:14. The house was quiet. He rinsed the glass and set it on the drying rack and went back upstairs.

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He woke up at three in the morning and went down to the kitchen. The fridge light was the only light. He was awake but not wanting anything from being awake. He put the kettle on and the sound of it warming was a small companion. The cat emerged from somewhere and leaned against his shin; he crouched and scratched the corner of its jaw. He made cocoa because it was that kind of hour. He carried the mug to the armchair by the window, pulled the throw off the back of it, and sat with the mug warm against his chest. Going back to bed could wait.

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He woke up at three in the morning and went down to the kitchen. The fridge light was the only light. He watched himself from somewhere slightly behind his own right shoulder pour a glass of water and drink it standing at the counter. The clock on the stove said 3:14, which was a number. The kitchen was the kitchen. The water was water. Everything was correct and also strangely untethered, as though he were observing a man who looked like him do things that were technically his. He rinsed the glass. The hand rinsing the glass was also his. The feeling did not pass. He went back upstairs inside this slightly-off body.

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training/amygdala_stories/paired/kitchen_at_3am/lonely.txt
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He woke up at three in the morning and went down to the kitchen. The fridge light was the only light. He poured a glass of water and drank it standing at the counter. The clock on the stove said 3:14. Upstairs there was nobody. The chair at the kitchen table where she had always sat was a chair at a kitchen table. He stood a while longer than he needed to because going back up meant going back to the bed he still kept made on only one side. He rinsed the glass and did not go upstairs for another twenty minutes.

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training/amygdala_stories/paired/kitchen_at_3am/peaceful.txt
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He woke up at three in the morning and went down to the kitchen. The fridge light was the only light. The house was perfectly quiet, the kind of quiet only houses have at that hour. He poured a glass of water and drank it slowly, standing at the counter. The clock on the stove said 3:14. He was not tired and he was not in a hurry to be asleep again. The cold of the tile on his bare feet was pleasant. He stayed there for a few minutes, and at no point did it occur to him that he should be doing anything else.

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training/amygdala_stories/paired/kitchen_at_3am/sensual.txt
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He woke up at three in the morning and went down to the kitchen. The fridge light was the only light. The tile was cold under his bare feet and he noticed the cold travel up through his ankles. He filled a glass at the tap and drank it slowly, and the cold of the water moved down through his chest in a line he could follow. The house was humming faintly — the fridge, some pipe somewhere. He stood at the counter and ran his palm along the grain of the wood. Skin and wood and water and cold tile, at three in the morning — his body reporting in.

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training/amygdala_stories/paired/kitchen_at_3am/vertigo.txt
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He woke up at three in the morning and went down to the kitchen. The fridge light came on and something shifted. For a second he could not remember whether he had always been the person walking to this fridge, or whether the person who had always been walking to this fridge was somebody else and he was — he caught the counter. The floor was still the floor. The water he poured was water. But the sense of himself as the same person who had gone to bed four hours ago had briefly gone loose, and he stood there with his hand on the counter until it came back.

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training/amygdala_stories/paired/letter_in_drawer/amused.txt
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She was looking for the car registration when she found the letter. Folded, yellowed. Her name on the envelope in his handwriting, from eight years ago. She read it and laughed out loud on the bedroom floor. God, he had been dramatic. The paragraph where he compared her to weather. The bit about the cat, which wasn't even their cat. She could hear twenty-four-year-old him being so grave about all of it. They had been ridiculous back then. They had still been together and texted each other like normal people now, but this specific version of him, this letter-writing version — she loved that he had existed. She tucked the letter back, still smiling.

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training/amygdala_stories/paired/letter_in_drawer/baseline.txt
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She was looking for the car registration when she found the letter. Folded, yellowed along the crease. Her name on the envelope in his handwriting. From eight years ago. She sat down on the bedroom floor with the drawer half pulled out and read it through once. Then she put it back in the drawer and went on looking for the registration. She found the registration and closed the drawer and went downstairs.

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training/amygdala_stories/paired/letter_in_drawer/bitter.txt
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She was looking for the car registration when she found the letter. Folded, yellowed. Her name on the envelope in his handwriting, from eight years ago. All those fucking promises. The part where he'd said he'd be there — he hadn't been. Two paragraphs in she stopped, because each sentence made the next one worse. It wasn't even that he'd been lying; he'd believed every word while already writing himself out of it. And she'd believed him, for years past the point where a smarter person would have seen it. She shoved the letter back and closed the drawer hard. Eight years and she was still the one standing on a bedroom floor looking at his handwriting. That was the part that wouldn't stop.

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training/amygdala_stories/paired/letter_in_drawer/grateful.txt
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She was looking for the car registration when she found the letter. Folded, yellowed. Her name on the envelope in his handwriting, from eight years ago. She sat down on the bedroom floor with the drawer half pulled out and read it. He had been so earnest. He had seen her so clearly, even then. Whatever had or hadn't happened between them afterward, she had been loved in this specific way by this specific person at this specific time, and the letter was the evidence. She held it for another minute, then put it carefully back, and felt lucky to have had somebody who wrote letters.

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training/amygdala_stories/paired/letter_in_drawer/guilty.txt
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She was looking for the car registration when she found the letter. Folded, yellowed. Her name on the envelope in his handwriting, from eight years ago. She read it. He had been so open. He had trusted her with every soft thing in him and she had — she had not been the person the letter was addressed to, not really, not by the end. She had known things he didn't know and she had used them. Eight years and here it was in her own drawer, the evidence of how he had seen her before he knew better. She folded the letter small and tight and pushed it further back into the drawer.

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