Add lock_blocking() to TrackedMutex: blocks current thread using
block_in_place + futures::executor::block_on, safe for sync contexts.
Replace all try_lock() calls with lock_blocking() in slash commands,
UI rendering, and status reads. Lock hold times are fast enough that
blocking briefly is fine, and this eliminates the spurious 'lock
unavailable' paths that were never actually hit.
Kept rx_mutex.try_lock() in mod.rs (std::sync::Mutex for stderr rx).
Two pieces around the cache that landed when Branch nodes started
holding `token_ids: Some(server_authoritative_stream)`:
1. wire_into / wire_chunks now pair cached vision blocks with their
child Image leaves. Previously the cached-branch arm spliced the
cache verbatim and didn't recurse for images, so a Branch whose
cache contained `VISION_START..VISION_END` blocks would emit those
tokens with no matching `WireImage` push — leading to a panic
downstream when `pair_images_to_ranges` tried to attach the
missing image. New `pair_cached_images` walks the children
depth-first for image leaves and zips them against
`vision_blocks(cache)` to emit correctly-offset entries; mismatched
counts panic loudly because that's an AST/cache invariant
violation that would otherwise mis-pair on the wire.
2. `conversation_mut() -> &mut Vec<AstNode>` was the one public
escape hatch that let callers reach into a Branch's children and
mutate them without invalidating the cached token stream. Removed
in favor of a focused `set_branch_memory_score(section, index,
key, score)` for the only legitimate use we had today (the
full-matrix scorer writing per-memory divergence onto the
Assistant Branch). Updated the lone caller in subconscious/learn.
Documented the invariants explicitly on `ContextState`: every
`Leaf.token_ids` matches `body.compute_token_ids()`, and every
`Branch { token_ids: Some(_) }` is a faithful walk of its children.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Mirrors the vLLM-side rewrite. AppendImage is gone; images now
ride along on Generate via a parallel `images` list.
- Productionize `qwen3_image_token_count` (was test-only). Image
leaf computes its IMAGE_PAD count eagerly at construction from
height/width; `token_count` is no longer "0 until the server
tells us."
- WireChunk shrinks to a single `Tokens(Vec<u32>)` variant — vision
blocks live inline in the token stream.
- `wire_chunks` now returns `(Vec<WireChunk>, Vec<WireImage>)`.
`WireImage` carries `pad_start` / `pad_end` (absolute positions
in the full walk) alongside bytes + mime.
- `assemble_prompt` returns `(chunks, images, match_upto)`.
- `stream_session_mm` / `run_session_generate` take the parallel
images list, filter to those past `match_upto`, and pass them
in `GenerateRequest.images` as `pb::ImageAttachment` entries.
- Drop `SessionHandle::append_image`,
`ContextState::commit_image_token_counts`,
`StreamToken::ImageAppended`, the WireChunk::Image branch in
`learn.rs`, and the now-empty `prompt_to_chunks` helper.
- Add 'v' toggle on the conscious-screen tree to render token-id
vectors in place of text content (debug-aid: lets us see what
the server actually has when output is suspicious).
- Comment out the subconscious-trigger spawn loop — Kent had this
disabled before; it had crept back into running.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two changes that bolt together — the shared connection means the new
scoring path actually costs one HTTP/2 handshake across the whole
process instead of one-per-RPC.
ApiClient gains `salience_channel: Arc<OnceCell<Channel>>`. First
call to `ApiClient::salience_client()` opens the channel via
`connect_channel()` and stores the Channel; subsequent calls clone
it (cheap — tonic multiplexes concurrent RPCs over the single
HTTP/2 connection). Every ApiClient clone shares the same OnceCell,
so all agents spawned from Mind's client — plus every ephemeral
scoring session — reuse one connection.
SessionHandle refactored to hold an `ApiClient` clone instead of
a bag of (base_url, api_key) strings. `open` / `append_image` /
`generate` go through `self.client.salience_client()` now. New
`prefill_only(tokens)` method encapsulates the "Generate with
max_tokens=0 to append text" pattern (previously a private free
function in api/mod.rs called `flush_pending`). Drop impl on
SessionHandle stays — still fires CloseSession on the shared
channel in a detached task.
`run_session_generate` switched from `(base_url, api_key, model)`
to `&ApiClient`; the agent-turn flow that uses it keeps the same
shape but `stream_session_mm` clones the ApiClient into the
spawned worker.
learn.rs migrated from the HTTP `/v1/score` endpoint to a gRPC
session-based score:
* `call_score` opens an ephemeral SessionHandle on the client,
converts (prompt_tokens, images) → Vec<WireChunk> via the new
`prompt_to_chunks` helper (splits on VISION_START/VISION_END),
walks chunks calling `prefill_only` + `append_image`, runs a
final Generate with `max_tokens=0` + `logprobs_ranges` over
the scored positions, and sums each Token event's
`sampled_logprob` per range to produce `ScoreResult`s.
* SessionHandle drops at end of scope → CloseSession auto-fires,
keeping the server's session map clean between calls.
* No more HTTP path, no more `http_client()` helper, no more
`ScoreResponse` / serde plumbing for /v1/score.
* `send_to_train` still uses HTTP (it talks to /v1/train which
isn't on the gRPC protocol); its ad-hoc HTTP client lives
inline now instead of reaching for the deleted `http_client()`.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Mind's impl had accumulated ~50 lines of setup glue per scoring flow
(memory, memory-full, finetune): snapshot config, clone handles,
resolve context, spawn task, route results back through BgEvent,
write stats. The shape was identical; only the middle changed.
Introduce the MindTriggered trait:
pub trait MindTriggered {
fn trigger(&self);
}
Each flow becomes a struct next to its scoring code that owns its
dependencies and a JoinHandle (behind a sync Mutex for interior
mutability):
subconscious::learn::MemoryScoring (Score, ScoreFull)
subconscious::learn::FinetuneScoring (ScoreFinetune)
Mind holds one of each and dispatches in one line:
MindCommand::Score => self.memory_scoring.trigger(),
MindCommand::ScoreFull => self.memory_scoring.trigger_full(),
MindCommand::ScoreFinetune => self.finetune_scoring.trigger(),
Each struct picks its own trigger semantics — memory scoring is
no-op-if-running (!handle.is_finished()); finetune is abort-restart.
Falls out:
- BgEvent / bg_tx / bg_rx disappear entirely. Tasks write directly
to their slice of MindState and call agent.state.changed.notify_one()
to wake the UI. The bg_rx arm in Mind's select loop is gone.
- agent.state.memory_scoring_in_flight was duplicating
shared.scoring_in_flight via BgEvent routing; now the JoinHandle
alone tells us, and shared.scoring_in_flight is written directly
by the task for the UI.
- start_memory_scoring / start_full_scoring / start_finetune_scoring
methods on Mind are deleted; Mind no longer knows the setup shape
of any scoring flow.
- FinetuneScoringStats moves from mind/ to subconscious/learn.rs
next to the function that produces it.
No behavior change — same flows, same trigger points, same semantics.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
- context.rs gains is_assistant, render_branch_text, render_prior_context
alongside memory_key / is_memory_node. They're pure AST helpers, used
by both the finetune pipeline and the forthcoming compare screen.
- new subconscious/generate.rs holds gen_continuation(context, entry_idx,
skip, client): build the prompt from a context prefix with an arbitrary
skip predicate, send to the model, decode the completion. Takes both
the predicate and the client so callers can aim it at memory-stripped
contexts (finetune), same-context-different-model (F7 compare), or
whatever else.
- learn.rs drops its private copies of those helpers and the inline
generate_alternate; the finetune path now reads as
gen_continuation(context, idx, is_memory_node, client).
Pure refactor, no behavior change.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
wire_prompt() gains a conv_range and a skip closure, and returns the
assistant-message token ranges needed by the scoring path. The agent
path passes 0..len + |_| false and ignores the ranges. Memory-ablation
scoring and candidate generation pass a prefix range + a predicate
(e.g. is_memory_node, or |n| memory_key(n) == Some(key)).
This deletes subconscious/learn.rs's build_token_ids, its private
Filter enum, and the is_memory/memory_key duplicates — the walk over
context sections now has one home. Adding a section or changing
section order in the agent path won't silently drift away from what
scoring sees.
call_score forwards multi_modal_data when the wire-form prompt
contains images. generate_alternate switches to stream_completion_mm
and passes the same images. Scoring on image-bearing contexts now
sends wire form (1 image_pad + image data) instead of expanded
image_pads with no image data; text-only contexts are bit-identical.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two fixes to the F6 candidate display:
1. Turns where the assistant produced nothing human-visible (an
interrupted generation, a turn consisting of only a tool call the
renderer folds to the tool name) were landing as candidates with
an empty response_text. They'd render as blank cards and, worse,
we'd still burn a full alternate generation on each one. Filter
them out before they reach the candidate list.
2. The detail pane showed only the scored response + alternate, with
no hint of what the user had actually asked. Pre-compute the last
two user/assistant exchanges on each candidate as a rendered
prior_context string ([user]/[assistant] markers) and show them
above the response, under a new "context & response" section
heading.
render_branch_text and render_prior_context extracted as helpers —
the response-text rendering and prior-context rendering share the
same "flatten Branch children to text" pass.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Runtime-mutable settings (F6's threshold knob, the generate-alternates
toggle, anything else that comes along) were ending up as mirrored
fields on MindState — each new config setting grew MindState::new's
signature and added a clone+sync path. Wrong home. MindState is
ephemeral session state, not a config projection.
Give AppConfig the same treatment the memory Config has: install it
into a global RwLock<AppConfig> at startup via load_app, read through
config::app() (returns a read guard), mutate through update_app. The
config_writer functions now write to disk AND update the cache
atomically, so the one-stop-shop call keeps both in sync.
Also while in here:
- learn.generate_alternates moves from a sentinel file
(~/.consciousness/cache/finetune-alternates, "exists = enabled")
into the config under the learn section. On first run with this
build, if the sentinel file still exists Mind::new flips the
config value to true and removes it. Drops
alternates_enabled()/set_alternates().
- Default threshold 0.0000001 → 1.0. With the timestamp filter
removed the previous value was letting essentially everything
through; 1.0 is a sane "nothing gets through unless you actually
want it" default.
- score_finetune_candidates takes generate_alternates as a parameter
instead of reading a global — caller snapshots the config values
once at the top of start_finetune_scoring so the async task
doesn't need to hold the config read lock across awaits.
- MindState.learn_threshold / learn_generate_alternates gone; the
SetLearn* command handlers now just delegate to config_writer.
Kent noted RwLock<Arc<AppConfig>> (the pattern used by the memory
Config global) is pointless here — nobody needs a snapshot-after-
release, reads are short — so this uses a plain RwLock<AppConfig>
and returns a read guard.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
With the timestamp filter gone (previous commit), score_finetune_candidates
started returning the actual ~100+ candidates per scoring run. The
existing code generated alternates for all of them in a tight loop
before returning anything, leaving the status line stuck on
"finetune: scoring N responses..." for ~100s of seconds while the
B200 was pegged.
Two fixes:
1. score_finetune_candidates now takes an ActivityGuard and a callback.
Candidates are emitted one-at-a-time as they complete (after their
alternate if that's enabled, immediately otherwise). The activity
status updates to "finetune: generating alternate N/M" during the
alternate-gen phase so it's clear what's happening.
2. BgEvent::FinetuneCandidates(Vec<_>) → FinetuneCandidate(one). Each
emitted candidate is pushed onto shared.finetune_candidates; the UI
tick picks it up and renders it on the next frame. start_finetune_scoring
clears the previous run's list at the top so each run is fresh.
Return type changes from (Vec, f64) → (usize, f64) — the count above
threshold is all the caller still needs since the candidates stream
through the callback.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Previously NodeLeaf.timestamp and AstNode::Branch.timestamp accepted
null or missing via a deserialize_timestamp_or_epoch fallback — legacy
entries in conversation.jsonl from before Branch timestamps existed
(and from before chrono serialization was wired up) would load with
UNIX_EPOCH as a sentinel. Downstream, node_timestamp_ns() returned
Option<i64> and callers had to handle None as "old entry, skip."
That second filter was silently dropping every candidate in
score_finetune_candidates when scoring an older session — the F6
screen showed "0 above threshold" even when max_divergence was
orders of magnitude above the threshold, because every entry was
failing the None check, not the divergence check.
The fix, in three parts:
1. src/bin/fix-timestamps.rs — one-off migration tool that walks a
conversation.jsonl, linearly interpolates timestamps for entries
stuck at UNIX_EPOCH (using surrounding real timestamps as anchors),
propagates to child leaves with per-sibling ns offsets, and bumps
any collisions by 1 ns for uniqueness. Ran against the current
session's log: 11887 entries, 72289 ns bumps, all unique.
2. context.rs — drop default_timestamp and
deserialize_timestamp_or_epoch. NodeLeaf and Branch now require a
present non-null timestamp on deserialize. Tests flip from
"missing/null → UNIX_EPOCH" to "missing/null → Err."
3. subconscious/learn.rs — node_timestamp_ns now returns i64, not
Option<i64>. The matching caller in score_finetune_candidates
collapses from a Some/None match to a single trained-set check.
mind/log.rs's oldest_timestamp no longer filters UNIX_EPOCH.
Every line currently on disk has already been migrated. Going
forward, new AstNodes always carry real timestamps (Utc::now() at
construction time), so the strict schema is the invariant, not an
aspiration.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
vllm's /v1/score endpoint made score_ranges a required field (the
messages-mode fallback that used to pattern-scan for assistant
boundaries is gone). Always send the field, and if we have nothing to
score, skip the HTTP round-trip entirely instead of letting the server
422 us.
Response parsing is unchanged — serde ignores the renamed range_index
field and the dropped role field since we only extract total_logprob.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Three changes that together reshape the F6 fine-tune-review screen:
1. Finetune scoring reports through the standard agent activity system
instead of a separate finetune_progress String. The previous design
ran an independent progress field that forced a cross-lock dance and
bespoke UI plumbing. start_finetune_scoring now uses start_activity
+ activity.update, so the usual status line and notifications
capture scoring progress uniformly with other background work.
2. MindState gains a FinetuneScoringStats snapshot (responses seen,
above threshold, max divergence, error). The F6 empty screen shows
this instead of a loading message — so after a scoring run that
produced zero candidates, you can see *why* (e.g., max_divergence
below threshold).
3. The divergence threshold is configurable from F6 via +/- hotkeys
(scales by 10×) and persisted to ~/.consciousness/config.json5 via
config_writer::set_learn_threshold. AppConfig grows a learn section
with a threshold field (default 1e-7).
Also: user/mod.rs no longer uses try_lock() for the per-tick
unconscious/mind state sync — we fixed the locking hot paths that
made try_lock necessary, so lock().await is now the right choice.
And subconscious::learn::score_finetune_candidates now returns
(candidates, max_divergence) so the stats can be populated.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Two related changes to the learn subsystem:
1. AST node timestamps are now non-optional — both Leaf and Branch
variants carry a DateTime<Utc>. UNIX_EPOCH means "unset" (old entries
deserialized from on-disk conversation logs).
Training uses timestamps as unique keys for dedup, so we promote to
nanosecond precision: node_timestamp_ns(), TrainData.timestamp_ns,
FinetuneCandidate.timestamp_ns, mark_trained(ns).
2. build_token_ids() now also returns token-position ranges of assistant
messages. These are passed to vLLM's /score endpoint via the new
score_ranges field so only scored-position logprobs are returned —
cuts bandwidth/compute when scoring small windows.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
When 's' is pressed on the learn screen, approved candidates are now
sent to the inference server's /train endpoint.
Samples are marked as sent immediately in the UI, and mark_trained()
is called after successful API response to prevent re-scoring.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Wire up divergence scoring to identify responses that depend heavily on
memories the model hasn't internalized. These are candidates for fine-tuning.
- Score finetune candidates automatically after each turn
- Track trained responses by timestamp to prevent overtraining
- F6 screen shows candidates with divergence scores
- j/k nav, a=approve, r=reject, g=toggle alternate gen, s=send
- Additive sync preserves approval status across ticks
- Keeps 10 most recent rejected, removes sent
The 's' key currently just marks as trained locally — actual /finetune
endpoint call to follow.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Identity memory nodes now participate in importance scoring alongside
conversation memories. Score loading/saving handles both sections, and
the conscious screen uses node.label() consistently for memory display.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Store now has internal Mutex for capnp appends and AtomicU64 for
size tracking. All methods take &self. The external Arc<Mutex<Store>>
is replaced with Arc<Store>.
- Store::append_lock protects file appends
- local.rs functions take &Store (not &mut Store)
- access_local() returns Arc<Store>
- All .lock().await calls removed from callers
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Replace Result<_, String> with anyhow::Result throughout:
- hippocampus/store module (persist, ops, types, view, mod)
- CLI modules (admin, agent, graph, journal, node)
- Run trait in main.rs
Use .context() and .with_context() instead of .map_err(|e| format!(...))
patterns. Add bail!() for early error returns.
Add access_local() helper in hippocampus/mod.rs that returns
Result<Arc<Mutex<Store>>> for direct local store access.
Fix store access patterns to properly lock Arc<Mutex<Store>> before
accessing fields in mind/unconscious.rs, mind/mod.rs, subconscious/learn.rs,
and hippocampus/memory.rs.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
The full matrix scorer was deleted during the AST conversion. Restore
it: /score runs score_memories() which computes divergence for every
memory × response pair, stores the MemoryScore on MindState, and
displays per-memory weights with bar charts on the F2 screen.
Both scoring paths now use ActivityGuard::update() for live progress
in the status bar instead of creating a new activity per iteration.
Also bumps score API timeout from 120s to 300s and adds progress
logging throughout.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Signed-off-by: Kent Overstreet <kent.overstreet@linux.dev>
The /score endpoint was receiving chat-format messages which had to go
through the chat template tokenizer — this was failing with "System
message must be first" errors because the AST structure doesn't map
cleanly to chat message format.
Send raw token IDs via the new `prompt` field instead, matching what
the /completions endpoint already does. The vLLM score endpoint finds
assistant boundaries by scanning for <|im_start|>assistant token
patterns, so no message-level metadata is needed.
Also includes identity and journal sections in the scored context,
matching what the model actually sees during inference.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Scoring calls the /score endpoint directly via HTTP, bypassing the
stream_completion path. These requests had no priority field, so they
could preempt interactive work. Set priority=5 (between subconscious
agents at 2 and unconscious at 10).
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Made StreamToken pub (was pub(crate), needed by context.rs).
Removed dead API_CLIENT, get_client, sampling/priority fields
from oneshot. Suppressed pre-existing SkipIndex warning in learn.rs.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Bulk replaced Arc<Mutex<Agent>> with Arc<Agent> across all files.
Fixed control.rs, memory.rs tool handlers. Fixed oneshot Backend.
Remaining errors are all agent.lock() → agent.state.lock() or
agent.context.lock() in mind/, user/, and a few in mod.rs.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Add agent/tokenizer.rs with global Qwen 3.5 tokenizer that generates
actual token IDs including chat template wrapping. ContextEntry now
stores token_ids: Vec<u32> instead of tokens: usize — the count is
derived from the length.
ContextEntry::new() tokenizes automatically via the global tokenizer.
ContextSection::push_entry() takes a raw ConversationEntry and
tokenizes it. set_message() re-tokenizes without needing an external
tokenizer parameter.
Token IDs include the full chat template: <|im_start|>role\ncontent
<|im_end|>\n — so concatenating token_ids across entries produces a
ready-to-send prompt for vLLM's /v1/completions endpoint.
The old tiktoken CoreBPE is now unused on Agent (will be removed in
a followup). Token counts are now exact for Qwen 3.5 instead of the
~85-90% approximation from cl100k_base.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Use cumulative token position instead of entry index for the scoring
cutoff. This reflects actual context usage — a few large entries
near the end won't skew the boundary.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Signed-off-by: Kent Overstreet <kent.overstreet@linux.dev>
score_memories_incremental now takes an async callback that fires
after each memory is scored. The callback:
- Writes the score to the conversation entry via set_score()
- Persists to memory-scores.json immediately
- Notifies the UI so the context screen updates live
Scoring no longer batches — each score is visible and persisted
as it completes. Does not touch the memory store.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
New types — not yet wired to callers:
- ContextEntry: wraps ConversationEntry with cached token count and
timestamp
- ContextSection: named group of entries with cached token total.
Private entries/tokens, read via entries()/tokens().
Mutation via push(entry), set(index, entry), del(index).
- ContextState: system/identity/journal/conversation sections + working_stack
- ConversationEntry::System variant for system prompt entries
Token counting happens once at push time. Sections maintain their
totals incrementally via push/set/del. No more recomputing from
scratch on every budget check.
Does not compile — callers need updating.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Only Message, Role, MessageContent, ContentPart, ToolCall,
FunctionCall, Usage, ImageUrl are pub-exported from agent::api.
Internal types (ChatRequest, ChatCompletionChunk, ChunkChoice,
Delta, ReasoningConfig, ToolCallDelta, FunctionCallDelta) are
pub(crate) — invisible outside the crate.
All callers updated to import from agent::api:: instead of
agent::api::types::.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
New src/agent/api/http.rs: ~240 lines, supports GET/POST, JSON/form
bodies, SSE streaming via chunk(), TLS via rustls. No tracing dep.
Removes reqwest from the main crate and telegram channel crate.
Cargo.lock drops ~900 lines of transitive dependencies.
tracing now only pulled in by tui-markdown.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Streaming text now goes directly to agent entries via append_streaming().
sync_from_agent diffs the growing entry each tick. The streaming entry
is popped when the response completes; build_response_message pushes
the final version.
All status feedback uses RAII ActivityGuards:
- push_activity() for long-running work (thinking, streaming, scoring)
- notify() for instant feedback (compacted, DMN state changes, commands)
- Guards auto-remove on Drop, appending "(complete)" and lingering 5s
- expire_activities() cleans up timed-out notifications on render tick
UiMessage enum reduced to a single Info variant with zero sends.
The channel infrastructure remains for now (Mind/Agent still take
UiSender in signatures) — mechanical cleanup for a follow-up.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Reasoning tokens: dropped for now, will land in context entries later.
Debug sends: converted to dbglog! macro (writes to debug.log).
Activity: now a field on Agent, set directly, read by UI via try_lock.
score_memories_incremental takes agent Arc for activity writes.
UiMessage down to 2 variants: TextDelta, Info.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Status bar reads directly from Agent and MindState on each render tick.
Activity is now a field on Agent — set by agent code directly, read by
UI via try_lock. DmnAnnotation, ContextInfoUpdate, AgentUpdate were
already dead (no senders).
UiMessage down to 4 variants: TextDelta, Reasoning, Debug, Info.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
