Set busy_timeout before the WAL switch and retry the pragma batch on
'database is locked' within the same 5 s budget: while a file is still in
rollback mode, a shared-lock holder upgrading for the journal_mode change
gets SQLITE_BUSY without the busy handler running, so busy_timeout alone
doesn't cover parallel opens of a fresh file. Adds a 16-thread stress test
and drops the per-module DB_INIT workaround in ai::handlers tests.
Closes#157
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Long insight/persona chats on llama-swap failed with "exceeds the
available context size" even after raising Context in the app:
- llama-server fixes its window at launch (-c) and ignores num_ctx, but
the history budget trusted the requested num_ctx, so raising it kept
MORE history than the server could hold. LlmClient::context_window()
now reports the served window (llama-swap: /upstream/<model>/props,
cached 10 min) and the budget uses min(requested, served).
- apply_context_budget could only drop tool-call pairs, so plain Q&A
chats never shrank. It now also drops whole earlier exchanges (keeping
user/assistant alternation) and, last, the current turn's tool results,
never the in-progress question.
- The budget only ran once, before the tool loop. ContextFitter now
fits every LLM call (on a copy; the stored transcript is untouched)
and, on a context-overflow error, shrinks the budget from the server's
reported token counts and retries once.
GET /insights/models reports each llama-swap model's context_window when
it's known without loading the model (cached, or currently running), for
the app's Context picker.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Found by the parity capture: both insight-generation handlers run the
generation inside a nested tokio::task::spawn (panic isolation) that was
not instrumented, so the background ai.insight.* trace still split off
from its 202 request. The watcher's boot-time availability probe ran
before the first tick span, leaving its count_for_library DB spans as
roots; it now opens `watcher.initial_probe`.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
trace_db_call no longer takes an OpenTelemetry Context: it opens a
db.<query_type>.<operation> span (same name via otel.name, same
db.query_type / db.operation attrs) on the caller's ambient tracing span
and runs the DAO body inside it. The cx / ctx / context parameter is gone
from every DAO trait method, impl and call site, and from the helpers and
background drains that only threaded it.
The remaining request-handler spans (files, tags, faces, image, video,
favorites, duplicates, memories) move to #[instrument]; the
Context::current_with_span / with_remote_span_context plumbing is gone, as
is extract_context_from_request (the tracing-actix-web root span covers
it). otel::set_span_attributes replaces BoxedSpan::set_attributes.
Background-job raw spans (thumbnails, video actors) are left for #142.
tests/otel_db_spans.rs pins name, attributes, status and nesting (across
an instrumented spawn); otel_traceparent now uses an #[instrument]
handler. CLAUDE.md documents the tracing conventions.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
LlmClient methods no longer take an OpenTelemetry Context. llm.request is
now a tracing span (otel.kind=client, llm.provider/operation/model) that
parents on the caller's ambient span; traceparent injection reads the
span's OTel context via OpenTelemetrySpanExt.
The src/ai feature spans (handlers, insight generation, chat, persona
chat, daily summaries, TTS) move to #[instrument] / info_span!, and every
LLM-bearing tokio::spawn carries its parent span with .instrument(): the
streaming chat turn (chat_turn_stream_inner), async chat / persona turns,
background insight jobs and TTS jobs. Sub-spans in the agentic loops
(ai.chat.loop, ai.agentic.loop) instrument each LLM / tool await.
DAO calls keep their explicit Context (derived from the tracing span)
until the DAO migration (#141).
tests/otel_llm_spans.rs pins nesting + traceparent for the blocking and
streaming paths across a spawn on a multi-thread runtime.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The legacy SSE streaming path (chat_turn_stream -> run_streaming_turn ->
run_bootstrap/continuation_streaming) built its LLM spans from
Context::current(), which is always the empty root context here: the codebase
threads OTel contexts explicitly and never attaches one, there is no
context-aware tokio layer, and the turn runs on a tokio::spawn. So every
llm.request span on that path was an orphaned root span, uncorrelated with the
HTTP request.
Thread the handler's extracted request context down through
chat_turn_stream -> chat_turn_stream_inner -> run_streaming_turn ->
run_bootstrap_streaming / run_continuation_streaming, and use it for the
describe_image and agentic-loop calls. Mirrors the non-streaming path, which
already threads parent_cx -> loop_cx.
Also rename the block-scoped db 'cx' in run_streaming_turn to db_cx so it no
longer shadows the new parameter.
Closes#127.
Grows user_ai_prefs with five nullable owner-default columns and makes
them the server-side fallback layer: request param -> stored pref ->
hard default, at insight generation (agentic + standard), all three
insight-chat turn variants, and reel creation.
- PUT /prefs accepts the fields with the existing three-state partial
update semantics (TriState generalized to TriState<T> for the numeric
reel_max_segments); validation (canonical BackendKind::parse,
MemoriesSpan::from_str) runs before any write.
- GET /prefs returns the new fields.
- capture_prefs reads the existing row and carries the AI owner-defaults
through unchanged (the upsert overwrites all columns), mirroring only
the effective reel span/segments.
- MemoriesSpan gains Display + FromStr so stored spans round-trip
losslessly.
1463/0 tests (+27), clippy clean.
Persona chat stored a flat Vec<ChatMessage> keyed on (user_id, persona_id),
which meant one rolling transcript per persona and no way to revisit a turn.
This moves it onto the same ChatHistoryStore tree the file chat uses and
gives conversations their own identity.
Storage
- messages_json now holds a serialized ChatHistoryStore. Reads accept the
old flat array and upgrade it in place, so existing transcripts survive
without a data migration. The upgrade drops the v1 seed greeting, which
the flat renderer hid but the tree renderer would surface as a bubble the
user has never seen.
- New migration re-keys persona_chat_conversations on an opaque
conversation_id and adds title + created_at, so one persona can hold any
number of separate threads. Every DAO read and write is scoped by user_id
as well: a conversation id is a bearer token for someone's transcript and
must never grant access on its own.
- The per-conversation lock and in-flight turn slot key on conversation_id,
so two threads with the same persona can run turns concurrently.
Endpoints
- POST/DELETE /persona_chat/conversations — start and remove a thread,
replacing /persona_chat/reset.
- GET /persona_chat/conversations — the chat list, with snippet and counts
derived from each tree's active branch.
- POST /persona_chat/rewind, POST /persona_chat/switch-branch,
GET /persona_chat/branches — rewind and fork, mirroring the file chat.
Index 0 is rewindable here (it is the user's own first question, not a
synthetic prompt) and re-anchors on the seed node.
- history/turn/rewind/switch-branch/branches all key on conversation_id;
history gained branch_id and now returns real fork_info, active_leaf_id
and viewing_branch_id instead of placeholders.
The turn body no longer carries a persona at all — it is read from the
stored conversation, so a stale client cannot swap a thread's voice midway.
Titles
After the first turn persists, the conversation is named from its opening
exchange on the same backend the turn ran on. Small models wrap titles in
quotes, prefix them with "Title:" and append explanations, so sanitize_title
strips all of that and truncates on a character boundary. Any failure falls
back to the user's opening question. Generation runs after persistence: a
failed title must not cost the turn.
Fixes found along the way
- insight_chat: both file-chat turn paths captured path.len() before
apply_context_budget drained messages out of the middle, then sliced
messages[path_len..] for the new tree nodes. Once truncation fired that
dropped the user turn from the tree or panicked on an out-of-range start
index. Now read after the budget pass as history_len.
- Persona chat had no context budget at all and hardcoded truncated: false
in the done frame, so a rolling transcript grew unbounded.
- A cancelled turn persisted a half-finished transcript and pushed a second
terminal frame; it now returns early like the file chat.
- The seeded system prompt was frozen at conversation creation, so editing a
persona never reached a thread already in flight. Re-resolved per turn.
- turn_count was overwritten each write with the per-turn message delta;
it is now the cumulative assistant-turn count on the active branch.
- is_initial is always false: the file chat reserves it for its synthetic
"describe this photo" prompt, and marking a persona chat's first question
with it made the opening reply impossible to regenerate.
600 lib tests pass, clippy --all-targets clean.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- PersonaChatSession dispatches turns through the shared agent loop,
persisting transcripts keyed on (user_id, persona_id) in the new
persona_chat table (migration included).
- GET /persona_chat/history returns a rendered transcript (tool
invocations folded, is_initial flag) matching the file-chat shape.
- POST /persona_chat/turn returns 202 with a turn_id; SSE replay and
cancel reuse turn_replay_impl/cancel_turn_impl, extracted from the
actix-attributed handlers so both route families share the logic.
- POST /persona_chat/reset clears the persona transcript.
- Concurrent dispatches for the same (user, persona) are rejected with
409 via an in-flight gate (InFlightPersonaTurns) whose RAII guard
drops with the spawned turn task, freeing the slot on completion,
error, or abort.
ForkInfo now carries the divergence node's id, and the branches endpoint
accepts node_id + viewing_branch_id to return only the position-ranked
sibling branches at that fork (snippets taken from after the divergence,
skipping tool scaffolding) instead of every leaf in the tree. Options in
the same subtree as the viewing leaf anchor to that leaf so clients can
reliably identify "the branch I'm on". Tree-wide listing is unchanged
when node_id is absent.
- llm_client: descendant_leaves + branch_options_at helpers; optional
position rank on BranchLeafInfo.
- insight_chat: ForkInfo.node_id set during render_tree_path; get_branches
takes the scoping params.
- handlers: dedicated ChatBranchesQuery; unknown node_id maps to 400.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Store chat history as a tree in training_messages instead of a flat
array. Rewind now sets active_leaf_id to the target node rather than
truncating, so discarded paths survive as alternate branches. Fork
indicators ("X/Y") mark divergence points and let the client load or
switch to alternate paths.
- llm_client: StoredChatNode/ChatHistoryStore with tree traversal
helpers (path_to_leaf, children_of, fork_at_node, leaves*, etc.) and
from_flat_array for backward-compatible reads of the old flat format.
- insight_chat: load_history/chat_turn/rewind_history operate on the
tree; new switch_branch and get_branches. render_tree_path walks every
node (including non-rendered tool-dispatch nodes) when computing fork
info, so a fork whose diverging child is a tool call still surfaces on
the following rendered message.
- handlers: GET /insights/chat/branches, POST /insights/chat/switch-branch,
and a branch_id param on the history endpoint.
Backward-compatible: flat arrays are converted to a tree lazily on read.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
New optional SamplingOverride forwarded to llama-server as
chat_template_kwargs.enable_thinking (gates Qwen3-style reasoning
blocks). None leaves the template default; other backends ignore it.
Wired through the agentic-insight and chat-turn request bodies/handlers.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Implement end-to-end nightly pre-generation of memory reels with agentic
scripting that grounds narration in calendar, location, messages, and RAG.
Sections A-E from the plan:
A. Extract produce_reel pipeline core from run_reel_job with
ScripterMode::Fast/Agentic and progress callbacks.
B. Agentic scripter: factor run_readonly_tool_loop from the insight
generator, build read-only tool gate, prompt builder with GPS, and
generate_script_agentic with fallback to fast path.
C. Precomputed reels ledger (SQLite table + DAO), GET /reels/precomputed
handler with validity gate, GET /reels/by-key/{key}/video streaming,
and normalize_library_key helper.
D. Nightly scheduler: spawn_pregen_scheduler with configurable hour,
run_pregen_batch (day/week/month spans), pregen_one with dedup and
disk-check, secs_until_next_run_hour time math.
E. user_ai_prefs passive mirror table + DAO for param capture in
create_reel_handler and replay in the scheduler.
Also fixes resolve_library_param signature to take &[Library] and adds
resolve_library_param_state wrapper for AppState callers.
New files: migrations/2026-06-13-000000_add_precomputed_reels/,
migrations/2026-06-13-000010_add_user_ai_prefs/,
src/database/precomputed_reel_dao.rs,
src/database/user_ai_prefs_dao.rs
The GPU lease keeps per-request reqwest budgets from burning behind a
cross-model swap, but the job-level INSIGHT_GENERATION_TIMEOUT_SECS
wall-clock started at spawn — an insight queued behind a running TTS
synthesis parked its first chat call on the lease and timed out
("timeout after 180s") before chatterbox even finished loading.
Acquire-and-drop an LLM read lease before starting the job clock in
both insight handlers: the wait for the GPU happens before the
timeout begins, mirroring the per-request lease semantics. Dropped
immediately — holding it across the generation would deadlock the
chat calls' own lease acquisitions.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Queries embedded via llama-swap were searching corpora embedded via
Ollama (measured: spaces diverged). Introduce LocalLlm — the local
Ollama + llama-swap pair with LLM_BACKEND dispatch baked in — and route
all embedding writers through it; anything embedding via a concrete
client reintroduces the bug.
- search_rag: embed the model's query verbatim (no metadata boilerplate),
make date optional — no time-decay when omitted, so "when did X
happen?" queries rank purely by similarity across all time
- reembed_embeddings bin: re-embed summaries / calendar / search /
knowledge entities via the active backend, with old-new cosine report
per table and truncate-and-retry for inputs over the embed server's
physical batch size
- import_calendar, import_search_history: embed through LocalLlm
- search_messages / get_sms_messages: render sender → recipient so sent
messages are attributable to a conversation
- insight job failures: store the one-line anyhow context chain ({:#})
instead of the Debug dump the client was shown verbatim
- serialize env_dispatch tests behind a lock (parallel-runner flake)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
A request carrying persona_id but no system_prompt used to fall back to
the neutral default voice. Both agentic generation
(generate_agentic_insight_handler) and chat bootstrap now resolve the
persona's stored prompt from the persona store, with precedence:
explicit non-blank client system_prompt > persona store lookup >
existing default ("default" persona id behaves the same — used if the
store has a row, neutral default otherwise). Resolution happens at the
handler / bootstrap entry where the DAO is reachable; internals are
unchanged. resolve_bootstrap_system_prompt takes the resolved persona
prompt as a second argument, with precedence tests.
Also in insight_chat:
- Sync chat_turn no longer persists the synthetic "Please write your
final answer now without calling any more tools." user message pushed
on iteration exhaustion — extracted both streaming variants'
synthetic_idx pattern into push/remove_synthetic_final_prompt (the
remove is a defensive no-op on index drift) and applied it to all
three loops; round-trip test included.
- Strip leaked <think> blocks from the final content persisted as the
reply in chat_turn and both streaming AgenticLoopOutcomes (mid-stream
TextDeltas are untouched; the raw transcript keeps the block).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Expose GET /insights/history?path=... returning every generated version
of a photo's insight (current plus superseded), newest-first, backing the
mobile per-file insight history view.
- New get_insight_history_handler; reuses the existing get_insight_history
DAO method (removed its dead_code allow).
- impl From<PhotoInsight> for PhotoInsightResponse, collapsing the mapping
that was duplicated across the single-get and all-insights handlers.
- rate_insight_by_id DAO method + optional insight_id on RateInsightRequest
so previously generated versions can be approved/rejected (the path-based
rate only touches the current row).
- DAO tests for history ordering/scoping and id-targeted rating.
- cargo fmt normalized a multi-line assert in insight_chat.rs tests.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Replace the one-shot SSE chat stream with an async dispatch + reconnectable
replay flow so the mobile client survives backgrounding, network blips, and
OS-killed sockets without losing an in-flight agentic turn.
- TurnRegistry/TurnEntry: in-memory per-turn event buffer (cap 500, front
eviction) shared by the agentic loop (writer) and SSE replay readers.
ReplayOutcome + replay_from/next_batch distinguish Events/CaughtUp/Gone;
next_batch registers the Notify before reading state (no lost wakeup) and
drains every buffered event before signaling terminal, so the final
Done/Error is never dropped and the stream closes cleanly.
- Endpoints: POST /insights/chat/turn (202 + turn_id), GET
/insights/chat/turn/{id} (SSE replay, ?skip_before= resume, per-event seq,
410 on eviction), DELETE /insights/chat/turn/{id} (real task abort +
cooperative is_running() check at each loop boundary).
- Cancellation actually stops the task (AbortHandle stored on the entry) and
emits a Done{cancelled:true}; callers skip persistence on cancel.
- Background sweeper drops stale turns; interval clamped to <=300s.
- OpenTelemetry spans: ai.chat.turn.execute/replay/cancel.
- Legacy POST /insights/chat/stream path preserved unchanged.
Tests: registry coverage for terminal delivery (race guard), waiting, Gone,
abort, eviction; handler integration tests for 404/410, skip_before, seq
stamping, completed replay, and cancel.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- Add prompt_eval_count and eval_count columns to photo_insights so
token usage from llama-swap/Ollama is stored and returned by the API
- Fix agentic generator return: was (prompt_eval_count, eval_count),
handler destructured first element as insight_id — now returns
(insight_id, prompt_eval_count, eval_count)
- Wire prompt_eval_count/eval_count from DB into PhotoInsightResponse
instead of hardcoded None
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Replace impl ToString with impl Display for InsightJobStatus and
InsightGenerationType
- Rename from_str → parse to avoid confusion with std::str::FromStr
- Collapse nested if statements (handlers, insight_chat, insight_generator,
image handlers)
- Use is_multiple_of() instead of manual modulo checks
- Suppress deprecated diesel::dsl::count_distinct (no drop-in replacement
available in current Diesel version)
- Scope MutexGuard in synthesize_merge to drop before await
- Allow dead_code on generate_no_think, enumerate_indexable_files,
total_deleted (intended for future use)
- Allow type_complexity on Diesel query result tuples
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Fix query param mismatch: rename GenerationStatusQuery.file_path to
path so the client's app-resume buildQuery({ path: ... }) resolves
correctly instead of always getting 400
- Remove dead _lib_id bindings from both generate handlers
- Return 202 Accepted instead of 200 from generate endpoints
- Restore OpenTelemetry span instrumentation on generate handlers
- Remove stale UNIQUE constraint from initial migration (incompatible
with plain-INSERT DAO)
- Add tests for status guard: complete_job/fail_job are no-ops when
job is already cancelled, and cancel_job by id
- Persist generation params (num_ctx, temperature, top_p, top_k, min_p,
system_prompt, persona_id) on the photo_insights table for auditing
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Add insight_generation_jobs table migration and DAO
- Implement job lifecycle: create_or_get_active, complete, fail, cancel
- Refactor POST /insights/generate and /agentic to async spawn with timeout
- Add GET /insights/generation/status endpoint with job_id and file_path lookup
- Use String for enum fields in Diesel models to avoid private Bound type
- Add from_str() helpers on InsightJobStatus and InsightGenerationType
- Fix update_training_messages to return Result<usize, DbError>
- 7/7 DAO unit tests passing
llamacpp models now receive images via OpenAI content-parts instead of
the describe-then-inline strategy (hybrid mode unchanged). Fixes
assistant messages with tool_calls emitting content: null instead of ""
to satisfy strict Jinja template role-alternation checks. Adds debug
logging of message role sequences on llamacpp requests.
Introduces BackendKind enum, SamplingOverrides, and ResolvedBackend in
a new backend.rs module. InsightGenerator::resolve_backend centralises
client construction + vision capability detection — next step wires the
existing inline dispatch through it.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Reverts the per-request backend="llamacpp" value. Chat/vision/embedding
backend is now a deploy-time decision (LLM_BACKEND=ollama|llamacpp),
applied globally across chat, vision describe, and embeddings — so
embedding vectors stay in one space across the index.
- Per-request backend whitelist back to "local"|"hybrid". A request
arriving with backend="llamacpp" is rejected.
- LLM_BACKEND=llamacpp swaps the entire local stack to llama-swap:
chat hits the chat slot, describe hits the vision slot, embeddings
hit the embed slot. Hybrid mode still routes chat to OpenRouter
but uses LLM_BACKEND for the describe pass.
- Drops env vars HYBRID_VISION_BACKEND, LLAMA_SWAP_VISION_MODELS,
EMBEDDING_BACKEND (the last never shipped). Drops the
LlamaCppClient.vision_models allowlist — capability inference now
reports has_vision only for the configured vision_model slot.
- Drops the /insights/llamacpp/models handler. /insights/models is
the single endpoint; returns Ollama servers under LLM_BACKEND=ollama
and llama-swap slots (from LLAMA_SWAP_ALLOWED_MODELS) under
LLM_BACKEND=llamacpp. Same envelope shape either way.
- New ai::embed_one helper routes embeddings through llama-swap when
LLM_BACKEND=llamacpp (else Ollama). Wires it into the four
insight_generator embedding sites.
- Cross-replay matrix simplifies to pre-llamacpp shape (local↔local,
hybrid↔hybrid, hybrid→local allowed; local→hybrid rejected).
Wires a new LlamaCppClient (OpenAI-compatible /v1 wire format) alongside
OllamaClient and OpenRouterClient. Per-slot routing for chat/vision/embed
via env (LLAMA_SWAP_URL + *_MODEL vars); capability inference uses an
env allowlist since /v1/models doesn't report modality.
InsightGenerator + InsightChatService gain three-way dispatch on
chat_backend = "local" | "hybrid" | "llamacpp". Hybrid and llamacpp
share the describe-then-inline path (text-only chat after a separate
vision describe). HYBRID_VISION_BACKEND=llamacpp lets hybrid route its
describe pass through llama-swap's vision slot while chat still goes
to OpenRouter.
Cross-replay matrix added (validate_cross_replay): local<->llamacpp
and hybrid<->llamacpp allowed; local->hybrid and llamacpp->hybrid
rejected. New /insights/llamacpp/models handler mirrors the OpenRouter
shape.
No logic changes - line reflow, brace placement, and method-chain splits
across handlers / personas / state / faces / knowledge / insights_dao /
knowledge_dao / populate_knowledge. Picked up incidentally while running
fmt for the sms-search work.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
When a photo exists in more than one library and the user
regenerates its insight from library A's chat, the regenerate
streams cleanly, store_insight flips library A's old row to
is_current=false, and inserts a new is_current=true row tagged
(library A, rel_path). On the next history fetch the user sees
their old transcript — the regenerate appears to vanish.
The cause: get_insight(file_path) filters on rel_path + is_current
only, so library B's untouched is_current=true row for the same
rel_path satisfies the query and gets returned by SQLite's .first()
ahead of A's new row. Because get_insight is also what
chat_turn_stream uses to decide bootstrap vs. continuation, the
next chat turn after the shadow hit also routes against the
wrong insight, so update_training_messages corrupts library B's
transcript with library A's chat.
Fix: add get_current_insight_for_library(library_id, file_path)
filtered on (library_id, rel_path, is_current=true) and route the
chat surface (load_history, chat_turn{,_stream}, rewind_history)
through it. load_history falls back to the cross-library
get_insight when the scoped lookup misses — preserves the
"scalar data merges across libraries" intent for the case where
the active library has no insight but another does. The path-only
get_insight stays for callers that don't have library context
(populate_knowledge, the photo-grid metadata fetch).
chat_history_handler stops dropping the parsed library on the
floor and threads it through. Single-library deploys see no
behaviour change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two persona-infrastructure correctness fixes that go together because
the second one (FK with CASCADE) requires the first (preventing the
persona row from being mutated out from under its facts).
1. update_persona handler refuses name/systemPrompt edits to built-ins
(409). includeAllMemories stays editable — that's a per-user
preference, not the persona's identity. Mirrors the existing
delete_persona guard. The DAO is intentionally permissive so the
guard sits at the HTTP layer; persona_dao test pins that contract.
2. Migration 2026-05-10 adds user_id to entity_facts and a composite
FK (user_id, persona_id) -> personas(user_id, persona_id) ON DELETE
CASCADE. This closes two issues at once:
- Persona orphans: deleting a custom persona used to leave its
facts dangling forever, readable only via PersonaFilter::All.
CASCADE now wipes them with the persona row.
- Multi-user fact leakage: PersonaFilter::Single("default") used
to surface every user's default-scoped facts. PersonaFilter is
now { user_id, persona_id } and all read paths
(get_facts_for_entity, list_facts, get_recent_activity) filter
on user_id first. upsert_fact's dedup key extends to user_id so
identical claims under shared persona names from different
users no longer corroborate-bump each other's confidence.
- user_id threads from Claims.sub.parse::<i32>().unwrap_or(1) at
the chat / insight handlers through ChatTurnRequest, the
streaming agentic loop, execute_tool, and into the leaf tools
(tool_store_fact, tool_recall_facts_for_photo). The ".unwrap_or(1)"
accommodates Apollo's service token whose sub is non-numeric on
legacy mints.
- Backfill picks the smallest user_id matching each legacy fact's
persona_id so the FK holds for already-stored rows.
Five new knowledge_dao tests with FK-on connection: persona scoping
isolation, All-variant union per-user, dedup not crossing users,
CASCADE delete, FK rejection of unknown personas. Plus
dao_update_does_not_block_built_ins documenting where the
HTTP-layer guard lives.
Apollo coordinates separately — the matching changes there add the
/api/personas proxy and start sending persona_id on photo-chat turns.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Move personas off the mobile client into ImageApi as first-class
records, and scope entity_facts by persona so each one builds its own
voice over a shared entity graph. The new include_all_memories flag
lets a persona opt back into the full hive-mind pool for human
browsing of /knowledge/*; agentic generation always stays in-voice.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Tap-Discuss-on-no-insight previously failed silently: ImageApi's
/insights/chat/stream required an existing agentic insight, errored
when missing, and emitted the failure as `event: error` — which the
frontend SSE consumer ignored (it listens for `error_message`).
This commit closes both gaps with a server-side state machine:
- /insights/chat/stream now branches on insight presence. Missing
insight (or `regenerate: true` in the body) → bootstrap path:
builds [System(req.system_prompt), User(req.user_message + image)],
runs the agentic loop, generates a title, persists a new row via
store_insight (which auto-flips priors). Existing insight →
continuation path (unchanged behaviour).
- New `regenerate: bool` request field forces bootstrap even when an
insight exists. Takes precedence over `amend`.
- `done` SSE payload field-name alignment with Apollo's frontend
convention: prompt_eval_count → prompt_tokens, eval_count →
eval_tokens, num_ctx echo added.
- `amended_insight_id` semantics broaden — now populated whenever the
turn produced a new row (bootstrap, regenerate, or amend). Existing
amend clients keep working unchanged; new clients get the new row's
id for free.
- `event: error` → `event: error_message` so frontend errors stop
silently dropping.
Refactor: extracted run_streaming_agentic_loop, build_chat_clients,
and generate_title as shared helpers between bootstrap and
continuation. Continuation path's outer logic moves to
run_continuation_streaming with no behaviour change.
Mobile-ready: any client (Apollo backend, mobile, future) sends one
request to /insights/chat/stream and gets the right path. Apollo's
proxy stays a dumb pipe.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Append mode: applied ephemerally — original system message restored
before persistence so re-opens see the baked persona. Amend mode:
override stays in place and becomes the new insight row's system
message. Pattern mirrors annotate_system_with_budget.
Adds system_prompt field on both ChatTurnHttpRequest and ChatTurnRequest;
plumbs through chat_turn and chat_turn_stream identically.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Few-shot injection on /insights/generate/agentic: compresses prior
training_messages into trajectory blocks (tool calls + result summaries)
and injects into the system prompt. Hardcoded default ids with optional
request override.
- New fewshot_source_ids column on photo_insights (+ migration) to track
which exemplars influenced a given row, for downstream training-set
filtering. Chat amend rows stamp None with a lineage note.
- Ollama client now remembers which server (primary/fallback) most
recently succeeded and tries it first on the next call, via a shared
Arc<AtomicBool>. Avoids re-404ing the primary on every agent iteration
when the chosen model only lives on the fallback.
- Demote noisy logs: daily_summary "Summary match" lines to debug;
inner chat_with_tools non-2xx body log from error to warn (outer
layer owns the terminal-error signal).
- Drift-guard tests for summarize_tool_result covering the success /
empty / error / unknown shape for every tool.
- Tidy: three pre-existing clippy warnings cleaned up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add LlmClient::chat_with_tools_stream and SSE endpoint
POST /insights/chat/stream that emits text deltas, tool_call /
tool_result pairs, truncated notice, and a terminal done frame as the
agentic loop runs.
- Ollama: parses NDJSON from /api/chat stream, accumulates content
deltas, emits Done with tool_calls from the final chunk.
- OpenRouter: parses OpenAI-compatible SSE, reassembles tool_call
argument deltas by index, asks for stream_options.include_usage.
- InsightChatService spawns the loop on a tokio task, feeds events
through an mpsc channel, persists training_messages at the end.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
load_history now groups preceding tool_call + tool_result scaffolding
under each assistant reply as `tools: [{name, arguments, result}]`.
Result bodies over 2000 chars are truncated for payload size with a
`result_truncated` flag; the full value remains in training_messages.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Rewind: POST /insights/chat/rewind truncates training_messages at a
given rendered index, dropping the target message plus any preceding
tool-call scaffolding. The initial user prompt is protected.
Metrics: log prompt_eval_count/duration and eval_count/duration from
every Ollama chat response, rendered as tokens + ms + tok/s.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds POST /insights/chat and GET /insights/chat/history. Replays the
stored agentic conversation through the same backend the insight was
generated with (or a per-turn override), runs a short tool-calling
loop, and persists the extended history in append or amend mode.
Backend switching: same-backend or hybrid->local replay verbatim;
local->hybrid is rejected in v1 (would require on-the-fly vision
description rewrite).
Per-(library, file) async mutex serialises concurrent turns. Soft
context budget drops oldest tool_call+result pairs when the
serialized history exceeds num_ctx - 2048 tokens.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Add OPENROUTER_ALLOWED_MODELS env var and GET /insights/openrouter/models
endpoint returning the curated list verbatim. Drop the live capability
precheck in hybrid mode — trust the operator's allowlist; bad ids surface
as a chat-call error.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a `backend` column to photo_insights (default 'local', migration
2026-04-20-000000) and a corresponding optional `backend` field on the
agentic request. When a request sets backend=hybrid:
- The local Ollama vision model is called once via describe_image to
produce a text description.
- The description is inlined into the first user message as text —
no base64 image is ever sent to the chat model.
- The agentic tool-calling loop and title generation route through an
OpenRouterClient (dispatched via &dyn LlmClient), letting the user
pick any tool-capable model from OpenRouter per request.
- describe_photo is removed from the offered tools since the description
is already present.
Embeddings and vision stay on local Ollama regardless of backend.
Hybrid mode requires OPENROUTER_API_KEY; handlers return a clear error
when hybrid is requested without it, and also when the selected
OpenRouter model lacks tool-calling support.
AppState gains an optional openrouter client built from
OPENROUTER_API_KEY / OPENROUTER_BASE_URL / OPENROUTER_DEFAULT_MODEL /
OPENROUTER_EMBEDDING_MODEL / attribution headers. Default model is
anthropic/claude-sonnet-4.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Silence forward-looking dead_code on unused DAO modules, annotate
individual placeholder items, rewrite tautological assert!(true/false)
in token tests as panic! arms, and pick up fmt drift.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Tags and insights now follow content across libraries via content_hash
lookups on the read path, so the same file indexed at different rel_paths
in multiple libraries shares its annotations. Recursive tag search scopes
hits to the selected library by checking each tagged rel_path against
the library's disk (with a content-hash sibling fallback so tags attached
under one library's rel_path still match a content-equivalent file in
another). The /image and /image/metadata handlers fall back across
libraries when the file isn't under the resolved one, so union-mode
search results (which carry no library attribution in the response)
still serve correctly.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Expose Ollama sampling params through the insight generation endpoints
so users can tune creativity/determinism per request. All four are
optional — omitted values fall through to the model's server-side
defaults.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Captures prompt_eval_count and eval_count from Ollama /api/chat responses
during the agentic loop and returns them in POST /insights/generate/agentic
so the frontend can display context window usage to the user.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Sanitise tool call arguments before re-sending in conversation history: non-object values (bool, string, null) that some models produce are normalised to {} to prevent Ollama 500s
- Map 'error parsing tool call' Ollama 500 to HTTP 400 with a descriptive message listing compatible models (llama3.1, llama3.2, qwen2.5, mistral-nemo)
- Add reverse_geocode tool backed by existing Nominatim helper; description hints model can chain it after get_location_history results
- Make get_sms_messages contact parameter optional (was required, forcing the model to guess); executor now passes None to fall back to all-contacts search
- Log tool result outcomes at warn level for errors/empty results, info for successes; log SMS API errors with full detail; log full request body on Ollama 500
- Strengthen system prompt to require 3-4 tool calls before final answer
- Try fallback server when checking model capabilities (primary-only check caused 500 for models only on fallback)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Register the agentic insight endpoint that validates tool-calling capability,
runs the agentic loop, and returns the stored PhotoInsightResponse. Returns 400
for unsupported models, 500 for other errors. Max iterations configurable via
AGENTIC_MAX_ITERATIONS env var (default 10).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
