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of a photo's insight (current plus superseded), newest-first, backing the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>
The truncation budget estimated message size by serializing the full
ChatMessage array, including the base64 image persisted in the first
user message. A 1024px JPEG is hundreds of KB of base64 characters —
8-19x the entire ~24KB text budget at the default num_ctx — and the
image lives in the protected prefix that's never dropped. The budget
check was therefore essentially always over, dropping all tool history
and firing the "trimmed context" banner on every turn for vision
backends that inline images.
estimate_bytes now strips image payloads before counting and charges a
flat IMAGE_TOKENS_EACH per image instead, so the budget reflects real
text token pressure. Adds a regression test covering a short
conversation with one large image.
Co-Authored-By: Claude Opus 4.8 <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
When backend=hybrid with LLM_BACKEND=llamacpp, the user-selected model
(an OpenRouter id like "google/gemini-3-flash-preview") was being applied
to the local LlamaCppClient's primary_model and vision_model. This caused
describe_image to send the OpenRouter model name to llama-swap, which
returned 400 because it has no such slot.
Guard the local-client model override with !is_hybrid so it only applies
in local-only mode (where the user is selecting a different local model).
Bump to v1.2.0.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- sms search tool: accept contact name, trim/validate, skip when
contact_id is set, pass to API client
- sms_client: new contact field in SmsSearchParams, URL-encode on wire
- Tool description clarifies contact_id takes precedence when both given
- Add parse_title_body helper for LLM response parsing
- llamacpp backend improvements
Replace 5 copies of the ~80-line backend resolution pattern with a
single InsightGenerator::resolve_backend() builder that returns a
ResolvedBackend (chat + local clients, BackendKind enum, images_inline
flag). Tool dispatch now takes &ResolvedBackend instead of
&OllamaClient + model + backend strings.
Remove duplicated ollama/openrouter/llamacpp fields from
InsightChatService — InsightGenerator owns them and resolve_backend
uses them. Delete build_chat_clients (replaced by resolve_backend).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.
Probe-phase scaffolding for CLIP semantic search. Adds the column
that will hold per-photo embeddings, the HTTP client to Apollo's
inference service, and a throwaway probe binary so we can eyeball
search-result quality on the live library before building the
persistence layer (backlog drain, /photos/search endpoint, UI).
- migrations/2026-05-14-000000_add_clip_embedding/ — adds
image_exif.clip_embedding (BLOB) and clip_model_version (TEXT),
plus a partial index on (clip_embedding IS NULL AND content_hash
IS NOT NULL) for the future backfill drain.
- src/database/models.rs — extends ImageExif struct to match.
- src/ai/clip_client.rs — encode_image / encode_text / health,
same Permanent/Transient/Disabled taxonomy as face_client.
- src/bin/probe_clip_search.rs — --query <q> --library N --limit M
--top K. Encodes a sample and prints top-K cosine similarities.
No DB writes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Bundles three coupled changes so agent-side mutations stay
auditable and reversible:
1. Audit columns on entity_facts —
`last_modified_by_model` / `last_modified_by_backend` /
`last_modified_at`. Stamped on every mutation path
(update_fact, supersede_fact, manual PATCH, manual supersede,
the new revert). NULL on rows never touched since creation.
Partial index on `last_modified_at WHERE NOT NULL` keeps the
"show me recent edits" feed fast without bloating from legacy
rows.
2. Per-persona gate `personas.allow_agent_corrections` (BOOLEAN,
default 0). Defense in depth at two layers:
- build_tool_definitions: when off, `update_fact` and
`supersede_fact` aren't in the catalog at all, so even a
hallucinated tool call by the model fails fast.
- tool_update_fact / tool_supersede_fact: re-checks the persona
flag at call time and returns an explicit "corrections
disabled" error if it's somehow off (e.g. flag flipped mid-
loop).
ToolGateOpts grows the flag; current_gate_opts splits into
`current_gate_opts` (no persona context, defaults closed) +
`current_gate_opts_for_persona` for chat callers that have a
persona id. Both call sites in insight_chat are updated.
3. Revert action — new DAO method `revert_supersession` +
`POST /knowledge/facts/{id}/restore`. Flips status back to
'active', clears `superseded_by`, clears `valid_until` (we
don't track whether it was hand-set vs auto-stamped, so the
safe reset is to drop it — user can re-bound after). Stamps
`last_modified_*` so the revert itself is attributable.
Manual paths (PATCH / supersede via HTTP, plus restore) stamp the
audit columns with `("manual", "manual")`. Agent paths stamp the
loop-time chat model and backend (mirroring the existing
created_by_* convention).
FactDetail in the HTTP response now carries the audit triple
alongside the existing provenance. Apollo wires the new field set
in the matching commit.
PersonaView / UpdatePersonaRequest grow `allowAgentCorrections`;
the PersonaPatch + InsertPersona + bulk_import paths thread it.
317 lib tests pass, including unchanged update_fact / supersede
DAO tests (now passing audit=None — None means "no provenance
context to attribute", legacy semantics).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds two nullable TEXT columns to entity_facts —
`created_by_model` (LLM identifier) and `created_by_backend`
("local" / "hybrid" / "manual" / NULL) — so the curator can audit
which configurations produce good fact-keeping and which produce
noise.
photo_insights already carries model_version + backend, and
entity_facts.source_insight_id links to it, but:
- source_insight_id is set post-loop, so chat-continuation and
regenerated-insight facts lose the link.
- JOINing per read is more friction than embedding provenance on
the row itself.
- Manual facts (POST /knowledge/facts) have no insight at all and
need their own "manual" provenance marker.
Threading: execute_tool grows `model` + `backend` params, passed
from the three call sites (agentic insight loop, chat single-turn,
chat stream) using the loop-time `chat_backend.primary_model()` +
`effective_backend` already in scope. tool_store_fact stamps the
new fact accordingly; manual create_fact stamps backend="manual".
Legacy rows leave both NULL — pre-tracking data can't be back-
filled reliably from training_messages without burning compute.
Indexes are partial (WHERE NOT NULL) so legacy rows don't bloat
them, and "show me all facts from model X" stays fast.
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>
The bootstrap system message gave the model a file path and (in
hybrid mode) a visual description, but no temporal anchor. Models
defaulted to today's date when calling get_sms_messages — Nov 2014
photos were getting "2024-03-11" passed as `date`, missing every
historical message and leading the model to confidently misreport
context.
This commit folds two more EXIF-sourced facts into the
--- PHOTO CONTEXT --- block:
Date taken: <YYYY-MM-DD or "unknown">
GPS: <lat, lon to 4dp> (omitted when no GPS)
Resolution waterfall for date_taken matches the documented canonical
date pipeline at the EXIF / filename steps, but intentionally stops
short of the fs-time fallback `generate_agentic_insight_for_photo`
uses — for chat we'd rather show "unknown" than mislead the model
with an inode mtime. GPS is taken straight from EXIF when both
lat/lon are populated; absent GPS suppresses the line entirely so
the model doesn't hallucinate coordinates.
InsightGenerator gains a `fetch_exif(file_path)` accessor (crate-
visible) so the chat service doesn't need its own ExifDao plumbing.
build_bootstrap_system_message picks up two new params (date,
gps); existing tests updated and 5 new tests cover:
- date present / absent / waterfall (EXIF wins, filename fallback,
None when neither source has it)
- GPS present / absent
- ordering (path → date → visual)
Total insight_chat unit tests: 33 (up from 27).
After refresh, the rendered transcript was showing two unwanted
artifacts in the initial user bubble:
Photo file path: pics/DSC_5171.jpg
please tell me about this photo and what was going on around it
Please write your final answer now without calling any more tools.
Two distinct bugs:
1. Bootstrap was prepending `Photo file path: <path>` (and, in
hybrid mode, the visual description block) into the user-turn
content. The model needed it to call file_path-keyed tools, but
the user could see it in their own bubble on replay.
2. The no-tools fallback ("Please write your final answer now…")
was a synthetic user message we never stripped from history,
so it persisted into training_messages, rendered as a second
user bubble, AND wiped the prior tool-call accumulator inside
load_history (user-turn handler clears pending_tools), which
is why the tool invocations disappeared from the assistant
bubble after refresh.
Fixes:
- New `build_bootstrap_system_message` helper composes the persona
with a `--- PHOTO CONTEXT ---` block (path + optional visual
description). Lives in the system message, not the user turn.
The user's bubble shows only what they typed.
- Streaming agentic loop's no-tools fallback now records its
insertion index and removes the synthetic user prompt from
`messages` after the model responds. Final assistant content
stays — it reads coherently on replay without the synthetic
prompt above it. Applies to both bootstrap and continuation.
3 new tests cover the system-message composer (path-only, with
visual block, persona-trim). Total insight_chat unit tests: 27.
Bug: bootstrap user_content was just the user's typed message (plus
the hybrid visual description). Tools that take a file_path arg —
recall_facts_for_photo, get_file_tags, get_faces_in_photo — had no
way to learn the canonical path. Small models would invent
placeholders like "input_file_0.png" or call the tool with a name
guessed from a hidden multimodal input handle, neither of which
matched any real photo.
Fix: prepend a single-line "Photo file path: <normalized>\n\n" block
to user_content. Same shape generate_agentic_insight_for_photo
already uses for non-chat callers — kept the bootstrap minimal
(no date / GPS / tags pre-stuffing; the agentic loop can fetch
those via tools when needed).
Hybrid still injects the visual description block between the path
block and the user message; local mode just gets path + user text.
resolve_bootstrap_system_prompt and resolve_bootstrap_backend run on
every bootstrap turn — they pick the persisted system prompt and the
chosen backend label. They were inline conditionals before; pulling
them out makes the rules testable without spinning up the full
streaming stack.
9 new tests cover:
- system prompt fallback to BOOTSTRAP_DEFAULT_SYSTEM_PROMPT for None,
empty string, whitespace-only
- supplied non-empty prompts pass through verbatim, with interior
newlines / spacing preserved (Apollo personas use multi-line tool
listings)
- backend defaults to "local" for None / empty
- "local" / "hybrid" accepted case-insensitively with edge-trim
- unknown labels return a descriptive error
Total insight_chat tests: 24 (up from 15). No behaviour change.
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>
No behavior change — purely whitespace/line-break cleanup that had
accumulated since the last format run.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Tools whose backing tables are empty (calendar, location_history,
daily_summaries) drop out of the catalog so the LLM doesn't waste
iteration budget calling them only to receive "no results found".
Vision and apollo gates already existed; this generalizes the pattern.
search_messages gains start_ts/end_ts/contact_id filters (date filter
is a client-side post-filter; SMS-API only accepts contact_id natively
on the search endpoint).
Descriptions follow a consistent convention: one sentence (what +
when), param semantics, examples for tools with non-obvious param
choices. No more all-caps headers, no more identity-prescriptive
language inside descriptions.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Trim the override input once via Option::map(str::trim).filter(...).
- Use matches!() in restore_system_prompt_override's Prepended arm so
it reads consistently with the Replaced arm.
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>
Branch B of the multi-library data-model rollout. tagged_photo and
photo_insights now follow the bytes (content_hash), not the path,
matching the policy pinned in CLAUDE.md "Multi-library data model".
Branch A's availability probe and EXIF scoping land first; this
branch builds on top.
Migration (2026-05-01-000000_hash_keyed_derived_data)
Adds nullable content_hash columns to tagged_photo and photo_insights,
with partial indexes on the non-null subset to keep the index small
during the transitional window. The migration backfills from
image_exif:
* tagged_photo joins on rel_path alone (no library_id available);
* photo_insights joins on (library_id, rel_path), unambiguous.
Rows whose image_exif hash isn't known yet stay null and the runtime
reconciliation pass populates them as the hash backlog drains.
Insert-time population
TagDao::tag_file looks up image_exif.content_hash by rel_path before
inserting; the hash is written into the new column.
InsightDao::store_insight does the same scoped to (library_id,
rel_path). Caller-supplied hash on InsertPhotoInsight wins; otherwise
the DAO does the lookup. Both paths fall back to None if the hash
isn't known yet — reconciliation backfills.
Reconciliation (database/reconcile.rs)
Three idempotent passes the watcher runs once per tick after the
per-library backfill loop:
1. tagged_photo NULL hashes → populate from image_exif by rel_path.
2. photo_insights NULL hashes → populate by (library_id, rel_path).
3. photo_insights scalar merge — when multiple is_current rows
share a content_hash, keep the earliest generated_at as
current; demote the rest. Demoted rows keep their data so
/insights/history is unaffected; only the "current" pointer
narrows to one per hash.
No filesystem dependency, so reconcile doesn't need the availability
gate; runs every tick. Logs once when something changed, debug
otherwise.
Tags are set-valued under the policy (union on read, already
DISTINCT in queries), so there is no analogous tag-collapse pass —
duplicate (tag_id, content_hash) rows across libraries are
harmless.
Read paths are unchanged in this branch — lookup_tags_batch's
existing rel_path-via-hash-sibling expansion still produces the
correct merge. A follow-up can simplify reads to use the new column
directly for performance.
Tests: 217 pass (212 pre-existing + 5 new in reconcile covering
NULL-fill, hash-not-yet-known no-op, library scoping on insights,
earliest-wins collapse, idempotency).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Optional integration with the sibling Apollo project's user-defined
Places (name + lat/lon + radius_m + description + category). When
APOLLO_API_BASE_URL is set, the per-photo location resolver folds the
most-specific containing Place into the LLM prompt's location string —
"Home (My house in Cambridge) — near Cambridge, MA" rather than the
city name alone. Smallest-radius wins; Apollo sorts server-side via
/api/places/contains, so the carousel badge in Apollo and the prompt
string here always agree.
Adds an agentic tool `get_personal_place_at(latitude, longitude)` that
the LLM can call during chat continuation. Tool description tells the
model the call returns the user's free-text notes, not just a name.
Deliberately narrow — no enumerate-all variant, lat/lon required.
Unset APOLLO_API_BASE_URL = legacy Nominatim-only path, tool is not
registered. 5 s timeout; all errors degrade silently.
Tests: 5 unit tests for compose_location_string (Apollo only, Nominatim
only, both, both-with-description, neither).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The mobile client's regenerate-after-failure flow sends a discard index
equal to the server's rendered count (its optimistic user bubble for the
failed turn was never persisted). find_raw_cut treated this as out of
range, surfacing as "Chat rewind failed: discard_from_rendered_index out
of range" and blocking the retry.
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>
- Inject the max-iterations budget into the agentic system prompt for
both insight generation and chat turns. Chat does this per-turn by
appending a note to the replayed system message and restoring it
before persistence so the note doesn't accumulate across turns.
- Stop deleting entity_photo_links at the start of agentic insight
generation. The clear made recall_facts_for_photo always return
empty, wasting a tool call and discarding knowledge from prior runs.
Re-linking the same entity is already an INSERT OR IGNORE no-op.
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>
Cameron Cordes
Cameron