Add OpenTelemetry tracing to every LLM client call so an insight/chat
turn is a single trace from HTTP entry to each downstream inference.
- LlmClient trait: thread a &opentelemetry::Context through all 5
methods (generate, chat_with_tools, chat_with_tools_stream,
generate_embeddings, describe_image), matching the DAO convention of
explicit context threading rather than ambient Context::current().
- otel::start_llm_request_span: single shared helper that every
provider (openrouter / llamacpp / ollama) funnels through, so the
span shape is identical: name llm.request, SpanKind::Client,
llm.provider / llm.operation / llm.model.
- Per-method spans in all three clients with the spec's operation enum
(chat | chat_stream | embeddings | describe_image | generate); stream
spans are owned and moved into the async_stream generator so they end
when the response drains, not when headers arrive.
- llm.tokens.input / llm.tokens.output recorded on non-stream and
stream completion; llm.cost_usd from usage.cost (OpenRouter);
http.status_code + error status on failure.
- W3C traceparent injected on outgoing LLM HTTP requests (llama-swap,
Ollama, OpenRouter) gated on span_context.is_valid() so debug
no-op-tracer builds don't send an all-zero parent.
- Turn-level spans (ai.chat.turn.execute, ai.persona_chat.turn.execute)
now carry llm.backend + llm.model; with_entry streaming paths thread
the turn context (turn_cx) to child describe_image / agentic-loop
calls instead of the ambient context.
clippy clean; 690 lib + 775 integration + 4 doc tests green.
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>
ALL-mode over-constrains NL queries — the model maps several query words to
tags and few photos carry every one, zeroing the candidate set. Switch to
ANY (a photo matches if it has any named tag); the semantic CLIP ranking
provides precision within that pool. Exclude tags still filter out.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
When structured filters are present they're the constraint and CLIP only ranks
within the candidate set, so drop the global similarity threshold for that
case. Previously the 0.2 whole-library threshold ran BEFORE intersecting with
the filters, discarding filter-matching photos that scored just under it (e.g.
a 2022 beach photo at 0.18) — producing after_struct_filter=0 even when matches
existed. Plain semantic (no filters) keeps the user's threshold.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Pin the NL->structured translation to a small, fast model that can stay
co-resident with CLIP (and the chat model) so it never evicts them on a tight
VRAM budget. Precedence: UNIFIED_SEARCH_MODEL env > client-selected model >
configured default. Logs the effective model (backend.model()) so model A/B
tests are visible. Documented in .env.example.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Log the translated query (semantic/tags/place/date/media + has_struct), the
tag-filter file count, candidate-row + allowed-hash counts, and the CLIP
considered/hits/after-filter counts. Pinpoints which stage drops results to
zero (over-extracted filter, tag path mismatch, Any/All over-constraint, or
CLIP threshold). info-level for now while debugging.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Add an optional `model` query param to /photos/search/unified, passed into
resolve_backend's overrides. The client sends the user's currently-selected
local model so the translation step reuses an already-loaded model instead of
forcing a llama-swap eviction + cold start. Falls back to the configured
default when absent. Still local only (no hybrid).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Composes the two existing engines (Path A orchestration):
- Translate NL -> StructuredQuery via local LLM, respecting LLM_BACKEND
(resolve_backend(Local) -> ollama or llama-swap; no hybrid).
- Forward-geocode the place name into a gps circle.
- Structured filters (tags/EXIF/geo/date/media) build a candidate set of EXIF
rows; CLIP ranks within it, joined by content_hash. Degenerate cases match
existing behavior: semantic-only -> plain CLIP; filters-only -> date-sorted.
- Echoes the interpreted query (incl. resolved place) for editable client chips.
Refactor: extracted reusable cores from clip_search (score_photos, resolve_hits,
parse_library_scope, score_error_response) shared by both endpoints. Removed the
Phase 1 allow-until-wired attributes now that nl_query + geo are consumed.
fmt + clippy clean; 23 backend tests pass (7 geo, 12 nl_query, 4 unified).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Cameron Cordes
