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>
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>
Addresses the P2 refactoring follow-ups parked on issue #130:
- otel::with_traceparent(builder, &SpanContext) — one shared W3C
traceparent injector. Replaces the 4-line inline header loop in
llamacpp (3x) and openrouter (3x) and removes ollama's private
inject_traceparent method (4 call sites now use the shared helper).
Takes &SpanContext (not &dyn Span) because the Span trait is not dyn
compatible in opentelemetry 0.31.
- otel::record_http_failure(span, status, message) and
otel::set_http_status(span, status) — collapse the repeated
'set http.status_code attribute + set_status(Status::error)'
boilerplate across all three clients (~10 sites).
Net -45 lines. The remaining if-let-Some(span.as_mut()) guards (token
attributes, parse-failure status) stay inline rather than growing a
SpanHandle wrapper type for a pattern that's already readable.
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>
llama-swap runs chat/vision/Chatterbox as a mutually-exclusive set on
one GPU and HOLDS a request for a non-resident model until the resident
model drains, then swaps. That hold burned the holder's reqwest timeout
(measured: a queued TTS lost 77s behind one LLM turn; an LLM request
behind a synthesis waited the entire remaining synth), so concurrent
insight + read-aloud timed out instead of queueing.
ai::gpu adds a fair RwLock lease acquired before each request is sent,
so cross-model waits happen before the HTTP timeout starts: chat/vision
share the read lease, TTS synthesis and voice-library ops (which spin
Chatterbox up) take the write lease, and embeddings take none (the
embed slot is in llama-swap's always-resident group). Speech jobs now
flip queued->running only after acquiring the GPU, letting the client
anchor its poll deadline to that transition.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- DELETE /tts/voices/{name}: remove a cloned voice via the llama-swap
passthrough (upstream chatterbox-tts-api exposes DELETE /voices/{name}).
- POST/GET/DELETE /tts/speech/jobs: durable job flow for long syntheses —
dispatch returns 202 + job id, the synth queues on the GPU permit instead
of fast-failing 429, and clients poll for the result (kept ~10 min).
- GET /tts/voices now serves an in-memory cache so listing voices doesn't
make llama-swap spin up the TTS model (evicting the resident LLM);
invalidated on create/delete, ?refresh=1 forces an upstream re-query.
- Created voice names are tagged with LLAMA_SWAP_TTS_REF_SECONDS (e.g.
grandma-30s) so the library shows which ref length produced each clone.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Ollama >=0.8 can stream tool_calls incrementally across NDJSON chunks;
chat_with_tools_stream did `tool_calls = Some(tcs)` per chunk, so only
the last chunk's calls survived assembly and earlier calls were silently
dropped. Append into the accumulator instead.
- ollama: append_streamed_tool_calls helper + tests covering two calls
arriving in separate chunks and the single-chunk batch case.
- llamacpp: the SSE delta assembly was already correct (per-index
BTreeMap, same-index argument fragments concatenate, distinct indexes
accumulate); extracted it into apply_tool_call_deltas /
finalize_tool_calls and added tests pinning that behavior.
- llm_client: new shared strip_think_blocks (moved from ollama's private
extract_final_answer, which now delegates) so the tool-calling final
content paths can reuse it; unit tests for tagged/plain/unclosed/empty
cases.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Long insights are chunked + synthesized server-side and can run past the shared
180s chat/embedding client timeout, causing spurious timeouts. /tts/speech now
uses a per-request timeout from LLAMA_SWAP_TTS_REQUEST_TIMEOUT_SECONDS
(default 600), overriding the client default without affecting chat/embeddings.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
/tts/speech now normalizes input before synthesis: unwraps markdown
links/images to visible text, drops heading/list/blockquote/emphasis
markers and URLs, strips emoji (which non-turbo Chatterbox mispronounces
or skips), and collapses whitespace. Centralized in clean_for_tts so the
app, WebUI, and curl all get clean audio. Bracketed tags are deliberately
preserved for a future Turbo (paralinguistic) switch.
Adds optional exaggeration / cfg_weight / temperature to the request,
clamped to Chatterbox's documented ranges and forwarded on the speech
body. Unit tests cover markdown/emoji/URL stripping and tag preservation.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
LlamaCppClient gains text_to_speech (OpenAI /audio/speech), list_voices and
create_voice (voice library at the swap-root /upstream/<model>/voices
passthrough), plus a tts_model slot configured via LLAMA_SWAP_TTS_MODEL
(default "chatterbox").
New Claims-gated routes:
- POST /tts/speech -> { audio_base64, format } for data: URI playback
- GET /tts/voices -> voice library passthrough
- POST /tts/voices/upload -> clone a voice from an uploaded clip (multipart)
- POST /tts/voices/from-library -> clone from a library file (ffmpeg-extracts
audio from video; audio forwarded as-is)
Security: voice_name sanitized to [A-Za-z0-9_-] (it becomes an upstream
filename), 25 MB upload cap, library refs restricted to real audio/video,
path confined via is_valid_full_path. Adds is_audio_file + unit tests for the
sanitizer, mime guesser, and swap-root derivation.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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
Cover the properties that prevent mid-turn model swaps in llama-swap
exclusive mode: vision_model defaults to primary, cloned local client
mirrors the user-selected model, embeddings stay on their own slot.
Also test the content:null serialization for tool-calling messages.
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.
