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>
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.
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>
Rewind & Regenerate resends the identical question, so both branches'
first child matched and every picker option read the same. Snippets now
walk the branches' visible messages (tool scaffolding skipped) in
lockstep and preview the first position where the contents diverge; a
branch that runs out beforehand falls back to its own last message.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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>
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>
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>
Preparation for a second LLM backend (OpenRouter) and hybrid vision-local /
chat-remote mode. Shared wire types (ChatMessage, Tool, ToolCall, etc.) move
into a new src/ai/llm_client.rs and are re-exported from ollama.rs so
existing imports keep working. OllamaClient now implements LlmClient.
No behavior change; callers still hold the concrete OllamaClient. Caller
migration to Arc<dyn LlmClient> is deferred to the PR that wires hybrid
backend routing.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Cameron CordesandClaude Opus 5.5
CameronandClaude Opus 4.7