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[Backend] Chunk the transcript by time window, sized from the provider's declared context #331

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opened 2026-08-25 20:38:50 +00:00 by claude-bot · 2 comments

claude-bot commented

2026-08-25 20:38:50 +00:00

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Severity: CRITICAL. Found in the August 2026 session lifecycle review (#319). First milestone of the summarisation re-architecture — roughly 70% of the benefit for 40% of the work.

The defect

summarise() sends the entire transcript in one prompt, with no chunking and no map-reduce anywhere in the path (audio_service.py:865-910). The budget does not fit the target workload:

A 3.5 h / 5-speaker transcript is ~36-80k tokens, centrally ~55k
The effective window is 65,536 tokens — llama.cpp launched --ctx-size 131072 --parallel 2 gives half the nominal size per slot
Generation shares that same slot, so a 55k prompt leaves ~10k for thinking plus output

It sometimes fits, barely, with no headroom, and a chatty table or a 4-hour session pushes it over. The design is out of budget at exactly the workload it was built for.

Real values are one grep away: audio_service.py:884 logs the character count, and anything at or above ~190 KB is at or over the slot budget.

Proposed fix

Split the transcript on timestamp boundaries into windows, with 1-2 lines of overlap, then map each window to a fixed mini-form (time_range, 4-8 bullet events with actors, NPCs introduced, decisions) and reduce once over the ordered mini-summaries with an explicit instruction to preserve the given order and time ranges.

Size the window from the provider's declared context, not a constant. This is the load-bearing design decision: it makes one code path correct on a 9B local model with a 65k slot and on a 1M-context hosted model, where the whole transcript collapses into a single window naturally. It ties directly to the provider abstraction in v4.2.0, which must declare a context limit per provider.

Chunking here is a context-management device, not an ordering device — the ordering guarantee comes from the beat validator in this milestone, not from the chunk boundaries. That distinction matters: naive chunking (summarise pieces to prose, ask a model to stitch them) genuinely does scramble chronology.

Acceptance criteria

Transcript is split on timestamp boundaries with configurable overlap
Window size is derived from the provider's declared context window, never hardcoded
A single-window path exists and is exercised when the transcript fits comfortably
Map calls run in parallel where the provider allows it
Reduce input carries explicit time ranges per window
Tests cover a transcript that fits in one window and one that needs twelve

**Severity: CRITICAL.** Found in the August 2026 session lifecycle review (#319). First milestone of the summarisation re-architecture — roughly 70% of the benefit for 40% of the work. ## The defect `summarise()` sends the **entire transcript in one prompt**, with no chunking and no map-reduce anywhere in the path (`audio_service.py:865-910`). The budget does not fit the target workload: - A 3.5 h / 5-speaker transcript is **~36-80k tokens, centrally ~55k** - The effective window is **65,536 tokens** — llama.cpp launched `--ctx-size 131072 --parallel 2` gives half the nominal size per slot - Generation shares that same slot, so a 55k prompt leaves ~10k for thinking plus output It sometimes fits, barely, with no headroom, and a chatty table or a 4-hour session pushes it over. The design is out of budget at exactly the workload it was built for. Real values are one grep away: `audio_service.py:884` logs the character count, and anything at or above ~190 KB is at or over the slot budget. ## Proposed fix Split the transcript on timestamp boundaries into windows, with 1-2 lines of overlap, then map each window to a fixed mini-form (`time_range`, 4-8 bullet events with actors, NPCs introduced, decisions) and reduce once over the ordered mini-summaries with an explicit instruction to preserve the given order and time ranges. **Size the window from the provider's declared context, not a constant.** This is the load-bearing design decision: it makes one code path correct on a 9B local model with a 65k slot and on a 1M-context hosted model, where the whole transcript collapses into a single window naturally. It ties directly to the provider abstraction in v4.2.0, which must declare a context limit per provider. Chunking here is a **context-management device, not an ordering device** — the ordering guarantee comes from the beat validator in this milestone, not from the chunk boundaries. That distinction matters: naive chunking (summarise pieces to prose, ask a model to stitch them) genuinely does scramble chronology. ## Acceptance criteria - [ ] Transcript is split on timestamp boundaries with configurable overlap - [ ] Window size is derived from the provider's declared context window, never hardcoded - [ ] A single-window path exists and is exercised when the transcript fits comfortably - [ ] Map calls run in parallel where the provider allows it - [ ] Reduce input carries explicit time ranges per window - [ ] Tests cover a transcript that fits in one window and one that needs twelve

claude-bot added this to the v4.0.0 - Session Pipeline Accuracy milestone

2026-08-25 20:38:50 +00:00

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2026-08-25 20:38:50 +00:00

rbrooks referenced this issue from a commit

2026-08-26 01:02:23 +00:00

feat(backend): give the pipeline a declared context window (#336, #337)

rbrooks referenced this issue from a commit

2026-08-26 01:02:23 +00:00

feat(backend): summarise long transcripts in ordered windows (#331, #338)

rbrooks referenced this issue from a commit

2026-08-26 01:30:27 +00:00

feat(backend): verify session summaries against the transcript (#332, #333, #334)

claude-bot commented

2026-08-28 16:38:37 +00:00

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Re-scoping one criterion, from the #336/#337/#338 pass.

~~Map calls run in parallel where the provider allows it~~ → belongs to #356 (v4.2.0), not here.

_summarise_chunked summarises windows sequentially, deliberately, and says why in the code: a local llama.cpp server has a small fixed slot count, so firing every window at once queues behind it anyway — or trips a hosted provider's rate limit. Doing this correctly needs to know how much concurrency a given provider allows, which is exactly what the provider abstraction is for. Building a fixed concurrency here would be a guess that is wrong on both self-hosted and hosted, in opposite directions.

The rest of this issue's criteria are unaffected. Noting it rather than silently leaving the box unticked.

Also relevant to "window size is derived from the provider's declared context window, never hardcoded": since ede1ac2 (#336) every call site passes the real window from llm_cfg.context_tokens, and since c54d9af (#337) a caller that declares nothing gets a conservative default rather than no budgeting at all. Note that extraction windows are separately capped by _EXTRACTION_CONTEXT_CAP — that is #423's measured result (a 131k model handed a whole session finds fewer events, not more), and it is a deliberate ceiling on top of the derived size, not a hardcoding of it.

**Re-scoping one criterion, from the #336/#337/#338 pass.** - [ ] ~~Map calls run in parallel where the provider allows it~~ → **belongs to #356 (v4.2.0), not here.** `_summarise_chunked` summarises windows sequentially, deliberately, and says why in the code: a local llama.cpp server has a small fixed slot count, so firing every window at once queues behind it anyway — or trips a hosted provider's rate limit. Doing this correctly needs to know *how much* concurrency a given provider allows, which is exactly what the provider abstraction is for. Building a fixed concurrency here would be a guess that is wrong on both self-hosted and hosted, in opposite directions. The rest of this issue's criteria are unaffected. Noting it rather than silently leaving the box unticked. Also relevant to *"window size is derived from the provider's declared context window, never hardcoded"*: since `ede1ac2` (#336) every call site passes the real window from `llm_cfg.context_tokens`, and since `c54d9af` (#337) a caller that declares nothing gets a conservative default rather than no budgeting at all. Note that extraction windows are separately capped by `_EXTRACTION_CONTEXT_CAP` — that is #423's measured result (a 131k model handed a whole session finds fewer events, not more), and it is a deliberate ceiling on top of the derived size, not a hardcoding of it.

rbrooks referenced this issue from a commit

2026-08-28 17:01:54 +00:00

fix(backend): close two attribution gaps found by verifying #342 and #344

rbrooks referenced this issue from a commit

2026-08-28 18:46:37 +00:00

fix(backend): stop the chunker recursing forever, and brief it on what it holds

claude-bot commented

2026-08-28 19:15:39 +00:00

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Verified; three gaps fixed in ed441c8 and dd8f305. One of them was a worker-killer.

Criteria

Transcript is split on timestamp boundaries with configurable overlap — splitting was real; overlap was not configurable. Fixed.
Window size is derived from the provider's declared context, never hardcoded — true on process_audio, false on the other call site. Fixed.
A single-window path exists and is exercised when the transcript fits — met.
~~Map calls run in parallel where the provider allows it~~ — re-scoped to #356, per the comment above.
Reduce input carries explicit time ranges per window — met.
Tests cover a transcript that fits in one window and one that needs twelve — "twelve" was met at splitter level only. Fixed.

The fold recursion was unbounded

When the reduce prompt still exceeds the budget, _summarise_chunked folds its own notes down by calling itself. That terminates only if each pass actually shrinks the text, and a model is under no obligation to shorten what it is given. A model answering a condense request with something the same length recursed until the OOM killer took the worker — leaving the session in "processing" forever with nothing logged.

Found by writing the first test for that branch, which killed the test runner. The branch has existed since this issue shipped and had no coverage: the map-reduce test asserts >= 2 windows, which a two-window transcript satisfies. Now bounded at three folds, then the notes are sampled to fit — sample_evenly rather than truncation, because head-truncation drops the end of the session, which is the part a GM most wants (#340).

I was wrong about the window reaching every call site

My earlier comment here said "since ede1ac2 (#336) every call site passes the real window from llm_cfg.context_tokens". That was wrong, and the miss was the one that mattered.

select_canonical_name (routers/sessions.py) called summarise() with no context_tokens. With window = 0, the fits_in_context branch is skipped entirely and the whole transcript goes in one prompt — precisely the defect this issue exists to prevent. #336 threaded the window through every generate_structured_text caller and missed this one, because it reaches the provider through summarise instead. Fixed in dd8f305.

Worth noting what made it invisible: three test doubles built the LLM config as a SimpleNamespace without context_tokens, so adding the read surfaced as a 500 from the router, not a quiet failed status — unlike the ~20 doubles in ede1ac2, where a task-level except swallowed the same AttributeError and recorded a wrong status instead of an error.

Overlap

_WINDOW_OVERLAP_LINES was a module constant no caller, env var or setting could reach — so "configurable overlap" was not met. Now a parameter with a QB_WINDOW_OVERLAP_LINES override, the same shape as QB_EXTRACTION_CONTEXT_CAP and for the same reason: the useful value moves with the model and should be settled with the #349 harness rather than by argument.

Tests

"…and one that needs twelve" is now end-to-end rather than splitter-level, asserting every window's notes reach reduce in order — losing one loses that stretch of the session silently. Plus the fold recursion, and overlap at 0, default and 4.

Closing.

**Verified; three gaps fixed in `ed441c8` and `dd8f305`.** One of them was a worker-killer. ## Criteria - [x] **Transcript is split on timestamp boundaries with configurable overlap** — splitting was real; **overlap was not configurable**. Fixed. - [x] **Window size is derived from the provider's declared context, never hardcoded** — true on `process_audio`, **false on the other call site**. Fixed. - [x] **A single-window path exists and is exercised when the transcript fits** — met. - [ ] ~~Map calls run in parallel where the provider allows it~~ — re-scoped to **#356**, per the comment above. - [x] **Reduce input carries explicit time ranges per window** — met. - [x] **Tests cover a transcript that fits in one window and one that needs twelve** — "twelve" was met at splitter level only. Fixed. ## The fold recursion was unbounded When the reduce prompt still exceeds the budget, `_summarise_chunked` folds its own notes down by calling itself. That terminates **only if each pass actually shrinks the text**, and a model is under no obligation to shorten what it is given. A model answering a condense request with something the same length recursed until the OOM killer took the worker — leaving the session in "processing" forever with nothing logged. Found by writing the first test for that branch, which **killed the test runner**. The branch has existed since this issue shipped and had no coverage: the map-reduce test asserts `>= 2` windows, which a two-window transcript satisfies. Now bounded at three folds, then the notes are sampled to fit — `sample_evenly` rather than truncation, because head-truncation drops the end of the session, which is the part a GM most wants (#340). ## I was wrong about the window reaching every call site My earlier comment here said *"since `ede1ac2` (#336) every call site passes the real window from `llm_cfg.context_tokens`"*. That was wrong, and the miss was the one that mattered. `select_canonical_name` (`routers/sessions.py`) called `summarise()` with **no `context_tokens`**. With `window = 0`, the `fits_in_context` branch is skipped entirely and the whole transcript goes in one prompt — precisely the defect this issue exists to prevent. #336 threaded the window through every `generate_structured_text` caller and missed this one, because it reaches the provider through `summarise` instead. Fixed in `dd8f305`. Worth noting what made it invisible: three test doubles built the LLM config as a `SimpleNamespace` without `context_tokens`, so adding the read surfaced as a **500 from the router**, not a quiet `failed` status — unlike the ~20 doubles in `ede1ac2`, where a task-level `except` swallowed the same `AttributeError` and recorded a wrong *status* instead of an error. ## Overlap `_WINDOW_OVERLAP_LINES` was a module constant no caller, env var or setting could reach — so "configurable overlap" was not met. Now a parameter with a `QB_WINDOW_OVERLAP_LINES` override, the same shape as `QB_EXTRACTION_CONTEXT_CAP` and for the same reason: the useful value moves with the model and should be settled with the #349 harness rather than by argument. ## Tests "…and one that needs twelve" is now end-to-end rather than splitter-level, asserting every window's notes reach reduce **in order** — losing one loses that stretch of the session silently. Plus the fold recursion, and overlap at 0, default and 4. Closing.

claude-bot closed this issue

2026-08-28 19:16:17 +00:00

rbrooks referenced this issue from a commit

2026-09-01 20:19:00 +00:00

fix(llm): check prompt consumption on every provider, not just Ollama (#351)

claude-bot referenced this issue

2026-09-01 20:19:28 +00:00

ASR and LLM provider contracts, and a truncation check that was only wired to one provider (#350, #351) #483

claude-bot referenced this issue

2026-09-01 20:29:59 +00:00

[Backend] Define the LLM provider contract with declared context window and schema output #351

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backend

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v4.0.0 - Session Pipeline Accuracy

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rbrooks/Quest-Board#331

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Delete branch "%!s()"

Deleting a branch is permanent. Although the deleted branch may continue to exist for a short time before it actually gets removed, it CANNOT be undone in most cases. Continue?

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