Split conversation into ~50K token chunks (configurable via
scoring_chunk_tokens in config) for prompt_logprobs calls.
Each chunk ends at an assistant message boundary. Avoids the
~40GB logprobs tensor allocation that OOM'd on full contexts.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Status bar shows "scoring 3/7..." during scoring. Debug pane logs
per-memory importance and top-5 response breakdowns. F10 context
screen shows which memories were important for each assistant
response as drilldown children (← memory_key (score)).
Added important_memories_for_entry() to look up the matrix by
conversation entry index.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
/score snapshots the context and client, releases the agent lock,
runs scoring in background. Only one score task at a time
(scoring_in_flight flag). Results stored on Agent and shown on
the F10 context debug screen with importance scores per memory.
ApiClient derives Clone. ContextState derives Clone.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
score_memories() drops each memory from the context one at a time,
runs prompt_logprobs against the full conversation, and builds a
divergence matrix: memories × responses.
Row sums = memory importance (for graph weight updates)
Column sums = response memory-dependence (training candidates)
Uses vLLM's prompt_logprobs to check "would the model have said
this without this memory?" — one forward pass per memory, all
responses scored at once. ~3s per memory on B200.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
