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hsd2514andZohaib Hassnain
8a4ebafb9a
* fix(context): honor explicit causal edges in decision tracing trace_decision_causality() inferred causes purely from shared NER entities plus timestamp ordering, so relationships recorded through add_causal_relationship() never affected the trace. When entity extraction returned nothing, trace_decision_chain() came back empty even though an explicit CAUSED edge was stored in the graph. Traverse the explicit CAUSED/INFLUENCED/PRECEDENT_FOR edges first, since they are the ground truth the caller recorded, and keep the entity and timestamp inference as an additive fallback for pairs with no explicit link. Edges whose source has no decision record (for example a graph restored via from_dict) are skipped so a stale edge cannot abort the trace. analyze_decision_influence() now reports explicitly linked decisions as direct influence rather than surfacing them only as indirect, and no longer lists the same decision under both direct and indirect. Closes #975 * fix(context): address review feedback on causal edge tracing Follow-up to the explicit causal edge fix, covering the issues raised in review. A stored edge weight of 0.0 was coerced to the 1.0 default by a truthiness check, inflating confidence_decay in the causal chain report. add_edge() is public and can create causal edges with any weight, so use an explicit None check instead. Explicit causes were collected into a dict keyed by source_id, so multiple causal edges between the same pair of decisions overwrote each other and only the last was traced. Collect every edge instead, keeping a separate set of source ids for the entity fallback exclusion. Cycle detection used a single traversal-wide visited set, so a decision reached through one branch became unreachable through another and branching graphs silently lost valid chains. Detect cycles per path instead; max_depth still bounds the traversal. Build a reverse index of causal edges once per call rather than scanning the edge list at every visited node, and use edge_type_index in the influence analysis. The three causal edge types are now a shared constant. Adds regression tests for zero weights, parallel edges, branching graphs and cycle termination. * fix(context): bound causal trace and report truncation Per-path cycle detection keeps branching graphs correct but makes the traversal combinatorial in max_depth: on a densely connected graph the number of distinct causal paths grows by roughly the branching factor per level, so a raised max_depth could return hundreds of thousands of chain reports and take seconds of CPU. Add a max_chains bound, defaulting to 10000. Rather than dropping chains silently, which is the exact failure this fix set out to eliminate, the traversal stops at the bound and appends a {"truncated": True, ...} marker so callers can always tell the trace is incomplete. A warning is logged with the same detail. Pass max_chains=None for the previous unbounded behaviour. Graphs that fit within the bound are unaffected. --------- Co-authored-by: Zohaib Hassnain <109234410+ZohaibHassan16@users.noreply.github.com>