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@cpdata cpdata commented Oct 14, 2025

Summary

  • extend the GraphDriver contract and every driver implementation to honor namespace and entity-label filters, wiring the behaviour through MemoryManager, the MeshMind client, and REST/gRPC search/list endpoints
  • build a developer-facing wiki under docs/ plus ENVIRONMENT_NEEDS.md and RESUME_NOTES.md, and update contributor instructions so documentation stays synchronized with code changes
  • expand regression coverage and examples so SQLite/fake drivers, service stubs, and the CLI demo all exercise the new filtering semantics

Testing

  • pytest

https://chatgpt.com/codex/tasks/task_b_68ee1c9ee92c83218cbbebce8b0667b8

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Comment on lines +54 to +55
subset = mems[: cfg.rerank_k]
reranked_subset = apply_reranker(query, subset, cfg.rerank_k, reranker)

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P1 Badge Use ranked results for reranker subset

The hybrid search path builds baseline from hybrid_search, but the reranker is invoked with mems[:cfg.rerank_k]. mems is the unranked, filtered input order, so the LLM reranker operates on arbitrary memories instead of the top-scoring candidates from the hybrid search. In practice the reranker can ignore the best matches and reorder unrelated items, degrading result quality. The reranker should consume baseline[:cfg.rerank_k] (the ranked list) before merging back into ordered.

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