Expand optional dependencies and document provisioning workflows#9
Expand optional dependencies and document provisioning workflows#9
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| mems = _apply_filters(memories, namespace, entity_labels, cfg) | ||
| ranked = hybrid_search(query, mems, cfg) | ||
| # Return only Memory objects | ||
| return [m for m, _ in ranked] | ||
| baseline = [m for m, _ in ranked] | ||
| if not baseline: | ||
| return [] | ||
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| if reranker is None: | ||
| return baseline[: cfg.top_k] | ||
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| subset = mems[: cfg.rerank_k] | ||
| reranked_subset = apply_reranker(query, subset, cfg.rerank_k, reranker) | ||
| ordered: List[Memory] = [] |
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The rerank path slices the first rerank_k items from the filtered input (subset = mems[: cfg.rerank_k]). mems retains the caller’s original order and is never sorted by relevance, whereas baseline contains the hybrid‑search ordering. When the input sequence is not already sorted, the reranker never sees the actual top hybrid candidates, so high‑scoring memories can never be reranked to the top. This yields inconsistent results whenever callers pass memories in arbitrary order (e.g. from a database or API). The subset should be derived from the hybrid ranking (baseline[:cfg.rerank_k]) rather than the unsorted input.
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Summary
uv --system, and document the workflow across README and supporting guidesmeshmind/tests/docker/, and authoring a dedicated SETUP guideTesting
https://chatgpt.com/codex/tasks/task_b_68ee1c9ee92c83218cbbebce8b0667b8