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https://github.com/agentscope-ai/ReMe.git
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db4763aead
- Add CompressorStep (reme/steps/evolve/compressor.py) for direct LLM text compression with optional query-guided relevance filtering - Extend search_v2_step to support query-aware and query-independent session transcript compression via _compress injected kwargs - Refactor _source_format.py: split into render_chunk_entries + join_chunk_entries; session chunks now render line-aligned with L<n>: prefixes for verbatim/compressed parity - Add JOB_TOOLS and INJECTED_JOB_KWARGS to BaseAgenticAnswerStep for per-subclass tool and parameter injection - LmeAgenticAnswerStep injects _search._compress payload to enable query-aware compression during benchmark evaluation - Record compression ablation results in result-longmemeval.md - Add unit tests for CompressorStep and search compression paths
63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
"""``vector_search_step`` — plain vector search with tool_context dedup."""
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from typing import Final
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from ._dedup import _ToolContextDedupMixin
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from ._source_format import ALL_RETURNED_MESSAGE, NO_RESULTS_MESSAGE, join_chunk_entries
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from ._source_format import merge_session_chunk_intervals, render_chunk_entries
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from ..base_step import BaseStep
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from ...components import R
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_MAX_CANDIDATES: Final = 200
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_CANDIDATE_MULTIPLIER: Final = 10
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@R.register("vector_search_step")
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class VectorSearchStep(_ToolContextDedupMixin, BaseStep):
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"""Vector-only search: retrieve, filter by min_score, dedup by tool_context, truncate."""
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def __init__(self, *args, seen_ttl_hours: float = 24, include_source: bool = True, **kwargs):
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super().__init__(*args, **kwargs)
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self.seen_ttl_hours = seen_ttl_hours
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self.include_source = include_source
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async def execute(self):
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assert self.context is not None
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query: str = (self.context.get("query", "") or "").strip()
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limit: int = int(self.context.get("limit") or 5)
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min_score: float = float(self.context.get("min_score") or 0.0)
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tool_context_id: str = (self.context.get("tool_context_id", "") or "").strip()
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if not query:
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self.context.response.success = False
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self.context.response.answer = "Error: query cannot be empty"
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return self.context.response
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assert limit > 0, f"limit must be positive, got {limit}"
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candidates = min(_MAX_CANDIDATES, max(1, limit * _CANDIDATE_MULTIPLIER))
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results = await self.file_store.vector_search(query, candidates, {})
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self.logger.info(f"[{self.name}] query={query!r} candidates={candidates} hits={len(results)}")
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if min_score > 0.0:
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results = [chunk for chunk in results if chunk.score >= min_score]
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pre_dedup_count = 0
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if tool_context_id:
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pre_dedup_count = len(results)
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results, _ = self._dedupe_tool_context(results, tool_context_id, limit)
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else:
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results = results[:limit]
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dialog_dir = self.config_value("dialog_dir")
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entries = render_chunk_entries(
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merge_session_chunk_intervals(results, dialog_dir),
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dialog_dir,
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include_source=self.include_source,
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)
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self.context.response.answer = join_chunk_entries(entries)
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if not results:
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self.context.response.answer = ALL_RETURNED_MESSAGE if pre_dedup_count > 0 else NO_RESULTS_MESSAGE
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self.context.response.metadata["results"] = [
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c.model_dump(exclude_none=True, exclude={"embedding"}) for c in results
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]
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return self.context.response
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