mirror of
https://github.com/agentscope-ai/ReMe.git
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8465f6d06e
docs(protocol): add typed edge link protocol documentation Add comprehensive documentation for the link protocol supporting typed edges in body text. This includes specification for three legal inline forms (bare wikilink, line-level Dataview, inline-bracketed Dataview), predicate syntax rules, and the machine-managed Relations section convention for organizing discovered edges. fix(memory): update path reference from vault_root to working_dir Change the memory_create operation's path anchoring from vault_root to working_dir to maintain consistency with the current working directory configuration. refactor(components): remove edge_extractor module and simplify parsing Remove the edge_extractor component module entirely and inline edge extraction logic directly into LinkedFileParser using parse_wikilinks utility. This simplifies the architecture by eliminating the separate edge extraction component and delegating edge discovery to the maintainer's enrichment operations. feat(parser): update parse method signature and simplify edge extraction Modify LinkedFileParser to return (FileNode, list[FileChunk]) tuple instead of ParsedFile, remove dependency on BaseEdgeExtractor, and implement direct wikilink parsing from body text only. ```
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
"""Chunk-search helpers — small pure functions used by file_store backends.
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Pulled out of `BaseFileStore` so the abstract base only carries graph
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mechanics. Both `LocalFileStore` and `SqliteFileStore` consume these
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to score / filter candidate chunks.
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"""
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from __future__ import annotations
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from reme2.schema import ChunkFilter, FileChunk
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def keyword_score(query: str, text: str) -> float:
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"""Word-overlap score with phrase bonus. Range [0, 1].
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`match_count / n_words` baseline + 0.2 if the full query phrase
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appears verbatim. Returns 0 when no query word hits the text.
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"""
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words = query.split()
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if not words:
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return 0.0
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query_lower = query.lower()
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words_lower = [w.lower() for w in words]
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text_lower = text.lower()
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n_words = len(words)
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match_count = sum(1 for w in words_lower if w in text_lower)
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if match_count == 0:
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return 0.0
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base = match_count / n_words
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phrase_bonus = 0.2 if n_words > 1 and query_lower in text_lower else 0.0
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return min(1.0, base + phrase_bonus)
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def filter_chunks(
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chunks: list[FileChunk], chunk_filter: ChunkFilter | None,
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) -> list[FileChunk]:
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"""Restrict `chunks` to those whose path passes the (compiled) filter."""
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if chunk_filter is None or chunk_filter.resolved_paths is None:
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return chunks
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return [c for c in chunks if chunk_filter.match_path(c.path)]
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