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b7d0804 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | from pathlib import Path
path = Path("app/graph/graph_context_service.py")
path.write_text(r'''
import re
from typing import Dict, Any, List, Optional
from app.graph.graph_storage import read_document_graph
STOPWORDS = {
"what", "is", "are", "the", "a", "an", "of", "to", "and", "or",
"in", "on", "for", "with", "from", "by", "how", "why", "explain",
"define", "meaning", "does", "do", "it", "this", "that"
}
def tokenize_query(query: str) -> List[str]:
words = re.findall(r"[a-zA-Z0-9_]+", (query or "").lower())
return [
word for word in words
if word not in STOPWORDS and len(word) > 1
]
def tokenize_entity_name(name: str) -> List[str]:
return re.findall(r"[a-zA-Z0-9_]+", (name or "").lower())
def entity_relevance_score(entity, query_terms: List[str]) -> float:
if not query_terms:
return 0.0
name_lower = entity.name.lower()
entity_id_lower = entity.entity_id.lower()
name_tokens = tokenize_entity_name(entity.name)
entity_id_tokens = tokenize_entity_name(entity.entity_id.replace("_", " "))
score = 0.0
for term in query_terms:
# Exact entity match
if term == name_lower or term == entity_id_lower:
score += 10.0
continue
# Token-level match. This prevents rag matching paragraph.
if term in name_tokens:
score += 6.0
continue
if term in entity_id_tokens:
score += 5.0
continue
# Only allow substring match for longer terms.
# Example: "retrieval" can match "retrieval-augmented generation".
# But short acronyms like rag/api/llm should not match inside random words.
if len(term) >= 4 and term in name_lower:
score += 2.0
if score > 0:
score += min(entity.mention_count, 10) * 0.15
return score
def build_graph_context_for_query(
document_id: Optional[str],
query: str,
limit: int = 8
) -> Dict[str, Any]:
if not document_id:
return {
"graph_available": False,
"reason": "No document_id provided.",
"matched_entities": [],
"matched_relations": [],
"context_text": ""
}
graph = read_document_graph(document_id)
if graph is None:
return {
"graph_available": False,
"reason": "Graph not built for this document.",
"matched_entities": [],
"matched_relations": [],
"context_text": ""
}
query_terms = tokenize_query(query)
scored_entities = []
for entity in graph.entities:
score = entity_relevance_score(entity, query_terms)
if score > 0:
scored_entities.append((score, entity))
scored_entities.sort(key=lambda item: item[0], reverse=True)
matched_entities = [
entity for score, entity in scored_entities[:limit]
]
matched_entity_ids = {
entity.entity_id for entity in matched_entities
}
matched_relations = []
for relation in graph.relations:
if (
relation.source_entity_id in matched_entity_ids
or relation.target_entity_id in matched_entity_ids
):
matched_relations.append(relation)
matched_relations = sorted(
matched_relations,
key=lambda relation: relation.weight,
reverse=True
)[:limit]
context_text = build_graph_context_text(
matched_entities=matched_entities,
matched_relations=matched_relations
)
return {
"graph_available": True,
"document_id": document_id,
"source_file_name": graph.source_file_name,
"query_terms": query_terms,
"matched_entities": [
{
"entity_id": entity.entity_id,
"name": entity.name,
"entity_type": entity.entity_type,
"mention_count": entity.mention_count,
"pages": entity.pages[:10],
"chunk_ids": entity.chunk_ids[:10]
}
for entity in matched_entities
],
"matched_relations": [
{
"relation_id": relation.relation_id,
"source": relation.source_name,
"relation_type": relation.relation_type,
"target": relation.target_name,
"weight": relation.weight,
"pages": relation.pages[:10],
"chunk_ids": relation.chunk_ids[:10]
}
for relation in matched_relations
],
"context_text": context_text
}
def build_graph_context_text(
matched_entities,
matched_relations
) -> str:
lines = []
if matched_entities:
lines.append("Relevant graph entities:")
for entity in matched_entities:
pages = ", ".join(str(page) for page in entity.pages[:5])
lines.append(
f"- {entity.name} ({entity.entity_type}), mentions={entity.mention_count}, pages={pages}"
)
if matched_relations:
lines.append("")
lines.append("Relevant graph relations:")
for relation in matched_relations:
lines.append(
f"- {relation.source_name} --{relation.relation_type}--> {relation.target_name} "
f"(weight={relation.weight})"
)
return "\n".join(lines).strip()
''', encoding="utf-8")
print("Fixed graph query matching. Short acronyms like RAG will no longer match inside words like Paragraph.")
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