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# =====================================================
# 1. Remove BOM from Python files
# =====================================================
for path in Path("app").rglob("*.py"):
text = path.read_text(encoding="utf-8-sig")
text = text.replace("\ufeff", "")
path.write_text(text, encoding="utf-8")
print("BOM cleanup completed.")
# =====================================================
# 2. Create graph-guided retriever
# =====================================================
Path("app/graph/graph_guided_retriever.py").write_text(r'''
from typing import Dict, Any, List, Optional
from app.graph.graph_context_service import build_graph_context_for_query
from app.storage.processed_storage import read_processed_chunks
def get_value(obj, key: str, default=None):
if isinstance(obj, dict):
return obj.get(key, default)
return getattr(obj, key, default)
def normalize_chunk_id(value) -> str:
if value is None:
return ""
return str(value)
def build_chunk_lookup(chunks: List[Any]) -> Dict[str, Any]:
lookup = {}
for index, chunk in enumerate(chunks):
chunk_id = (
get_value(chunk, "chunk_id")
or get_value(chunk, "id")
or f"chunk_{index}"
)
lookup[normalize_chunk_id(chunk_id)] = chunk
return lookup
def extract_text_preview(chunk, max_chars: int = 500) -> str:
text = (
get_value(chunk, "content")
or get_value(chunk, "text")
or ""
)
text = str(text).replace("\\n", " ").strip()
if len(text) > max_chars:
return text[:max_chars] + "..."
return text
def score_graph_chunks(
graph_context: Dict[str, Any]
) -> Dict[str, Dict[str, Any]]:
"""
Scores chunks using matched graph entities and relations.
Higher score means the chunk is more graph-relevant to the query.
"""
chunk_scores: Dict[str, Dict[str, Any]] = {}
matched_entities = graph_context.get("matched_entities", [])
matched_relations = graph_context.get("matched_relations", [])
for entity in matched_entities:
mention_count = entity.get("mention_count", 1) or 1
base_score = 3.0 + min(mention_count, 10) * 0.2
for chunk_id in entity.get("chunk_ids", []):
cid = normalize_chunk_id(chunk_id)
if not cid:
continue
if cid not in chunk_scores:
chunk_scores[cid] = {
"score": 0.0,
"matched_entities": [],
"matched_relations": []
}
chunk_scores[cid]["score"] += base_score
chunk_scores[cid]["matched_entities"].append(entity.get("name"))
for relation in matched_relations:
weight = relation.get("weight", 1) or 1
base_score = 2.0 + min(weight, 10) * 0.3
relation_label = (
f'{relation.get("source")} '
f'--{relation.get("relation_type")}--> '
f'{relation.get("target")}'
)
for chunk_id in relation.get("chunk_ids", []):
cid = normalize_chunk_id(chunk_id)
if not cid:
continue
if cid not in chunk_scores:
chunk_scores[cid] = {
"score": 0.0,
"matched_entities": [],
"matched_relations": []
}
chunk_scores[cid]["score"] += base_score
chunk_scores[cid]["matched_relations"].append(relation_label)
return chunk_scores
def graph_guided_retrieve(
document_id: Optional[str],
query: str,
graph_entity_limit: int = 8,
top_k: int = 5
) -> Dict[str, Any]:
"""
Returns graph-selected chunks for a query.
This is a debug/research endpoint.
It helps us inspect whether the graph is selecting useful evidence.
"""
if not document_id:
return {
"status": "failed",
"message": "document_id is required.",
"results": []
}
chunks = read_processed_chunks(document_id)
if chunks is None:
return {
"status": "failed",
"message": "No processed chunks found. Upload/process the document first.",
"document_id": document_id,
"results": []
}
graph_context = build_graph_context_for_query(
document_id=document_id,
query=query,
limit=graph_entity_limit
)
if not graph_context.get("graph_available"):
return {
"status": "failed",
"message": graph_context.get("reason", "Graph context not available."),
"document_id": document_id,
"graph_context": graph_context,
"results": []
}
chunk_lookup = build_chunk_lookup(chunks)
chunk_scores = score_graph_chunks(graph_context)
ranked = sorted(
chunk_scores.items(),
key=lambda item: item[1]["score"],
reverse=True
)
results = []
for rank, (chunk_id, info) in enumerate(ranked[:top_k], start=1):
chunk = chunk_lookup.get(chunk_id)
if chunk is None:
continue
results.append(
{
"rank": rank,
"chunk_id": chunk_id,
"graph_score": round(info["score"], 4),
"page_number": get_value(chunk, "page_number"),
"source_file_name": (
get_value(chunk, "source_file_name")
or get_value(chunk, "file_name")
or get_value(chunk, "filename")
),
"matched_entities": sorted(set(info["matched_entities"])),
"matched_relations": sorted(set(info["matched_relations"])),
"text_preview": extract_text_preview(chunk)
}
)
return {
"status": "success",
"document_id": document_id,
"query": query,
"graph_available": True,
"graph_entity_limit": graph_entity_limit,
"top_k": top_k,
"matched_entity_count": len(graph_context.get("matched_entities", [])),
"matched_relation_count": len(graph_context.get("matched_relations", [])),
"returned_chunks": len(results),
"matched_entities": graph_context.get("matched_entities", []),
"matched_relations": graph_context.get("matched_relations", []),
"results": results
}
''', encoding="utf-8")
# =====================================================
# 3. Patch main.py
# =====================================================
main_path = Path("app/main.py")
text = main_path.read_text(encoding="utf-8-sig")
text = text.replace("\ufeff", "")
if "from app.graph.graph_guided_retriever import graph_guided_retrieve" not in text:
text = "from app.graph.graph_guided_retriever import graph_guided_retrieve\n" + text
old_phases = [
"Phase 15 - Graph-Augmented Answering",
"Phase 14.1 - Graph Visualization UI",
"Phase 14 - Graph Foundation Entity Relation Extraction"
]
for old in old_phases:
text = text.replace(old, "Phase 16 - Graph-Guided Retrieval Debug Layer")
if "# Graph-guided retrieval endpoint" not in text:
text += '''
# Graph-guided retrieval endpoint
@app.get("/documents/{document_id}/graph/retrieve")
def graph_guided_retrieval_endpoint(
document_id: str,
query: str = Query(..., min_length=1),
graph_entity_limit: int = Query(8, ge=1, le=30),
top_k: int = Query(5, ge=1, le=20)
):
result = graph_guided_retrieve(
document_id=document_id,
query=query,
graph_entity_limit=graph_entity_limit,
top_k=top_k
)
if result.get("status") == "failed":
raise HTTPException(
status_code=400,
detail=result
)
return result
'''
main_path.write_text(text, encoding="utf-8")
print("Phase 16 graph-guided retrieval patch applied successfully.")
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