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62516b8 | 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 | from __future__ import annotations
from src.agent.rag import MaterialRAGStore
from src.agent.types import GenerateType, SourceRef
def build_rag_context(
*,
rag_store: MaterialRAGStore,
user_id: str,
document_id: str,
filename: str,
file_type: str,
extracted_text: str,
generate_types: list[GenerateType],
) -> tuple[str, list[SourceRef], list[str]]:
warnings: list[str] = []
try:
chunk_count, index_warnings = rag_store.index_material(
user_id=user_id,
document_id=document_id,
filename=filename,
file_type=file_type,
text=extracted_text,
)
warnings.extend(index_warnings)
if chunk_count <= 0:
warnings.append(
"RAG indexing produced no chunks; using extracted text fallback."
)
return extracted_text, [], warnings
except Exception as exc:
warnings.append(f"RAG indexing failed; using extracted text fallback: {exc}")
return extracted_text, [], warnings
queries = build_rag_queries(
extracted_text,
generate_types=generate_types,
)
docs, retrieval_warnings = rag_store.retrieve_for_generation(
user_id=user_id,
document_id=document_id,
queries=queries,
)
warnings.extend(retrieval_warnings)
if not docs:
warnings.append("RAG retrieval returned no chunks; using extracted text fallback.")
return extracted_text, [], warnings
context = "\n\n".join(doc.page_content for doc in docs)
sources: list[SourceRef] = []
for doc in docs:
metadata = doc.metadata or {}
sources.append(
SourceRef(
chunk_id=metadata.get("chunk_id"),
source_id=metadata.get("document_id"),
excerpt=doc.page_content[:200],
)
)
return context, sources, warnings
def build_lkpd_rag_context(
*,
rag_store: MaterialRAGStore,
user_id: str,
document_id: str,
filename: str,
file_type: str,
extracted_text: str,
) -> tuple[str, list[SourceRef], list[str]]:
warnings: list[str] = []
try:
chunk_count, index_warnings = rag_store.index_material(
user_id=user_id,
document_id=document_id,
filename=filename,
file_type=file_type,
text=extracted_text,
)
warnings.extend(index_warnings)
if chunk_count <= 0:
warnings.append(
"RAG indexing produced no chunks; using extracted text fallback."
)
return extracted_text, [], warnings
except Exception as exc:
warnings.append(f"RAG indexing failed; using extracted text fallback: {exc}")
return extracted_text, [], warnings
queries = build_lkpd_rag_queries(extracted_text)
docs, retrieval_warnings = rag_store.retrieve_for_generation(
user_id=user_id,
document_id=document_id,
queries=queries,
)
warnings.extend(retrieval_warnings)
if not docs:
warnings.append("RAG retrieval returned no chunks; using extracted text fallback.")
return extracted_text, [], warnings
context = "\n\n".join(doc.page_content for doc in docs)
sources: list[SourceRef] = []
for doc in docs:
metadata = doc.metadata or {}
sources.append(
SourceRef(
chunk_id=metadata.get("chunk_id"),
source_id=metadata.get("document_id"),
excerpt=doc.page_content[:200],
)
)
return context, sources, warnings
def build_rag_queries(
extracted_text: str,
*,
generate_types: list[GenerateType],
) -> list[str]:
topic_hint = " ".join(extracted_text.split()[:40])
queries = [f"konsep utama materi {topic_hint}"]
if "summary" in generate_types:
queries.append(f"ringkasan konsep utama materi {topic_hint}")
if "mcq" in generate_types:
queries.append(
f"fakta penting dan konsep untuk kuis pilihan ganda {topic_hint}"
)
if "essay" in generate_types:
queries.append(f"pemahaman mendalam untuk soal essay {topic_hint}")
return queries
def build_lkpd_rag_queries(extracted_text: str) -> list[str]:
topic_hint = " ".join(extracted_text.split()[:40])
return [
f"konsep utama dan tujuan pembelajaran materi {topic_hint}",
f"langkah kegiatan praktikum atau aktivitas pembelajaran {topic_hint}",
f"indikator penilaian dan rubrik tugas untuk materi {topic_hint}",
]
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