Files
breakpilot-lehrer/klausur-service/backend/self_rag.py
Benjamin Admin bd4b956e3c [split-required] Split final 43 files (500-668 LOC) to complete refactoring
klausur-service (11 files):
- cv_gutter_repair, ocr_pipeline_regression, upload_api
- ocr_pipeline_sessions, smart_spell, nru_worksheet_generator
- ocr_pipeline_overlays, mail/aggregator, zeugnis_api
- cv_syllable_detect, self_rag

backend-lehrer (17 files):
- classroom_engine/suggestions, generators/quiz_generator
- worksheets_api, llm_gateway/comparison, state_engine_api
- classroom/models (→ 4 submodules), services/file_processor
- alerts_agent/api/wizard+digests+routes, content_generators/pdf
- classroom/routes/sessions, llm_gateway/inference
- classroom_engine/analytics, auth/keycloak_auth
- alerts_agent/processing/rule_engine, ai_processor/print_versions

agent-core (5 files):
- brain/memory_store, brain/knowledge_graph, brain/context_manager
- orchestrator/supervisor, sessions/session_manager

admin-lehrer (5 components):
- GridOverlay, StepGridReview, DevOpsPipelineSidebar
- DataFlowDiagram, sbom/wizard/page

website (2 files):
- DependencyMap, lehrer/abitur-archiv

Other: nibis_ingestion, grid_detection_service, export-doclayout-onnx

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-25 09:41:42 +02:00

39 lines
1.0 KiB
Python

"""
Self-RAG / Corrective RAG Module — barrel re-export.
All implementation split into:
self_rag_grading — document relevance grading, filtering, decisions
self_rag_retrieval — query reformulation, retrieval loop, info
IMPORTANT: Self-RAG is DISABLED by default for privacy reasons!
When enabled, search queries and retrieved documents are sent to OpenAI API.
Based on research:
- Self-RAG (Asai et al., 2023)
- Corrective RAG (Yan et al., 2024)
"""
# Grading: relevance, filtering, decisions, groundedness
from self_rag_grading import ( # noqa: F401
SELF_RAG_ENABLED,
OPENAI_API_KEY,
SELF_RAG_MODEL,
RELEVANCE_THRESHOLD,
GROUNDING_THRESHOLD,
MAX_RETRIEVAL_ATTEMPTS,
RetrievalDecision,
SelfRAGError,
grade_document_relevance,
grade_documents_batch,
filter_relevant_documents,
decide_retrieval_strategy,
grade_answer_groundedness,
)
# Retrieval: reformulation, loop, info
from self_rag_retrieval import ( # noqa: F401
reformulate_query,
self_rag_retrieve,
get_self_rag_info,
)