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Phase 1 (LLM Quality): - Add format=json to all Ollama payloads (obligation_extractor, control_generator, citation_backfill) - Add Chain-of-Thought analysis steps to Pass 0a/0b system prompts Phase 2 (Retrieval Quality): - Hybrid search via Qdrant Query API with RRF fusion + automatic text index (legal_rag.go) - Fallback to dense-only search if Query API unavailable - Cross-encoder re-ranking with BGE Reranker v2 (RERANK_ENABLED=false by default) - CPU-only PyTorch dependency to keep Docker image small Phase 3 (Data Layer): - Cross-regulation dedup pass (threshold 0.95) links controls across regulations - DedupResult.link_type field distinguishes dedup_merge vs cross_regulation - Chunk size defaults updated 512/50 → 1024/128 for new ingestions only - Existing collections and controls are NOT affected Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
38 lines
940 B
Python
38 lines
940 B
Python
"""
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Configuration for RAG Service
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"""
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from pydantic_settings import BaseSettings
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from typing import Optional
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class Settings(BaseSettings):
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"""Application settings loaded from environment variables."""
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# Service
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environment: str = "development"
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port: int = 8082
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# Qdrant
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qdrant_url: str = "http://localhost:6333"
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qdrant_collection: str = "legal_documents"
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# Ollama
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ollama_url: str = "http://localhost:11434"
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embedding_model: str = "bge-m3"
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llm_model: str = "qwen2.5:32b"
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# Document Processing
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# NOTE: Changed from 512/50 to 1024/128 for improved retrieval quality.
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# Existing collections (ingested with 512/50) are NOT affected —
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# new settings apply only to new ingestions.
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chunk_size: int = 1024
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chunk_overlap: int = 128
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# Legal Corpus
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corpus_path: str = "./legal-corpus"
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class Config:
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env_file = ".env"
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env_file_encoding = "utf-8"
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