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"""
Centralized configuration helpers for the Mixedbread CVE RAG workflow.
"""
from __future__ import annotations
import os
from dataclasses import dataclass
from pathlib import Path
@dataclass
class Settings:
"""Container for frequently used paths and model configuration."""
project_root: Path
zip_path: Path
cve_root: Path
corpus_path: Path
chroma_dir: Path
chroma_collection: str
embed_model: str
rerank_model: str
hf_token: str # Optional, only needed for downloading models from HF Hub
embed_batch_size: int
device: str # "cpu" or "cuda" for GPU
def load_settings() -> Settings:
root = Path(__file__).resolve().parents[1]
data_root = root / "data" / "cvelistV5-main"
artifacts_dir = root / "rag_mixedbread" / "artifacts"
chroma_dir = root / "rag_mixedbread" / "index"
hf_token = os.environ.get("HF_API_TOKEN", "")
settings = Settings(
project_root=root,
zip_path=root / "testing" / "cvelistV5-main.zip",
cve_root=data_root,
corpus_path=artifacts_dir / "cve_corpus.jsonl",
chroma_dir=chroma_dir,
chroma_collection=os.environ.get("RAG_CHROMA_COLLECTION", "cve_chunks"),
embed_model=os.environ.get(
"RAG_EMBED_MODEL", "mixedbread-ai/mxbai-embed-large-v1"
),
rerank_model=os.environ.get(
"RAG_RERANK_MODEL", "mixedbread-ai/mxbai-rerank-base-v2"
),
hf_token=hf_token, # Optional, only needed for private models or rate-limited downloads
embed_batch_size=int(os.environ.get("RAG_EMBED_BATCH", "8")),
device=os.environ.get("RAG_DEVICE", "cpu"), # "cpu" or "cuda"
)
artifacts_dir.mkdir(parents=True, exist_ok=True)
chroma_dir.mkdir(parents=True, exist_ok=True)
return settings
__all__ = ["Settings", "load_settings"]
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