49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
import os
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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DATA_DIR = ROOT / "data"
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STATIC_DIR = ROOT / "static"
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EMBEDDING_MODEL = "BAAI/bge-small-zh-v1.5"
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COLLECTION_NAME = "knowledge_bge_small_zh_v15"
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CHUNK_SIZE = 360
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CHUNK_OVERLAP = 60
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DEFAULT_TOP_K = 4
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LOW_SCORE = 0.45
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os.environ.setdefault("ANONYMIZED_TELEMETRY", "False")
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os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
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def load_dotenv() -> None:
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path = ROOT / ".env"
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if not path.exists():
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return
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for raw in path.read_text(encoding="utf-8").splitlines():
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line = raw.strip()
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if not line or line.startswith("#") or "=" not in line:
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continue
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key, value = line.split("=", 1)
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key = key.strip()
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value = value.strip().strip('"').strip("'")
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if key and key not in os.environ:
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os.environ[key] = value
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load_dotenv()
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def llm_config() -> dict | None:
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key = os.getenv("OPENAI_API_KEY", "").strip()
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openai_base = os.getenv("OPENAI_BASE_URL", "").strip()
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if key and openai_base:
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model = os.getenv("OPENAI_MODEL", "gpt-4o-mini").strip() or "gpt-4o-mini"
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return {"provider": "openai", "key": key, "base": openai_base.rstrip("/"), "model": model}
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base = os.getenv("OLLAMA_BASE_URL", "http://10.7.9.3:11434").strip()
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if not base:
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return None
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model = os.getenv("OLLAMA_MODEL", "qwen3-vl:4b").strip() or "qwen3-vl:4b"
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return {"provider": "ollama", "key": "ollama", "base": base.rstrip("/"), "model": model}
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