Spaces:
Sleeping
Sleeping
File size: 1,779 Bytes
402298d 8fb6d95 402298d a59bb17 402298d b49feb6 a123e22 402298d 35eb91f b27db4d 402298d b49feb6 a123e22 402298d |
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 |
"""Configuration management for WorkWise backend"""
import os
from dotenv import load_dotenv
load_dotenv()
BASE_DIR = "/home/user/app"
DATA_DIR = os.path.join(BASE_DIR, "data")
os.makedirs(DATA_DIR, exist_ok=True)
class Settings:
"""Application settings loaded from environment variables"""
# Faiss (local) configuration
FAISS_INDEX_PATH: str = os.path.join(DATA_DIR, "faiss.index")
FAISS_PAYLOADS_PATH: str = os.path.join(DATA_DIR, "faiss_payloads.json")
# Qdrant Configuration
QDRANT_URL: str = os.getenv("QDRANT_URL", "http://localhost:6333")
QDRANT_API_KEY: str = os.getenv("QDRANT_API_KEY", "")
QDRANT_COLLECTION_NAME: str = os.getenv("QDRANT_COLLECTION_NAME", "jira_tickets")
# Hugging Face Configuration
HF_API_URL: str = os.getenv("HF_API_URL", "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1")
HF_TOKEN: str = os.getenv("HF_TOKEN", "")
# Embedding Model
#EMBEDDING_MODEL: str = os.getenv("EMBEDDING_MODEL", "sentence-transformers/all-MiniLM-L6-v2")
#EMBEDDING_MODEL: str = os.getenv("EMBEDDING_MODEL", "sentence-transformers/multi-qa-MiniLM-L6-cos-v1")
#EMBEDDING_MODEL: str = os.getenv("EMBEDDING_MODEL", "BAAI/bge-small-en-v1.5")
EMBEDDING_MODEL: str = os.getenv("EMBEDDING_MODEL", "intfloat/e5-large-v2")
# Server Configuration
HOST: str = os.getenv("HOST", "0.0.0.0")
PORT: int = int(os.getenv("PORT", 7860))
LOG_LEVEL: str = os.getenv("LOG_LEVEL", "debug")
# CORS
ALLOWED_ORIGINS: list = os.getenv("ALLOWED_ORIGINS", "http://localhost:5173").split(",")
# Vector Search
TOP_K: int = 5
SCORE_THRESHOLD: float = 0.0
VECTOR_SIZE = 1024 # Adjust based on embedding model used
settings = Settings()
|