mirror of
https://github.com/RooVetGit/Roo-Code.git
synced 2026-09-05 08:10:14 +00:00
6fe86f074f
- Implement pluggable reranker architecture with HTTP API communication - Add LocalReranker implementation for self-hosted reranking services - Include reference Python reranker service with Docker support - Add comprehensive UI configuration options in settings - Implement proper error handling and fallback mechanisms - Add extensive test coverage for all reranking functionality - Support internationalization for all 18 languages - Update documentation with feature overview and usage instructions The extension now supports optional reranking of code search results through external services, improving search relevance while maintaining user privacy and control.
70 lines
No EOL
2.1 KiB
Python
70 lines
No EOL
2.1 KiB
Python
"""
|
|
Configuration constants and settings for the reranker service.
|
|
"""
|
|
|
|
import os
|
|
from typing import Optional
|
|
|
|
# Model configuration
|
|
DEFAULT_MODEL_NAME = "cross-encoder/ms-marco-MiniLM-L-6-v2"
|
|
MODEL_CACHE_DIR = os.getenv("MODEL_CACHE_DIR", "/app/.cache/models")
|
|
|
|
# API configuration
|
|
API_HOST = os.getenv("API_HOST", "0.0.0.0")
|
|
API_PORT = int(os.getenv("API_PORT", "8080"))
|
|
API_WORKERS = int(os.getenv("API_WORKERS", "1"))
|
|
|
|
# Reranking configuration
|
|
DEFAULT_MAX_RESULTS = 20
|
|
MAX_ALLOWED_RESULTS = 100
|
|
MIN_ALLOWED_RESULTS = 1
|
|
MAX_DOCUMENT_LENGTH = 10000 # Maximum characters per document
|
|
|
|
# Performance configuration
|
|
REQUEST_TIMEOUT = int(os.getenv("REQUEST_TIMEOUT", "30")) # seconds
|
|
BATCH_SIZE = int(os.getenv("BATCH_SIZE", "32"))
|
|
|
|
# Logging configuration
|
|
LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
|
|
LOG_FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
|
|
|
|
# CORS configuration
|
|
CORS_ORIGINS = os.getenv("CORS_ORIGINS", "http://localhost:*,http://127.0.0.1:*").split(",")
|
|
|
|
# Device configuration
|
|
FORCE_CPU = os.getenv("FORCE_CPU", "false").lower() == "true"
|
|
|
|
# Model warmup configuration
|
|
WARMUP_ON_START = os.getenv("WARMUP_ON_START", "true").lower() == "true"
|
|
|
|
# Health check configuration
|
|
HEALTH_CHECK_TIMEOUT = 5 # seconds
|
|
|
|
def get_model_name() -> str:
|
|
"""Get the model name from environment or use default."""
|
|
return os.getenv("MODEL_NAME", DEFAULT_MODEL_NAME)
|
|
|
|
def get_device() -> str:
|
|
"""Determine the device to use for model inference."""
|
|
if FORCE_CPU:
|
|
return "cpu"
|
|
|
|
try:
|
|
import torch
|
|
return "cuda" if torch.cuda.is_available() else "cpu"
|
|
except ImportError:
|
|
return "cpu"
|
|
|
|
def validate_config() -> None:
|
|
"""Validate configuration settings."""
|
|
if API_PORT < 1 or API_PORT > 65535:
|
|
raise ValueError(f"Invalid API_PORT: {API_PORT}")
|
|
|
|
if API_WORKERS < 1:
|
|
raise ValueError(f"Invalid API_WORKERS: {API_WORKERS}")
|
|
|
|
if REQUEST_TIMEOUT < 1:
|
|
raise ValueError(f"Invalid REQUEST_TIMEOUT: {REQUEST_TIMEOUT}")
|
|
|
|
if BATCH_SIZE < 1:
|
|
raise ValueError(f"Invalid BATCH_SIZE: {BATCH_SIZE}") |