config.py

"""
Configuration settings for the Executive Education RAG Chatbot.
PLEASE CONSIDER READING THE 'docs/configuration_system_documentation.md' TO PROPERLY USE THE NEW CONFIGURATION SYSTEM.
"""
# ========================================= General Configuration ===========================================

# A list of ISO 639 language codes. Defines a list of languages in which 
# the application can operate. Defaults to ['en', 'de'].
AVAILABLE_LANGUAGES = ['en', 'de']

# A string representing a path (relative to the project root or absolute) to the directory
# where the data output files such as scraping or document processing outputs will be stored.
DATA_PATH = 'data'

# A string representing a path (relative to the project root or absolute) to the directory
# where the loging files will be stored.
LOGS_PATH = 'logs'

# =================================== Conversation State Configuration ======================================

# A boolean; either True or False. Enables the collection of user preferences 
# during conversation to avoid repetetive questions. Defaults to True. 
TRACK_USER_PROFILE = True 

# An integer. Defines the amount of user messages after which the language 
# of the conversation will be locked. If set to 0, the language will not be locked.
LOCK_LANGUAGE_AFTER_N_MESSAGES = 3

# An integer. Sets the maximum amount of conversation turns as the sum of user queries
# and agent responses. The conversation ends after the maximum turns amount is reached.
MAX_CONVERSATION_TURNS = 20

# ============================================ LLM Configuration ============================================

# A string, either 'openai', 'groq', 'open_router' or 'ollama' (local).
# Defines the main model provider for the application.
LLM_PROVIDER = 'openai' 

# A string. Defines the model that will be used by the application agents. 
OPENAI_MODEL = 'gpt-5.1'
# GROQ_MODEL = 
# OLLAMA_MODEL = 
# OPEN_ROUTER_MODEL = 

# ==================================== Weaviate Database Configuration ======================================

# A boolean; either True or False. 
# Defines whether the database is set as a local instance (via Docker container), 
# or as a cloud service. More information on https://docs.weaviate.io/weaviate.
WEAVIATE_IS_LOCAL = False

# A string. Defines the name of the colletions stored in the database.
# For each available language a new collection will be created
# with set name <WEAVIATE_COLLECTION_BASENAME>_<LANGUAGE>.
WEAVIATE_COLLECTION_BASENAME = 'hsg_rag_content'

# A string; either 'manual', 'filesystem' (local instance), 's3' (AWS).
# Defines the service for storing the database backups.
# More information on https://docs.weaviate.io/deploy/configuration/backups.
WEAVIATE_BACKUP_METHOD = 'manual'

# A string representing a path in the system where backups will be stored 
# only if WEAVIATE_BACKUP_METHOD is set to 'manual'.
BACKUPS_PATH = 'data/database/backups'

# A string representing a system path where collection properties will be stored.
PROPERTIES_PATH = 'data/database'

# A string representing a system path where property strategies will be stored.
# More information on property strategies in the documentation.
STRATEGIES_PATH = 'data/database/strategies'

# An integer. Defines a connection timeout to the cloud weaviate service (in seconds). 
# Defaults to 90.
WEAVIATE_INIT_TIMEOUT = 90

# An integer. Defines the query response time limit upon querying the database (in seconds). 
# Defaults to 60.
WEAVIATE_QUERY_TIMEOUT = 60

# An integer. Defines the chunk insertion time limit when importing new chunks to database (in seconds).
# Defaults to 600
WEAVIATE_INSERT_TIMEOUT = 600

# ========================================== Cache Configuration ============================================

# A string; either 'local', 'cloud' (Redis) or 'dict'. Defaults to 'cloud'.
# Sets the default cache mode. More information on cache modes in documentation.
CACHE_MODE = 'cloud'

# An integer. Sets the reset time (time to live) in seconds for the cache storage.
# The cache storage will be cleared upon reset time exceedance.
# Defaults to 86400 seconds (24 hours).
CACHE_TTL = 86400 

# An integer. Maximum amount of cached messages that will be held in the cache storage.
# Defaults to 1000.
CACHE_MAX_SIZE = 1000 

# A string. Defines the IP adress to access the local cache storage. Defaults to 'localhost'.
CACHE_LOCAL_HOST = 'localhost'

# An integer. Defines the port for accessing the local cache storage. Defaults to 6379.
CACHE_LOCAL_PORT = 6379 

# ===================================== Data Processing Configuration =======================================

# A string representing the name of an embeding model for embedding generation.
# The parameter MAX_TOKENS must match this model's maximum token amount.
EMBEDDING_MODEL = 'sentence-transformers/multi-qa-mpnet-base-dot-v1'

# A float in range from 0 to 1. Sets the threshold for english language in the language detector.
# If the language detection certanty is lower than the threshold, the English language will be returned.
LANG_AMBIGUITY_THRESHOLD = 0.6

# An integer. Defines the maximum amount of tokens pro single chunk.
MAX_TOKENS = 512

# An integer. Defines the amount of overlapping tokens between chunks to keep the context. 
CHUNK_OVERLAP = 100


# An integer representing seconds. Defines the maximum waiting time for the target server 
# responses during the scraping procedures.
SCRAPING_TIMEOUT = 30 

# An integer. Defines the maximum amount of additional tries that will be performed 
# if the initial request to the server failed.
SCRAPING_MAX_RETRIES = 3

# An integer representing seconds. Defines the waiting interval between two server calls.
# This value might be overwritten by the delay set by the server.
SCRAPING_CRAWL_DELAY = 1

# An integer. Defines the backoff base value for retries with exponential backoff.
# The higher is the number, the longer is the waiting interval between subsequent retries going to be.
SCRAPING_BACKOFF_RATE = 1.25

# A list of string URLs. Defines the starting points for the website scraping.
SCRAPING_TARGET_URLS = [
    # 'https://emba.unisg.ch/',       # EMBA HSG root
    'https://embax.ch/',            # emba X root
]

# Scraping Priority Interval in days
SCRAPING_PRIO_INTERVAL = {
    "high": 1,
    "medium": 7,
    "low": 30
}

# ======================================== Agent Chain Configuration ========================================

# A boolean; either True or False. Activates the response quality evaluation procedure
# for agentic responses. Defaults to True.
ENABLE_EVALUATE_RESPONSE_QUALITY = True

# A float in range from 0 to 1. Sets the treshold value for the quality evaluation.
# The fallback mechanism will be activated if the quality of the agentic response 
# is lower than the confidence threshold.
CONFIDENCE_THRESHOLD = 0.6

# An integer. Defines the amount of chunks that should be retrieved from the database 
# upon querying by subagents during conversation. Defaults to 4.
TOP_K_RETRIEVAL = 4  

# An integer. Sets the amount of model invocation retries after which the fallback model 
# will be invoked. Defaults to 3.
MODEL_MAX_RETRIES = 3

# An integer. Sets the maximum amount of words in the response from the lead agent.
MAX_RESPONSE_WORDS_LEAD = 100 

# An integer. Sets the maximum amount of words in the response for subagents.
MAX_RESPONSE_WORDS_SUBAGENT = 200

# A boolean; either True or False. If response chunking is enabled, long responses 
# from the lead agent will be split and retuned through multiple conversation turns.
ENABLE_RESPONSE_CHUNKING = True

# ========================================== Notification Configuration =====================================

NOTIFY_ENABLE_EMAIL_ALERTS= True
NOTIFY_ENABLE_SLACK_ALERTS = True

# ===========================================================================================================