"""
Configuration for the Agentic Memory demo.
Manages OpenSearch connection, memory container IDs, and model settings.
"""

import os
from dotenv import load_dotenv

load_dotenv()

# OpenSearch cluster
OPENSEARCH_ENDPOINT = os.getenv("OPENSEARCH_ENDPOINT", "")
AWS_REGION = os.getenv("AWS_REGION", "us-east-1")

# Bedrock model for agents
BEDROCK_MODEL_ID_FAST = os.getenv(
    "BEDROCK_MODEL_ID_FAST",
    "us.amazon.nova-micro-v1:0"
)
BEDROCK_MODEL_ID = os.getenv(
    "BEDROCK_MODEL_ID",
    "us.anthropic.claude-sonnet-4-6"
)

# Embedding model for memory containers
EMBEDDING_MODEL_ID = os.getenv("EMBEDDING_MODEL_ID", "")
EMBEDDING_DIMENSION = int(os.getenv("EMBEDDING_DIMENSION", "1024"))

# LLM model for memory processing (extraction/summarization)
MEMORY_LLM_MODEL_ID = os.getenv("MEMORY_LLM_MODEL_ID", "")

# Single memory container (holds all memory types: long-term, sessions, working, history)
CONTAINER_NAME = "product-search-agent"
CONTAINER_ID = os.getenv("MEMORY_CONTAINER_ID")

# Persona IDs
PERSONAS = {
    "sarah": "user1",
    "alex": "user2",
}

# Persona genders (used for gender_affinity matching in product personalization)
PERSONA_GENDERS = {
    "user1": "female",
    "user2": "male",
}