spring.application.name=rag-service

# --- LLM Provider: Groq (OpenAI-compatible API) ---
spring.ai.openai.base-url=${SPRING_AI_OPENAI_BASE_URL:https://api.groq.com/openai/v1}
spring.ai.openai.api-key=${SPRING_AI_OPENAI_API_KEY:}
spring.ai.openai.chat.model=${SPRING_AI_OPENAI_CHAT_MODEL:llama-3.3-70b-versatile}

# --- Embedding model: use local pgvector with a lightweight model ---
# Groq does not provide an embedding endpoint, so we disable OpenAI embedding auto-config
# and rely on the pgvector store's existing embedding setup
spring.ai.openai.embedding.enabled=false

jwt.secret=${JWT_SECRET:ywfI6dBznYmHbokihB/OBzZz6E0Fj+6PiqrM8dQ5c3t0HeYarblCbOGM8vQtOt472AtQ+MsCH7OVIKHOzjrPsQ==}
jwt.expiration=103600000
spring.datasource.url=${SPRING_DATASOURCE_URL:jdbc:postgresql://localhost:5432/ragdb}
spring.datasource.username=${SPRING_DATASOURCE_USERNAME:postgres}
spring.datasource.password=${SPRING_DATASOURCE_PASSWORD:postgres}
logging.level.org.springframework.ai.chat.client.advisor=DEBUG
logging.level.org.springframework.web.servlet.mvc.method.annotation.RequestMappingHandlerMapping=DEBUG
logging.level.org.springframework.web=DEBUG
logging.level.org.flywaydb=DEBUG
logging.level.com.balex.rag.controller=DEBUG
app.document.chunk-size=200
server.compression.enabled=false
server.tomcat.connection-timeout=60000
spring.mvc.async.request-timeout=60000
end.points.users=/users
end.points.id=/{id}
end.points.all=/all
end.points.create=/create
end.points.userinfo=/userinfo
end.points.refresh.token=/refresh/token
end.points.auth=/auth
end.points.login=/login
end.points.register=/register
end.points.chat=/chat
end.points.entry=/entry
end.points.document=/documents
rag.rerank-fetch-multiplier=2
#Swagger
swagger.servers.first=http://localhost:8080
springdoc.swagger-ui.path=/swagger-ui.html
springdoc.api-docs.path=/v3/api-docs
#Kafka
spring.kafka.bootstrap-servers=${KAFKA_BOOTSTRAP_SERVERS:localhost:9092}
analytics.kafka.topic=user-events