# Configuration for the educational RAG agent
# Adjust paths and model names as needed for your environment

knowledge_base:
  data_dir: "data"          # Directory containing .txt documents
  embedding_model: "all-MiniLM-L6-v2"  # SentenceTransformer model for embeddings
  vector_store: "faiss"     # Type of vector store (currently only FAISS supported)

language_model:
  model_name: "gpt2"        # Hugging Face model for generation
  max_length: 512           # Max token length for generated responses

retrieval:
  top_k: 3                  # Number of top passages to retrieve per query

logging:
  level: "INFO"             # Logging level (DEBUG, INFO, WARNING, ERROR)