feat: solution for 'Агент с RAG-памятью'
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2026-07-01 10:57:34 +03:00
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# 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)