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# Configuration for the educational RAG agent
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# Adjust paths and model names as needed for your environment
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knowledge_base:
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data_dir: "data" # Directory containing .txt documents
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embedding_model: "all-MiniLM-L6-v2" # SentenceTransformer model for embeddings
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vector_store: "faiss" # Type of vector store (currently only FAISS supported)
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language_model:
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model_name: "gpt2" # Hugging Face model for generation
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max_length: 512 # Max token length for generated responses
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retrieval:
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top_k: 3 # Number of top passages to retrieve per query
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logging:
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level: "INFO" # Logging level (DEBUG, INFO, WARNING, ERROR)
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