# ============================================================
# General & System Configurations
# ============================================================
project_name: "Emasters_Group-2_CapstoneProject"
seed: 42
device: "auto"  # Options: 'auto', 'cuda', 'mps', 'cpu'
dtype: "float16"  # Options: 'float16', 'bfloat16', 'float32'

# ============================================================
# Model & Tokenizer Configs
# ============================================================
model_name: "Qwen/Qwen2.5-Coder-0.5B-Instruct"
lora_adapter_path: "models/checkpoints/lora_finetuned"

# ============================================================
# Embedding Model Configs
# ============================================================
embedding_model: "BAAI/bge-small-en-v1.5"  # used only when embedding.source == "external"
embedding:
  source: "external"  # Options: 'code_lm' or 'external'
  external_model: "BAAI/bge-small-en-v1.5"
  max_length: 512
  batch_size: 32

# ============================================================
# Retrieval Settings (semantic + AST/structural hybrid search)
# ============================================================
retrieval:
  hybrid: true
  structural_weight_alpha: 0.7
  structural_language: "mongodb"
  topk_sweep: [1, 2, 3, 5, 8]

# ============================================================
# Large-LLM baseline (for "small code LM vs. LLM" comparison)
# ============================================================
llm_baseline:
  backend: "api"  # Options: 'local' or 'api'
  local_model: "Qwen/Qwen2.5-Coder-0.5B-Instruct"
  api_model: "gemini-3.6-flash"
  api_key_env: "GEMINI_API_KEY"
  max_new_tokens: 256
  temperature: 0.2

# ============================================================
# Dataset Paths
# ============================================================
data_paths:
  raw_dir: "data/raw"
  spider: "spider_real_execution_mongo.json"

# ============================================================
# FAISS Vector & AST Index Paths
# ============================================================
indices_paths:
  faiss_index: "data/indices/faiss_semantic.index"
  ast_store: "data/indices/ast_store.pkl"

# ============================================================
# Text Generation Parameters
# ============================================================
generation:
  max_length: 512
  temperature: 0.2
  top_k: 50
  top_p: 0.95
  num_return_sequences: 1

# ============================================================
# Evaluation Settings
# ============================================================
evaluation:
  device: "cpu"  # CodeBERTScore device
  bertscore_model: "microsoft/codebert-base"
  bertscore_local_path: "models/codebert-base"
  timeout_seconds: 3

# ============================================================
# LoRA Training Settings
# ============================================================
lora:
  r: 16
  alpha: 32
  dropout: 0.05
  batch_size: 1
  learning_rate: 0.0002
  epochs: 1
  output_dir: "models/checkpoints/lora_finetuned"
  trainable_fraction: 1.0  # Fraction of LoRA parameters to train (0.0 to 1.0)

# ============================================================
# Indexing Settings
# ============================================================
indexing:
  top_k: 3
  vector_dim: 1024  # Must match embedding model's output dimension

# ============================================================
# Outputs & Evaluation Paths
# ============================================================
outputs:
  logs_dir: "output/logs"
  metrics_path: "output/metrics/evaluation_metrics.json"
  plots_dir: "output/plots"
  generated_dir: "output/generated"