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ReeCall / ReeCallGNN) that learns file-level dependency embeddings for TypeScript/JavaScript codebases and retrieves relevant engineering context as structured JSON.| Property | Detail |
|---|---|
| Architecture | MLP Encoder + 1-Hop Neighbor Mean Aggregation + Projection Head |
| Parameters | ~50,000 (CPU-friendly) |
| Input Feature Dim | 128 (structural metrics, language, file roles, symbol & path hashes) |
| Embedding Dim | 32 (L2-normalized for cosine similarity) |
| Training Objective | Co-import prediction via Contrastive Loss (margin=0.5) |
1import torch
2from reecall_ai import ReeCall
3
4# Initialize ReeCall model
5model = ReeCall(input_dim=128, hidden_dim=64, embed_dim=32)
6
7# Load checkpoint weights
8checkpoint = torch.load("model.pt", map_location="cpu", weights_only=True)
9model.load_state_dict(checkpoint)
10model.eval()
11
12print("ReeCall model loaded successfully!")model.pt: Trained PyTorch ReeCall model state dict (~200KB).file_embeddings.npy: Example 32-dim normalized file embeddings matrix.default.ini: Model & training hyperparameters config.