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reecall.ai
omghante/reecall.ai
reecall.ai is a feature extraction model from omghante. Use it when you need embeddings to search or compare text. The card lists the license as mit.
ReeCall.ai is a lightweight (~50K parameters) Graph Neural Network (ReeCall / ReeCallGNN) that learns file-level dependency embeddings for TypeScript/JavaScript codebases and retrieves relevant engineering context as…
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Public
Updated Aug 7, 2026
Repo size
672 KB
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0
Public
Repo makeup
Click a slice to open those files.
.pt111 KB · 96%
At a glance
- Task
- Feature Extraction
- License
- mit
- Access
- Public
- Created
- May 28, 2026
- Updated
- Aug 7, 2026
- SHA
- 87c7a9ba
Datasets
Model card
From the Hugging Face model README
ReeCall.ai — Checkpoint (v1)
ReeCall.ai is a lightweight (~50K parameters) Graph Neural Network (ReeCall / ReeCallGNN) that learns file-level dependency embeddings for TypeScript/JavaScript codebases and retrieves relevant engineering context as structured JSON.
Model Properties
| 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) |
Quick Usage
import torch
from reecall_ai import ReeCall
# Initialize ReeCall model
model = ReeCall(input_dim=128, hidden_dim=64, embed_dim=32)
# Load checkpoint weights
checkpoint = torch.load("model.pt", map_location="cpu", weights_only=True)
model.load_state_dict(checkpoint)
model.eval()
print("ReeCall model loaded successfully!")
Files in Repository
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.
Repository & Documentation
- GitHub Repository: https://github.com/om-ghante/reecall.ai
- License: MIT
- Task
- Feature Extraction
- License
- mit
- Languages
- en
- Created
- May 28, 2026
- Updated
- Aug 7, 2026