Instructions to use alirezaaminzadeh/enterprise-rag-studio with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use alirezaaminzadeh/enterprise-rag-studio with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("alirezaaminzadeh/enterprise-rag-studio", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
OrgMind RAG β Hybrid TF-IDF Retrieval Index
Aria AI Engineering Team Β· Space Β· Dataset
Model Description
Char_wb n-gram TF-IDF embedding index for bilingual (EN/FA) enterprise policy retrieval. Powers OrgMind RAG Studio hybrid search (cosine similarity + keyword reranking).
Intended Use
- Internal knowledge base Q&A with citations
- HR / HSE / Procurement / IT policy lookup
- Not a replacement for legal, HR, or safety professional advice
Training
python scripts/build_dataset.py
python scripts/train_index.py
Metrics
See dataset/eval_results.json and dataset/benchmark_report.json after python scripts/benchmark.py.
Limitations
- Extractive answers only in default pipeline
- Coverage limited to indexed documents
- TF-IDF may underperform dense embeddings on paraphrased queries
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