metadata
language: en
tags:
- neuro-symbolic
- search
- information-retrieval
license: apache-2.0
Neuro-Symbolic Search Model
A hybrid search system combining neural embeddings with symbolic reasoning.
Features
- 🧠 Neural semantic search using sentence transformers
- 🔧 Symbolic rule-based filtering
- 🚀 Fast and lightweight
- 📊 Supports metadata filtering
Quick Start
from model import SimpleNeuroSymbolicSearch
# Initialize
search = SimpleNeuroSymbolicSearch()
# Index documents
documents = ["Document 1", "Document 2", "Document 3"]
metadata = [
{'topic': 'ai', 'level': 'beginner'},
{'topic': 'ml', 'level': 'advanced'},
{'topic': 'ai', 'level': 'intermediate'}
]
search.index_documents(documents, metadata)
# Search with filters
results = search.search("artificial intelligence topic:ai", top_k=5)
for r in results:
print(f"{r['rank']}. {r['document']} (score: {r['score']:.3f})")
Search Syntax
- Simple search:
machine learning - With filters:
machine learning topic:ml level:beginner - Boolean operators:
data science topic:ml AND level:advanced
Model Details
- Base Model: all-MiniLM-L6-v2
- Embedding Dimension: 384
- Search Method: Cosine Similarity + Rule Filtering
Requirements
torch>=2.0.0
sentence-transformers>=2.2.0
transformers>=4.30.0
Citation
@misc{neuro-symbolic-search-2025,
title={Simple Neuro-Symbolic Search},
author={Your Name},
year={2025},
url={https://huggingface.co/YOUR_USERNAME/neuro-symbolic-search}
}