Sentence Similarity
sentence-transformers
ONNX
Safetensors
OpenVINO
modernbert
loss:OnlineContrastiveLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use redis/langcache-embed-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use redis/langcache-embed-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("redis/langcache-embed-v1") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
Added ArXiv paper reference
Browse files
README.md
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### BibTeX
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#### Redis Langcache-embed Models
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#### Sentence Transformers
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```bibtex
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### BibTeX
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#### Redis Langcache-embed Models
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```bibtex
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@inproceedings{langcache-embed-v1,
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title = "Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data",
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author = "Gill, Cechmanek, Hutcherson, Rajamohan, Agarwal, Gulzar, Singh, Dion",
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month = "04",
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year = "2025",
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url = "https://arxiv.org/abs/2504.02268",
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}
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```
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#### Sentence Transformers
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```bibtex
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