Model reference · open weights
langcache-embed is an open-weight embedding model from redis. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | redis |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 149M |
| Context | 8k tokens |
| Runs with | sentence-transformers |
| Based on | redis/langcache-embed-v1 |
| Released | 2025-05-21 |
| Popularity | 1k downloads / month |
| Licence | Unknown |
About
This is a sentence-transformers model finetuned from redis/langcache-embed-v1 on the triplet dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("redis/langcache-embed-v2")
# Run inference
sentences = [
'What are some examples of crimes understood as a moral turpitude?',
'What are some examples of crimes of moral turpitude?',
'What are some examples of crimes understood as a legal aptitude?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Dataset: triplet
Size: 36,864 training samples
Columns: anchor, positive, negative_1, negative_2, and negative_3
Samples:
| anchor | positive | negative_1 | negative_2 | negative_3 |
|---|---|---|---|---|
| Is life really what I make of it? | Life is what you make it? | Is life hardly what I take of it? | Life is not entirely what I make of it. | Is life not what I make of it? |
| When you visit a website, can a person running the website see your IP address? | Does every website I visit knows my public ip address? | When you avoid a website, can a person hiding the website see your MAC address? | When you send an email, can the recipient see your physical location? | When you visit a website, a person running the website cannot see your IP address. |
| What are some cool features about iOS 10? | What are the best new features of iOS 10? | iOS 10 received criticism for its initial bugs and performance issues, and some users found the redesigned apps less intuitive compared to previous versions. | What are the drawbacks of using Android 14? | iOS 10 was widely criticized for its bugs, removal of beloved features, and generally being a downgrade from previous versions. |
Loss: MatryoshkaLoss with these parameters:
{
"loss": "CachedMultipleNegativesRankingLoss",
"matryoshka_dims": [768,512,256,128,64],
"matryoshka_weights": [1,1,1,1,1],
"n_dims_per_step": -1
}
We encourage you to cite our work if you use our models or build upon our findings.
@inproceedings{langcache-embed-v1,
title = "Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data",
author = "Gill, Cechmanek, Hutcherson, Rajamohan, Agarwal, Gulzar, Singh, Dion",
month = "04",
year = "2025",
url = "https://arxiv.org/abs/2504.02268",
}
@inproceedings{reimers-2019-sentence-bert,
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys langcache-embed for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (langcache-embed below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"langcache-embed","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.