Model reference · open weights

langcache-embed

Available as managed deployment Embeddings redis Embeddings 1 variants 1k dl/mo

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 byredis
TypeEmbedding models
TaskEmbeddings
Parameters (lead)149M
Context8k tokens
Runs withsentence-transformers
Based onredis/langcache-embed-v1
Released2025-05-21
Popularity1k downloads / month
LicenceUnknown

About

What langcache-embed is

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.

Read the full model card

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: redis/langcache-embed-v1
  • Maximum Sequence Length: 8192 tokens
  • Output Dimensionality: 768 dimensions
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • triplet

Model Sources

Full Model Architecture

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})
)

Usage

Direct Usage (Sentence Transformers)

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]

Training Details

  • Dataset: triplet

  • Size: 36,864 training samples

  • Columns: anchor, positive, negative_1, negative_2, and negative_3

  • Samples:

    anchorpositivenegative_1negative_2negative_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
    }
    

Evaluation

Citation

Redis Langcache-embed Models

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",
}
Sentence Transformers
@inproceedings{reimers-2019-sentence-bert,
    

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

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.

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