Qdrant / constella-nano

constella-nano

constella-nano is a 34,540,672-parameter English query encoder for semantic search. It offers better retrieval quality than constella-zero while remaining much smaller than the Stella model used to encode documents.

It produces normalized 1024-dimensional vectors that search documents encoded by stella-en-400M-v5-doc-onnx . The same document index also works with the faster constella-zero query encoder.

Research preview: Native FastEmbed support currently requires the Constella preview branch shown below. The published evaluation is limited to the results described in this card. Full BEIR benchmarking is underway. Results from that broader evaluation are not included yet.

The Constella family

The name Constella combines "constellation" and "Stella." The document embeddings are the fixed stars, and the query encoder navigates their shared vector space.

Nano and Zero are swappable at query time. Both can search the same document index, so you can choose between them without re-encoding documents or rebuilding the collection. Their rankings differ: Nano has higher retrieval scores on the six reported datasets, while Zero is the faster option.

Installation

Native FastEmbed support is currently available from the Constella preview branch:

pip install "fastembed @ git+https://github.com/Dylancouzon/fastembed.git@constella-research-preview"
pip install qdrant-client

Usage

Encode documents once with the document model, then encode queries with constella-nano. The example below creates an in-memory Qdrant collection, but the vectors can be used with any vector database that supports cosine similarity.

from fastembed import TextEmbedding
from qdrant_client import QdrantClient, models

NAME = "Qdrant/constella-nano"
DOC_NAME = "Qdrant/stella-en-400M-v5-doc-onnx"

documents = [
    "mRNA vaccines deliver messenger RNA encoding a viral antigen.",
    "The Treaty of Westphalia ended the Thirty Years' War in 1648.",
]

document_model = TextEmbedding(DOC_NAME)

client = QdrantClient(":memory:")
client.create_collection(
    "documents",
    vectors_config=models.VectorParams(size=1024, distance=models.Distance.COSINE),
)
client.upsert(
    "documents",
    points=[
        models.PointStruct(id=i, vector=embedding.tolist(), payload={"text": text})
        for i, (text, embedding) in enumerate(
            zip(documents, document_model.embed(documents))
        )
    ],
)

query_model = TextEmbedding(NAME)
query_embedding = next(iter(query_model.embed(["how do mRNA vaccines work?"])))
results = client.query_points(
    "documents", query=query_embedding.tolist(), limit=2
).points

for result in results:
    print(result.score, result.payload["text"])

FastEmbed applies attention-masked mean pooling and L2 normalization. Do not use the document model as an unprompted query encoder. Use constella-nano, constella-zero, or Stella's prompted query path instead.

To switch from Nano to Zero without rebuilding the document index, change NAME to Qdrant/constella-zero .

How it works

Nano starts from BAAI/bge-small-en-v1.5 . Features from layers 12, 8, and 4 feed a learned linear projection into Stella's 1024-dimensional document space. The model was trained against frozen query embeddings from NovaSearch/stella_en_400M_v5 .

The model does not use a query prefix. Inputs are right-truncated at 512 tokens and dynamically padded to the longest input in each batch. The ONNX graph returns fp32 token embeddings; FastEmbed performs pooling and normalization.

Retrieval results

The table reports exact-search nDCG@10. ArguAna and FiQA are marked because the Stella teacher discloses training or evaluation contact with those datasets. Results on those two datasets should therefore be interpreted separately from the other four.

Note: Stella discloses training or evaluation contact with ArguAna and FiQA.

Nano passed the predefined superiority test against bge-small-en-v1.5 on both the four datasets without disclosed Stella contact and all six datasets. The mean nDCG@10 differences were +0.017648 and +0.027449, respectively.

Against LEAF asym, Nano passed the predefined superiority test across all six datasets with a +0.016181 mean nDCG@10 difference.

Query encoding cost

These measurements cover the query encoder only. They use batch size 1, four CPU threads, five warmups, and twenty synthetic 20-word queries in each of three fresh processes. They do not include vector search or end-to-end application latency.

Files

The repository contains a 137,802,369-byte fp32 ONNX graph plus its FastEmbed configuration and tokenizer files. The graph uses ONNX opset 17 and standard operators. It returns token embeddings; FastEmbed supplies pooling and normalization.

Training

The released checkpoint saw exactly 199,999,721 training examples in a 75% query and 25% document mix. Training data included queries from Amazon ESCI, HotpotQA, Mr. TyDi, NQ Open, SQuAD, TriviaQA, and PAQ, along with licensed documents and generated query forms. MS MARCO was used for validation only.

CC BY-SA sources retain the required attribution. PAQ is licensed under CC BY-SA 3.0.

Limitations

• The model is English-only and truncates inputs after 512 tokens.
• Document indexing still requires the 400M-parameter Stella document encoder.
• Stella discloses training or evaluation contact with ArguAna and FiQA, so the scores on those datasets are not treated as independent evidence.
• Nano is slower than constella-zero at query time.

License and provenance

The model is MIT licensed. It uses BAAI/bge-small-en-v1.5 at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a as its backbone and was distilled from NovaSearch/stella_en_400M_v5 at revision ffeb2b7ee715c226d4ffe5e4619f7dbb48624c20 . Both source models are MIT licensed.

The released ONNX graph has SHA-256 9ba0acf57b71dc31bc5512c5445078a797fa51cf3e85587d6b8a506bfc55dbc2 .