Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Sentence Transformers is a Python library for using and training embedding and reranker models for applications like retrieval augmented generation, semantic search, and more. With the v6.0 update, it gains a fourth model type: MultiVectorEncoder, for ColBERT-style late interaction retrieval. Any PyLate checkpoint and any Stanford-NLP ColBERT checkpoint loads straight into it, and colpali-engine models for visual document retrieval can be used too, through the same familiar API you already use for dense, sparse, and reranker models.

Where a regular embedding model compresses a whole text into one vector, a multi-vector model keeps one vector per token and scores query against document with the MaxSim operator. That preserves token-level matching information that a single vector has to average away, which usually means stronger retrieval at the cost of a bigger index. It's also the state of the art for visual document retrieval, where a text query is matched against page images directly, with no OCR step in between.

In this blogpost, we'll show you how to use these models: loading the various checkpoint formats, encoding and scoring, plugging them into a search stack, running them on page images, and keeping the index affordable. Everything below runs on a plain pip install -U sentence-transformers.

Table of Contents

• What are Multi-Vector Models?
  • The MaxSim Operator
  • What You Gain, and What It Costs
• Installation
• Loading a Model
  • Inspecting What a Checkpoint Configured
• Encoding Queries and Documents
• Scoring with MaxSim
  • Score Magnitude and MeanMaxSim
• Semantic Search
• Retrieve and Rerank
• Indexing
• Visual Document Retrieval
• Audio Retrieval
• Video Retrieval
• Interpretability
• Token Pooling
• Speeding Up Inference
• Evaluating a Model
• Coming from PyLate or colpali-engine
• Supported Models
• Acknowledgements
• Additional Resources

What are Multi-Vector Models?

A dense embedding model reads a text and returns a single fixed-size vector. Everything the model noticed has to fit in those 384, 768, or 1024 numbers, and similarity is one dot product between two such summaries. This works remarkably well, but the compression is lossy in a specific way: a rare entity, an exact identifier, or one crucial clause in a long passage all have to compete for room in the same vector. A query with several requirements at once runs into the same wall. For "green sofa with wooden legs and rounded cushions", a single vector has to blend all four into one point, so a green sofa with the wrong legs ends up sitting close to the one you actually asked for.

A multi-vector model (also called a late-interaction or ColBERT-style model, after the ColBERT paper) skips that compression. It runs the same transformer, but instead of pooling the token embeddings into one vector, it projects each token embedding down to a small dimension (classically 128) and keeps all of them. A 9-token document becomes a 9x128 matrix, not a 1x128 vector.

The interaction between query and document is then deferred until scoring time, which is where the name "late interaction" comes from. A cross-encoder interacts early: both texts go through the model together, which is accurate but leaves nothing to precompute, since every document has to be re-encoded for each new query. A bi-encoder, which is what the dense embedding model above is, barely interacts at all (one dot product between two finished summaries), and that is exactly what lets you encode a collection once and query it fast. Late interaction sits in between: documents are still encoded independently and can be indexed offline, but scoring compares every query token against every document token, which leaves far more room for the two to interact.

The MaxSim Operator

Scoring uses MaxSim: for each query token, take its highest similarity against any document token, then sum those maxima across the query.

$$\text{MaxSim}(Q,D)=\sum _{Q_i\in Q}\underset{D_j\in D}{\max }{Q}_i\cdot D_j\backslash text\{MaxSim\}(Q,\; D)\; =\; \backslash sum\_\{Q\_i\; \backslash in\; Q\}\; \backslash max\_\{D\_j\; \backslash in\; D\}\; Q\_i\; \backslash cdot\; D\_j$$

Because the token embeddings are L2-normalized, each of those dot products is a cosine similarity in [-1, 1], so the whole sum lands within [-num_query_tokens, num_query_tokens].

You can read the operator as a soft alignment: every query token points at the one document token that best explains it, and the score is how well the document supports the query overall.

The alignment doesn't have to be lexical, since the token embeddings are contextualized. Encode "Where do penguins live?" against "Penguins inhabit Antarctica." with lightonai/mLateOn and the query token live finds its best match on inhabit at 0.94, a word it shares no characters with! That is the thing lexical retrieval cannot do, BM25 and its relatives need the term itself, so synonyms and paraphrases slip past them. Dense embedding models bridge that gap as well, of course. What late interaction adds is that it does so without giving up the other direction: when an exact match is what matters (a product code, a surname, a function name), MaxSim still has that token sitting there on its own, where a single-vector model had to average it in with everything else. It isn't one-to-one either, since several query tokens routinely settle on the same document token.

What You Gain, and What It Costs

You gain retrieval quality, particularly on queries where one specific piece of a document is what makes it relevant, on multi-requirement queries like the sofa above where each requirement gets to find its own evidence, and on out-of-domain data where a dense model's compression was tuned for a different distribution. That compression is learned from the training queries, so the model learns to keep what they needed and drop everything else, which may include exactly what your production queries ask about. The effect grows with document length, since more text has to fit in the same fixed vector.

The cost is index size. One vector per token instead of one vector per document is a lot more vectors, only partly offset by the smaller dimension. Encoding 4,874 Natural Questions passages with lightonai/LateOn produced 608,414 token vectors, an average of 124.8 per passage:

That's about 42x the storage of the MiniLM index, or 62 KiB per passage. However, indexes are often compressed, e.g. the same 608,414 vectors take 92 MB as a fast-plaid index, since PLAID stores a centroid id plus a quantized residual per vector rather than the vector itself. For scale, a 4096-dimensional dense model like Qwen3-Embedding-8B would need about 80 MB for these same 4,874 passages, so a compressed multi-vector index sits in the same territory as the dense indexes people already run. Token Pooling cuts the vector count before any of that, and Retrieve and Rerank avoids building an index at all.

PyLate comes up throughout this post, so briefly: Sentence Transformers handled dense and sparse models but not late interaction, so LightOn built PyLate on top of it to close that gap, adding the training, inference, and retrieval pieces these models need. Much of what you'll load below was trained with it, and LightOn built an ecosystem around it too, including fast-plaid, the late-interaction index that turns up in Indexing. With v6.0 those capabilities live in Sentence Transformers itself.

With the tradeoff in mind, let's get a model running.

Installation

Multi-vector models work with a plain install:

pip install -U sentence-transformers

For ColPali-style visual document retrieval, you also need the image dependencies (see Installation for all extras, and Multimodal Embedding & Reranker Models for multimodal support in general):

pip install -U "sentence-transformers[image]"

Sentence Transformers v6.0 requires transformers v5.x, torch 2.2+, and huggingface-hub v1.x. If you pin any of those lower, plan the upgrade first. See the Migration Guide for the full list of breaking changes.

Loading a Model

Loading a multi-vector model looks exactly like loading any other Sentence Transformers model:

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("lightonai/LateOn")

To find models that work, look for the multi-vector and sentence-transformers tags on the Hub. Any model with those tags loads with the line above, whether it started life as a PyLate checkpoint, a Stanford-NLP ColBERT checkpoint, or a ColPali-family model for visual document retrieval. We're working through the ecosystem to get that tag onto every model that works, so the list keeps growing.

Underneath, MultiVectorEncoder reads each of the formats these checkpoints have been published in over the years, so PyLate and Stanford-NLP checkpoints load directly even where the tag hasn't been added yet:

from sentence_transformers import MultiVectorEncoder



model = MultiVectorEncoder("lightonai/LateOn")
model = MultiVectorEncoder("mixedbread-ai/mxbai-edge-colbert-v0-17m")
model = MultiVectorEncoder("LiquidAI/LFM2.5-ColBERT-350M", trust_remote_code=True)



model = MultiVectorEncoder("colbert-ir/colbertv2.0")
model = MultiVectorEncoder("answerdotai/answerai-colbert-small-v1")


model = MultiVectorEncoder("answerdotai/ModernBERT-base")

Visual document retrieval models are the exception. ColPali-family checkpoints ship in colpali-engine's own format, which carries no information Sentence Transformers can use, so each one needs a small configuration added to its repository before it loads. Most of that work is done and waiting to be merged. See Supported Models for the current state and how to load them today.

Inspecting What a Checkpoint Configured

Multi-vector models carry a handful of recipe knobs that differ per checkpoint: marker prefixes for queries and documents, length caps, whether queries are padded out with [MASK] tokens, and which tokens are skipped when scoring documents. All of them live in the module configs, so print(model) shows you exactly what you loaded. Here's the original ColBERTv2 checkpoint, which pads every query to exactly 32 tokens and truncates documents at 180:

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("colbert-ir/colbertv2.0")
print(model)
"""
MultiVectorEncoder(
  (0): Transformer({..., 'document_length': 180,
                    'query_expansion': {'strategy': 'fixed', 'attend': False, 'token': None, 'length': 32}})
  (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, ...})
  (2): MultiVectorMask({'skiplist_words': ['!', '"', '#', ...], 'skiplist_tasks': ['document'], ...})
  (3): Normalize({...})
)
"""
print(model.prompts)

That's the classic ColBERT pipeline: a Transformer producing contextualized token embeddings, a token-level Dense projecting each of them to 128 dimensions, a MultiVectorMask deciding which tokens count during scoring, and a token-level Normalize. Other checkpoints fill in different values. lightonai/GTE-ModernColBERT-v1 uses the same four modules with [Q] and [D] prompts, no query expansion, and caps of 48 and 300.

You rarely need to touch any of this, since every released checkpoint configures its own. It matters when you build a model from a bare backbone, which is covered in Creating Custom Models.

One value is worth checking against your own data, though. document_length truncates, so anything past it never reaches the index. For example, a 662-token passage through LateOn's cap of 300 comes back as 273 vectors, with the rest of the passage simply gone. Most of these checkpoints were trained on short passages, so if your chunks are longer than the cap, you can lift it for a single call with encode_document(..., processing_kwargs={"text": {"max_length": 512}}), keeping in mind that you would be running the model past the length it was trained on and that the index grows roughly in proportion. Multi-vector models tend to tolerate that well. On MLDR, a long-document retrieval benchmark, the multilingual siblings of the pair above show the gap clearly: mLateOn scores 77.92 against mDenseOn's 51.59.

Read the full original article:

HuggingFace Blog