pplx-embed-v1-late-0.6b

Name: pplx-embed-v1-late-0.6b
Author: perplexity-ai

by perplexity-ai

Late-interaction (ColBERT-style) embedding model from Perplexity AI

4.9Kdl/month

0.6Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

perplexity-ai/pplx-embed-v1-late-0.6b

Feature URI

mixpeek://text_extractor@v1/perplexity_pplx_embed_late_06b_v1

Overview

pplx-embed-v1-late is a 0.6B parameter late-interaction embedding model from Perplexity AI that uses ColBERT-style token-level representations with MaxSim scoring. Unlike dense single-vector embeddings, it produces 128-dimensional vectors for each token, enabling fine-grained matching that captures partial document relevance. It outperforms ColBERT-zero on BEIR (56.61 nDCG@10) and jina-colbert-v2 on MIRACL multilingual retrieval (66.62).

Architecture

Late-interaction architecture based on the pplx-embed-v1-0.6b backbone. Produces per-token 128-dimensional vectors instead of a single document vector. Scoring uses MaxSim: for each query token, find the maximum similarity to any document token, then sum across query tokens. This enables fine-grained partial matching that dense embeddings miss. Optimized CUDA and Metal kernels available for efficient scoring.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so pplx-embed-v1-late-0.6b runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
  "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      collection_id: "col_your_collection",
      documents: [
        {
          document_id: "asset-00412",
          // The vector name has to match a vector index on the collection.
          vectors: { "text-embedding": yourVector },
          payload: { source_key: "archive/2026/asset-00412" },
        },
      ],
    }),
  },
);

// Managed alternative, if this exact model is not the requirement:
// text_extractor@v1 runs intfloat/multilingual-e5-large-instruct
// (1024-d) over a bucket, with no inference of your own.