README.md

metadata

license: apache-2.0
pipeline_tag: feature-extraction
tags:
  - feature-extraction
  - sentence-similarity
  - mteb
  - sentence-transformers
language:
  - multilingual

pplx-embed-1: Diffusion-LM for Dense and Contextual Retrieval

pplx-embed-1 and pplx-embed-1-context are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks.

• Use pplx-embed-1 for independent text embedding (queries, documents, semantic search)
• Use pplx-embed-1-context for document chunks in RAG systems where surrounding context matters

Models

Model	Dimensions	Context	MRL	Quantization	Instruction	Pooling
pplx-embed-1-0.6B	1024	32K	Yes	INT8/BINARY	No	Mean
pplx-embed-1-4B	2560	32K	Yes	INT8/BINARY	No	Mean
pplx-embed-1-context-0.6B	1024	32K	Yes	INT8/BINARY	No	Mean
pplx-embed-1-context-4B	2560	32K	Yes	INT8/BINARY	No	Mean

_{All models are built on diffusion continued pre-trained Qwen3 at Perplexity AI.}

_{Many modern embedding models rely on instruction tuning, where users prepend an instruction string to the text being embedded. This can yield a 2%-3% lift on benchmarks, but it also introduces prompt-selection overhead and can make indexing pipelines brittle (small instruction changes can shift embedding space). We deliberately avoid this requirement: you can embed the text you want to index directly, without having to choose or maintain an instruction prefix.}

Usage

Via API (Standard Embeddings)

curl -X POST https://api.perplexity.ai/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "texts": [
      "Scientists explore the universe driven by curiosity.",
      "Children learn through curious exploration.",
      "Historical discoveries began with curious questions.",
      "Animals use curiosity to adapt and survive.",
      "Philosophy examines the nature of curiosity.",
    ],
    "model": "pplx-embed-1-4B"
  }'

Via API (Contextualized Embeddings)

curl -X POST https://api.perplexity.ai/v1/contextualizedembeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": [
      [
        "Curiosity begins in childhood with endless questions about the world.",
        "As we grow, curiosity drives us to explore new ideas and challenge assumptions.",
        "Scientific breakthroughs often start with a simple curious question."
      ],
      [
        "The curiosity rover explores Mars, searching for signs of ancient life.",
        "Each discovery on Mars sparks new questions about our place in the universe."
      ]
    ],
    "model": "pplx-embed-1-context-4B"
  }'

Using Transformers

from transformers import AutoModel

model = AutoModel.from_pretrained(
    "perplexity-ai/pplx-embed-1-0.6B",
    trust_remote_code=True
)
texts = [
    "Scientists explore the universe driven by curiosity.",
    "Children learn through curious exploration.",
    "Historical discoveries began with curious questions.",
    "Animals use curiosity to adapt and survive.",
    "Philosophy examines the nature of curiosity.",
]

embeddings = model.encode(texts) # Shape: (5, 1024)

model_ctx = AutoModel.from_pretrained(
    "perplexity-ai/pplx-embed-1-context-0.6B",
    trust_remote_code=True
)

doc_chunks = [
    [
        "Curiosity begins in childhood with endless questions about the world.",
        "As we grow, curiosity drives us to explore new ideas.",
        "Scientific breakthroughs often start with a curious question."
    ],
    [
        "The curiosity rover explores Mars searching for ancient life.",
        "Each discovery on Mars sparks new questions about the universe."
    ]
]
# Returns list of numpy arrays (one per document)
# embeddings[0].shape = (3, 1024), embeddings[1].shape = (2, 1024)
embeddings = model_ctx.encode(doc_chunks)

Using SentenceTransformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "perplexity-ai/pplx-embed-1-0.6B",
    trust_remote_code=True
)

texts = [
    "Scientists explore the universe driven by curiosity.",
    "Children learn through curious exploration.",
    "Historical discoveries began with curious questions.",
    "Animals use curiosity to adapt and survive.",
    "Philosophy examines the nature of curiosity.",
]

embeddings = model.encode(texts) # Shape: (5, 1024)

model_ctx = SentenceTransformer(
    "perplexity-ai/pplx-embed-1-context-0.6B",
    trust_remote_code=True
)

doc_chunks = [
    [
        "Curiosity begins in childhood with endless questions about the world.",
        "As we grow, curiosity drives us to explore new ideas.",
        "Scientific breakthroughs often start with a curious question."
    ],
    [
        "The curiosity rover explores Mars searching for ancient life.",
        "Each discovery on Mars sparks new questions about the universe."
    ]
]
# Returns list of numpy arrays (one per document)
# embeddings[0].shape = (3, 1024), embeddings[1].shape = (2, 1024)
embeddings = model_ctx.encode(doc_chunks)

Technical Details

For comprehensive technical details and evaluation results, see our paper on arXiv.

Contact

• Website: https://perplexity.ai
• API Support: api-support@perplexity.ai