pplx embed v1 0.6b EU AI Act Compliance Profile

pplx-embed-v1 and pplx-embed-context-v1 are state-of-the-art text embedding models optimized for real-world, web-scale retrieval tasks.

• Use pplx-embed-v1 for independent text embedding (queries, documents, semantic search)
• Use pplx-embed-context-v1 for document chunks in RAG systems where surrounding context matters

[!IMPORTANT] pplx-embed-v1 and pplx-embed-context-v1 natively produce unnormalized int8-quantized embeddings. Ensure that you compare them via cosine similarity.

Models

| Model | Dimensions | Context | MRL | Quantization | Instruction | Pooling | |:-----:|:----------:|:-------:|:---:|:------------:|:-----------:|:-------:| | pplx-embed-v1-0.6B | 1024 | 32K | Yes | INT8/BINARY | No | Mean | | pplx-embed-v1-4B | 2560 | 32K | Yes | INT8/BINARY | No | Mean | | pplx-embed-context-v1-0.6B | 1024 | 32K | Yes | INT8/BINARY | No | Mean | | pplx-embed-context-v1-4B | 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

curl -X POST https://api.perplexity.ai/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      "Scientists explo

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