perplexity-ai/pplx-embed-v2-context-9b

pplx-embed-v2-context: Multimodal Contextual Embeddings

pplx-embed-v2-context-9b is a state-of-the-art vision-language embedding model for retrieval over text, images, and visual documents. It supports contextual and non-contextual embeddings: In contextual mode, the model embeds chunks of a document with the surrounding context in view. In non-contextual mode, text inputs are embedded in isolation. Its 1024-dimensional MRL prefix shares an embedding space with pplx-embed-v2-context-0.6b. Documents can be embedded with one model and queried with another.

This model also supports contextual chunking for visual inputs. Chunked images can be retrieved via standard dense ANN indices. Chunking images can substantially improve retrieval performance.

For benchmark results and details on training, refer to our technical report.

When encoding chunks in contextual mode, the document chunks should be non-overlapping.

Queries and documents are encoded with different methods: use encode_queries for queries and encode for document chunks. The model is trained with separate query and document prefixes, and encoding queries with encode silently degrades retrieval quality.

Like pplx-embed-context-v1, the model natively produces unnormalized int8-quantized embeddings. Compare embeddings with cosine similarity, or pass normalize_embeddings=True and use the dot product.

Text Embeddings

Requires transformers>=5.4.0, torch, numpy, safetensors, tqdm, Pillow, and torchvision. Install flash-linear-attention and causal-conv1d for the fast linear-attention path. The model uses custom code, so load it with trust_remote_code=True.

from transformers import AutoModel

model = AutoModel.from_pretrained(
    "perplexity-ai/pplx-embed-v2-context-9b",
    trust_remote_code=True,
).to("cuda")

# Two documents, consisting of several chunks
documents = [
    [
        "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.",
    ],
]

# Contextual mode
# One (chunk_count, 2048) array per document:
# chunk_embeddings[0].shape == (3, 2048), chunk_embeddings[1].shape == (2, 2048)
chunk_embeddings = model.encode(documents, normalize_embeddings=True)

# Each query is a single-chunk row.
queries = [["What drives scientific breakthroughs?"]]
query_embeddings = model.encode_queries(queries, normalize_embeddings=True)

scores = chunk_embeddings[0] @ query_embeddings[0][0]

# Non-contextual mode: flatten chunks into a batch of singletons
chunk_embeddings = model.encode(
    [[chunk] for doc in documents for chunk in doc],
    normalize_embeddings=True,
)

encode(documents, ...) and encode_queries(queries, ...) accept:

Argument	Default	Description
batch_size	32	Documents (or queries) per forward pass
normalize_embeddings	False	L2-normalize outputs
convert_to_numpy	True	Return NumPy arrays; False returns CPU tensors
show_progress_bar	False	Show a progress bar
device	None	Move the model to this device before encoding

The text API does not truncate inputs. Split or truncate documents that exceed the configured input length before encoding. To obtain one embedding per document, pass each document as a single-chunk list.

Matryoshka dimensions

The model was trained with Matryoshka losses at 1024 and 2048 dimensions. To use 1024-dimensional embeddings, take the first 1024 values of each unnormalized embedding and normalize afterwards:

import numpy as np

emb = model.encode(documents)  # unnormalized int8 values
emb_1024 = [e[:, :1024] / np.linalg.norm(e[:, :1024], axis=-1, keepdims=True) for e in emb]

Apply the same truncation and normalization to image embeddings and query embeddings. Use 1024 dimensions when comparing against embeddings from the 0.6B model. Other truncation sizes were not trained.

Image Embeddings

Use encode_images() to embed document pages or other images as contextual chunks. Pass a list of PIL images; each image produces one embedding per horizontal band. Use encode_queries() for the corresponding text queries.

import torch
from PIL import Image
from transformers import AutoModel

model = AutoModel.from_pretrained(
    "perplexity-ai/pplx-embed-v2-context-9b",
    trust_remote_code=True,
    dtype=torch.bfloat16,
    attn_implementation="sdpa",
).to("cuda").eval()

with Image.open("page.png") as page:
    image_embeddings = model.encode_images(
        [page.convert("RGB")],
        chunks=8,
        min_pixels=16384,
        max_pixels=1800000,
        normalize_embeddings=True,
    )

query_embeddings = model.encode_queries(
    [["What is the company's annual revenue?"]],
    normalize_embeddings=True,
)
page_score = (image_embeddings[0] @ query_embeddings[0][0]).max()

Each image is encoded in one contextual forward pass, with its merged visual tokens divided into whole-row horizontal bands. Native three-axis MRoPE and image wrappers are preserved. Separator tokens between bands use the midpoint of the adjacent rows and the horizontal center of the image as their spatial position. Only visual content is pooled; prefixes, wrappers, and separators are excluded.

encode_images() returns one (actual_chunks, 2048) array per page. The actual chunk count is capped at the number of merged image rows. Use cosine similarity per chunk and the maximum chunk score for page retrieval, as shown above with normalized vectors.

Image argument	Default	Description
chunks	8	Requested number of horizontal bands per page
min_pixels	16384	Processor minimum image pixel budget
max_pixels	1800000	Processor maximum image pixel budget
batch_size	1	Images per forward pass

The image API also accepts normalize_embeddings, convert_to_numpy, show_progress_bar, and device with the defaults listed above. prepare_images(images, chunks=8, min_pixels=16384, max_pixels=1800000) exposes the input tensors, chunk spans, masks, and position IDs without running the model.

Citation

@article{eslami2026contextual,
  title   = {Contextual Embeddings Beyond the Gold Chunk},
  author  = {Eslami, Sedigheh and Krimmel, Markus and Barber, William and Chaffin, Antoine and Schall, Maximilian and Golde, Jonas and Milliken, Louis and Glazkova, Ekaterina and Bykov, Denis and Wang, Bo},
  journal = {arXiv preprint arXiv:2610.38854},
  year    = {2026},
  doi     = {10.48550/arXiv.2610.38854},
  url     = {https://arxiv.org/abs/2610.38854}
}