feat(perplexity): add embedding support for pplx-embed-v1 models (#22610 )

* feat: add Perplexity embedding support (pplx-embed-v1)

Add support for Perplexity AI's embedding models via the LLM HTTP handler:

Models:
- pplx-embed-v1-0.6b (1024 dims, 32K context, $0.004/1M tokens)
- pplx-embed-v1-4b (2560 dims, 32K context, $0.03/1M tokens)

Implementation:
- PerplexityEmbeddingConfig in litellm/llms/perplexity/embedding/
- Registered in ProviderConfigManager, __init__.py lazy imports, main.py dispatch
- Model pricing added to model_prices_and_context_window.json
- Supports dimensions and encoding_format parameters
- Uses base_llm_http_handler.embedding() pattern

Tests:
- 19 unit tests covering transformation, params, URLs, provider config, model info

Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>

* docs: add Perplexity AI embeddings documentation

- Create providers/perplexity_embedding.md with SDK and proxy usage examples
- Convert Perplexity from flat doc to category in sidebars.js
- Category includes existing chat/responses doc + new embeddings doc
- Covers pplx-embed-v1-0.6b and pplx-embed-v1-4b models
- Documents supported parameters (dimensions, encoding_format)
- Includes proxy config and curl examples

Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>

* fix: decode Perplexity base64_int8 embeddings to OpenAI-format float arrays

Perplexity returns embeddings as base64-encoded signed int8 values by default,
not float arrays like OpenAI. This commit adds decoding in
transform_embedding_response so the proxy returns standard OpenAI-compatible
float arrays (normalized to [-1, 1]).

- Added _decode_base64_embedding() static method
- Handles both base64 strings (decoded) and float lists (passthrough)
- Added 3 new tests for base64 decoding + passthrough

Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>

---------

Co-authored-by: Cursor Agent <cursoragent@cursor.com>
Co-authored-by: Ishaan Jaff <ishaan-jaff@users.noreply.github.com>

2026-03-02 17:37:50 -08:00

3.2 KiB

Raw Blame History

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

Perplexity Embeddings

https://docs.perplexity.ai/docs/embeddings/quickstart

LiteLLM supports Perplexity's pplx-embed embedding models for web-scale text retrieval.

API Key

# env variable
os.environ['PERPLEXITYAI_API_KEY']

Sample Usage - Embedding

from litellm import embedding
import os

os.environ['PERPLEXITYAI_API_KEY'] = ""

response = embedding(
    model="perplexity/pplx-embed-v1-0.6b",
    input=["good morning from litellm"],
)
print(response)

Setup config.yaml

model_list:
  - model_name: pplx-embed-v1-0.6b
    litellm_params:
      model: perplexity/pplx-embed-v1-0.6b
      api_key: os.environ/PERPLEXITYAI_API_KEY
  - model_name: pplx-embed-v1-4b
    litellm_params:
      model: perplexity/pplx-embed-v1-4b
      api_key: os.environ/PERPLEXITYAI_API_KEY

Start proxy

litellm --config /path/to/config.yaml

Test it!

curl http://0.0.0.0:4000/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "pplx-embed-v1-0.6b",
    "input": ["good morning from litellm"]
  }'

Supported Parameters

Perplexity embeddings support the following optional parameters:

Parameter	Type	Description
`dimensions`	int	Output embedding dimensions. 128–1024 for 0.6b models, 128–2560 for 4b models. Defaults to max.
`encoding_format`	string	`"base64_int8"` (default) or `"base64_binary"` for compressed output.

Example with Parameters

from litellm import embedding
import os

os.environ['PERPLEXITYAI_API_KEY'] = ""

response = embedding(
    model="perplexity/pplx-embed-v1-4b",
    input=["Your text here"],
    dimensions=512,
)
print(f"Embedding dimensions: {len(response.data[0]['embedding'])}")

curl http://0.0.0.0:4000/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-1234" \
  -d '{
    "model": "pplx-embed-v1-4b",
    "input": ["Your text here"],
    "dimensions": 512
  }'

Supported Models

All models listed on the Perplexity Embeddings docs are supported. Use model=perplexity/<model-name>.

Model Name	Dimensions	Max Tokens	Price (per 1M tokens)	Function Call
pplx-embed-v1-0.6b	1024	32K	$0.004	`embedding(model="perplexity/pplx-embed-v1-0.6b", input)`
pplx-embed-v1-4b	2560	32K	$0.03	`embedding(model="perplexity/pplx-embed-v1-4b", input)`

Key Specifications

Max texts per request: 512
Max tokens per input: 32,768
Combined request limit: 120,000 tokens
Matryoshka dimension reduction — reduce dimensions to 128+ for faster search and reduced storage
No instruction prefix required — embed text directly
Unnormalized embeddings — use cosine similarity for comparison

3.2 KiB Raw Blame History Unescape Escape