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>
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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
```python
# env variable
os.environ['PERPLEXITYAI_API_KEY']
```
## Sample Usage - Embedding
<Tabs>
<TabItem value="sdk" label="SDK">
```python
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)
```
</TabItem>
<TabItem value="proxy" label="Proxy">
1. Setup config.yaml
```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
```
2. Start proxy
```bash
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
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"]
}'
```
</TabItem>
</Tabs>
## 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
<Tabs>
<TabItem value="sdk" label="SDK">
```python
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'])}")
```
</TabItem>
<TabItem value="proxy" label="Proxy">
```bash
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
}'
```
</TabItem>
</Tabs>
## Supported Models
All models listed on the [Perplexity Embeddings docs](https://docs.perplexity.ai/docs/embeddings/quickstart) 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

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@ -876,7 +876,14 @@ const sidebars = {
"providers/openrouter",
"providers/sarvam",
"providers/ovhcloud",
"providers/perplexity",
{
type: "category",
label: "Perplexity AI",
items: [
"providers/perplexity",
"providers/perplexity_embedding",
]
},
"providers/petals",
"providers/poe",
"providers/publicai",