Documentation Index
Fetch the complete documentation index at: https://docs.deepinfra.com/llms.txt
Use this file to discover all available pages before exploring further.
DeepInfra supports the OpenAI embeddings API for all embedding models.
The endpoint is:
POST https://api.deepinfra.com/v1/openai/embeddings
Example
from openai import OpenAI
openai = OpenAI(
api_key="$DEEPINFRA_TOKEN",
base_url="https://api.deepinfra.com/v1/openai",
)
input_text = "The food was delicious and the waiter..."
# Or a list: ["hello", "world"]
embeddings = openai.embeddings.create(
model="Qwen/Qwen3-Embedding-8B",
input=input_text,
encoding_format="float"
)
print(embeddings.data[0].embedding)
print(embeddings.usage.prompt_tokens)
Batch embeddings
Pass an array as input to embed multiple texts in a single request:
embeddings = openai.embeddings.create(
model="Qwen/Qwen3-Embedding-8B",
input=["Hello", "World", "How are you?"],
encoding_format="float"
)
for i, item in enumerate(embeddings.data):
print(f"Text {i}: {item.embedding[:5]}...") # First 5 dims
Supported parameters
| Parameter | Notes |
|---|
model | Embedding model name |
input | String or array of strings |
encoding_format | float only |
Available models
Browse all embedding models — includes Qwen3 Embedding, BAAI/bge, sentence-transformers, and more.
