Embeddings#

Learn how to create text embeddings in Xinference.

Introduction#

Text embeddings are used to quantify how related different pieces of text are. They can be used in various applications including search, clustering, recommendations, anomaly detection, diversity measurement, and classification.

An embedding is a vector of floating point numbers. The proximity between two vectors serves as an indicator of their similarity. Less distance implies a higher correlation, while a larger distance indicates reduced correlation.

Embedding models in Xinference can be invoked through the Embeddings API to create embeddings. The Embeddings API mimics OpenAI’s create embeddings API.

API ENDPOINT	OpenAI-compatible ENDPOINT
Embeddings API	/v1/embeddings

Supported models#

You can examine all the builtin embedding models in Xinference.

Embedding engines#

When launching an embedding model, you can pick the serving engine with the model_engine parameter (--model-engine on the command line):

sentence_transformers: the default engine, available for all embedding models.
vllm: high-throughput serving for supported model families — currently models whose names start with bge, gte, text2vec, m3e, Qwen3, or bce (e.g. bce-embedding-base_v1).
flag: FlagEmbedding-based engine; also supports hybrid (sparse+dense) output, see the FAQ below.
llama.cpp: serve GGUF-format embedding models.

Truncating input#

The Embeddings API accepts an optional truncate_prompt_tokens parameter to cap the token length of each input before encoding:

unset / null: no truncation.
a positive integer N: truncate each input to at most N tokens.
-1: truncate to the model’s own maximum input length.

curl -X 'POST' \
  'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/embeddings' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "<MODEL_UID>",
    "input": "A very long document ...",
    "truncate_prompt_tokens": 512
  }'

Quickstart#

We can try Embeddings API out either via cURL, OpenAI Client, or Xinference’s python client:

curl -X 'POST' \
  'http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1/embeddings' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "<MODEL_UID>",
    "input": "What is the capital of China?"
  }'

import openai

client = openai.Client(
  api_key="cannot be empty",
  base_url="http://<XINFERENCE_HOST>:<XINFERENCE_PORT>/v1"
)
client.embeddings.create(
  model=model_uid,
  input=["What is the capital of China?"]
)

from xinference.client import Client

client = Client("http://<XINFERENCE_HOST>:<XINFERENCE_PORT>")

model = client.get_model("<MODEL_UID>")
input = "What is the capital of China?"
model.create_embedding(input)

{
  "object": "list",
  "model": "<MODEL_UID>",
  "data": [{
    "index": 0,
    "object": "embedding",
    "embedding": [
      -0.014207549393177032,
      -0.01832585781812668,
      ...
      -0.03009396605193615,
      0.05420297756791115]
  }],
  "usage": {
    "prompt_tokens": 37,
    "total_tokens": 37
  }
}

FAQ#

Does the LLM in Xinference support Embeddings API?#

No. Xinference doesn’t provide embed API for LLMs due to considerations of performance.

Does Embeddings API provides integration method for LangChain?#

Yes, you can refer to the related sections in LangChain’s respective official Xinference documentation. Here is the link: Text Embedding Models: Xinference

Does Embeddings API support hrbrid model?#

Yes, you can use flag as the engine to deploy the model and call Embeddings API by setting the extra parameter return_parse=True which will return sparse vectors.