# Embeddings

> Send text to /v1/embeddings on an embedding endpoint and get back vectors you can store and compare for search, RAG and clustering.

Source: https://bitsrun.com/docs/api/embeddings/
Updated: 2026-11-09

An embedding is a list of numbers (a vector) that captures what a piece of text means. Texts with similar meaning get vectors that sit close together. Apps use them for semantic search, for RAG (retrieval-augmented generation: finding the right documents to hand a chat model), for grouping similar items and for spotting duplicates.

## Deploy an embedding model

Deploy an embedding model like any other model, for example the Hugging Face repo `Snowflake/snowflake-arctic-embed-l-v2.0` or `Qwen/Qwen3-Embedding-0.6B`:

```bash
bitsrun deploy hf:Snowflake/snowflake-arctic-embed-l-v2.0 --name "Docs search"
```

Embedding models are small, so they fit the smallest GPU size (NVIDIA L4). The card shows **Model type: Embeddings** once it is created. See [Deploy your first model](https://bitsrun.com/docs/get-started/first-endpoint/) for the dashboard steps.

## Get vectors

Send one string or a list of strings as `input`:

```python
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://01k8y4000d3m00000000000012.endpoint.bitsrun.com/v1",
    api_key=os.environ["BITSRUN_API_KEY"],
)
result = client.embeddings.create(
    model="snowflake-arctic-embed-l-v2.0",
    input=["How do I reset my password?", "Where can I change my login email?"],
)
for item in result.data:
    print(len(item.embedding), item.embedding[:4])
```

```bash
curl https://01k8y4000d3m00000000000012.endpoint.bitsrun.com/v1/embeddings \
  -H "Authorization: Bearer $BITSRUN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "arctic", "input": "How do I reset my password?"}'
```

BitsRun passes `input`, `encoding_format` and `dimensions` (for models that can shorten their vectors) and drops other fields.

## Use them in LangChain

```python
import os
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    base_url="https://01k8y4000d3m00000000000012.endpoint.bitsrun.com/v1",
    api_key=os.environ["BITSRUN_API_KEY"],
    model="snowflake-arctic-embed-l-v2.0",
    check_embedding_ctx_length=False,
)
vector = embeddings.embed_query("How do I reset my password?")
```

`check_embedding_ctx_length=False` stops LangChain from splitting text with an OpenAI-only tokenizer before it sends it.

## Tips

- **Use one model for both sides.** Vectors from different models cannot be compared. Embed your documents and your search queries with the same endpoint.
- **Send batches.** One request with 64 short texts is faster than 64 requests.
- **Keep it awake while you index.** A big import sends many requests in a row, so the endpoint stays awake and you pay only for the time it works. It sleeps again after its idle time.
- **Give each app its own key.** A key limited to the embedding endpoint cannot reach your chat endpoints. See [API keys](https://bitsrun.com/docs/api/api-keys/).

## What can go wrong

- **`404 not_found` on `/v1/chat/completions`**: embedding endpoints only answer `/v1/embeddings` and `/v1/models`.
- **`400 context_too_long` or a cut-off result**: each model reads a limited amount of text at once. Split long documents into chunks of a few hundred words.
- **`model_starting`**: the endpoint took longer than 8 minutes to start, or you sent `X-BitsRun-Wait: 0` (a `503`). Send the request again; embedding models usually start in seconds on a warm GPU.