# Embeddings

URL: https://bridge.wekoya.tech/docs/embeddings
Updated: 2026-11-08

> Turn text into vectors for search, recommendations and "chat with your documents", on OpenAI's POST /v1/embeddings. gemini-embedding-2 (₦297 per million…

Turn text into vectors for search, recommendations and "chat with your documents", on OpenAI's `POST /v1/embeddings`. `gemini-embedding-2` (₦297 per million tokens, 8,192 tokens a text) or `gemini-embedding-001` (₦222.75, 2,048 a text). Up to 2,048 texts a request. Vectors come back unit length, 3,072 numbers unless you ask for fewer. `gemini-embedding-001` does not report its token count, so it is billed on an estimate, marked `"usage_status": "estimated"`.

```
result = client.embeddings.create(
    model="gemini-embedding-2",
    input=["How do I pay my electricity bill?", "Buy airtime"],
    dimensions=768,                         # optional: 128 to 3,072
    extra_body={"task_type": "RETRIEVAL_QUERY"},  # optional: what the vectors are for
)
vector = result.data[0].embedding           # unit length: cosine similarity is a dot product
```