embeddings

client.embeddings is the Retrieval surface's recall lane: turn text into vectors for semantic search and RAG. It exposes the embed capability lane as a single callable:

• embeddings(input, input_type=None): embed one string or a list, order-preserving.

Two facts set this namespace apart from the rest of the SDK:

• Its own route, not chat.completions. embeddings(...) hits POST /v1/embeddings directly (OpenAI-shaped request and response). You never pick a serving model or GPU; Pareta resolves the model behind the lane (qwen-embed-1 — Qwen3-Embedding-0.6B, the open embedder that beats text-embedding-3-large on the measured CUAD recall benchmark).
• Metered per input token. Each call debits your org balance by the tokens embedded — $0.004 per 1M tokens, 5× under OpenAI text-embedding-3-small and 32× under 3-large. An empty balance raises InsufficientCreditsError (402).

from pareta import Pareta

pa = Pareta()  # PARETA_API_KEY from the environment

# Index side: embed documents raw.
docs = ["Delaware law governs.", "30-day termination notice.", "Notices in writing."]
doc_vecs = pa.embeddings(docs).vectors

# Search side: embed the QUERY with input_type="query" — retrieval queries
# embed differently from passages (the model's retrieval instruction).
q = pa.embeddings("which state's law applies?", input_type="query").vectors[0]

# Vectors are unit-normalized: cosine similarity is a plain dot product.
best = max(range(len(docs)), key=lambda i: sum(a * b for a, b in zip(q, doc_vecs[i])))

import { Pareta } from "pareta";

const pa = Pareta.fromEnv();
const docVecs = (await pa.embeddings(docs)).vectors;
const q = (await pa.embeddings("which state's law applies?", { inputType: "query" })).vectors[0];
// unit vectors: cosine is a plain dot product
const dot = (a: number[], b: number[]) => a.reduce((s, v, i) => s + v * b[i], 0);
const best = docVecs.reduce((bi, v, i) => (dot(q, v) > dot(q, docVecs[bi]) ? i : bi), 0);

embeddings()

pa.embeddings(input, *, input_type=None) -> Embeddings

Parameter	Type	Notes
input	str | Sequence[str]	Text(s) to embed. Up to 512 per call; each is truncated at 512 tokens.
input_type	"query" | "document" | None	"query" applies the retrieval-query embedding; default (None/"document") embeds raw.

Returns an Embeddings. Raises ValueError locally on empty input (before any network call).

The Embeddings object

Accessor	Type	Meaning
.vectors	list[list[float]]	Unit-normalized 1024-dim vectors, in your input order.
.model	str	The lane's serving model (qwen-embed-1).
.prompt_tokens	int	Tokens embedded — the metered unit.
len(result)	int	Number of vectors.

The RAG stack on Pareta

Embeddings are the recall stage; pair them with rerank for precision — retrieve a wide candidate set by cosine, then let pareta-rerank-1 re-score the top 50–100. Both stages are benchmarkable on your own data (the text-embedding and document-reranking catalog tasks score nDCG@10 against your graded relevance), and both are measured on the same public pools, so the leaderboard shows exactly what each stage buys.