/*
 * Copyright 2024 CloudWeGo Authors
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package embedding

import "context"

// Embedder converts a batch of strings into dense vector representations.
//
// EmbedStrings returns one vector per input text, in the same order. The
// vector length (dimensions) is fixed by the underlying model and identical
// for every text in the batch.
//
// The returned [][]float64 maps as:
//
//	embeddings[i]  →  vector for texts[i]
//	len(embeddings[i])  →  model's embedding dimension (e.g. 1536 for ada-002)
//
// Both [Indexer] and [Retriever] use an Embedder to convert documents and
// queries into vectors. They must share the exact same model — mismatched
// dimensions or model families break semantic similarity.
//
//go:generate  mockgen -destination ../../internal/mock/components/embedding/Embedding_mock.go --package embedding -source interface.go
type Embedder interface {
	EmbedStrings(ctx context.Context, texts []string, opts ...Option) ([][]float64, error) // invoke
}
