build(deps): bump github.com/blevesearch/bleve/v2 from 2.3.10 to 2.4.0

Bumps [github.com/blevesearch/bleve/v2](https://github.com/blevesearch/bleve) from 2.3.10 to 2.4.0.
- [Release notes](https://github.com/blevesearch/bleve/releases)
- [Commits](https://github.com/blevesearch/bleve/compare/v2.3.10...v2.4.0)

---
updated-dependencies:
- dependency-name: github.com/blevesearch/bleve/v2
  dependency-type: direct:production
  update-type: version-update:semver-minor
...

Signed-off-by: dependabot[bot] <support@github.com>

vendor/github.com/blevesearch/bleve_index_api/vector.go

@@ -0,0 +1,69 @@
+//  Copyright (c) 2023 Couchbase, Inc.
+//
+// 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.
+
+//go:build vectors
+// +build vectors
+
+package index
+
+type VectorField interface {
+	Vector() []float32
+	// Dimensionality of the vector
+	Dims() int
+	// Similarity metric to be used for scoring the vectors
+	Similarity() string
+	// nlist/nprobe config (recall/latency) the index is optimized for
+	IndexOptimizedFor() string
+}
+
+// -----------------------------------------------------------------------------
+
+const (
+	EuclideanDistance = "l2_norm"
+
+	// dotProduct(vecA, vecB) = vecA . vecB = |vecA| * |vecB| * cos(theta);
+	//  where, theta is the angle between vecA and vecB
+	// If vecA and vecB are normalized (unit magnitude), then
+	// vecA . vecB = cos(theta), which is the cosine similarity.
+	// Thus, we don't need a separate similarity type for cosine similarity
+	CosineSimilarity = "dot_product"
+)
+
+const DefaultSimilarityMetric = EuclideanDistance
+
+// Supported similarity metrics for vector fields
+var SupportedSimilarityMetrics = map[string]struct{}{
+	EuclideanDistance: {},
+	CosineSimilarity:  {},
+}
+
+// -----------------------------------------------------------------------------
+
+const (
+	IndexOptimizedForRecall  = "recall"
+	IndexOptimizedForLatency = "latency"
+)
+
+const DefaultIndexOptimization = IndexOptimizedForRecall
+
+var SupportedVectorIndexOptimizations = map[string]int{
+	IndexOptimizedForRecall:  0,
+	IndexOptimizedForLatency: 1,
+}
+
+// Reverse maps vector index optimizations': int -> string
+var VectorIndexOptimizationsReverseLookup = map[int]string{
+	0: IndexOptimizedForRecall,
+	1: IndexOptimizedForLatency,
+}