feat(embedding): 嵌入（n-gram 哈希向量 + 关键词 FTS + OpenAI 兼容 API 钩子 + Manager 选择）

internal/runtime/embedding/embedding.go

@@ -0,0 +1,34 @@
+// Package embedding provides text-to-vector embedding and keyword full-text
+// search for the Cyrene runtime.
+//
+// It implements the vectorization path described in DevDocs/design/04-运行时.md
+// §7: an Embedder interface with two implementations — a dependency-free
+// n-gram hashing embedder (the offline default) and an optional OpenAI-
+// compatible /embeddings API embedder — plus a keyword full-text search (FTS)
+// inverted index.
+//
+// The runtime has two coexisting retrieval levels: the n-gram/API vectors are
+// semantic-level, while the FTS inverted index is keyword-level. Persistent
+// vector storage (SQLite vector extension / pgvector) is deliberately out of
+// scope for this package and belongs to milestone M4 (see
+// DevDocs/开发时间线.md); M1 only performs embedding and indexes for search.
+package embedding
+
+import "context"
+
+// Vector is a dense embedding: one float32 per dimension.
+type Vector []float32
+
+// Embedder converts a batch of texts into dense vectors of a fixed dimension.
+//
+// Implementations must be deterministic for identical input and safe for
+// concurrent use. Embed returns one Vector per input text, aligned by index;
+// every returned vector has Dim() elements. ctx controls cancellation and must
+// be honored: a canceled context returns immediately with a non-nil error.
+type Embedder interface {
+	// Embed returns one vector per input text, in the same order as texts.
+	Embed(ctx context.Context, texts []string) ([]Vector, error)
+	// Dim returns the fixed number of dimensions in every produced vector. It
+	// must match the length of each vector returned by Embed.
+	Dim() int
+}