feat(Phase 4): Memory & Intelligence — memory, log, summarization, skills (#91)

Co-authored-by: Jason Woltje <jason@diversecanvas.com>
Co-committed-by: Jason Woltje <jason@diversecanvas.com>

packages/memory/src/vector-store.ts

@@ -0,0 +1,39 @@
+/**
+ * VectorStore interface — abstraction over pgvector that allows future
+ * swap to Qdrant, Pinecone, etc.
+ */
+export interface VectorStore {
+  /** Store an embedding with an associated document ID. */
+  store(documentId: string, embedding: number[], metadata?: Record<string, unknown>): Promise<void>;
+
+  /** Search for similar embeddings, returning document IDs and distances. */
+  search(
+    queryEmbedding: number[],
+    limit?: number,
+    filter?: Record<string, unknown>,
+  ): Promise<VectorSearchResult[]>;
+
+  /** Delete an embedding by document ID. */
+  remove(documentId: string): Promise<void>;
+}
+
+export interface VectorSearchResult {
+  documentId: string;
+  distance: number;
+  metadata?: Record<string, unknown>;
+}
+
+/**
+ * EmbeddingProvider interface — generates embeddings from text.
+ * Implemented by the gateway using the configured LLM provider.
+ */
+export interface EmbeddingProvider {
+  /** Generate an embedding vector for the given text. */
+  embed(text: string): Promise<number[]>;
+
+  /** Generate embeddings for multiple texts in batch. */
+  embedBatch(texts: string[]): Promise<number[][]>;
+
+  /** The dimensionality of the embeddings this provider generates. */
+  dimensions: number;
+}