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>

apps/gateway/src/memory/embedding.service.ts

@@ -0,0 +1,69 @@
+import { Injectable, Logger } from '@nestjs/common';
+import type { EmbeddingProvider } from '@mosaic/memory';
+
+const DEFAULT_MODEL = 'text-embedding-3-small';
+const DEFAULT_DIMENSIONS = 1536;
+
+interface EmbeddingResponse {
+  data: Array<{ embedding: number[]; index: number }>;
+  model: string;
+  usage: { prompt_tokens: number; total_tokens: number };
+}
+
+/**
+ * Generates embeddings via the OpenAI-compatible embeddings API.
+ * Supports OpenAI, Azure OpenAI, and any provider with a compatible endpoint.
+ */
+@Injectable()
+export class EmbeddingService implements EmbeddingProvider {
+  private readonly logger = new Logger(EmbeddingService.name);
+  private readonly apiKey: string | undefined;
+  private readonly baseUrl: string;
+  private readonly model: string;
+
+  readonly dimensions = DEFAULT_DIMENSIONS;
+
+  constructor() {
+    this.apiKey = process.env['OPENAI_API_KEY'];
+    this.baseUrl = process.env['EMBEDDING_API_URL'] ?? 'https://api.openai.com/v1';
+    this.model = process.env['EMBEDDING_MODEL'] ?? DEFAULT_MODEL;
+  }
+
+  get available(): boolean {
+    return !!this.apiKey;
+  }
+
+  async embed(text: string): Promise<number[]> {
+    const results = await this.embedBatch([text]);
+    return results[0]!;
+  }
+
+  async embedBatch(texts: string[]): Promise<number[][]> {
+    if (!this.apiKey) {
+      this.logger.warn('No OPENAI_API_KEY configured — returning zero vectors');
+      return texts.map(() => new Array<number>(this.dimensions).fill(0));
+    }
+
+    const response = await fetch(`${this.baseUrl}/embeddings`, {
+      method: 'POST',
+      headers: {
+        'Content-Type': 'application/json',
+        Authorization: `Bearer ${this.apiKey}`,
+      },
+      body: JSON.stringify({
+        model: this.model,
+        input: texts,
+        dimensions: this.dimensions,
+      }),
+    });
+
+    if (!response.ok) {
+      const body = await response.text();
+      this.logger.error(`Embedding API error: ${response.status} ${body}`);
+      throw new Error(`Embedding API returned ${response.status}`);
+    }
+
+    const json = (await response.json()) as EmbeddingResponse;
+    return json.data.sort((a, b) => a.index - b.index).map((d) => d.embedding);
+  }
+}