Feat : Skills

open-sse/handlers/embeddingProviders/gemini.js

@@ -0,0 +1,42 @@
+// Google Gemini embeddings — embedContent / batchEmbedContents
+const BASE = "https://generativelanguage.googleapis.com/v1beta";
+
+function modelPath(model) {
+  return model.startsWith("models/") ? model : `models/${model}`;
+}
+
+export default {
+  buildUrl: (model, creds, { input } = {}) => {
+    const apiKey = creds.apiKey || creds.accessToken;
+    const path = modelPath(model);
+    const op = Array.isArray(input) ? "batchEmbedContents" : "embedContent";
+    return `${BASE}/${path}:${op}?key=${encodeURIComponent(apiKey)}`;
+  },
+  buildHeaders: () => ({ "Content-Type": "application/json" }),
+  buildBody: (model, { input }) => {
+    const m = modelPath(model);
+    if (Array.isArray(input)) {
+      return { requests: input.map((text) => ({ model: m, content: { parts: [{ text: String(text) }] } })) };
+    }
+    return { model: m, content: { parts: [{ text: String(input) }] } };
+  },
+  normalize: (responseBody, model) => {
+    if (responseBody.object === "list" && Array.isArray(responseBody.data)) return responseBody;
+    let items = [];
+    if (Array.isArray(responseBody.embeddings)) {
+      items = responseBody.embeddings.map((emb, idx) => ({
+        object: "embedding",
+        index: idx,
+        embedding: emb.values || [],
+      }));
+    } else if (responseBody.embedding?.values) {
+      items = [{ object: "embedding", index: 0, embedding: responseBody.embedding.values }];
+    }
+    return {
+      object: "list",
+      data: items,
+      model,
+      usage: { prompt_tokens: 0, total_tokens: 0 },
+    };
+  },
+};