Fix/gemini

backend/MCP_Execution_Server/src/config.ts

@@ -10,11 +10,11 @@ export const config = {
     apiKey: process.env.NEYNAR_API_KEY ?? "0x",
     signerUuid: process.env.NEYNAR_SIGNER_UUID ?? "0x",
   },
-  gemini:{
-    apiKey: process.env.GEMINI_API_KEY ?? "0x",
+  gemini: {
+    apiKey: process.env.GEMINI_API_KEY ?? "",
   },
   openai: {
-    apiKey: process.env.OPENAI_API_KEY,
+    apiKey: process.env.OPENAI_API_KEY ?? "",
   },
   supabase: {
     SUPABASE_URL: process.env.SUPABASE_URL ?? "",

backend/MCP_Execution_Server/src/index.ts

@@ -24,8 +24,8 @@ async function main() {
     config.reclaim.appId,
     config.reclaim.appSecret,
   );
-  const geminiai = new GeminiAiService(config.gemini.apiKey as string);
-  const ai = new AIService(config.openai.apiKey as string);
+  const geminiai = new GeminiAiService(config.gemini.apiKey);
+  const ai = new AIService(config.openai.apiKey);
   const avs = new AVSService(
     config.network.rpcBaseAddress,
     config.network.privateKey,
@@ -41,7 +41,7 @@ async function main() {
     avs,
     ipfs,
   );
-  const user = new UserService(neynar, ai, db);
+  const user = new UserService(neynar, ai, db, geminiai);
 
   // Initialize services
   const services = {

backend/MCP_Execution_Server/src/services/gemini_ai.service.ts

@@ -1,4 +1,4 @@
-import { GoogleGenAI } from "@google/genai";
+import { GoogleGenAI, Type } from "@google/genai";
 
 export class GeminiAiService {
   private ai: GoogleGenAI;
@@ -9,7 +9,7 @@ export class GeminiAiService {
     this.ai = new GoogleGenAI({
       apiKey: this.geminiApiKey,
     });
-    this.geminiModel = "gemini-2.5-flash";
+    this.geminiModel = "gemini-2.5-flash-preview-04-17";
     this.embeddingModel = "embedding-001";
   }
 
@@ -42,10 +42,17 @@ Bad output:
     `;
 
     try {
+      /**
+       * TODO: Fix this
+       *
+       * Received error:
+       * ClientError: got status: 404 Not Found. {"error":{"code":404,"message":"models/gemini-2.5-flash is not found for API version v1beta, or is not supported for generateContent. Call ListModels to see the list of available models and their supported methods.","status":"NOT_FOUND"}}
+       */
       const result = await this.ai.models.generateContent({
         model: this.geminiModel,
         contents: prompt,
       });
+
       return result.text;
     } catch (err: any) {
       console.error("summarizeUserContext error", err);
@@ -151,8 +158,7 @@ ${formattedTrendingFeeds}
 7. AVOID talking about airdrops and giveaways.
 </instructions>
 
-<output_format>
-You MUST respond with ONLY a valid JSON object containing exactly these two fields:
+<output_guidelines>
 - replyText: A string with the message "You should connect with [author_username], who said: '[cast_text]'" - Max 60-70 words, 320 characters.
 - link: A string with the URL "https://warpcast.com/[author_username]/[cast_hash]" - this should MATCH the cast you selected and not a fabricated/random one.
 
@@ -169,30 +175,36 @@ If and only if you find no relevant casts, respond with this exact format:
   "replyText": "No relevant trending casts found in the provided data.",
   "link": ""
 }
-</output_format>
-
-<response_requirements>
-Your response should be a valid JSON object with no additional text or explanation.
-</response_requirements>
+</output_guidelines>
     `;
 
     try {
       const result = await this.ai.models.generateContent({
         model: this.geminiModel,
         contents: prompt,
+        config: {
+          responseMimeType: "application/json",
+          responseSchema: {
+            type: Type.OBJECT,
+            properties: {
+              replyText: { type: Type.STRING },
+              link: { type: Type.STRING },
+            },
+            required: ["replyText", "link"],
+          },
+        },
       });
-      const content = result.text?.trim();
-
+      const content = result.text;
       try {
         return content
           ? JSON.parse(content)
           : {
-              replyText:
-                "No relevant trending casts found in the provided data.",
-              link: "",
-            };
+            replyText:
+              "No relevant trending casts found in the provided data.",
+            link: "",
+          };
       } catch (jsonErr) {
-        console.warn("Response was not valid JSON:", content);
+        console.warn("Response was not valid JSON Gemini:", content);
         return { error: "Invalid JSON format in AI response", raw: content };
       }
     } catch (err: any) {

backend/MCP_Execution_Server/src/services/user.service.ts

@@ -1,7 +1,7 @@
 import type { NeynarService } from "./neynar.service.js";
 import type { AIService } from "./ai.service.js";
 import type { DBService } from "./db.service.js";
-import { GeminiAiService } from "./gemini_ai.service.js";
+import type { GeminiAiService } from "./gemini_ai.service.js";
 
 export enum FID_STATUS {
   NOT_EXIST = "NOT_EXIST",
@@ -23,28 +23,17 @@ export class UserService {
     try {
       if (this.geminiService) {
         const summary = await this.geminiService.summarizeUserContext(userData);
+        console.log("Summary generated by Gemini");
         if (summary) return summary;
       }
+      console.log("Summary generated by AIService");
       return await this.aiService.summarizeUserContext(userData);
     } catch (e) {
       console.warn("Fallback to AIService for summarizeUserContext");
       return await this.aiService.summarizeUserContext(userData);
     }
   }
 
-  private async generateEmbeddingsSafe(text: string): Promise<any | null> {
-    try {
-      if (this.geminiService) {
-        const embeddings = await this.geminiService.generateEmbeddings(text);
-        if (embeddings) return embeddings;
-      }
-      return await this.aiService.generateEmbeddings(text);
-    } catch (e) {
-      console.warn("Fallback to AIService for generateEmbeddings");
-      return await this.aiService.generateEmbeddings(text);
-    }
-  }
-
   private async generateReplyForCastSafe(input: {
     userCast: string;
     castSummary: string;
@@ -158,7 +147,7 @@ export class UserService {
         const userData = await this.neynarService.aggregateUserData(fid);
         const summary = await this.summarizeUserContextSafe(userData);
         if (!summary) throw new Error("Summary generation failed");
-        const embeddings = await this.generateEmbeddingsSafe(summary);
+        const embeddings = await this.aiService.generateEmbeddings(summary);
         if (!embeddings) throw new Error("Embedding generation failed");
 
         const { success } = await this.db.registerAndSubscribeFID(
@@ -259,7 +248,8 @@ export class UserService {
 
       // Step 2: Generate embeddings for the received cast
       const castSummary = await this.findMeaningFromTextSafe(cast.text);
-      const castEmbeddings = await this.generateEmbeddingsSafe(castSummary);
+      const castEmbeddings =
+        await this.aiService.generateEmbeddings(castSummary);
       if (!castEmbeddings) {
         throw new Error("Embedding generation failed for the cast text");
       }