feat: update configuration and initialization for MongoDB and PostgreSQL stores

- Removed optional WorkOS configuration from .env.example.
- Initialized MongoDB store and created a vector index for embeddings in mongodb.ts.
- Initialized PostgreSQL store and logged index details in pg-storage.ts.
- Updated memory configuration to use HNSW index type in mongodb.ts.
- Adjusted import statement for ExtractParams to use type import in upstashMemory.ts.
- Added CORS configuration to mastra index for better API access control.
- Added authentication logic using Supabase in auth.ts.

Author: ssdeanx

.env.example

@@ -67,6 +67,3 @@ NEXT_PUBLIC_MASTRA_API_URL='http://localhost:4111'
 # Example placeholders for local testing
 LOCAL_DEV='true'
 
-# WorkOS Configuration (optional, for authentication)
-WORKOS_API_KEY=sk_live_...
-WORKOS_CLIENT_ID=client_...

lib/auth.ts

@@ -0,0 +1,10 @@
+import { createClient } from "@supabase/supabase-js";
+
+const supabase = createClient("<supabase-url>", "<supabase-key>");
+
+const authTokenResponse = await supabase.auth.signInWithPassword({
+  email: "<user's email>",
+  password: "<user's password>",
+});
+
+const accessToken = authTokenResponse.data?.session?.access_token;

src/mastra/config/mongodb.ts

@@ -30,6 +30,16 @@ const mongoStore = new MongoDBStore({
   url: process.env.MONGODB_URI ?? 'mongodb://localhost:27017',
   dbName: process.env.MONGODB_DATABASE ?? 'AgentStack',
 });
+
+await mongoStore.init();
+//const allIndexes = await mongoStore.listIndexes();
+//console.log(allIndexes);
+//log.info('MongoDB Store initialized with MongoDBVector support, all indexes:', {
+//    indexCount: allIndexes.length,
+//    indexes: allIndexes,
+//});
+
+
 /**
  * Initialize MongoDB Vector store with proper configuration
  */
@@ -38,6 +48,11 @@ const mongoVector = new MongoDBVector({
   dbName: MONGODB_CONFIG.dbName,
 });
 
+// Create a vector index for embeddings
+await mongoVector.createIndex({
+  indexName: "ultra",
+  dimension: 1536,
+});
 
 /**
  * * Memory configuration using MongoDB Vector & Storage
@@ -64,9 +79,12 @@ export const mongoMemory = new Memory({
             scope: 'resource', // 'resource' | 'thread'
             // HNSW index configuration to support high-dimensional embeddings (>2000 dimensions)
             indexConfig: {
-                type: 'flat', // flat index type (supports dimensions > 4000, unlike HNSW limit of 2000)
+                type: 'hnsw', // flat index type (supports dimensions > 4000, unlike HNSW limit of 2000)
                 metric: 'cosine', // Distance metric for normalized embeddings
-                ivf: {lists: 4000},
+                hnsw: {
+                    m: 16, // Number of bi-directional links created for every new element
+                    efConstruction: 200, // Size of the dynamic candidate list for construction
+                },
                 }
         },
         // Enhanced working memory with supported template

src/mastra/config/pg-storage.ts

@@ -1,3 +1,4 @@
+
 import { Memory } from '@mastra/memory'
 import { PgVector, PostgresStore } from '@mastra/pg'
 import { createVectorQueryTool, createGraphRAGTool } from '@mastra/rag'
@@ -11,7 +12,6 @@ import { google } from '@ai-sdk/google'
 //import type { CoreMessage } from '@mastra/core';
 import { maskStreamTags } from '@mastra/core';
 import { z } from 'zod'
-import { MDocument } from "@mastra/rag";
 
 // Use the proper CoreMessage type from @mastra/core
 // This replaces the custom extension that was causing type conflicts
@@ -68,6 +68,14 @@ export const pgStore = new PostgresStore({
     keepAliveInitialDelayMillis: 0,
 })
 
+await pgStore.init();
+const allIndexes = await pgStore.listIndexes();
+console.log(allIndexes);
+log.info('PostgreSQL Store initialized with PgVector support, all indexes:', {
+    indexCount: allIndexes.length,
+    indexes: allIndexes,
+});
+
 // PgVector configuration for 1568 dimension embeddings (gemini-embedding-002)
 export const pgVector = new PgVector({
     connectionString:

src/mastra/config/upstashMemory.ts

@@ -10,7 +10,7 @@ import { google } from '@ai-sdk/google'
 import { AISpanType } from '@mastra/core/ai-tracing';
 import type { TracingContext } from '@mastra/core/ai-tracing';
 import { log } from './logger';
-import { ExtractParams } from '@mastra/rag';
+import type { ExtractParams } from '@mastra/rag';
 
 /**
  * Redefine CoreMessage to include a metadata property for custom data.

src/mastra/index.ts

@@ -637,6 +637,12 @@ export const mastra = new Mastra({
         }
       }),
     ],
+    cors: {
+      origin: ["*"], // Allow specific origins or '*' for all
+      allowMethods: ["GET", "POST", "PUT", "DELETE", "OPTIONS"],
+      allowHeaders: ["Content-Type", "Authorization"],
+      credentials: false,
+    },
     middleware: [
       async (c, next) => {
         const runtimeContext = c.get("runtimeContext");