ci: fix unhandled typescript exceptions and strict eslint errors caught by the pipeline

packages/payload-ai/src/tools/memoryDb.ts

@@ -1,37 +1,39 @@
-import { tool } from 'ai'
-import { z } from 'zod'
-import { QdrantClient } from '@qdrant/js-client-rest'
+import { tool } from "ai";
+import { z } from "zod";
+import { QdrantClient } from "@qdrant/js-client-rest";
 
 // Qdrant initialization
 // This requires the user to have Qdrant running and QDRANT_URL/QDRANT_API_KEY environment variables set
 const qdrantClient = new QdrantClient({
-    url: process.env.QDRANT_URL || 'http://localhost:6333',
-    apiKey: process.env.QDRANT_API_KEY,
-})
+  url: process.env.QDRANT_URL || "http://localhost:6333",
+  apiKey: process.env.QDRANT_API_KEY,
+});
 
-const MEMORY_COLLECTION = 'mintel_ai_memory'
+const MEMORY_COLLECTION = "mintel_ai_memory";
 
 // Ensure collection exists on load
 async function initQdrant() {
-    try {
-        const res = await qdrantClient.getCollections()
-        const exists = res.collections.find((c: any) => c.name === MEMORY_COLLECTION)
-        if (!exists) {
-            await qdrantClient.createCollection(MEMORY_COLLECTION, {
-                vectors: {
-                    size: 1536, // typical embedding size, adjust based on the embedding model used
-                    distance: 'Cosine',
-                },
-            })
-            console.log(`Qdrant collection '${MEMORY_COLLECTION}' created.`)
-        }
-    } catch (error) {
-        console.error('Failed to initialize Qdrant memory collection:', error)
+  try {
+    const res = await qdrantClient.getCollections();
+    const exists = res.collections.find(
+      (c: any) => c.name === MEMORY_COLLECTION,
+    );
+    if (!exists) {
+      await qdrantClient.createCollection(MEMORY_COLLECTION, {
+        vectors: {
+          size: 1536, // typical embedding size, adjust based on the embedding model used
+          distance: "Cosine",
+        },
+      });
+      console.log(`Qdrant collection '${MEMORY_COLLECTION}' created.`);
     }
+  } catch (error) {
+    console.error("Failed to initialize Qdrant memory collection:", error);
+  }
 }
 
 // Call init, but don't block
-initQdrant()
+initQdrant();
 
 /**
  * Returns memory tools for the AI SDK.
@@ -40,76 +42,99 @@ initQdrant()
  * by a utility function, or we use Qdrant's FastEmbed (if running their specialized container).
  */
 export const generateMemoryTools = (userId: string | number) => {
-    return {
-        save_memory: tool({
-            description: 'Save an important preference, fact, or instruction about the user to long-term memory. Only use this when explicitly asked or when it is clearly a long-term preference.',
-            parameters: z.object({
-                fact: z.string().describe('The fact or instruction to remember.'),
-                category: z.string().optional().describe('An optional category like "preference", "rule", or "project_detail".'),
-            }),
-            // @ts-ignore - AI SDK strict mode bug
-            execute: async ({ fact, category }: { fact: string; category?: string }) => {
-                // In a real scenario, you MUST generate embeddings for the 'fact' string here
-                // using OpenAI or another embedding provider before inserting into Qdrant.
-                // const embedding = await generateEmbedding(fact)
+  return {
+    save_memory: tool({
+      description:
+        "Save an important preference, fact, or instruction about the user to long-term memory. Only use this when explicitly asked or when it is clearly a long-term preference.",
+      parameters: z.object({
+        fact: z.string().describe("The fact or instruction to remember."),
+        category: z
+          .string()
+          .optional()
+          .describe(
+            'An optional category like "preference", "rule", or "project_detail".',
+          ),
+      }),
+      // @ts-expect-error - AI SDK strict mode bug
+      execute: async ({
+        fact,
+        category,
+      }: {
+        fact: string;
+        category?: string;
+      }) => {
+        // In a real scenario, you MUST generate embeddings for the 'fact' string here
+        // using OpenAI or another embedding provider before inserting into Qdrant.
+        // const embedding = await generateEmbedding(fact)
 
-                try {
-                    // Mock embedding payload for demonstration
-                    const mockEmbedding = new Array(1536).fill(0).map(() => Math.random())
+        try {
+          // Mock embedding payload for demonstration
+          const mockEmbedding = new Array(1536)
+            .fill(0)
+            .map(() => Math.random());
 
-                    await qdrantClient.upsert(MEMORY_COLLECTION, {
-                        wait: true,
-                        points: [
-                            {
-                                id: crypto.randomUUID(),
-                                vector: mockEmbedding,
-                                payload: {
-                                    userId: String(userId), // Partition memory by user
-                                    fact,
-                                    category,
-                                    createdAt: new Date().toISOString(),
-                                },
-                            },
-                        ],
-                    })
-                    return { success: true, message: `Successfully remembered: "${fact}"` }
-                } catch (error) {
-                    console.error("Qdrant save error:", error)
-                    return { success: false, error: 'Failed to save to memory database.' }
-                }
+          await qdrantClient.upsert(MEMORY_COLLECTION, {
+            wait: true,
+            points: [
+              {
+                id: crypto.randomUUID(),
+                vector: mockEmbedding,
+                payload: {
+                  userId: String(userId), // Partition memory by user
+                  fact,
+                  category,
+                  createdAt: new Date().toISOString(),
+                },
+              },
+            ],
+          });
+          return {
+            success: true,
+            message: `Successfully remembered: "${fact}"`,
+          };
+        } catch (error) {
+          console.error("Qdrant save error:", error);
+          return {
+            success: false,
+            error: "Failed to save to memory database.",
+          };
+        }
+      },
+    }),
+
+    search_memory: tool({
+      description:
+        "Search the user's long-term memory for past factual context, preferences, or rules.",
+      parameters: z.object({
+        query: z.string().describe("The search string to find in memory."),
+      }),
+      // @ts-expect-error - AI SDK strict mode bug
+      execute: async ({ query }: { query: string }) => {
+        // Generate embedding for query
+        const mockQueryEmbedding = new Array(1536)
+          .fill(0)
+          .map(() => Math.random());
+
+        try {
+          const results = await qdrantClient.search(MEMORY_COLLECTION, {
+            vector: mockQueryEmbedding,
+            limit: 5,
+            filter: {
+              must: [
+                {
+                  key: "userId",
+                  match: { value: String(userId) },
+                },
+              ],
             },
-        }),
+          });
 
-        search_memory: tool({
-            description: 'Search the user\'s long-term memory for past factual context, preferences, or rules.',
-            parameters: z.object({
-                query: z.string().describe('The search string to find in memory.'),
-            }),
-            // @ts-ignore - AI SDK strict mode bug
-            execute: async ({ query }: { query: string }) => {
-                // Generate embedding for query
-                const mockQueryEmbedding = new Array(1536).fill(0).map(() => Math.random())
-
-                try {
-                    const results = await qdrantClient.search(MEMORY_COLLECTION, {
-                        vector: mockQueryEmbedding,
-                        limit: 5,
-                        filter: {
-                            must: [
-                                {
-                                    key: 'userId',
-                                    match: { value: String(userId) }
-                                }
-                            ]
-                        }
-                    })
-
-                    return results.map((r: any) => r.payload?.fact || '')
-                } catch (error) {
-                    console.error("Qdrant search error:", error)
-                    return []
-                }
-            }
-        })
-    }
-}
+          return results.map((r: any) => r.payload?.fact || "");
+        } catch (error) {
+          console.error("Qdrant search error:", error);
+          return [];
+        }
+      },
+    }),
+  };
+};