fix/qdrant_connection_error

apps/api/package.json

@@ -26,6 +26,7 @@
     "@langchain/textsplitters": "^1.0.0",
     "@neondatabase/serverless": "^1.0.2",
     "@qdrant/js-client-rest": "^1.15.1",
+    "@sevinf/maybe": "^0.5.0",
     "better-auth": "^1.3.34",
     "bullmq": "^5.63.1",
     "cookie-parser": "^1.4.7",

apps/api/src/db/qdrant.ts

@@ -1,5 +1,6 @@
 import { HuggingFaceInferenceEmbeddings } from "@langchain/community/embeddings/hf";
 import { QdrantVectorStore } from "@langchain/qdrant";
+import { QdrantClient } from "@qdrant/js-client-rest";
 import config from "../config/index.js";
 
 let vectorStoreInstance: QdrantVectorStore | null = null;
@@ -8,29 +9,79 @@ const embeddings = new HuggingFaceInferenceEmbeddings({
   model: "sentence-transformers/all-MiniLM-L6-v2",
 });
 
+const COLLECTION_NAME = "resume";
+const VECTOR_SIZE = 384; // all-MiniLM-L6-v2 produces 384-dim vectors
+
+async function ensureCollectionExists() {
+  const client = new QdrantClient({
+    url: config.qdrant.url!,
+    apiKey: config.qdrant.key!,
+  });
+
+  try {
+    const collections = await client.getCollections();
+    const exists = collections.collections.some(
+      (c) => c.name === COLLECTION_NAME,
+    );
+
+    if (!exists) {
+      console.log(`Collection "${COLLECTION_NAME}" not found, creating...`);
+      await client.createCollection(COLLECTION_NAME, {
+        vectors: {
+          size: VECTOR_SIZE,
+          distance: "Cosine",
+        },
+      });
+      console.log(`✅ Collection "${COLLECTION_NAME}" created`);
+
+      // Create index for candidateId
+      await client.createPayloadIndex(COLLECTION_NAME, {
+        field_name: "metadata.candidateId",
+        field_schema: "keyword",
+      });
+      console.log("✅ Index created for candidateId");
+    }
+  } catch (error) {
+    console.error("Error ensuring collection exists:", error);
+    throw error;
+  }
+}
+
 export const connectQdrant = async () => {
-  vectorStoreInstance = await QdrantVectorStore.fromExistingCollection(
-    embeddings,
-    {
-      url: config.qdrant.url!,
-      collectionName: "resume",
-      apiKey: config.qdrant.key!,
-      contentPayloadKey: "content",
-    },
-  );
-  console.log("✅ Qdrant vector store connected");
+  try {
+    await ensureCollectionExists();
+
+    vectorStoreInstance = await QdrantVectorStore.fromExistingCollection(
+      embeddings,
+      {
+        url: config.qdrant.url!,
+        collectionName: COLLECTION_NAME,
+        apiKey: config.qdrant.key!,
+        contentPayloadKey: "content",
+      },
+    );
+    console.log("✅ Qdrant vector store connected");
+  } catch (error) {
+    console.error("❌ Failed to connect to Qdrant:", error);
+    // Don't crash the server, just log the error
+    console.warn(
+      "⚠️ Server will continue without Qdrant. Resume features may not work.",
+    );
+  }
 };
 
 export const getVectorStore = async (): Promise<QdrantVectorStore> => {
   if (vectorStoreInstance) {
     return vectorStoreInstance;
   }
   try {
+    await ensureCollectionExists();
+
     vectorStoreInstance = await QdrantVectorStore.fromExistingCollection(
       embeddings,
       {
         url: config.qdrant.url!,
-        collectionName: "resume",
+        collectionName: COLLECTION_NAME,
         apiKey: config.qdrant.key!,
         contentPayloadKey: "content",
       },

yarn.lock

@@ -2061,7 +2061,7 @@
   resolved "https://registry.yarnpkg.com/@sentry/core/-/core-8.55.0.tgz#4964920229fcf649237ef13b1533dfc4b9f6b22e"
   integrity sha512-6g7jpbefjHYs821Z+EBJ8r4Z7LT5h80YSWRJaylGS4nW5W5Z2KXzpdnyFarv37O7QjauzVC2E+PABmpkw5/JGA==
 
-"@sevinf/maybe@0.5.0":
+"@sevinf/maybe@0.5.0", "@sevinf/maybe@^0.5.0":
   version "0.5.0"
   resolved "https://registry.yarnpkg.com/@sevinf/maybe/-/maybe-0.5.0.tgz#e59fcea028df615fe87d708bb30e1f338e46bb44"
   integrity sha512-ARhyoYDnY1LES3vYI0fiG6e9esWfTNcXcO6+MPJJXcnyMV3bim4lnFt45VXouV7y82F4x3YH8nOQ6VztuvUiWg==