diff --git a/src/collection.cpp b/src/collection.cpp
index bee1eb99..19463522 100644
--- a/src/collection.cpp
+++ b/src/collection.cpp
@@ -1233,6 +1233,11 @@ Option<nlohmann::json> Collection::search(std::string raw_query,
                     continue;
                 }
 
+                if(embedding_fields.find(search_field.name) == embedding_fields.end()) {
+                    std::string error = "Vector field `" + search_field.name + "` is not an auto-embedding field, do not use `query_by` with it, use `vector_query` instead.";
+                    return Option<nlohmann::json>(400, error);
+                }
+
                 TextEmbedderManager& embedder_manager = TextEmbedderManager::get_instance();
                 auto embedder_op = embedder_manager.get_text_embedder(search_field.embed[fields::model_config]);
                 if(!embedder_op.ok()) {
diff --git a/test/collection_vector_search_test.cpp b/test/collection_vector_search_test.cpp
index 28dd21ba..c3a608ba 100644
--- a/test/collection_vector_search_test.cpp
+++ b/test/collection_vector_search_test.cpp
@@ -2177,4 +2177,42 @@ TEST_F(CollectionVectorTest, HybridSearchOnlyKeyworMatchDoNotHaveVectorDistance)
 
     ASSERT_EQ(1, hybrid_results.get()["hits"].size());
     ASSERT_EQ(0, hybrid_results.get()["hits"][0].count("vector_distance"));
+}
+
+
+TEST_F(CollectionVectorTest, QueryByNotAutoEmbeddingVectorField) {
+    nlohmann::json schema = R"({
+                    "name": "test",
+                    "fields": [
+                        {
+                            "name": "title",
+                            "type": "string"
+                        },
+                        {
+                        "name": "embedding",
+                        "type": "float[]",
+                        "num_dim": 384
+                        }
+                    ]
+                    })"_json;
+    
+    TextEmbedderManager::set_model_dir("/tmp/typesense_test/models");
+
+    auto collection_create_op = collectionManager.create_collection(schema);
+    ASSERT_TRUE(collection_create_op.ok());
+
+    auto coll = collection_create_op.get();
+
+    auto search_res = coll->search("john", {"title", "embedding"}, "", {}, {}, {0}, 20, 1, FREQUENCY, {true}, Index::DROP_TOKENS_THRESHOLD,
+                                 spp::sparse_hash_set<std::string>(),
+                                 spp::sparse_hash_set<std::string>(), 10, "", 30, 5,
+                                 "", 10, {}, {}, {}, 0,
+                                 "<mark>", "</mark>", {}, 1000, true, false, true, "", false, 6000 * 1000, 4, 7,
+                                 fallback,
+                                 4, {off}, 32767, 32767, 2,
+                                 false, true, "embedding:([0.96826, 0.94, 0.39557, 0.306488])");
+    
+    ASSERT_FALSE(search_res.ok());
+
+    ASSERT_EQ("Vector field `embedding` is not an auto-embedding field, do not use `query_by` with it, use `vector_query` instead.", search_res.error());
 }
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