diff --git a/misinformation/test/test_multimodal_search.py b/misinformation/test/test_multimodal_search.py
index 64bf7fd..51e1fe8 100644
--- a/misinformation/test/test_multimodal_search.py
+++ b/misinformation/test/test_multimodal_search.py
@@ -668,6 +668,222 @@ model_type = "blip"
 # model_type = "clip_vitl14_336"
 
 
+pre_proc_pic_blip2_blip_albef = [
+    -1.0039474964141846,
+    -1.0039474964141846,
+    -0.8433647751808167,
+    -0.6097899675369263,
+    -0.5951915383338928,
+    -0.6243883967399597,
+    -0.6827820539474487,
+    -0.6097899675369263,
+    -0.7119789123535156,
+    -1.0623412132263184,
+]
+pre_proc_pic_clip_vitl14 = [
+    -0.7995694875717163,
+    -0.7849710583686829,
+    -0.7849710583686829,
+    -0.7703726291656494,
+    -0.7703726291656494,
+    -0.7849710583686829,
+    -0.7849710583686829,
+    -0.7703726291656494,
+    -0.7703726291656494,
+    -0.7703726291656494,
+]
+
+pre_proc_pic_clip_vitl14_336 = [
+    -0.7995694875717163,
+    -0.7849710583686829,
+    -0.7849710583686829,
+    -0.7849710583686829,
+    -0.7849710583686829,
+    -0.7849710583686829,
+    -0.7849710583686829,
+    -0.9163569211959839,
+    -1.149931788444519,
+    -1.0039474964141846,
+]
+
+pre_proc_text_blip2_blip_albef = (
+    "the bird sat on a tree located at the intersection of 23rd and 43rd streets"
+)
+
+pre_proc_text_clip_clip_vitl14_clip_vitl14_336 = (
+    "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
+)
+
+pre_extracted_feature_img_blip2 = [
+    0.04566730558872223,
+    -0.042554520070552826,
+    -0.06970272958278656,
+    -0.009771779179573059,
+    0.01446065679192543,
+    0.10173682868480682,
+    0.007092420011758804,
+    -0.020045937970280647,
+    0.12923966348171234,
+    0.006452132016420364,
+]
+
+pre_extracted_feature_img_blip = [
+    -0.02480311505496502,
+    0.05037587881088257,
+    0.039517853409051895,
+    -0.06994109600782394,
+    -0.12886561453342438,
+    0.047039758414030075,
+    -0.11620642244815826,
+    -0.003398326924070716,
+    -0.07324369996786118,
+    0.06994668394327164,
+]
+
+pre_extracted_feature_img_albef = [
+    0.08971136063337326,
+    -0.10915573686361313,
+    -0.020636577159166336,
+    0.048121627420186996,
+    -0.05943416804075241,
+    -0.129856139421463,
+    -0.0034469354432076216,
+    0.017888527363538742,
+    -0.03284582123160362,
+    -0.1037328764796257,
+]
+
+pre_extracted_feature_img_clip = [
+    0.01621132344007492,
+    -0.004035486374050379,
+    -0.04304071143269539,
+    -0.03459808602929115,
+    0.016922621056437492,
+    -0.025056276470422745,
+    -0.04178355261683464,
+    0.02165347896516323,
+    -0.003224249929189682,
+    0.020485712215304375,
+]
+
+pre_extracted_feature_img_parsing_clip = [
+    0.01621132344007492,
+    -0.004035486374050379,
+    -0.04304071143269539,
+    -0.03459808602929115,
+    0.016922621056437492,
+    -0.025056276470422745,
+    -0.04178355261683464,
+    0.02165347896516323,
+    -0.003224249929189682,
+    0.020485712215304375,
+]
+
+pre_extracted_feature_img_clip_vitl14 = [
+    -0.023943455889821053,
+    -0.021703708916902542,
+    0.035043686628341675,
+    0.019495919346809387,
+    0.014351222664117813,
+    -0.008634116500616074,
+    0.01610446907579899,
+    -0.003426523646339774,
+    0.011931191198527813,
+    0.0008691544644534588,
+]
+
+pre_extracted_feature_img_clip_vitl14_336 = [
+    -0.15060146152973175,
+    -0.1998099535703659,
+    0.5503129363059998,
+    0.2589969336986542,
+    -0.0182882659137249,
+    -0.12753525376319885,
+    0.018985718488693237,
+    -0.17110440135002136,
+    0.02220013737678528,
+    0.01086437702178955,
+]
+
+pre_extracted_feature_text_blip2 = [
+    -0.1384204626083374,
+    -0.008662976324558258,
+    0.006269007455557585,
+    0.03151319921016693,
+    0.060558050870895386,
+    -0.03230040520429611,
+    0.015861615538597107,
+    -0.11856459826231003,
+    -0.058296192437410355,
+    0.03699290752410889,
+]
+
+pre_extracted_feature_text_blip = [
+    0.0118643119931221,
+    -0.01291718054562807,
+    -0.0009687161073088646,
+    0.01428765058517456,
+    -0.05591396614909172,
+    0.07386433333158493,
+    -0.11475936323404312,
+    0.01620068959891796,
+    0.0062415082938969135,
+    0.0034833091776818037,
+]
+
+pre_extracted_feature_text_albef = [
+    -0.06229640915989876,
+    0.11278597265481949,
+    0.06628583371639252,
+    0.1649140566587448,
+    0.068987175822258,
+    0.006291372701525688,
+    0.03244050219655037,
+    -0.049556829035282135,
+    0.050752390176057816,
+    -0.0421440489590168,
+]
+
+pre_extracted_feature_text_clip = [
+    0.018169036135077477,
+    0.03634127229452133,
+    0.025660742074251175,
+    0.009149895049631596,
+    -0.035570453852415085,
+    0.033126577734947205,
+    -0.004808237310498953,
+    -0.0031453112605959177,
+    -0.02194291725754738,
+    0.024019461125135422,
+]
+
+pre_extracted_feature_text_clip_vitl14 = [
+    -0.0055463071912527084,
+    0.006908962037414312,
+    -0.019450219348073006,
+    -0.018097277730703354,
+    0.017567576840519905,
+    -0.03828490898013115,
+    -0.03781530633568764,
+    -0.023951737210154533,
+    0.01365653332322836,
+    -0.02341713197529316,
+]
+
+pre_extracted_feature_text_clip_vitl14_336 = [
+    -0.1172553077340126,
+    0.07105237245559692,
+    -0.283934086561203,
+    -0.24353823065757751,
+    0.22662702202796936,
+    -0.472959041595459,
+    -0.5191791653633118,
+    -0.29402273893356323,
+    0.22669515013694763,
+    -0.32044747471809387,
+]
+
+
 @pytest.mark.parametrize(
     (
         "pre_multimodal_device",
@@ -676,61 +892,109 @@ model_type = "blip"
         "pre_proc_text",
         "pre_extracted_feature_img",
         "pre_extracted_feature_text",
+        "pre_extracted_feature_img2",
     ),
     [
         pytest.param(
             device("cuda"),
             "blip2",
-            [
-                -1.0039474964141846,
-                -1.0039474964141846,
-                -0.8433647751808167,
-                -0.6097899675369263,
-                -0.5951915383338928,
-                -0.6243883967399597,
-                -0.6827820539474487,
-                -0.6097899675369263,
-                -0.7119789123535156,
-                -1.0623412132263184,
-            ],
-            "the bird sat on a tree located at the intersection of 23rd and 43rd streets",
-            [
-                0.04566730558872223,
-                -0.042554520070552826,
-                -0.06970272958278656,
-                -0.009771779179573059,
-                0.01446065679192543,
-                0.10173682868480682,
-                0.007092420011758804,
-                -0.020045937970280647,
-                0.12923966348171234,
-                0.006452132016420364,
-            ],
-            [
-                -0.1384204626083374,
-                -0.008662976324558258,
-                0.006269007455557585,
-                0.03151319921016693,
-                0.060558050870895386,
-                -0.03230040520429611,
-                0.015861615538597107,
-                -0.11856459826231003,
-                -0.058296192437410355,
-                0.03699290752410889,
-            ],
+            pre_proc_pic_blip2_blip_albef,
+            pre_proc_text_blip2_blip_albef,
+            pre_extracted_feature_img_blip2,
+            pre_extracted_feature_text_blip2,
+            pre_extracted_feature_img_blip2,
+            marks=pytest.mark.skipif(
+                gpu_is_not_available, reason="gpu_is_not_availible"
+            ),
+        ),
+        (
+            device("cuda"),
+            "blip",
+            pre_proc_pic_blip2_blip_albef,
+            pre_proc_text_blip2_blip_albef,
+            pre_extracted_feature_img_blip,
+            pre_extracted_feature_text_blip,
+            pre_extracted_feature_img_blip,
+        ),
+        pytest.param(
+            device("cuda"),
+            "blip",
+            pre_proc_pic_blip2_blip_albef,
+            pre_proc_text_blip2_blip_albef,
+            pre_extracted_feature_img_blip,
+            pre_extracted_feature_text_blip,
+            pre_extracted_feature_img_blip,
+            marks=pytest.mark.skipif(
+                gpu_is_not_available, reason="gpu_is_not_availible"
+            ),
+        ),
+        (
+            device("cpu"),
+            "albef",
+            pre_proc_pic_blip2_blip_albef,
+            pre_proc_text_blip2_blip_albef,
+            pre_extracted_feature_img_albef,
+            pre_extracted_feature_text_albef,
+            pre_extracted_feature_img_albef,
+        ),
+        pytest.param(
+            device("cuda"),
+            "albef",
+            pre_proc_pic_blip2_blip_albef,
+            pre_proc_text_blip2_blip_albef,
+            pre_extracted_feature_img_albef,
+            pre_extracted_feature_text_albef,
+            pre_extracted_feature_img_albef,
+            marks=pytest.mark.skipif(
+                gpu_is_not_available, reason="gpu_is_not_availible"
+            ),
+        ),
+        (
+            device("cpu"),
+            "clip_base",
+            pre_proc_pic_clip_vitl14,
+            pre_proc_text_clip_clip_vitl14_clip_vitl14_336,
+            pre_extracted_feature_img_parsing_clip,
+            pre_extracted_feature_text_clip,
+            pre_extracted_feature_img_clip,
+        ),
+        pytest.param(
+            device("cuda"),
+            "clip_base",
+            pre_proc_pic_clip_vitl14,
+            pre_proc_text_clip_clip_vitl14_clip_vitl14_336,
+            pre_extracted_feature_img_parsing_clip,
+            pre_extracted_feature_text_clip,
+            pre_extracted_feature_img_clip,
+            marks=pytest.mark.skipif(
+                gpu_is_not_available, reason="gpu_is_not_availible"
+            ),
+        ),
+        (
+            device("cpu"),
+            "clip_vitl14",
+            pre_proc_pic_clip_vitl14,
+            pre_proc_text_clip_clip_vitl14_clip_vitl14_336,
+            pre_extracted_feature_img_clip_vitl14,
+            pre_extracted_feature_text_clip_vitl14,
+            pre_extracted_feature_img_clip_vitl14,
+        ),
+        pytest.param(
+            device("cuda"),
+            "clip_vitl14",
+            pre_proc_pic_clip_vitl14,
+            pre_proc_text_clip_clip_vitl14_clip_vitl14_336,
+            pre_extracted_feature_img_clip_vitl14,
+            pre_extracted_feature_text_clip_vitl14,
+            pre_extracted_feature_img_clip_vitl14,
             marks=pytest.mark.skipif(
                 gpu_is_not_available, reason="gpu_is_not_availible"
             ),
         ),
-        #        (device("cpu"),"blip"),
-        #        (device("cpu"),"albef"),
-        #        (device("cpu"),"clip_base"),
-        #        (device("cpu"),"clip_vitl14"),
         #        (device("cpu"),"clip_vitl14_336"),
-        #        pytest.param( device("cuda"),"blip", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
-        #        pytest.param( device("cuda"),"albef", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
-        #        pytest.param( device("cuda"),"clip_base", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
-        #        pytest.param( device("cuda"),"clip_vitl14", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
+        #
+        #
+        #
         #        pytest.param( device("cuda"),"clip_vitl14_336", marks=pytest.mark.skipif(gpu_is_not_available, reason="gpu_is_not_availible"),),
     ],
 )
@@ -741,6 +1005,7 @@ def test_parsing_images(
     pre_proc_text,
     pre_extracted_feature_img,
     pre_extracted_feature_text,
+    pre_extracted_feature_img2,
 ):
     mydict = {
         "IMG_2746": {"filename": "./test/data/IMG_2746.png"},
@@ -777,9 +1042,8 @@ def test_parsing_images(
     assert processed_text == pre_proc_text
 
     search_query = [
-        {
-            "text_input": "The bird sat on a tree located at the intersection of 23rd and 43rd streets."
-        }
+        {"text_input": test_querry},
+        {"image": TEST_IMAGE_2},
     ]
     multi_features_stacked = ms.MultimodalSearch.querys_processing(
         mydict, search_query, model, txt_processor, vis_processor, pre_model
@@ -791,5 +1055,11 @@ def test_parsing_images(
             is True
         )
 
+    for i, num in zip(range(10), multi_features_stacked[1, 10:20].tolist()):
+        assert (
+            math.isclose(num, pre_extracted_feature_img2[i], rel_tol=related_error)
+            is True
+        )
+
     del model, vis_processor, txt_processor
     cuda.empty_cache()