fix: add missing parameters in recommend batch (#338)

qdrant_client/local/qdrant_local.py

@@ -3,7 +3,6 @@
 import logging
 import os
 import shutil
-from copy import deepcopy
 from io import TextIOWrapper
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Union
 
@@ -224,6 +223,14 @@ def recommend_batch(
                 with_payload=request.with_payload,
                 with_vectors=request.with_vector,
                 score_threshold=request.score_threshold,
+                using=request.using,
+                lookup_from_collection=self._get_collection(request.lookup_from.collection)
+                if request.lookup_from
+                else None,
+                lookup_from_vector_name=request.lookup_from.vector
+                if request.lookup_from
+                else None,
+                strategy=request.strategy,
             )
             for request in requests
         ]

tests/congruence_tests/test_common.py

@@ -119,7 +119,7 @@ def compare_scored_record(
         point1.id == point2.id
     ), f"point1[{idx}].id = {point1.id}, point2[{idx}].id = {point2.id}"
     # adjust precision depending on the magnitude of score
-    max_difference = 1e-4 * 10**(math.floor(math.log(abs(point2.score), 10)))
+    max_difference = 1e-4 * 10 ** (math.floor(math.log(abs(point2.score), 10)))
     assert (
         abs(point1.score - point2.score) < max_difference
     ), f"point1[{idx}].score = {point1.score}, point2[{idx}].score = {point2.score}, max_difference = {max_difference}"
@@ -135,7 +135,12 @@ def compare_records(res1: list, res2: list) -> None:
         res1_item = res1[i]
         res2_item = res2[i]
 
-        if isinstance(res1_item, models.ScoredPoint) and isinstance(res2_item, models.ScoredPoint):
+        if isinstance(res1_item, list) and isinstance(res2_item, list):
+            compare_records(res1_item, res2_item)
+
+        elif isinstance(res1_item, models.ScoredPoint) and isinstance(
+            res2_item, models.ScoredPoint
+        ):
             compare_scored_record(res1_item, res2_item, i)
 
         elif isinstance(res1_item, models.Record) and isinstance(res2_item, models.Record):

tests/congruence_tests/test_group_recommend.py

@@ -11,7 +11,6 @@
     init_remote,
 )
 from tests.fixtures.filters import one_random_filter_please
-from tests.fixtures.payload import one_random_payload_please
 
 secondary_collection_name = "secondary_collection"
 
@@ -35,7 +34,7 @@ def simple_recommend_groups_image(self, client: QdrantBase) -> models.GroupsResu
             group_size=self.group_size,
             search_params=models.SearchParams(exact=True),
         )
-        
+
     def simple_recommend_groups_best_scores(self, client: QdrantBase) -> models.GroupsResult:
         return client.recommend_groups(
             collection_name=COLLECTION_NAME,

tests/congruence_tests/test_recommendation.py

@@ -8,21 +8,22 @@
     COLLECTION_NAME,
     compare_client_results,
     generate_fixtures,
+    image_vector_size,
     init_client,
     init_local,
     init_remote,
-    image_vector_size,
 )
 from tests.fixtures.filters import one_random_filter_please
 
 secondary_collection_name = "secondary_collection"
 
+
 class TestSimpleRecommendation:
-
+    __test__ = False
+
     def __init__(self):
         self.query_image = np.random.random(image_vector_size).tolist()
 
-
     @classmethod
     def simple_recommend_image(cls, client: QdrantBase) -> List[models.ScoredPoint]:
         return client.recommend(
@@ -82,19 +83,22 @@ def filter_recommend_text(
             limit=10,
             using="text",
         )
-        
+
     @classmethod
     def best_score_recommend(cls, client: QdrantBase) -> List[models.ScoredPoint]:
         return client.recommend(
             collection_name=COLLECTION_NAME,
-            positive=[10, 20,],
+            positive=[
+                10,
+                20,
+            ],
             negative=[],
             with_payload=True,
             limit=10,
             using="image",
             strategy=models.RecommendStrategy.BEST_SCORE,
         )
-    
+
     @classmethod
     def only_negatives_best_score_recommend(cls, client: QdrantBase) -> List[models.ScoredPoint]:
         return client.recommend(
@@ -106,7 +110,7 @@ def only_negatives_best_score_recommend(cls, client: QdrantBase) -> List[models.
             using="image",
             strategy=models.RecommendStrategy.BEST_SCORE,
         )
-    
+
     @classmethod
     def avg_vector_recommend(cls, client: QdrantBase) -> List[models.ScoredPoint]:
         return client.recommend(
@@ -118,7 +122,7 @@ def avg_vector_recommend(cls, client: QdrantBase) -> List[models.ScoredPoint]:
             using="image",
             strategy=models.RecommendStrategy.AVERAGE_VECTOR,
         )
-        
+
     def recommend_from_raw_vectors(self, client: QdrantBase) -> List[models.ScoredPoint]:
         return client.recommend(
             collection_name=COLLECTION_NAME,
@@ -128,7 +132,7 @@ def recommend_from_raw_vectors(self, client: QdrantBase) -> List[models.ScoredPo
             limit=10,
             using="image",
         )
-    
+
     def recommend_from_raw_vectors_and_ids(self, client: QdrantBase) -> List[models.ScoredPoint]:
         return client.recommend(
             collection_name=COLLECTION_NAME,
@@ -139,6 +143,32 @@ def recommend_from_raw_vectors_and_ids(self, client: QdrantBase) -> List[models.
             using="image",
         )
 
+    @staticmethod
+    def recommend_batch(client: QdrantBase) -> List[List[models.ScoredPoint]]:
+        return client.recommend_batch(
+            collection_name=COLLECTION_NAME,
+            requests=[
+                models.RecommendRequest(
+                    positive=[3],
+                    negative=[],
+                    limit=1,
+                    using="image",
+                    strategy=models.RecommendStrategy.AVERAGE_VECTOR,
+                ),
+                models.RecommendRequest(
+                    positive=[10],
+                    negative=[],
+                    limit=2,
+                    using="image",
+                    strategy=models.RecommendStrategy.BEST_SCORE,
+                    lookup_from=models.LookupLocation(
+                        collection=secondary_collection_name,
+                        vector="image",
+                    ),
+                ),
+            ],
+        )
+
 
 def test_simple_recommend() -> None:
     fixture_records = generate_fixtures()
@@ -160,10 +190,15 @@ def test_simple_recommend() -> None:
     compare_client_results(local_client, remote_client, searcher.simple_recommend_negative)
     compare_client_results(local_client, remote_client, searcher.recommend_from_another_collection)
     compare_client_results(local_client, remote_client, searcher.best_score_recommend)
-    compare_client_results(local_client, remote_client, searcher.only_negatives_best_score_recommend)
+    compare_client_results(
+        local_client, remote_client, searcher.only_negatives_best_score_recommend
+    )
     compare_client_results(local_client, remote_client, searcher.avg_vector_recommend)
     compare_client_results(local_client, remote_client, searcher.recommend_from_raw_vectors)
-    compare_client_results(local_client, remote_client, searcher.recommend_from_raw_vectors_and_ids)
+    compare_client_results(
+        local_client, remote_client, searcher.recommend_from_raw_vectors_and_ids
+    )
+    compare_client_results(local_client, remote_client, searcher.recommend_batch)
 
     for _ in range(10):
         query_filter = one_random_filter_please()