Remove dependecies

profiling/test_import_and_query.py

@@ -1,14 +1,12 @@
 import time
-from typing import List
 import uuid
+from typing import List
 
 import h5py  # type: ignore
+from _pytest.fixtures import SubRequest
 
 import weaviate
 import weaviate.classes as wvc
-
-from _pytest.fixtures import SubRequest
-
 from weaviate.collections.collection import Collection
 from .conftest import get_file_path
 
@@ -68,25 +66,6 @@ def load_records_v4(collection: Collection, vectors: List[List[float]]) -> None:
     print(f"V4: Finished writing {len(vectors)} records in {time.time()-start}s")
 
 
-def load_records_v3(client: weaviate.Client, vectors: List[List[float]], name: str) -> None:
-    start = time.time()
-
-    client.batch.configure(batch_size=1000, num_workers=2)
-
-    with client.batch as batch:
-        for i, vector in enumerate(vectors):
-            data_object = {"i": i}
-
-            batch.add_data_object(
-                data_object=data_object,
-                vector=vector,
-                class_name=name,
-                uuid=uuid.UUID(int=i),
-            )
-
-    print(f"V3: Finished writing {len(vectors)} records in {time.time()-start}s")
-
-
 def query_v4(
     collection: Collection, vectors: List[List[float]], neighbours: List[List[int]], ef: int
 ) -> None:
@@ -107,40 +86,6 @@ def query_v4(
     )
 
 
-def query_v3(
-    collection: Collection,
-    client: weaviate.Client,
-    vectors: List[List[float]],
-    neighbours: List[List[int]],
-    ef: int,
-) -> None:
-    collection.config.update(vector_index_config=wvc.config.Reconfigure.VectorIndex.hnsw(ef=ef))
-    start = time.time()
-    recall = 0.0
-
-    for i, vec in enumerate(vectors):
-        res = (
-            client.query.get(collection.name, ["i _additional{id}"])
-            .with_near_vector(
-                {
-                    "vector": vec,
-                }
-            )
-            .with_limit(LIMIT)
-            .do()
-        )
-        res_ids = [
-            uuid.UUID(res["_additional"]["id"]).int for res in res["data"]["Get"][collection.name]
-        ]
-        ideal_neighbors = set(neighbours[i][:LIMIT])
-
-        recall += len(ideal_neighbors.intersection(res_ids)) / LIMIT
-
-    print(
-        f"V3: Querying {len(vectors)} records with ef {ef} in {time.time()-start}s with recall {recall/len(vectors)}"
-    )
-
-
 def run_v4(file: str, name: str, efc: int, m: int) -> None:
     sift_file = get_file_path(file)
 
@@ -156,32 +101,6 @@ def run_v4(file: str, name: str, efc: int, m: int) -> None:
         query_v4(collection, vectors_test, ideal_neighbors, ef)
 
 
-def run_v3(file: str, name: str, efc: int, m: int) -> None:
-    sift_file = get_file_path(file)
-
-    f = h5py.File(sift_file)
-    vectors_import = f["train"]
-    vectors_test = f["test"]
-    ideal_neighbors = f["neighbors"]
-
-    client = weaviate.Client(url="http://localhost:8080")
-
-    # use v4 client to create schema to avoid duplicate code
-    clientv4 = weaviate.connect_to_local()
-    collection = create_schema(clientv4, name, efc, m, 1, "l2-squared")
-    load_records_v3(client, vectors_import, name)
-    for ef in EF_VALUES:
-        query_v3(collection, client, vectors_test, ideal_neighbors, ef)
-
-
-def test_sift_v3(request: SubRequest) -> None:
-    run_v3(file="sift-128-euclidean.hdf5", name=request.node.name, efc=128, m=32)
-
-
-def test_dbpedia_v3(request: SubRequest) -> None:
-    run_v3(file="dbpedia-openai-1000k-angular.hdf5", name=request.node.name, efc=384, m=20)
-
-
 def test_sift_v4(request: SubRequest) -> None:
     run_v4(file="sift-128-euclidean.hdf5", name=request.node.name, efc=128, m=32)
 

requirements-devel.txt

@@ -1,4 +1,3 @@
-requests==2.32.3
 httpx==0.25.2
 validators==0.34.0
 authlib==1.3.1

setup.cfg

@@ -20,24 +20,14 @@ packages =
     weaviate
     weaviate.connect
     weaviate.collections
-    weaviate.schema
-    weaviate.schema.properties
-    weaviate.batch
     weaviate.backup
-    weaviate.classification
-    weaviate.contextionary
-    weaviate.data
-    weaviate.data.references
-    weaviate.data.replication
     weaviate.gql
-    weaviate.cluster
     weaviate.proto
     weaviate.proto.v1
 
 platforms = any
 include_package_data = True
 install_requires =
-    requests>=2.30.0,<3.0.0
     httpx>=0.25.0,<=0.27.0
     validators==0.34.0
     authlib>=1.2.1,<1.3.2