feat: implement keyword and hybrid search for Weaviate provider

tests/unit/providers/vector_io/conftest.py

@@ -26,13 +26,15 @@ from llama_stack.providers.remote.vector_io.milvus.milvus import MilvusIndex, Mi
 from llama_stack.providers.remote.vector_io.pgvector.config import PGVectorVectorIOConfig
 from llama_stack.providers.remote.vector_io.pgvector.pgvector import PGVectorIndex, PGVectorVectorIOAdapter
 from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
+from llama_stack.providers.remote.vector_io.weaviate.config import WeaviateVectorIOConfig
+from llama_stack.providers.remote.vector_io.weaviate.weaviate import WeaviateIndex, WeaviateVectorIOAdapter
 
 EMBEDDING_DIMENSION = 384
 COLLECTION_PREFIX = "test_collection"
 MILVUS_ALIAS = "test_milvus"
 
 
-@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma", "pgvector"])
+@pytest.fixture(params=["milvus", "sqlite_vec", "faiss", "chroma", "pgvector", "weaviate"])
 def vector_provider(request):
     return request.param
 
@@ -446,6 +448,75 @@ async def pgvector_vec_adapter(mock_inference_api, embedding_dimension):
 
                         yield adapter
                         await adapter.shutdown()
+def weaviate_vec_db_path():
+    return "localhost:8080"
+
+
+@pytest.fixture
+async def weaviate_vec_index(weaviate_vec_db_path, embedding_dimension):
+    import uuid
+
+    import weaviate
+
+    # Connect to local Weaviate instance
+    client = weaviate.connect_to_local(
+        host="localhost",
+        port=8080,
+    )
+
+    collection_name = f"{COLLECTION_PREFIX}_{uuid.uuid4()}"
+    index = WeaviateIndex(client=client, collection_name=collection_name)
+
+    # Create the collection for this test
+    import weaviate.classes as wvc
+    from weaviate.collections.classes.config import _CollectionConfig
+
+    from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collection_name
+
+    sanitized_name = sanitize_collection_name(collection_name, weaviate_format=True)
+    collection_config = _CollectionConfig(
+        name=sanitized_name,
+        vectorizer_config=wvc.config.Configure.Vectorizer.none(),
+        properties=[
+            wvc.config.Property(
+                name="chunk_content",
+                data_type=wvc.config.DataType.TEXT,
+            ),
+        ],
+    )
+    if not client.collections.exists(sanitized_name):
+        client.collections.create_from_config(collection_config)
+
+    yield index
+    await index.delete()
+    client.close()
+
+
+@pytest.fixture
+async def weaviate_vec_adapter(weaviate_vec_db_path, mock_inference_api, embedding_dimension):
+    config = WeaviateVectorIOConfig(
+        weaviate_cluster_url=weaviate_vec_db_path,
+        weaviate_api_key=None,
+        kvstore=SqliteKVStoreConfig(),
+    )
+    adapter = WeaviateVectorIOAdapter(
+        config=config,
+        inference_api=mock_inference_api,
+        files_api=None,
+    )
+    collection_id = f"weaviate_test_collection_{random.randint(1, 1_000_000)}"
+    await adapter.initialize()
+    await adapter.register_vector_db(
+        VectorDB(
+            identifier=collection_id,
+            provider_id="test_provider",
+            embedding_model="test_model",
+            embedding_dimension=embedding_dimension,
+        )
+    )
+    adapter.test_collection_id = collection_id
+    yield adapter
+    await adapter.shutdown()
 
 
 @pytest.fixture
@@ -457,6 +528,7 @@ def vector_io_adapter(vector_provider, request):
         "chroma": "chroma_vec_adapter",
         "qdrant": "qdrant_vec_adapter",
         "pgvector": "pgvector_vec_adapter",
+        "weaviate": "weaviate_vec_adapter",
     }
     return request.getfixturevalue(vector_provider_dict[vector_provider])