feat: implement keyword and hybrid search for Weaviate provider (#3264)

# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
- This PR implements keyword and hybrid search for Weaviate DB based on
its inbuilt functions.
- Added fixtures to conftest.py for Weaviate.
- Enabled integration tests for remote Weaviate on all 3 search modes.

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->
Closes #3010 

## Test Plan
<!-- Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.* -->
Unit tests and integration tests should pass on this PR.

tests/integration/vector_io/test_openai_vector_stores.py

@@ -22,16 +22,16 @@ def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models):
     vector_io_providers = [p for p in client_with_models.providers.list() if p.api == "vector_io"]
     for p in vector_io_providers:
         if p.provider_type in [
-            "inline::faiss",
-            "inline::sqlite-vec",
-            "inline::milvus",
             "inline::chromadb",
-            "remote::pgvector",
-            "remote::chromadb",
-            "remote::qdrant",
+            "inline::faiss",
+            "inline::milvus",
             "inline::qdrant",
-            "remote::weaviate",
+            "inline::sqlite-vec",
+            "remote::chromadb",
             "remote::milvus",
+            "remote::pgvector",
+            "remote::qdrant",
+            "remote::weaviate",
         ]:
             return
 
@@ -47,23 +47,25 @@ def skip_if_provider_doesnt_support_openai_vector_stores_search(client_with_mode
             "inline::milvus",
             "inline::chromadb",
             "inline::qdrant",
-            "remote::pgvector",
             "remote::chromadb",
-            "remote::weaviate",
-            "remote::qdrant",
             "remote::milvus",
+            "remote::pgvector",
+            "remote::qdrant",
+            "remote::weaviate",
         ],
         "keyword": [
+            "inline::milvus",
             "inline::sqlite-vec",
             "remote::milvus",
-            "inline::milvus",
             "remote::pgvector",
+            "remote::weaviate",
         ],
         "hybrid": [
-            "inline::sqlite-vec",
             "inline::milvus",
+            "inline::sqlite-vec",
             "remote::milvus",
             "remote::pgvector",
+            "remote::weaviate",
         ],
     }
     supported_providers = search_mode_support.get(search_mode, [])

tests/integration/vector_io/test_vector_io.py

@@ -138,8 +138,8 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding
 def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, embedding_model_id, embedding_dimension):
     vector_io_provider_params_dict = {
         "inline::milvus": {"score_threshold": -1.0},
-        "remote::qdrant": {"score_threshold": -1.0},
         "inline::qdrant": {"score_threshold": -1.0},
+        "remote::qdrant": {"score_threshold": -1.0},
     }
     vector_db_name = "test_precomputed_embeddings_db"
     register_response = client_with_empty_registry.vector_dbs.register(

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
 
@@ -448,6 +450,71 @@ async def pgvector_vec_adapter(mock_inference_api, embedding_dimension):
                         await adapter.shutdown()
 
 
+@pytest.fixture(scope="session")
+def weaviate_vec_db_path(tmp_path_factory):
+    db_path = str(tmp_path_factory.getbasetemp() / "test_weaviate.db")
+    return db_path
+
+
+@pytest.fixture
+async def weaviate_vec_index(weaviate_vec_db_path):
+    import pytest_socket
+    import weaviate
+
+    pytest_socket.enable_socket()
+    client = weaviate.connect_to_embedded(
+        hostname="localhost",
+        port=8080,
+        grpc_port=50051,
+        persistence_data_path=weaviate_vec_db_path,
+    )
+    index = WeaviateIndex(client=client, collection_name="Testcollection")
+    await index.initialize()
+    yield index
+    await index.delete()
+    client.close()
+
+
+@pytest.fixture
+async def weaviate_vec_adapter(weaviate_vec_db_path, mock_inference_api, embedding_dimension):
+    import pytest_socket
+    import weaviate
+
+    pytest_socket.enable_socket()
+
+    client = weaviate.connect_to_embedded(
+        hostname="localhost",
+        port=8080,
+        grpc_port=50051,
+        persistence_data_path=weaviate_vec_db_path,
+    )
+
+    config = WeaviateVectorIOConfig(
+        weaviate_cluster_url="localhost:8080",
+        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()
+    client.close()
+
+
 @pytest.fixture
 def vector_io_adapter(vector_provider, request):
     vector_provider_dict = {
@@ -457,6 +524,7 @@ 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])