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(