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.

llama_stack/providers/utils/memory/vector_store.py

@@ -50,6 +50,7 @@ class ChunkForDeletion(BaseModel):
 # Constants for reranker types
 RERANKER_TYPE_RRF = "rrf"
 RERANKER_TYPE_WEIGHTED = "weighted"
+RERANKER_TYPE_NORMALIZED = "normalized"
 
 
 def parse_pdf(data: bytes) -> str:
@@ -325,6 +326,8 @@ class VectorDBWithIndex:
                 weights = ranker.get("params", {}).get("weights", [0.5, 0.5])
                 reranker_type = RERANKER_TYPE_WEIGHTED
                 reranker_params = {"alpha": weights[0] if len(weights) > 0 else 0.5}
+            elif strategy == "normalized":
+                reranker_type = RERANKER_TYPE_NORMALIZED
             else:
                 reranker_type = RERANKER_TYPE_RRF
                 k_value = ranker.get("params", {}).get("k", 60.0)