feat: add input validation for search mode of rag query config (#2275)

# What does this PR do?
Adds input validation for mode in RagQueryConfig
This will prevent users from inputting search modes other than `vector`
and `keyword` for the time being with `hybrid` to follow when that
functionality is implemented.

## 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.*]
```
# Check out this PR and enter the LS directory
uv sync --extra dev
```
Run the quickstart
[example](https://llama-stack.readthedocs.io/en/latest/getting_started/#step-3-run-the-demo)
Alter the Agent to include a query_config
```
agent = Agent(
    client,
    model=model_id,
    instructions="You are a helpful assistant",
    tools=[
        {
            "name": "builtin::rag/knowledge_search",
            "args": {
                "vector_db_ids": [vector_db_id],
                "query_config": {
                    "mode": "i-am-not-vector", # Test for non valid search mode
                    "max_chunks": 6
                }
            },
        }
    ],
)
```
Ensure you get the following error:
```
400: {'errors': [{'loc': ['mode'], 'msg': "Value error, mode must be either 'vector' or 'keyword' if supported by the vector_io provider", 'type': 'value_error'}]}
```

## Running unit tests
```
uv sync --extra dev
uv run pytest tests/unit/rag/test_rag_query.py -v
```

[//]: # (## Documentation)

llama_stack/apis/tools/rag_tool.py

@@ -87,6 +87,20 @@ class RAGQueryGenerator(Enum):
     custom = "custom"
 
 
+@json_schema_type
+class RAGSearchMode(Enum):
+    """
+    Search modes for RAG query retrieval:
+    - VECTOR: Uses vector similarity search for semantic matching
+    - KEYWORD: Uses keyword-based search for exact matching
+    - HYBRID: Combines both vector and keyword search for better results
+    """
+
+    VECTOR = "vector"
+    KEYWORD = "keyword"
+    HYBRID = "hybrid"
+
+
 @json_schema_type
 class DefaultRAGQueryGeneratorConfig(BaseModel):
     type: Literal["default"] = "default"
@@ -128,7 +142,7 @@ class RAGQueryConfig(BaseModel):
     max_tokens_in_context: int = 4096
     max_chunks: int = 5
     chunk_template: str = "Result {index}\nContent: {chunk.content}\nMetadata: {metadata}\n"
-    mode: str | None = None
+    mode: RAGSearchMode | None = RAGSearchMode.VECTOR
     ranker: Ranker | None = Field(default=None)  # Only used for hybrid mode
 
     @field_validator("chunk_template")