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)

docs/_static/llama-stack-spec.html

@@ -14796,7 +14796,8 @@
                         "description": "Template for formatting each retrieved chunk in the context. Available placeholders: {index} (1-based chunk ordinal), {chunk.content} (chunk content string), {metadata} (chunk metadata dict). Default: \"Result {index}\\nContent: {chunk.content}\\nMetadata: {metadata}\\n\""
                     },
                     "mode": {
-                        "type": "string",
+                        "$ref": "#/components/schemas/RAGSearchMode",
+                        "default": "vector",
                         "description": "Search mode for retrieval—either \"vector\", \"keyword\", or \"hybrid\". Default \"vector\"."
                     },
                     "ranker": {
@@ -14831,6 +14832,16 @@
                     }
                 }
             },
+            "RAGSearchMode": {
+                "type": "string",
+                "enum": [
+                    "vector",
+                    "keyword",
+                    "hybrid"
+                ],
+                "title": "RAGSearchMode",
+                "description": "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"
+            },
             "RRFRanker": {
                 "type": "object",
                 "properties": {

docs/_static/llama-stack-spec.yaml

@@ -10346,7 +10346,8 @@ components:
             content string), {metadata} (chunk metadata dict). Default: "Result {index}\nContent:
             {chunk.content}\nMetadata: {metadata}\n"
         mode:
-          type: string
+          $ref: '#/components/schemas/RAGSearchMode'
+          default: vector
           description: >-
             Search mode for retrieval—either "vector", "keyword", or "hybrid". Default
             "vector".
@@ -10373,6 +10374,17 @@ components:
         mapping:
           default: '#/components/schemas/DefaultRAGQueryGeneratorConfig'
           llm: '#/components/schemas/LLMRAGQueryGeneratorConfig'
+    RAGSearchMode:
+      type: string
+      enum:
+        - vector
+        - keyword
+        - hybrid
+      title: RAGSearchMode
+      description: >-
+        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
     RRFRanker:
       type: object
       properties:

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")

tests/unit/rag/test_rag_query.py

@@ -8,6 +8,7 @@ from unittest.mock import AsyncMock, MagicMock
 
 import pytest
 
+from llama_stack.apis.tools.rag_tool import RAGQueryConfig
 from llama_stack.apis.vector_io import (
     Chunk,
     ChunkMetadata,
@@ -58,3 +59,14 @@ class TestRagQuery:
         )
         assert expected_metadata_string in result.content[1].text
         assert result.content is not None
+
+    async def test_query_raises_incorrect_mode(self):
+        with pytest.raises(ValueError):
+            RAGQueryConfig(mode="invalid_mode")
+
+    @pytest.mark.asyncio
+    async def test_query_accepts_valid_modes(self):
+        RAGQueryConfig()  # Test default (vector)
+        RAGQueryConfig(mode="vector")  # Test vector
+        RAGQueryConfig(mode="keyword")  # Test keyword
+        RAGQueryConfig(mode="hybrid")  # Test hybrid