chore: Rename RagTool FileSearchTool

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -86,7 +86,7 @@ from .safety import SafetyException, ShieldRunnerMixin
 TOOLS_ATTACHMENT_KEY_REGEX = re.compile(r"__tools_attachment__=(\{.*?\})")
 MEMORY_QUERY_TOOL = "knowledge_search"
 WEB_SEARCH_TOOL = "web_search"
-RAG_TOOL_GROUP = "builtin::rag"
+RAG_TOOL_GROUP = "builtin::file_search"
 
 logger = get_logger(name=__name__, category="agents::meta_reference")
 
@@ -927,14 +927,14 @@ class ChatAgent(ShieldRunnerMixin):
         """Parse a toolgroup name into its components.
 
         Args:
-            toolgroup_name: The toolgroup name to parse (e.g. "builtin::rag/knowledge_search")
+            toolgroup_name: The toolgroup name to parse (e.g. "builtin::file_search/knowledge_search")
 
         Returns:
             A tuple of (tool_type, tool_group, tool_name)
         """
         split_names = toolgroup_name_with_maybe_tool_name.split("/")
         if len(split_names) == 2:
-            # e.g. "builtin::rag"
+            # e.g. "builtin::file_search"
             tool_group, tool_name = split_names
         else:
             tool_group, tool_name = split_names[0], None

llama_stack/providers/inline/tool_runtime/file_search/__init__.py

@@ -12,8 +12,8 @@ from .config import RagToolRuntimeConfig
 
 
 async def get_provider_impl(config: RagToolRuntimeConfig, deps: dict[Api, Any]):
-    from .memory import MemoryToolRuntimeImpl
+    from .file_search import FileSearchToolRuntimeImpl
 
-    impl = MemoryToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference], deps[Api.files])
+    impl = FileSearchToolRuntimeImpl(config, deps[Api.vector_io], deps[Api.inference], deps[Api.files])
     await impl.initialize()
     return impl

llama_stack/providers/inline/tool_runtime/file_search/context_retriever.py

@@ -9,19 +9,19 @@ from jinja2 import Template
 
 from llama_stack.apis.common.content_types import InterleavedContent
 from llama_stack.apis.inference import OpenAIChatCompletionRequestWithExtraBody, OpenAIUserMessageParam
-from llama_stack.apis.tools.rag_tool import (
-    DefaultRAGQueryGeneratorConfig,
-    LLMRAGQueryGeneratorConfig,
-    RAGQueryGenerator,
-    RAGQueryGeneratorConfig,
+from llama_stack.apis.tools.file_search_tool import (
+    DefaultFileSearchGeneratorConfig,
+    FileSearchGenerator,
+    FileSearchGeneratorConfig,
+    LLMFileSearchGeneratorConfig,
 )
 from llama_stack.providers.utils.inference.prompt_adapter import (
     interleaved_content_as_str,
 )
 
 
-async def generate_rag_query(
-    config: RAGQueryGeneratorConfig,
+async def generate_file_search_query(
+    config: FileSearchGeneratorConfig,
     content: InterleavedContent,
     **kwargs,
 ):
@@ -29,25 +29,25 @@ async def generate_rag_query(
     Generates a query that will be used for
     retrieving relevant information from the memory bank.
     """
-    if config.type == RAGQueryGenerator.default.value:
-        query = await default_rag_query_generator(config, content, **kwargs)
-    elif config.type == RAGQueryGenerator.llm.value:
-        query = await llm_rag_query_generator(config, content, **kwargs)
+    if config.type == FileSearchGenerator.default.value:
+        query = await default_file_search_query_generator(config, content, **kwargs)
+    elif config.type == FileSearchGenerator.llm.value:
+        query = await llm_file_search_query_generator(config, content, **kwargs)
     else:
         raise NotImplementedError(f"Unsupported memory query generator {config.type}")
     return query
 
 
-async def default_rag_query_generator(
-    config: DefaultRAGQueryGeneratorConfig,
+async def default_file_search_query_generator(
+    config: DefaultFileSearchGeneratorConfig,
     content: InterleavedContent,
     **kwargs,
 ):
     return interleaved_content_as_str(content, sep=config.separator)
 
 
-async def llm_rag_query_generator(
-    config: LLMRAGQueryGeneratorConfig,
+async def llm_file_search_query_generator(
+    config: LLMFileSearchGeneratorConfig,
     content: InterleavedContent,
     **kwargs,
 ):

llama_stack/providers/inline/tool_runtime/file_search/file_search.py

@@ -23,11 +23,11 @@ from llama_stack.apis.common.content_types import (
 from llama_stack.apis.files import Files, OpenAIFilePurpose
 from llama_stack.apis.inference import Inference
 from llama_stack.apis.tools import (
+    FileSearchConfig,
+    FileSearchResult,
+    FileSearchToolRuntime,
     ListToolDefsResponse,
     RAGDocument,
-    RAGQueryConfig,
-    RAGQueryResult,
-    RAGToolRuntime,
     ToolDef,
     ToolGroup,
     ToolInvocationResult,
@@ -45,7 +45,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import interleaved_con
 from llama_stack.providers.utils.memory.vector_store import parse_data_url
 
 from .config import RagToolRuntimeConfig
-from .context_retriever import generate_rag_query
+from .context_retriever import generate_file_search_query
 
 log = get_logger(name=__name__, category="tool_runtime")
 
@@ -91,7 +91,7 @@ async def raw_data_from_doc(doc: RAGDocument) -> tuple[bytes, str]:
             return content_str.encode("utf-8"), "text/plain"
 
 
-class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRuntime):
+class FileSearchToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, FileSearchToolRuntime):
     def __init__(
         self,
         config: RagToolRuntimeConfig,
@@ -177,15 +177,15 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
         self,
         content: InterleavedContent,
         vector_db_ids: list[str],
-        query_config: RAGQueryConfig | None = None,
-    ) -> RAGQueryResult:
+        query_config: FileSearchConfig | None = None,
+    ) -> FileSearchResult:
         if not vector_db_ids:
             raise ValueError(
                 "No vector DBs were provided to the knowledge search tool. Please provide at least one vector DB ID."
             )
 
-        query_config = query_config or RAGQueryConfig()
-        query = await generate_rag_query(
+        query_config = query_config or FileSearchConfig()
+        query = await generate_file_search_query(
             query_config.query_generator_config,
             content,
             inference_api=self.inference_api,
@@ -218,7 +218,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
                 scores.append(score)
 
         if not chunks:
-            return RAGQueryResult(content=None)
+            return FileSearchResult(content=None)
 
         # sort by score
         chunks, scores = zip(*sorted(zip(chunks, scores, strict=False), key=lambda x: x[1], reverse=True), strict=False)  # type: ignore
@@ -269,7 +269,7 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
             )
         )
 
-        return RAGQueryResult(
+        return FileSearchResult(
             content=picked,
             metadata={
                 "document_ids": [c.document_id for c in chunks[: len(picked)]],
@@ -312,9 +312,9 @@ class MemoryToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime, RAGToolRunti
         vector_db_ids = kwargs.get("vector_db_ids", [])
         query_config = kwargs.get("query_config")
         if query_config:
-            query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config)
+            query_config = TypeAdapter(FileSearchConfig).validate_python(query_config)
         else:
-            query_config = RAGQueryConfig()
+            query_config = FileSearchConfig()
 
         query = kwargs["query"]
         result = await self.query(

llama_stack/providers/registry/tool_runtime.py

@@ -18,7 +18,7 @@ def available_providers() -> list[ProviderSpec]:
     return [
         InlineProviderSpec(
             api=Api.tool_runtime,
-            provider_type="inline::rag-runtime",
+            provider_type="inline::file_search-runtime",
             pip_packages=DEFAULT_VECTOR_IO_DEPS
             + [
                 "tqdm",
@@ -29,8 +29,8 @@ def available_providers() -> list[ProviderSpec]:
                 "sentencepiece",
                 "transformers",
             ],
-            module="llama_stack.providers.inline.tool_runtime.rag",
-            config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig",
+            module="llama_stack.providers.inline.tool_runtime.file_search",
+            config_class="llama_stack.providers.inline.tool_runtime.file_search.config.RagToolRuntimeConfig",
             api_dependencies=[Api.vector_io, Api.inference, Api.files],
             description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.",
         ),

llama_stack/providers/registry/vector_io.py

@@ -241,33 +241,33 @@ Two ranker types are supported:
      - alpha=1: Only use vector scores
      - alpha=0.5: Equal weight to both (default)
 
-Example using RAGQueryConfig with different search modes:
+Example using FileSearchConfig with different search modes:
 
 ```python
-from llama_stack.apis.tools import RAGQueryConfig, RRFRanker, WeightedRanker
+from llama_stack.apis.tools import FileSearchConfig, RRFRanker, WeightedRanker
 
 # Vector search
-config = RAGQueryConfig(mode="vector", max_chunks=5)
+config = FileSearchConfig(mode="vector", max_chunks=5)
 
 # Keyword search
-config = RAGQueryConfig(mode="keyword", max_chunks=5)
+config = FileSearchConfig(mode="keyword", max_chunks=5)
 
 # Hybrid search with custom RRF ranker
-config = RAGQueryConfig(
+config = FileSearchConfig(
     mode="hybrid",
     max_chunks=5,
     ranker=RRFRanker(impact_factor=50.0),  # Custom impact factor
 )
 
 # Hybrid search with weighted ranker
-config = RAGQueryConfig(
+config = FileSearchConfig(
     mode="hybrid",
     max_chunks=5,
     ranker=WeightedRanker(alpha=0.7),  # 70% vector, 30% keyword
 )
 
 # Hybrid search with default RRF ranker
-config = RAGQueryConfig(
+config = FileSearchConfig(
     mode="hybrid", max_chunks=5
 )  # Will use RRF with impact_factor=60.0
 ```