Merge fb6763eef5 into 1d3f27fe5b

docs/source/distributions/self_hosted_distro/ollama.md

@@ -24,7 +24,7 @@ The `llamastack/distribution-ollama` distribution consists of the following prov
 | safety | `inline::llama-guard` |
 | scoring | `inline::basic`, `inline::llm-as-judge`, `inline::braintrust` |
 | telemetry | `inline::meta-reference` |
-| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
+| tool_runtime | `remote::brave-search`, `remote::tavily-search`, `inline::rag-runtime`, `inline::synthetic-data-kit`, `remote::model-context-protocol`, `remote::wolfram-alpha` |
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 

llama_stack/providers/inline/tool_runtime/synthetic-data-kit/__init__.py

@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any
+
+from llama_stack.providers.datatypes import Api
+
+from .config import SyntheticDataKitToolRuntimeConfig
+
+
+async def get_provider_impl(config: SyntheticDataKitToolRuntimeConfig, deps: dict[Api, Any]):
+    from .synthetic_data_kit import SyntheticDataKitToolRuntimeImpl
+
+    impl = SyntheticDataKitToolRuntimeImpl(config, deps[Api.files])
+    await impl.initialize()
+    return impl

llama_stack/providers/inline/tool_runtime/synthetic-data-kit/config.py

@@ -0,0 +1,15 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any
+
+from pydantic import BaseModel
+
+
+class SyntheticDataKitToolRuntimeConfig(BaseModel):
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
+        return {}

llama_stack/providers/inline/tool_runtime/synthetic-data-kit/synthetic_data_kit.py

@@ -0,0 +1,123 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+import asyncio
+import logging
+import mimetypes
+import os
+import tempfile
+from typing import Any
+
+from llama_stack.apis.common.content_types import URL
+from llama_stack.apis.files.files import Files
+from llama_stack.apis.tools import (
+    ListToolDefsResponse,
+    ToolDef,
+    ToolGroup,
+    ToolInvocationResult,
+    ToolParameter,
+    ToolRuntime,
+)
+from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
+from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type
+
+from .config import SyntheticDataKitToolRuntimeConfig
+
+log = logging.getLogger(__name__)
+
+
+class SyntheticDataKitToolRuntimeImpl(ToolGroupsProtocolPrivate, ToolRuntime):
+    def __init__(
+        self,
+        config: SyntheticDataKitToolRuntimeConfig,
+        files_api: Files,
+    ):
+        self.config = config
+        self.files_api = files_api
+
+    async def initialize(self):
+        pass
+
+    async def shutdown(self):
+        pass
+
+    async def register_toolgroup(self, toolgroup: ToolGroup) -> None:
+        pass
+
+    async def unregister_toolgroup(self, toolgroup_id: str) -> None:
+        return
+
+    async def list_runtime_tools(
+        self, tool_group_id: str | None = None, mcp_endpoint: URL | None = None
+    ) -> ListToolDefsResponse:
+        return ListToolDefsResponse(
+            data=[
+                ToolDef(
+                    name="convert_file_to_text",
+                    description="Convert a file to text",
+                    parameters=[
+                        ToolParameter(
+                            name="file_id",
+                            description="The id of the file to convert.",
+                            parameter_type="string",
+                        ),
+                    ],
+                ),
+            ]
+        )
+
+    async def invoke_tool(self, tool_name: str, kwargs: dict[str, Any]) -> ToolInvocationResult:
+        if tool_name != "convert_file_to_text":
+            raise ValueError(f"Unknown tool: {tool_name}")
+
+        file_id = kwargs["file_id"]
+        file_response = await self.files_api.openai_retrieve_file(file_id)
+        mime_type = self._guess_mime_type(file_response.filename)
+        content_response = await self.files_api.openai_retrieve_file_content(file_id)
+
+        mime_category = mime_type.split("/")[0] if mime_type else None
+        if mime_category == "text":
+            # Don't use synthetic-data-kit if the file is already text
+            content = content_from_data_and_mime_type(content_response.body, mime_type)
+            return ToolInvocationResult(
+                content=content,
+                metadata={},
+            )
+        else:
+            return await asyncio.to_thread(
+                self._synthetic_data_kit_convert, content_response.body, file_response.filename
+            )
+
+    def _guess_mime_type(self, filename: str) -> str | None:
+        mime_type, _ = mimetypes.guess_type(filename)
+        if mime_type is None and filename.endswith(".md"):
+            mime_type = "text/markdown"
+        return mime_type
+
+    def _synthetic_data_kit_convert(self, content_body: bytes, filename: str) -> ToolInvocationResult:
+        from synthetic_data_kit.core.ingest import process_file
+
+        try:
+            with tempfile.TemporaryDirectory() as tmpdir:
+                file_path = os.path.join(tmpdir, filename)
+                with open(file_path, "wb") as f:
+                    f.write(content_body)
+                output_path = process_file(file_path, tmpdir)
+                with open(output_path) as f:
+                    content = f.read()
+
+                    return ToolInvocationResult(
+                        content=content,
+                        metadata={},
+                    )
+        except Exception as e:
+            return ToolInvocationResult(
+                content="",
+                error_message=f"Error converting file: {e}",
+                error_code=1,
+                metadata={},
+            )

llama_stack/providers/inline/vector_io/faiss/__init__.py

@@ -16,6 +16,8 @@ async def get_provider_impl(config: FaissVectorIOConfig, deps: dict[Api, Any]):
 
     assert isinstance(config, FaissVectorIOConfig), f"Unexpected config type: {type(config)}"
 
-    impl = FaissVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files, None))
+    impl = FaissVectorIOAdapter(
+        config, deps[Api.inference], deps.get(Api.files, None), deps.get(Api.tool_runtime, None)
+    )
     await impl.initialize()
     return impl

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -18,6 +18,7 @@ from numpy.typing import NDArray
 from llama_stack.apis.files import Files
 from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.inference.inference import Inference
+from llama_stack.apis.tools.tools import ToolRuntime
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
     Chunk,
@@ -150,10 +151,17 @@ class FaissIndex(EmbeddingIndex):
 
 
 class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPrivate):
-    def __init__(self, config: FaissVectorIOConfig, inference_api: Inference, files_api: Files | None) -> None:
+    def __init__(
+        self,
+        config: FaissVectorIOConfig,
+        inference_api: Inference,
+        files_api: Files | None = None,
+        tool_runtime_api: ToolRuntime | None = None,
+    ) -> None:
         self.config = config
         self.inference_api = inference_api
         self.files_api = files_api
+        self.tool_runtime_api = tool_runtime_api
         self.cache: dict[str, VectorDBWithIndex] = {}
         self.kvstore: KVStore | None = None
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}

llama_stack/providers/inline/vector_io/sqlite_vec/__init__.py

@@ -15,6 +15,8 @@ async def get_provider_impl(config: SQLiteVectorIOConfig, deps: dict[Api, Any]):
     from .sqlite_vec import SQLiteVecVectorIOAdapter
 
     assert isinstance(config, SQLiteVectorIOConfig), f"Unexpected config type: {type(config)}"
-    impl = SQLiteVecVectorIOAdapter(config, deps[Api.inference], deps.get(Api.files, None))
+    impl = SQLiteVecVectorIOAdapter(
+        config, deps[Api.inference], deps.get(Api.files, None), deps.get(Api.tool_runtime, None)
+    )
     await impl.initialize()
     return impl

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -17,6 +17,7 @@ from numpy.typing import NDArray
 
 from llama_stack.apis.files.files import Files
 from llama_stack.apis.inference.inference import Inference
+from llama_stack.apis.tools.tools import ToolRuntime
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
     Chunk,
@@ -419,10 +420,13 @@ class SQLiteVecVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtoc
     and creates a cache of VectorDBWithIndex instances (each wrapping a SQLiteVecIndex).
     """
 
-    def __init__(self, config, inference_api: Inference, files_api: Files | None) -> None:
+    def __init__(
+        self, config, inference_api: Inference, files_api: Files | None, tool_runtime_api: ToolRuntime | None
+    ) -> None:
         self.config = config
         self.inference_api = inference_api
         self.files_api = files_api
+        self.tool_runtime_api = tool_runtime_api
         self.cache: dict[str, VectorDBWithIndex] = {}
         self.openai_vector_stores: dict[str, dict[str, Any]] = {}
 

llama_stack/providers/registry/tool_runtime.py

@@ -34,6 +34,14 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.tool_runtime.rag.config.RagToolRuntimeConfig",
             api_dependencies=[Api.vector_io, Api.inference],
         ),
+        InlineProviderSpec(
+            api=Api.tool_runtime,
+            provider_type="inline::synthetic-data-kit",
+            pip_packages=["synthetic-data-kit"],
+            module="llama_stack.providers.inline.tool_runtime.synthetic-data-kit",
+            config_class="llama_stack.providers.inline.tool_runtime.synthetic-data-kit.config.SyntheticDataKitToolRuntimeConfig",
+            api_dependencies=[Api.files],
+        ),
         remote_provider_spec(
             api=Api.tool_runtime,
             adapter=AdapterSpec(

llama_stack/providers/registry/vector_io.py

@@ -24,7 +24,7 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
             deprecation_warning="Please use the `inline::faiss` provider instead.",
             api_dependencies=[Api.inference],
-            optional_api_dependencies=[Api.files],
+            optional_api_dependencies=[Api.files, Api.tool_runtime],
         ),
         InlineProviderSpec(
             api=Api.vector_io,
@@ -33,7 +33,7 @@ def available_providers() -> list[ProviderSpec]:
             module="llama_stack.providers.inline.vector_io.faiss",
             config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
             api_dependencies=[Api.inference],
-            optional_api_dependencies=[Api.files],
+            optional_api_dependencies=[Api.files, Api.tool_runtime],
         ),
         # NOTE: sqlite-vec cannot be bundled into the container image because it does not have a
         # source distribution and the wheels are not available for all platforms.
@@ -44,7 +44,7 @@ def available_providers() -> list[ProviderSpec]:
             module="llama_stack.providers.inline.vector_io.sqlite_vec",
             config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
             api_dependencies=[Api.inference],
-            optional_api_dependencies=[Api.files],
+            optional_api_dependencies=[Api.files, Api.tool_runtime],
         ),
         InlineProviderSpec(
             api=Api.vector_io,
@@ -54,7 +54,7 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
             deprecation_warning="Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.",
             api_dependencies=[Api.inference],
-            optional_api_dependencies=[Api.files],
+            optional_api_dependencies=[Api.files, Api.tool_runtime],
         ),
         remote_provider_spec(
             Api.vector_io,

llama_stack/providers/utils/memory/openai_vector_store_mixin.py

@@ -6,14 +6,14 @@
 
 import asyncio
 import logging
-import mimetypes
 import time
 import uuid
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, cast
 
 from llama_stack.apis.files import Files
 from llama_stack.apis.files.files import OpenAIFileObject
+from llama_stack.apis.tools.tools import ToolRuntime
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import (
     Chunk,
@@ -36,7 +36,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreSearchResponse,
     VectorStoreSearchResponsePage,
 )
-from llama_stack.providers.utils.memory.vector_store import content_from_data_and_mime_type, make_overlapped_chunks
+from llama_stack.providers.utils.memory.vector_store import make_overlapped_chunks
 
 logger = logging.getLogger(__name__)
 
@@ -54,6 +54,7 @@ class OpenAIVectorStoreMixin(ABC):
     # These should be provided by the implementing class
     openai_vector_stores: dict[str, dict[str, Any]]
     files_api: Files | None
+    tool_runtime_api: ToolRuntime | None
 
     @abstractmethod
     async def _save_openai_vector_store(self, store_id: str, store_info: dict[str, Any]) -> None:
@@ -526,6 +527,14 @@ class OpenAIVectorStoreMixin(ABC):
             )
             return vector_store_file_object
 
+        if not hasattr(self, "tool_runtime_api") or not self.tool_runtime_api:
+            vector_store_file_object.status = "failed"
+            vector_store_file_object.last_error = VectorStoreFileLastError(
+                code="server_error",
+                message="Tool runtime API is not available",
+            )
+            return vector_store_file_object
+
         if isinstance(chunking_strategy, VectorStoreChunkingStrategyStatic):
             max_chunk_size_tokens = chunking_strategy.static.max_chunk_size_tokens
             chunk_overlap_tokens = chunking_strategy.static.chunk_overlap_tokens
@@ -536,11 +545,13 @@ class OpenAIVectorStoreMixin(ABC):
 
         try:
             file_response = await self.files_api.openai_retrieve_file(file_id)
-            mime_type, _ = mimetypes.guess_type(file_response.filename)
-            content_response = await self.files_api.openai_retrieve_file_content(file_id)
-
-            content = content_from_data_and_mime_type(content_response.body, mime_type)
-
+            tool_result = await self.tool_runtime_api.invoke_tool(
+                "convert_file_to_text",
+                {"file_id": file_id},
+            )
+            if tool_result.error_code or tool_result.error_message:
+                raise ValueError(f"Failed to convert file to text: {tool_result.error_message}")
+            content = cast(str, tool_result.content)  # The tool always returns strings
             chunks = make_overlapped_chunks(
                 file_id,
                 content,

llama_stack/templates/ollama/build.yaml

@@ -31,6 +31,7 @@ distribution_spec:
     - remote::brave-search
     - remote::tavily-search
     - inline::rag-runtime
+    - inline::synthetic-data-kit
     - remote::model-context-protocol
     - remote::wolfram-alpha
 image_type: conda

llama_stack/templates/ollama/ollama.py

@@ -36,6 +36,7 @@ def get_distribution_template() -> DistributionTemplate:
             "remote::brave-search",
             "remote::tavily-search",
             "inline::rag-runtime",
+            "inline::synthetic-data-kit",
             "remote::model-context-protocol",
             "remote::wolfram-alpha",
         ],
@@ -91,6 +92,10 @@ def get_distribution_template() -> DistributionTemplate:
             toolgroup_id="builtin::wolfram_alpha",
             provider_id="wolfram-alpha",
         ),
+        ToolGroupInput(
+            toolgroup_id="builtin::document_conversion",
+            provider_id="synthetic-data-kit",
+        ),
     ]
 
     return DistributionTemplate(

llama_stack/templates/ollama/run-with-safety.yaml

@@ -115,6 +115,9 @@ providers:
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
     config: {}
+  - provider_id: synthetic-data-kit
+    provider_type: inline::synthetic-data-kit
+    config: {}
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
     config: {}
@@ -159,5 +162,7 @@ tool_groups:
   provider_id: rag-runtime
 - toolgroup_id: builtin::wolfram_alpha
   provider_id: wolfram-alpha
+- toolgroup_id: builtin::document_conversion
+  provider_id: synthetic-data-kit
 server:
   port: 8321

llama_stack/templates/ollama/run.yaml

@@ -113,6 +113,9 @@ providers:
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
     config: {}
+  - provider_id: synthetic-data-kit
+    provider_type: inline::synthetic-data-kit
+    config: {}
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
     config: {}
@@ -149,5 +152,7 @@ tool_groups:
   provider_id: rag-runtime
 - toolgroup_id: builtin::wolfram_alpha
   provider_id: wolfram-alpha
+- toolgroup_id: builtin::document_conversion
+  provider_id: synthetic-data-kit
 server:
   port: 8321

llama_stack/templates/starter/build.yaml

@@ -38,6 +38,7 @@ distribution_spec:
     - remote::brave-search
     - remote::tavily-search
     - inline::rag-runtime
+    - inline::synthetic-data-kit
     - remote::model-context-protocol
 image_type: conda
 additional_pip_packages:

llama_stack/templates/starter/run.yaml

@@ -163,6 +163,9 @@ providers:
   - provider_id: rag-runtime
     provider_type: inline::rag-runtime
     config: {}
+  - provider_id: synthetic-data-kit
+    provider_type: inline::synthetic-data-kit
+    config: {}
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
     config: {}
@@ -822,5 +825,7 @@ tool_groups:
   provider_id: tavily-search
 - toolgroup_id: builtin::rag
   provider_id: rag-runtime
+- toolgroup_id: builtin::document_conversion
+  provider_id: synthetic-data-kit
 server:
   port: 8321

llama_stack/templates/starter/starter.py

@@ -163,6 +163,7 @@ def get_distribution_template() -> DistributionTemplate:
             "remote::brave-search",
             "remote::tavily-search",
             "inline::rag-runtime",
+            "inline::synthetic-data-kit",
             "remote::model-context-protocol",
         ],
     }
@@ -214,6 +215,10 @@ def get_distribution_template() -> DistributionTemplate:
             toolgroup_id="builtin::rag",
             provider_id="rag-runtime",
         ),
+        ToolGroupInput(
+            toolgroup_id="builtin::document_conversion",
+            provider_id="synthetic-data-kit",
+        ),
     ]
     embedding_model = ModelInput(
         model_id="all-MiniLM-L6-v2",

tests/verifications/openai_api/fixtures/docs/llama_stack_and_models.md

@@ -0,0 +1,27 @@
+# Llama Stack
+
+## Llama Stack Overview
+
+Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides
+
+* Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.
+
+* Plugin architecture to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile.
+
+* Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly and reliably in any environment.
+
+* Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android.
+
+* Standalone applications as examples for how to build production-grade AI applications with Llama Stack.
+
+## Llama Stack Benefits
+
+* Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices.
+
+* Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior.
+
+* Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models.
+
+# Llama 4 Maverick
+
+Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts.

tests/verifications/openai_api/fixtures/docs/llama_stack_and_models.txt

@@ -0,0 +1,24 @@
+Llama Stack
+
+
+Llama Stack Overview
+
+Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides
+
+* Unified API layer for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry.
+* Plugin architecture to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile.
+* Prepackaged verified distributions which offer a one-stop solution for developers to get started quickly and reliably in any environment.
+* Multiple developer interfaces like CLI and SDKs for Python, Typescript, iOS, and Android.
+* Standalone applications as examples for how to build production-grade AI applications with Llama Stack.
+
+
+Llama Stack Benefits
+
+* Flexible Options: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices.
+* Consistent Experience: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior.
+* Robust Ecosystem: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models.
+
+
+Llama 4 Maverick
+
+Llama 4 Maverick is a Mixture-of-Experts (MoE) model with 17 billion active parameters and 128 experts.

tests/verifications/openai_api/fixtures/test_cases/responses.yaml

@@ -42,12 +42,40 @@ test_response_file_search:
         # vector_store_ids param for file_search tool gets added by the test runner
       file_content: "Llama 4 Maverick has 128 experts"
       output: "128"
+    - case_id: "llama_experts_docx"
+      input: "How many experts does the Llama 4 Maverick model have?"
+      tools:
+      - type: file_search
+        # vector_store_ids param for file_search toolgets added by the test runner
+      file_path: "docs/llama_stack_and_models.docx"
+      output: "128"
+    - case_id: "llama_experts_md"
+      input: "How many experts does the Llama 4 Maverick model have?"
+      tools:
+      - type: file_search
+        # vector_store_ids param for file_search toolgets added by the test runner
+      file_path: "docs/llama_stack_and_models.md"
+      output: "128"
     - case_id: "llama_experts_pdf"
       input: "How many experts does the Llama 4 Maverick model have?"
       tools:
       - type: file_search
         # vector_store_ids param for file_search toolgets added by the test runner
-      file_path: "pdfs/llama_stack_and_models.pdf"
+      file_path: "docs/llama_stack_and_models.pdf"
+      output: "128"
+    - case_id: "llama_experts_pptx"
+      input: "How many experts does the Llama 4 Maverick model have?"
+      tools:
+      - type: file_search
+        # vector_store_ids param for file_search toolgets added by the test runner
+      file_path: "docs/llama_stack_and_models.pptx"
+      output: "128"
+    - case_id: "llama_experts_txt"
+      input: "How many experts does the Llama 4 Maverick model have?"
+      tools:
+      - type: file_search
+        # vector_store_ids param for file_search toolgets added by the test runner
+      file_path: "docs/llama_stack_and_models.txt"
       output: "128"
 
 test_response_mcp_tool: