Merge remote-tracking branch 'upstream/main'

llama_stack/distribution/access_control/datatypes.py

@@ -5,9 +5,9 @@
 # the root directory of this source tree.
 
 from enum import Enum
+from typing import Self
 
 from pydantic import BaseModel, model_validator
-from typing_extensions import Self
 
 from .conditions import parse_conditions
 

llama_stack/distribution/build.py

@@ -43,23 +43,12 @@ def get_provider_dependencies(
     config: BuildConfig | DistributionTemplate,
 ) -> tuple[list[str], list[str]]:
     """Get normal and special dependencies from provider configuration."""
-    # Extract providers based on config type
     if isinstance(config, DistributionTemplate):
-        providers = config.providers
+        config = config.build_config()
+
+    providers = config.distribution_spec.providers
+    additional_pip_packages = config.additional_pip_packages
 
-        # TODO: This is a hack to get the dependencies for internal APIs into build
-        # We should have a better way to do this by formalizing the concept of "internal" APIs
-        # and providers, with a way to specify dependencies for them.
-        run_configs = config.run_configs
-        additional_pip_packages: list[str] = []
-        if run_configs:
-            for run_config in run_configs.values():
-                run_config_ = run_config.run_config(name="", providers={}, container_image=None)
-                if run_config_.inference_store:
-                    additional_pip_packages.extend(run_config_.inference_store.pip_packages)
-    elif isinstance(config, BuildConfig):
-        providers = config.distribution_spec.providers
-        additional_pip_packages = config.additional_pip_packages
     deps = []
     registry = get_provider_registry(config)
     for api_str, provider_or_providers in providers.items():
@@ -87,8 +76,7 @@ def get_provider_dependencies(
         else:
             normal_deps.append(package)
 
-    if additional_pip_packages:
-        normal_deps.extend(additional_pip_packages)
+    normal_deps.extend(additional_pip_packages or [])
 
     return list(set(normal_deps)), list(set(special_deps))
 
@@ -113,7 +101,7 @@ def build_image(
     template_or_config: str,
     run_config: str | None = None,
 ):
-    container_base = build_config.distribution_spec.container_image or "python:3.10-slim"
+    container_base = build_config.distribution_spec.container_image or "python:3.11-slim"
 
     normal_deps, special_deps = get_provider_dependencies(build_config)
     normal_deps += SERVER_DEPENDENCIES

llama_stack/distribution/build_conda_env.sh

@@ -49,7 +49,7 @@ ensure_conda_env_python310() {
   local env_name="$1"
   local pip_dependencies="$2"
   local special_pip_deps="$3"
-  local python_version="3.10"
+  local python_version="3.11"
 
   # Check if conda command is available
   if ! is_command_available conda; then

llama_stack/distribution/distribution.py

@@ -180,6 +180,7 @@ def get_provider_registry(
                         if provider_type_key in ret[api]:
                             logger.warning(f"Overriding already registered provider {provider_type_key} for {api.name}")
                         ret[api][provider_type_key] = spec
+                        logger.info(f"Successfully loaded external provider {provider_type_key}")
                     except yaml.YAMLError as yaml_err:
                         logger.error(f"Failed to parse YAML file {spec_path}: {yaml_err}")
                         raise yaml_err

llama_stack/distribution/providers.py

@@ -99,7 +99,7 @@ class ProviderImpl(Providers):
             try:
                 health = await asyncio.wait_for(impl.health(), timeout=timeout)
                 return api_name, health
-            except (asyncio.TimeoutError, TimeoutError):
+            except TimeoutError:
                 return (
                     api_name,
                     HealthResponse(

llama_stack/distribution/resolver.py

@@ -335,7 +335,7 @@ async def instantiate_provider(
         method = "get_auto_router_impl"
 
         config = None
-        args = [provider_spec.api, deps[provider_spec.routing_table_api], deps, run_config]
+        args = [provider_spec.api, deps[provider_spec.routing_table_api], deps, run_config, policy]
     elif isinstance(provider_spec, RoutingTableProviderSpec):
         method = "get_routing_table_impl"
 
@@ -394,9 +394,13 @@ def check_protocol_compliance(obj: Any, protocol: Any) -> None:
                     logger.error(f"Method {name} incompatible proto: {proto_params} vs. obj: {obj_params}")
                     missing_methods.append((name, "signature_mismatch"))
                 else:
-                    # Check if the method is actually implemented in the class
-                    method_owner = next((cls for cls in mro if name in cls.__dict__), None)
-                    if method_owner is None or method_owner.__name__ == protocol.__name__:
+                    # Check if the method has a concrete implementation (not just a protocol stub)
+                    # Find all classes in MRO that define this method
+                    method_owners = [cls for cls in mro if name in cls.__dict__]
+
+                    # Allow methods from mixins/parents, only reject if ONLY the protocol defines it
+                    if len(method_owners) == 1 and method_owners[0].__name__ == protocol.__name__:
+                        # Only reject if the method is ONLY defined in the protocol itself (abstract stub)
                         missing_methods.append((name, "not_actually_implemented"))
 
     if missing_methods:

llama_stack/distribution/routers/__init__.py

@@ -47,7 +47,7 @@ async def get_routing_table_impl(
 
 
 async def get_auto_router_impl(
-    api: Api, routing_table: RoutingTable, deps: dict[str, Any], run_config: StackRunConfig
+    api: Api, routing_table: RoutingTable, deps: dict[str, Any], run_config: StackRunConfig, policy: list[AccessRule]
 ) -> Any:
     from .datasets import DatasetIORouter
     from .eval_scoring import EvalRouter, ScoringRouter
@@ -78,7 +78,7 @@ async def get_auto_router_impl(
 
     # TODO: move pass configs to routers instead
     if api == Api.inference and run_config.inference_store:
-        inference_store = InferenceStore(run_config.inference_store)
+        inference_store = InferenceStore(run_config.inference_store, policy)
         await inference_store.initialize()
         api_to_dep_impl["store"] = inference_store
 

llama_stack/distribution/routers/inference.py

@@ -163,6 +163,9 @@ class InferenceRouter(Inference):
         messages: list[Message] | InterleavedContent,
         tool_prompt_format: ToolPromptFormat | None = None,
     ) -> int | None:
+        if not hasattr(self, "formatter") or self.formatter is None:
+            return None
+
         if isinstance(messages, list):
             encoded = self.formatter.encode_dialog_prompt(messages, tool_prompt_format)
         else:
@@ -423,6 +426,7 @@ class InferenceRouter(Inference):
         user: str | None = None,
         guided_choice: list[str] | None = None,
         prompt_logprobs: int | None = None,
+        suffix: str | None = None,
     ) -> OpenAICompletion:
         logger.debug(
             f"InferenceRouter.openai_completion: {model=}, {stream=}, {prompt=}",
@@ -453,6 +457,7 @@ class InferenceRouter(Inference):
             user=user,
             guided_choice=guided_choice,
             prompt_logprobs=prompt_logprobs,
+            suffix=suffix,
         )
 
         provider = self.routing_table.get_provider_impl(model_obj.identifier)
@@ -602,7 +607,7 @@ class InferenceRouter(Inference):
 
     async def health(self) -> dict[str, HealthResponse]:
         health_statuses = {}
-        timeout = 0.5
+        timeout = 1  # increasing the timeout to 1 second for health checks
         for provider_id, impl in self.routing_table.impls_by_provider_id.items():
             try:
                 # check if the provider has a health method
@@ -610,7 +615,7 @@ class InferenceRouter(Inference):
                     continue
                 health = await asyncio.wait_for(impl.health(), timeout=timeout)
                 health_statuses[provider_id] = health
-            except (asyncio.TimeoutError, TimeoutError):
+            except TimeoutError:
                 health_statuses[provider_id] = HealthResponse(
                     status=HealthStatus.ERROR,
                     message=f"Health check timed out after {timeout} seconds",

llama_stack/distribution/routers/vector_io.py

@@ -4,14 +4,32 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import asyncio
 from typing import Any
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
 )
-from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+from llama_stack.apis.models import ModelType
+from llama_stack.apis.vector_io import (
+    Chunk,
+    QueryChunksResponse,
+    SearchRankingOptions,
+    VectorIO,
+    VectorStoreDeleteResponse,
+    VectorStoreListResponse,
+    VectorStoreObject,
+    VectorStoreSearchResponsePage,
+)
+from llama_stack.apis.vector_io.vector_io import (
+    VectorStoreChunkingStrategy,
+    VectorStoreFileContentsResponse,
+    VectorStoreFileDeleteResponse,
+    VectorStoreFileObject,
+    VectorStoreFileStatus,
+)
 from llama_stack.log import get_logger
-from llama_stack.providers.datatypes import RoutingTable
+from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
 
 logger = get_logger(name=__name__, category="core")
 
@@ -34,6 +52,31 @@ class VectorIORouter(VectorIO):
         logger.debug("VectorIORouter.shutdown")
         pass
 
+    async def _get_first_embedding_model(self) -> tuple[str, int] | None:
+        """Get the first available embedding model identifier."""
+        try:
+            # Get all models from the routing table
+            all_models = await self.routing_table.get_all_with_type("model")
+
+            # Filter for embedding models
+            embedding_models = [
+                model
+                for model in all_models
+                if hasattr(model, "model_type") and model.model_type == ModelType.embedding
+            ]
+
+            if embedding_models:
+                dimension = embedding_models[0].metadata.get("embedding_dimension", None)
+                if dimension is None:
+                    raise ValueError(f"Embedding model {embedding_models[0].identifier} has no embedding dimension")
+                return embedding_models[0].identifier, dimension
+            else:
+                logger.warning("No embedding models found in the routing table")
+                return None
+        except Exception as e:
+            logger.error(f"Error getting embedding models: {e}")
+            return None
+
     async def register_vector_db(
         self,
         vector_db_id: str,
@@ -70,3 +113,272 @@ class VectorIORouter(VectorIO):
     ) -> QueryChunksResponse:
         logger.debug(f"VectorIORouter.query_chunks: {vector_db_id}")
         return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)
+
+    # OpenAI Vector Stores API endpoints
+    async def openai_create_vector_store(
+        self,
+        name: str,
+        file_ids: list[str] | None = None,
+        expires_after: dict[str, Any] | None = None,
+        chunking_strategy: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+        embedding_model: str | None = None,
+        embedding_dimension: int | None = None,
+        provider_id: str | None = None,
+        provider_vector_db_id: str | None = None,
+    ) -> VectorStoreObject:
+        logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
+
+        # If no embedding model is provided, use the first available one
+        if embedding_model is None:
+            embedding_model_info = await self._get_first_embedding_model()
+            if embedding_model_info is None:
+                raise ValueError("No embedding model provided and no embedding models available in the system")
+            embedding_model, embedding_dimension = embedding_model_info
+            logger.info(f"No embedding model specified, using first available: {embedding_model}")
+
+        vector_db_id = name
+        registered_vector_db = await self.routing_table.register_vector_db(
+            vector_db_id,
+            embedding_model,
+            embedding_dimension,
+            provider_id,
+            provider_vector_db_id,
+        )
+
+        return await self.routing_table.get_provider_impl(registered_vector_db.identifier).openai_create_vector_store(
+            vector_db_id,
+            file_ids=file_ids,
+            expires_after=expires_after,
+            chunking_strategy=chunking_strategy,
+            metadata=metadata,
+            embedding_model=embedding_model,
+            embedding_dimension=embedding_dimension,
+            provider_id=registered_vector_db.provider_id,
+            provider_vector_db_id=registered_vector_db.provider_resource_id,
+        )
+
+    async def openai_list_vector_stores(
+        self,
+        limit: int | None = 20,
+        order: str | None = "desc",
+        after: str | None = None,
+        before: str | None = None,
+    ) -> VectorStoreListResponse:
+        logger.debug(f"VectorIORouter.openai_list_vector_stores: limit={limit}")
+        # Route to default provider for now - could aggregate from all providers in the future
+        # call retrieve on each vector dbs to get list of vector stores
+        vector_dbs = await self.routing_table.get_all_with_type("vector_db")
+        all_stores = []
+        for vector_db in vector_dbs:
+            try:
+                vector_store = await self.routing_table.get_provider_impl(
+                    vector_db.identifier
+                ).openai_retrieve_vector_store(vector_db.identifier)
+                all_stores.append(vector_store)
+            except Exception as e:
+                logger.error(f"Error retrieving vector store {vector_db.identifier}: {e}")
+                continue
+
+        # Sort by created_at
+        reverse_order = order == "desc"
+        all_stores.sort(key=lambda x: x.created_at, reverse=reverse_order)
+
+        # Apply cursor-based pagination
+        if after:
+            after_index = next((i for i, store in enumerate(all_stores) if store.id == after), -1)
+            if after_index >= 0:
+                all_stores = all_stores[after_index + 1 :]
+
+        if before:
+            before_index = next((i for i, store in enumerate(all_stores) if store.id == before), len(all_stores))
+            all_stores = all_stores[:before_index]
+
+        # Apply limit
+        limited_stores = all_stores[:limit]
+
+        # Determine pagination info
+        has_more = len(all_stores) > limit
+        first_id = limited_stores[0].id if limited_stores else None
+        last_id = limited_stores[-1].id if limited_stores else None
+
+        return VectorStoreListResponse(
+            data=limited_stores,
+            has_more=has_more,
+            first_id=first_id,
+            last_id=last_id,
+        )
+
+    async def openai_retrieve_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreObject:
+        logger.debug(f"VectorIORouter.openai_retrieve_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store(vector_store_id)
+
+    async def openai_update_vector_store(
+        self,
+        vector_store_id: str,
+        name: str | None = None,
+        expires_after: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> VectorStoreObject:
+        logger.debug(f"VectorIORouter.openai_update_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store(
+            vector_store_id=vector_store_id,
+            name=name,
+            expires_after=expires_after,
+            metadata=metadata,
+        )
+
+    async def openai_delete_vector_store(
+        self,
+        vector_store_id: str,
+    ) -> VectorStoreDeleteResponse:
+        logger.debug(f"VectorIORouter.openai_delete_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        result = await provider.openai_delete_vector_store(vector_store_id)
+        # drop from registry
+        await self.routing_table.unregister_vector_db(vector_store_id)
+        return result
+
+    async def openai_search_vector_store(
+        self,
+        vector_store_id: str,
+        query: str | list[str],
+        filters: dict[str, Any] | None = None,
+        max_num_results: int | None = 10,
+        ranking_options: SearchRankingOptions | None = None,
+        rewrite_query: bool | None = False,
+    ) -> VectorStoreSearchResponsePage:
+        logger.debug(f"VectorIORouter.openai_search_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_search_vector_store(
+            vector_store_id=vector_store_id,
+            query=query,
+            filters=filters,
+            max_num_results=max_num_results,
+            ranking_options=ranking_options,
+            rewrite_query=rewrite_query,
+        )
+
+    async def openai_attach_file_to_vector_store(
+        self,
+        vector_store_id: str,
+        file_id: str,
+        attributes: dict[str, Any] | None = None,
+        chunking_strategy: VectorStoreChunkingStrategy | None = None,
+    ) -> VectorStoreFileObject:
+        logger.debug(f"VectorIORouter.openai_attach_file_to_vector_store: {vector_store_id}, {file_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_attach_file_to_vector_store(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+            attributes=attributes,
+            chunking_strategy=chunking_strategy,
+        )
+
+    async def openai_list_files_in_vector_store(
+        self,
+        vector_store_id: str,
+        limit: int | None = 20,
+        order: str | None = "desc",
+        after: str | None = None,
+        before: str | None = None,
+        filter: VectorStoreFileStatus | None = None,
+    ) -> list[VectorStoreFileObject]:
+        logger.debug(f"VectorIORouter.openai_list_files_in_vector_store: {vector_store_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_list_files_in_vector_store(
+            vector_store_id=vector_store_id,
+            limit=limit,
+            order=order,
+            after=after,
+            before=before,
+            filter=filter,
+        )
+
+    async def openai_retrieve_vector_store_file(
+        self,
+        vector_store_id: str,
+        file_id: str,
+    ) -> VectorStoreFileObject:
+        logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file: {vector_store_id}, {file_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+        )
+
+    async def openai_retrieve_vector_store_file_contents(
+        self,
+        vector_store_id: str,
+        file_id: str,
+    ) -> VectorStoreFileContentsResponse:
+        logger.debug(f"VectorIORouter.openai_retrieve_vector_store_file_contents: {vector_store_id}, {file_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_retrieve_vector_store_file_contents(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+        )
+
+    async def openai_update_vector_store_file(
+        self,
+        vector_store_id: str,
+        file_id: str,
+        attributes: dict[str, Any],
+    ) -> VectorStoreFileObject:
+        logger.debug(f"VectorIORouter.openai_update_vector_store_file: {vector_store_id}, {file_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_update_vector_store_file(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+            attributes=attributes,
+        )
+
+    async def openai_delete_vector_store_file(
+        self,
+        vector_store_id: str,
+        file_id: str,
+    ) -> VectorStoreFileDeleteResponse:
+        logger.debug(f"VectorIORouter.openai_delete_vector_store_file: {vector_store_id}, {file_id}")
+        # Route based on vector store ID
+        provider = self.routing_table.get_provider_impl(vector_store_id)
+        return await provider.openai_delete_vector_store_file(
+            vector_store_id=vector_store_id,
+            file_id=file_id,
+        )
+
+    async def health(self) -> dict[str, HealthResponse]:
+        health_statuses = {}
+        timeout = 1  # increasing the timeout to 1 second for health checks
+        for provider_id, impl in self.routing_table.impls_by_provider_id.items():
+            try:
+                # check if the provider has a health method
+                if not hasattr(impl, "health"):
+                    continue
+                health = await asyncio.wait_for(impl.health(), timeout=timeout)
+                health_statuses[provider_id] = health
+            except TimeoutError:
+                health_statuses[provider_id] = HealthResponse(
+                    status=HealthStatus.ERROR,
+                    message=f"Health check timed out after {timeout} seconds",
+                )
+            except NotImplementedError:
+                health_statuses[provider_id] = HealthResponse(status=HealthStatus.NOT_IMPLEMENTED)
+            except Exception as e:
+                health_statuses[provider_id] = HealthResponse(
+                    status=HealthStatus.ERROR, message=f"Health check failed: {str(e)}"
+                )
+        return health_statuses

llama_stack/distribution/server/auth_providers.py

@@ -9,12 +9,12 @@ import time
 from abc import ABC, abstractmethod
 from asyncio import Lock
 from pathlib import Path
+from typing import Self
 from urllib.parse import parse_qs
 
 import httpx
 from jose import jwt
 from pydantic import BaseModel, Field, field_validator, model_validator
-from typing_extensions import Self
 
 from llama_stack.distribution.datatypes import AuthenticationConfig, AuthProviderType, User
 from llama_stack.log import get_logger
@@ -84,6 +84,7 @@ def get_attributes_from_claims(claims: dict[str, str], mapping: dict[str, str])
 class OAuth2JWKSConfig(BaseModel):
     # The JWKS URI for collecting public keys
     uri: str
+    token: str | None = Field(default=None, description="token to authorise access to jwks")
     key_recheck_period: int = Field(default=3600, description="The period to recheck the JWKS URI for key updates")
 
 
@@ -246,9 +247,12 @@ class OAuth2TokenAuthProvider(AuthProvider):
             if self.config.jwks is None:
                 raise ValueError("JWKS is not configured")
             if time.time() - self._jwks_at > self.config.jwks.key_recheck_period:
+                headers = {}
+                if self.config.jwks.token:
+                    headers["Authorization"] = f"Bearer {self.config.jwks.token}"
                 verify = self.config.tls_cafile.as_posix() if self.config.tls_cafile else self.config.verify_tls
                 async with httpx.AsyncClient(verify=verify) as client:
-                    res = await client.get(self.config.jwks.uri, timeout=5)
+                    res = await client.get(self.config.jwks.uri, timeout=5, headers=headers)
                     res.raise_for_status()
                     jwks_data = res.json()["keys"]
                     updated = {}

llama_stack/distribution/server/quota.py

@@ -6,7 +6,7 @@
 
 import json
 import time
-from datetime import datetime, timedelta, timezone
+from datetime import UTC, datetime, timedelta
 
 from starlette.types import ASGIApp, Receive, Scope, Send
 
@@ -79,7 +79,7 @@ class QuotaMiddleware:
 
                 if int(prev) == 0:
                     # Set with expiration datetime when it is the first request in the window.
-                    expiration = datetime.now(timezone.utc) + timedelta(seconds=self.window_seconds)
+                    expiration = datetime.now(UTC) + timedelta(seconds=self.window_seconds)
                     await kv.set(key, str(count), expiration=expiration)
                 else:
                     await kv.set(key, str(count))

llama_stack/distribution/server/server.py

@@ -30,6 +30,7 @@ from fastapi.responses import JSONResponse, StreamingResponse
 from openai import BadRequestError
 from pydantic import BaseModel, ValidationError
 
+from llama_stack.apis.common.responses import PaginatedResponse
 from llama_stack.distribution.datatypes import AuthenticationRequiredError, LoggingConfig, StackRunConfig
 from llama_stack.distribution.distribution import builtin_automatically_routed_apis
 from llama_stack.distribution.request_headers import PROVIDER_DATA_VAR, User, request_provider_data_context
@@ -144,7 +145,7 @@ async def shutdown(app):
                 await asyncio.wait_for(impl.shutdown(), timeout=5)
             else:
                 logger.warning("No shutdown method for %s", impl_name)
-        except (asyncio.TimeoutError, TimeoutError):
+        except TimeoutError:
             logger.exception("Shutdown timeout for %s ", impl_name, exc_info=True)
         except (Exception, asyncio.CancelledError) as e:
             logger.exception("Failed to shutdown %s: %s", impl_name, {e})
@@ -230,7 +231,10 @@ def create_dynamic_typed_route(func: Any, method: str, route: str) -> Callable:
                     return StreamingResponse(gen, media_type="text/event-stream")
                 else:
                     value = func(**kwargs)
-                    return await maybe_await(value)
+                    result = await maybe_await(value)
+                    if isinstance(result, PaginatedResponse) and result.url is None:
+                        result.url = route
+                    return result
             except Exception as e:
                 logger.exception(f"Error executing endpoint {route=} {method=}")
                 raise translate_exception(e) from e

llama_stack/distribution/ui/page/distribution/__init__.py

@@ -0,0 +1,5 @@
+# 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.