adding the fiddlecube provider

llama_stack/providers/registry/safety.py

@@ -85,4 +85,13 @@ Provider `inline::meta-reference` for API `safety` does not work with the latest
                 config_class="llama_stack.providers.remote.safety.bedrock.BedrockSafetyConfig",
             ),
         ),
+        remote_provider_spec(
+            api=Api.safety,
+            adapter=AdapterSpec(
+                adapter_type="fiddlecube",
+                pip_packages=["httpx"],
+                module="llama_stack.providers.remote.safety.fiddlecube",
+                config_class="llama_stack.providers.remote.safety.fiddlecube.FiddlecubeSafetyConfig",
+            ),
+        ),
     ]

llama_stack/providers/remote/safety/fiddlecube/__init__.py

@@ -0,0 +1,18 @@
+# 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 .config import FiddlecubeSafetyConfig
+
+
+async def get_adapter_impl(config: FiddlecubeSafetyConfig, _deps) -> Any:
+    from .fiddlecube import FiddlecubeSafetyAdapter
+
+    impl = FiddlecubeSafetyAdapter(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/remote/safety/fiddlecube/config.py

@@ -0,0 +1,14 @@
+# 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 pydantic import BaseModel
+
+from llama_models.schema_utils import json_schema_type
+
+
+@json_schema_type
+class FiddlecubeSafetyConfig(BaseModel):
+    pass

llama_stack/providers/remote/safety/fiddlecube/fiddlecube.py

@@ -0,0 +1,104 @@
+# 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 json
+import logging
+
+from typing import Any, Dict, List
+
+from llama_stack.apis.inference import Message
+
+from llama_stack.apis.safety import (
+    RunShieldResponse,
+    Safety,
+    SafetyViolation,
+    ViolationLevel,
+)
+from llama_stack.apis.shields import Shield
+from llama_stack.providers.datatypes import ShieldsProtocolPrivate
+
+from .config import FiddlecubeSafetyConfig
+
+
+logger = logging.getLogger(__name__)
+
+
+class FiddlecubeSafetyAdapter(Safety, ShieldsProtocolPrivate):
+    def __init__(self, config: FiddlecubeSafetyConfig) -> None:
+        self.config = config
+        self.registered_shields = []
+
+    async def initialize(self) -> None:
+        pass
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def register_shield(self, shield: Shield) -> None:
+        pass
+
+    async def run_shield(
+        self, shield_id: str, messages: List[Message], params: Dict[str, Any] = None
+    ) -> RunShieldResponse:
+        # Set up FiddleCube API using httpx
+        # [TBD] convert the `messages` into format FiddleCube expects
+        # make a call to the API for guardrails
+        # convert the [TBD] response into the format RunShieldResponse expects
+        # return the response
+        return RunShieldResponse()
+
+        shield = await self.shield_store.get_shield(shield_id)
+        if not shield:
+            raise ValueError(f"Shield {shield_id} not found")
+
+        """This is the implementation for the bedrock guardrails. The input to the guardrails is to be of this format
+        ```content = [
+            {
+                "text": {
+                    "text": "Is the AB503 Product a better investment than the S&P 500?"
+                }
+            }
+        ]```
+        However the incoming messages are of this type UserMessage(content=....) coming from
+        https://github.com/meta-llama/llama-models/blob/main/models/llama3/api/datatypes.py
+
+        They contain content, role . For now we will extract the content and default the "qualifiers": ["query"]
+        """
+
+        shield_params = shield.params
+        logger.debug(f"run_shield::{shield_params}::messages={messages}")
+
+        # - convert the messages into format Bedrock expects
+        content_messages = []
+        for message in messages:
+            content_messages.append({"text": {"text": message.content}})
+        logger.debug(f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:")
+
+        response = self.bedrock_runtime_client.apply_guardrail(
+            guardrailIdentifier=shield.provider_resource_id,
+            guardrailVersion=shield_params["guardrailVersion"],
+            source="OUTPUT",  # or 'INPUT' depending on your use case
+            content=content_messages,
+        )
+        if response["action"] == "GUARDRAIL_INTERVENED":
+            user_message = ""
+            metadata = {}
+            for output in response["outputs"]:
+                # guardrails returns a list - however for this implementation we will leverage the last values
+                user_message = output["text"]
+            for assessment in response["assessments"]:
+                # guardrails returns a list - however for this implementation we will leverage the last values
+                metadata = dict(assessment)
+
+            return RunShieldResponse(
+                violation=SafetyViolation(
+                    user_message=user_message,
+                    violation_level=ViolationLevel.ERROR,
+                    metadata=metadata,
+                )
+            )
+
+        return RunShieldResponse()