add bedrock shields support

distributions/bedrock/run.yaml

@@ -26,9 +26,13 @@ providers:
       provider_type: meta-reference
       config: {}
   safety:
-    - provider_id: meta0
-      provider_type: meta-reference
-      config: {}
+    - provider_id: bedrock0
+      provider_type: remote::bedrock
+      config:
+        aws_access_key_id: <AWS_ACCESS_KEY_ID>
+        aws_secret_access_key: <AWS_SECRET_ACCESS_KEY>
+        aws_session_token: <AWS_SESSION_TOKEN>
+        region_name: <AWS_REGION>
   agents:
     - provider_id: meta0
       provider_type: meta-reference

docs/source/getting_started/distributions/remote_hosted_distro/bedrock.md

@@ -0,0 +1,58 @@
+# Bedrock Distribution
+
+### Connect to a Llama Stack Bedrock Endpoint
+- You may connect to Amazon Bedrock APIs for running LLM inference
+
+The `llamastack/distribution-bedrock` distribution consists of the following provider configurations.
+
+
+| **API**         	| **Inference** 	| **Agents**     	| **Memory**     	| **Safety**     	| **Telemetry**  	|
+|-----------------	|---------------	|----------------	|----------------	|----------------	|----------------	|
+| **Provider(s)** 	| remote::bedrock | meta-reference 	| meta-reference 	| remote::bedrock | meta-reference 	|
+
+
+### Docker: Start the Distribution (Single Node CPU)
+
+> [!NOTE]
+> This assumes you have valid AWS credentials configured with access to Amazon Bedrock.
+
+```
+$ cd distributions/bedrock && docker compose up
+```
+
+Make sure in your `run.yaml` file, your inference provider is pointing to the correct AWS configuration. E.g.
+```
+inference:
+  - provider_id: bedrock0
+    provider_type: remote::bedrock
+    config:
+      aws_access_key_id: <AWS_ACCESS_KEY_ID>
+      aws_secret_access_key: <AWS_SECRET_ACCESS_KEY>
+      aws_session_token: <AWS_SESSION_TOKEN>
+      region_name: <AWS_REGION>
+```
+
+### Conda llama stack run (Single Node CPU)
+
+```bash
+llama stack build --template bedrock --image-type conda
+# -- modify run.yaml with valid AWS credentials
+llama stack run ./run.yaml
+```
+
+### (Optional) Update Model Serving Configuration
+
+Use `llama-stack-client models list` to check the available models served by Amazon Bedrock.
+
+```
+$ llama-stack-client models list
++------------------------------+------------------------------+---------------+------------+
+| identifier                   | llama_model                  | provider_id   | metadata   |
++==============================+==============================+===============+============+
+| Llama3.1-8B-Instruct         | meta.llama3-1-8b-instruct-v1:0 | bedrock0     | {}         |
++------------------------------+------------------------------+---------------+------------+
+| Llama3.1-70B-Instruct        | meta.llama3-1-70b-instruct-v1:0 | bedrock0     | {}         |
++------------------------------+------------------------------+---------------+------------+
+| Llama3.1-405B-Instruct       | meta.llama3-1-405b-instruct-v1:0 | bedrock0     | {}         |
++------------------------------+------------------------------+---------------+------------+
+```

llama_stack/providers/adapters/safety/bedrock/bedrock.py

@@ -25,20 +25,33 @@ BEDROCK_SUPPORTED_SHIELDS = [
     ShieldType.generic_content_shield.value,
 ]
 
+def _create_bedrock_client(config: BedrockSafetyConfig, name: str) :
+    session_args = {
+        k: v
+        for k, v in dict(
+            aws_access_key_id=config.aws_access_key_id,
+            aws_secret_access_key=config.aws_secret_access_key,
+            aws_session_token=config.aws_session_token,
+            region_name=config.region_name,
+            profile_name=config.profile_name,
+        ).items()
+        if v is not None
+    }
+
+    boto3_session = boto3.session.Session(**session_args)
+
+    return boto3_session.client(name)
+
 
 class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):
     def __init__(self, config: BedrockSafetyConfig) -> None:
-        if not config.aws_profile:
-            raise ValueError(f"Missing boto_client aws_profile in model info::{config}")
         self.config = config
         self.registered_shields = []
 
     async def initialize(self) -> None:
         try:
-            print(f"initializing with profile --- > {self.config}")
-            self.boto_client = boto3.Session(
-                profile_name=self.config.aws_profile
-            ).client("bedrock-runtime")
+            self.bedrock_runtime_client = _create_bedrock_client(self.config, "bedrock-runtime")
+            self.bedrock_client = _create_bedrock_client(self.config, "bedrock")
         except Exception as e:
             raise RuntimeError("Error initializing BedrockSafetyAdapter") from e
 
@@ -49,12 +62,18 @@ class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):
         raise ValueError("Registering dynamic shields is not supported")
 
     async def list_shields(self) -> List[ShieldDef]:
-        raise NotImplementedError(
-            """
-            `list_shields` not implemented; this should read all guardrails from
-            bedrock and populate guardrailId and guardrailVersion in the ShieldDef.
-        """
-        )
+        response = self.bedrock_client.list_guardrails()
+        shields = []
+        for guardrail in response["guardrails"]:
+            # populate the shield def with the guardrail id and version
+            shield_def = ShieldDef(
+                identifier=guardrail["id"],
+                shield_type=ShieldType.generic_content_shield.value,
+                params={"guardrailIdentifier": guardrail["id"], "guardrailVersion": guardrail["version"]},
+            )
+            shields.append(shield_def)
+        return shields
+
 
     async def run_shield(
         self, shield_type: str, messages: List[Message], params: Dict[str, Any] = None
@@ -88,7 +107,7 @@ class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):
             f"run_shield::final:messages::{json.dumps(content_messages, indent=2)}:"
         )
 
-        response = self.boto_client.apply_guardrail(
+        response = self.bedrock_runtime_client.apply_guardrail(
             guardrailIdentifier=shield_params["guardrailIdentifier"],
             guardrailVersion=shield_params["guardrailVersion"],
             source="OUTPUT",  # or 'INPUT' depending on your use case

llama_stack/providers/adapters/safety/bedrock/config.py

@@ -5,12 +5,29 @@
 # the root directory of this source tree.
 
 from pydantic import BaseModel, Field
-
+from typing import Optional
 
 class BedrockSafetyConfig(BaseModel):
     """Configuration information for a guardrail that you want to use in the request."""
 
-    aws_profile: str = Field(
+    aws_access_key_id: Optional[str] = Field(
+        default=None,
+        description="The AWS access key to use. Default use environment variable: AWS_ACCESS_KEY_ID",
+    )
+    aws_secret_access_key: Optional[str] = Field(
+        default=None,
+        description="The AWS secret access key to use. Default use environment variable: AWS_SECRET_ACCESS_KEY",
+    )
+    aws_session_token: Optional[str] = Field(
+        default=None,
+        description="The AWS session token to use. Default use environment variable: AWS_SESSION_TOKEN",
+    )
+    region_name: Optional[str] = Field(
+        default=None,
+        description="The default AWS Region to use, for example, us-west-1 or us-west-2."
+        "Default use environment variable: AWS_DEFAULT_REGION",
+    )
+    profile_name: str = Field(
         default="default",
         description="The profile on the machine having valid aws credentials. This will ensure separation of creation to invocation",
     )