Python: Bedrock connector

python/.cspell.json

@@ -27,6 +27,7 @@
         "aiplatform",
         "azuredocindex",
         "azuredocs",
+        "boto",
         "contentvector",
         "contoso",
         "datamodel",

python/pyproject.toml

@@ -114,6 +114,9 @@ weaviate = [
 pandas = [
     "pandas ~= 2.2"
 ]
+aws = [
+    "boto3>=1.28.57",
+]
 
 [tool.uv]
 prerelease = "if-necessary-or-explicit"

python/semantic_kernel/connectors/ai/bedrock/README.md

@@ -0,0 +1,5 @@
+## Configure AWS CLI
+
+1. Install the AWS CLI
+2. Configure the AWS CLI with your AWS credentials
+3. Configure the AWS CLI with the correct region

python/semantic_kernel/connectors/ai/bedrock/bedrock_prompt_execution_settings.py

@@ -0,0 +1,44 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+
+from typing import Any
+
+from pydantic import Field
+
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+
+
+class BedrockPromptExecutionSettings(PromptExecutionSettings):
+    """Bedrock Prompt Execution Settings."""
+
+    temperature: float | None = Field(None, ge=0.0, le=1.0)
+    top_p: float | None = Field(None, ge=0.0, le=1.0)
+    top_k: int | None = Field(None, gt=0)
+    max_tokens: int | None = Field(None, gt=0)
+    stop: list[str] = Field(default_factory=list)
+
+
+class BedrockChatPromptExecutionSettings(BedrockPromptExecutionSettings):
+    """Bedrock Chat Prompt Execution Settings."""
+
+    tools: list[dict[str, Any]] | None = Field(
+        None,
+        max_length=64,
+        description="Do not set this manually. It is set by the service based on the function choice configuration.",
+    )
+    tool_choice: dict[str, Any] | None = Field(
+        None,
+        description="Do not set this manually. It is set by the service based on the function choice configuration.",
+    )
+
+
+class BedrockTextPromptExecutionSettings(BedrockPromptExecutionSettings):
+    """Bedrock Text Prompt Execution Settings."""
+
+    ...
+
+
+class BedrockEmbeddingPromptExecutionSettings(PromptExecutionSettings):
+    """Bedrock Embedding Prompt Execution Settings."""
+
+    ...

python/semantic_kernel/connectors/ai/bedrock/bedrock_settings.py

@@ -0,0 +1,34 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from typing import ClassVar
+
+from semantic_kernel.kernel_pydantic import KernelBaseSettings
+from semantic_kernel.utils.experimental_decorator import experimental_class
+
+
+@experimental_class
+class BedrockSettings(KernelBaseSettings):
+    """Amazon Bedrock service settings.
+
+    The settings are first loaded from environment variables with
+    the prefix 'BEDROCK_'.
+    If the environment variables are not found, the settings can
+    be loaded from a .env file with the encoding 'utf-8'.
+    If the settings are not found in the .env file, the settings
+    are ignored; however, validation will fail alerting that the
+    settings are missing.
+
+    Optional settings for prefix 'BEDROCK_' are:
+        - chat_model_id: str | None - The Amazon Bedrock chat model ID to use.
+            (Env var BEDROCK_CHAT_MODEL_ID)
+        - text_model_id: str | None - The Amazon Bedrock text model ID to use.
+            (Env var BEDROCK_TEXT_MODEL_ID)
+        - embedding_model_id: str | None - The Amazon Bedrock embedding model ID to use.
+            (Env var BEDROCK_EMBEDDING_MODEL_ID)
+    """
+
+    env_prefix: ClassVar[str] = "BEDROCK_"
+
+    chat_model_id: str | None = None
+    text_model_id: str | None = None
+    embedding_model_id: str | None = None

python/semantic_kernel/connectors/ai/bedrock/services/bedrock_base.py

@@ -0,0 +1,32 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from abc import ABC
+from functools import partial
+from typing import Any, ClassVar
+
+from semantic_kernel.connectors.ai.bedrock.services.model_provider.utils import run_in_executor
+from semantic_kernel.kernel_pydantic import KernelBaseModel
+
+
+class BedrockBase(KernelBaseModel, ABC):
+    """Amazon Bedrock Service Base Class."""
+
+    MODEL_PROVIDER_NAME: ClassVar[str] = "bedrock"
+
+    # Amazon Bedrock Clients
+    # Runtime Client: Use for inference
+    bedrock_runtime_client: Any
+    # Client: Use for model management
+    bedrock_client: Any
+
+    async def get_foundation_model_info(self, model_id: str) -> dict[str, Any]:
+        """Get the foundation model information."""
+        response = await run_in_executor(
+            None,
+            partial(
+                self.bedrock_client.get_foundation_model,
+                modelIdentifier=model_id,
+            ),
+        )
+
+        return response.get("modelDetails")