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Implements AWS Bedrock inference provider using OpenAI-compatible endpoint for Llama models available through Bedrock. Closes: #3410 ## What does this PR do? Adds AWS Bedrock as an inference provider using the OpenAI-compatible endpoint. This lets us use Bedrock models (GPT-OSS, Llama) through the standard llama-stack inference API. The implementation uses LiteLLM's OpenAI client under the hood, so it gets all the OpenAI compatibility features. The provider handles per-request API key overrides via headers. ## Test Plan **Tested the following scenarios:** - Non-streaming completion - basic request/response flow - Streaming completion - SSE streaming with chunked responses - Multi-turn conversations - context retention across turns - Tool calling - function calling with proper tool_calls format # Bedrock OpenAI-Compatible Provider - Test Results **Model:** `bedrock-inference/openai.gpt-oss-20b-1:0` --- ## Test 1: Model Listing **Request:** ```http GET /v1/models HTTP/1.1 ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: application/json { "data": [ {"identifier": "bedrock-inference/openai.gpt-oss-20b-1:0", ...}, {"identifier": "bedrock-inference/openai.gpt-oss-40b-1:0", ...} ] } ``` --- ## Test 2: Non-Streaming Completion **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "bedrock-inference/openai.gpt-oss-20b-1:0", "messages": [{"role": "user", "content": "Say 'Hello from Bedrock' and nothing else"}], "stream": false } ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: application/json { "choices": [{ "finish_reason": "stop", "message": {"content": "...Hello from Bedrock"} }], "usage": {"prompt_tokens": 79, "completion_tokens": 50, "total_tokens": 129} } ``` --- ## Test 3: Streaming Completion **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "bedrock-inference/openai.gpt-oss-20b-1:0", "messages": [{"role": "user", "content": "Count from 1 to 5"}], "stream": true } ``` **Response:** ```http HTTP/1.1 200 OK Content-Type: text/event-stream [6 SSE chunks received] Final content: "1, 2, 3, 4, 5" ``` --- ## Test 4: Error Handling - Invalid Model **Request:** ```http POST /v1/chat/completions HTTP/1.1 Content-Type: application/json { "model": "invalid-model-id", "messages": [{"role": "user", "content": "Hello"}], "stream": false } ``` **Response:** ```http HTTP/1.1 404 Not Found Content-Type: application/json { "detail": "Model 'invalid-model-id' not found. Use 'client.models.list()' to list available Models." } ``` --- ## Test 5: Multi-Turn Conversation **Request 1:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "My name is Alice"}] } ``` **Response 1:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Nice to meet you, Alice! How can I help you today?"} }] } ``` **Request 2 (with history):** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [ {"role": "user", "content": "My name is Alice"}, {"role": "assistant", "content": "...Nice to meet you, Alice!..."}, {"role": "user", "content": "What is my name?"} ] } ``` **Response 2:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Your name is Alice."} }], "usage": {"prompt_tokens": 183, "completion_tokens": 42} } ``` **Context retained across turns** --- ## Test 6: System Messages **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [ {"role": "system", "content": "You are Shakespeare. Respond only in Shakespearean English."}, {"role": "user", "content": "Tell me about the weather"} ] } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "Lo! I heed thy request..."} }], "usage": {"completion_tokens": 813} } ``` --- ## Test 7: Tool Calling **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "What's the weather in San Francisco?"}], "tools": [{ "type": "function", "function": { "name": "get_weather", "parameters": {"type": "object", "properties": {"location": {"type": "string"}}} } }] } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "finish_reason": "tool_calls", "message": { "tool_calls": [{ "function": {"name": "get_weather", "arguments": "{\"location\":\"San Francisco\"}"} }] } }] } ``` --- ## Test 8: Sampling Parameters **Request:** ```http POST /v1/chat/completions HTTP/1.1 { "messages": [{"role": "user", "content": "Say hello"}], "temperature": 0.7, "top_p": 0.9 } ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Hello! 👋 How can I help you today?"} }] } ``` --- ## Test 9: Authentication Error Handling ### Subtest A: Invalid API Key **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": "invalid-fake-key-12345"} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 400 Bad Request { "detail": "Invalid value: Authentication failed: Error code: 401 - {'error': {'message': 'Invalid API Key format: Must start with pre-defined prefix', ...}}" } ``` --- ### Subtest B: Empty API Key (Fallback to Config) **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": ""} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 200 OK { "choices": [{ "message": {"content": "...Hello! How can I assist you today?"} }] } ``` **Fell back to config key** --- ### Subtest C: Malformed Token **Request:** ```http POST /v1/chat/completions HTTP/1.1 x-llamastack-provider-data: {"aws_bedrock_api_key": "not-a-valid-bedrock-token-format"} {"model": "bedrock-inference/openai.gpt-oss-20b-1:0", ...} ``` **Response:** ```http HTTP/1.1 400 Bad Request { "detail": "Invalid value: Authentication failed: Error code: 401 - {'error': {'message': 'Invalid API Key format: Must start with pre-defined prefix', ...}}" } ```
78 lines
3.3 KiB
Python
78 lines
3.3 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from types import SimpleNamespace
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from openai import AuthenticationError
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from llama_stack.apis.inference import OpenAIChatCompletionRequestWithExtraBody
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from llama_stack.providers.remote.inference.bedrock.bedrock import BedrockInferenceAdapter
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from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
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def test_adapter_initialization():
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config = BedrockConfig(api_key="test-key", region_name="us-east-1")
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adapter = BedrockInferenceAdapter(config=config)
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assert adapter.config.auth_credential.get_secret_value() == "test-key"
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assert adapter.config.region_name == "us-east-1"
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def test_client_url_construction():
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config = BedrockConfig(api_key="test-key", region_name="us-west-2")
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adapter = BedrockInferenceAdapter(config=config)
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assert adapter.get_base_url() == "https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"
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def test_api_key_from_config():
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config = BedrockConfig(api_key="config-key", region_name="us-east-1")
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adapter = BedrockInferenceAdapter(config=config)
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assert adapter.config.auth_credential.get_secret_value() == "config-key"
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def test_api_key_from_header_overrides_config():
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"""Test API key from request header overrides config via client property"""
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config = BedrockConfig(api_key="config-key", region_name="us-east-1")
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adapter = BedrockInferenceAdapter(config=config)
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adapter.provider_data_api_key_field = "aws_bedrock_api_key"
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adapter.get_request_provider_data = MagicMock(return_value=SimpleNamespace(aws_bedrock_api_key="header-key"))
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# The client property is where header override happens (in OpenAIMixin)
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assert adapter.client.api_key == "header-key"
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async def test_authentication_error_handling():
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"""Test that AuthenticationError from OpenAI client is converted to ValueError with helpful message"""
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config = BedrockConfig(api_key="invalid-key", region_name="us-east-1")
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adapter = BedrockInferenceAdapter(config=config)
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# Mock the parent class method to raise AuthenticationError
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mock_response = MagicMock()
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mock_response.message = "Invalid authentication credentials"
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auth_error = AuthenticationError(message="Invalid authentication credentials", response=mock_response, body=None)
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# Create a mock that raises the error
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mock_super = AsyncMock(side_effect=auth_error)
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# Patch the parent class method
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original_method = BedrockInferenceAdapter.__bases__[0].openai_chat_completion
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BedrockInferenceAdapter.__bases__[0].openai_chat_completion = mock_super
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try:
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with pytest.raises(ValueError) as exc_info:
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params = OpenAIChatCompletionRequestWithExtraBody(
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model="test-model", messages=[{"role": "user", "content": "test"}]
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)
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await adapter.openai_chat_completion(params=params)
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assert "AWS Bedrock authentication failed" in str(exc_info.value)
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assert "Please verify your API key" in str(exc_info.value)
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finally:
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# Restore original method
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BedrockInferenceAdapter.__bases__[0].openai_chat_completion = original_method
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