start of bedrock branch

Author: tschellenbach

agents-core/vision_agents/core/deepseek.py

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+
+
+"""
+There are various hosting for model providers
+- baseten
+- bedrock (converse API: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock-runtime/client/converse.html#)
+- cerebras
+- openrouter
+- vertex
+- fal
+- modal
+- ollama
+- wandb
+- azure AI foundry
+- together AI (Together SDK)
+- novita AI (openai)
+- Fireworks
+- Replicate
+- Hyperbolic
+"""
+
+import os
+
+from dotenv import load_dotenv
+from openai import OpenAI
+
+load_dotenv()
+
+def main():
+
+    client = OpenAI(
+        api_key=os.environ["BASETEN_API_KEY"],
+        base_url="https://inference.baseten.co/v1"
+    )
+
+    response = client.chat.completions.create(
+        model="deepseek-ai/DeepSeek-V3.1",
+        messages=[
+            {
+                "role": "user",
+                "content": "Implement FizzBuzz in Python"
+            }
+        ],
+        stop=[],
+        stream=True,
+        stream_options={
+            "include_usage": True,
+            "continuous_usage_stats": True
+        },
+        top_p=1,
+        max_tokens=1000,
+        temperature=1,
+        presence_penalty=0,
+        frequency_penalty=0
+    )
+
+    for chunk in response:
+        if chunk.choices and chunk.choices[0].delta.content is not None:
+            print(chunk.choices[0].delta.content, end="", flush=True)
+
+
+if __name__ == '__main__':
+    main()

plugins/bedrock/README.md

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+# AWS Bedrock Plugin for Vision Agents
+
+AWS Bedrock LLM integration for Vision Agents framework.
+
+## Installation
+
+```bash
+pip install vision-agents-plugins-bedrock
+```
+
+## Usage
+
+```python
+from vision_agents.plugins import bedrock
+
+# Initialize the Bedrock LLM
+llm = bedrock.LLM(
+    model="anthropic.claude-3-5-sonnet-20241022-v2:0",
+    region_name="us-east-1"
+)
+
+# Simple response
+response = await llm.simple_response("Hello, how are you?")
+print(response.text)
+```
+
+## Configuration
+
+The plugin uses boto3 for AWS authentication. You can configure credentials using:
+- AWS credentials file (~/.aws/credentials)
+- Environment variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY)
+- IAM roles (when running on AWS services)
+
+## Supported Models
+
+All AWS Bedrock models are supported, including:
+- Claude 3.5 models (anthropic.claude-*)
+- Amazon Titan models (amazon.titan-*)
+- Meta Llama models (meta.llama-*)
+- And more
+
+See [AWS Bedrock documentation](https://docs.aws.amazon.com/bedrock/) for available models.
+

plugins/bedrock/py.typed

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+[build-system]
+requires = ["hatchling", "hatch-vcs"]
+build-backend = "hatchling.build"
+
+[project]
+name = "vision-agents-plugins-bedrock"
+dynamic = ["version"]
+description = "AWS Bedrock LLM integration for Vision Agents"
+readme = "README.md"
+keywords = ["bedrock", "aws", "LLM", "AI", "voice agents", "agents"]
+requires-python = ">=3.10"
+license = "MIT"
+dependencies = [
+    "vision-agents",
+    "boto3>=1.28.0",
+]
+
+[project.urls]
+Documentation = "https://visionagents.ai/"
+Website = "https://visionagents.ai/"
+Source = "https://github.com/GetStream/Vision-Agents"
+
+[tool.hatch.version]
+source = "vcs"
+raw-options = { root = "..", search_parent_directories = true, fallback_version = "0.0.0" }
+
+[tool.hatch.build.targets.wheel]
+packages = ["."]
+
+[tool.hatch.build.targets.sdist]
+include = ["/vision_agents"]
+
+[tool.uv.sources]
+vision-agents = { workspace = true }
+
+[dependency-groups]
+dev = [
+    "pytest>=8.4.1",
+    "pytest-asyncio>=1.0.0",
+]
+

plugins/bedrock/pyproject.toml

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+"""Tests for AWS Bedrock plugin."""
+import pytest
+from dotenv import load_dotenv
+
+from plugins.bedrock.vision_agents.plugins.bedrock.bedrock_llm import BedrockLLM
+from vision_agents.core.agents.conversation import InMemoryConversation
+from vision_agents.core.agents.conversation import Message
+from vision_agents.core.llm.events import LLMResponseChunkEvent
+
+load_dotenv()
+
+
+class TestBedrockLLM:
+    """Test suite for BedrockLLM class with real API calls."""
+
+    @pytest.fixture
+    async def llm(self) -> BedrockLLM:
+        """Test BedrockLLM initialization with a provided client."""
+        llm = BedrockLLM(
+            model="anthropic.claude-3-5-sonnet-20241022-v2:0",
+            region_name="us-east-1"
+        )
+        llm._conversation = InMemoryConversation("be friendly", [])
+        return llm
+
+    @pytest.mark.asyncio
+    async def test_message(self, llm: BedrockLLM):
+        messages = BedrockLLM._normalize_message("say hi")
+        assert isinstance(messages[0], Message)
+        message = messages[0]
+        assert message.original is not None
+        assert message.content == "say hi"
+
+    @pytest.mark.asyncio
+    async def test_advanced_message(self, llm: BedrockLLM):
+        advanced = {
+            "role": "user",
+            "content": [{"text": "Explain quantum entanglement in simple terms."}],
+        }
+        messages2 = BedrockLLM._normalize_message(advanced)
+        assert messages2[0].original is not None
+
+    @pytest.mark.integration
+    async def test_simple(self, llm: BedrockLLM):
+        response = await llm.simple_response(
+            "Explain quantum computing in 1 paragraph",
+        )
+        assert response.text
+
+    @pytest.mark.integration
+    async def test_native_api(self, llm: BedrockLLM):
+        response = await llm.converse(
+            messages=[{"role": "user", "content": [{"text": "say hi"}]}],
+        )
+
+        # Assertions
+        assert response.text
+
+    @pytest.mark.integration
+    async def test_stream(self, llm: BedrockLLM):
+        streamingWorks = False
+        
+        @llm.events.subscribe
+        async def passed(event: LLMResponseChunkEvent):
+            nonlocal streamingWorks
+            streamingWorks = True
+
+        await llm.converse_stream(
+            messages=[{"role": "user", "content": [{"text": "Explain magma to a 5 year old"}]}]
+        )
+        # Wait for all events in queue to be processed
+        await llm.events.wait()
+
+        assert streamingWorks
+
+    @pytest.mark.integration
+    async def test_memory(self, llm: BedrockLLM):
+        await llm.simple_response(
+            text="There are 2 dogs in the room",
+        )
+        response = await llm.simple_response(
+            text="How many paws are there in the room?",
+        )
+
+        assert "8" in response.text or "eight" in response.text
+
+    @pytest.mark.integration
+    async def test_native_memory(self, llm: BedrockLLM):
+        await llm.converse(
+            messages=[{"role": "user", "content": [{"text": "There are 2 dogs in the room"}]}],
+        )
+        response = await llm.converse(
+            messages=[
+                {"role": "user", "content": [{"text": "How many paws are there in the room?"}]}
+            ],
+        )
+        assert "8" in response.text or "eight" in response.text
+

plugins/bedrock/tests/__init__.py

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+# Namespace package
+

plugins/bedrock/tests/test_bedrock.py

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+from .bedrock_llm import BedrockLLM as LLM
+
+__all__ = ["LLM"]
+