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* Phase 2: Embedding clients for Ollama, Bedrock, and Azure AI Inference Add embedding client implementations to existing provider packages: - OllamaEmbeddingClient: Text embeddings via Ollama's embed API - BedrockEmbeddingClient: Text embeddings via Amazon Titan on Bedrock - AzureAIInferenceEmbeddingClient: Text and image embeddings via Azure AI Inference, supporting Content | str input with separate model IDs for text (AZURE_AI_INFERENCE_EMBEDDING_MODEL_ID) and image (AZURE_AI_INFERENCE_IMAGE_EMBEDDING_MODEL_ID) endpoints Additional changes: - Rename EmbeddingCoT -> EmbeddingT, EmbeddingOptionsCoT -> EmbeddingOptionsT - Add otel_provider_name passthrough to all embedding clients - Register integration pytest marker in all packages - Add lazy-loading namespace exports for Ollama and Bedrock embeddings - Add image embedding sample using Cohere-embed-v3-english - Add azure-ai-inference dependency to azure-ai package Part of #1188 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix mypy duplicate name and ruff lint issues - Rename second 'vector' variable to 'img_vector' in image embedding loop - Combine nested with statements in tests - Remove unused result assignments in tests Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updates from feedback * Fix CI failures in embedding usage handling - Fix Azure AI embedding mypy issues by normalizing vectors to list[float], safely accumulating optional usage token fields, and filtering None entries before constructing GeneratedEmbeddings - Avoid Bandit false positive by initializing usage details as an empty dict - Update OpenAI embedding tests to assert canonical usage keys (input_token_count/total_token_count) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Get Started with Microsoft Agent Framework Copilot Studio
Please install this package via pip:
pip install agent-framework-copilotstudio --pre
Copilot Studio Agent
The Copilot Studio agent enables integration with Microsoft Copilot Studio, allowing you to interact with published copilots through the Agent Framework.
Prerequisites
Before using the Copilot Studio agent, you need:
- Copilot Studio Environment: Access to a Microsoft Copilot Studio environment with a published copilot
- App Registration: An Azure AD App Registration with appropriate permissions for Power Platform API
- Environment Configuration: Set the required environment variables or pass them as parameters
Environment Variables
The following environment variables are used for configuration:
COPILOTSTUDIOAGENT__ENVIRONMENTID- Your Copilot Studio environment IDCOPILOTSTUDIOAGENT__SCHEMANAME- Your copilot's agent identifier/schema nameCOPILOTSTUDIOAGENT__AGENTAPPID- Your App Registration client IDCOPILOTSTUDIOAGENT__TENANTID- Your Azure AD tenant ID
Basic Usage Example
import asyncio
from agent_framework.microsoft import CopilotStudioAgent
async def main():
# Create agent using environment variables
agent = CopilotStudioAgent()
# Run a simple query
result = await agent.run("What is the capital of France?")
print(result)
asyncio.run(main())
Explicit Configuration Example
import asyncio
import os
from agent_framework.microsoft import CopilotStudioAgent, acquire_token
from microsoft_agents.copilotstudio.client import ConnectionSettings, CopilotClient, PowerPlatformCloud, AgentType
async def main():
# Acquire authentication token
token = acquire_token(
client_id=os.environ["COPILOTSTUDIOAGENT__AGENTAPPID"],
tenant_id=os.environ["COPILOTSTUDIOAGENT__TENANTID"]
)
# Create connection settings
settings = ConnectionSettings(
environment_id=os.environ["COPILOTSTUDIOAGENT__ENVIRONMENTID"],
agent_identifier=os.environ["COPILOTSTUDIOAGENT__SCHEMANAME"],
cloud=PowerPlatformCloud.PROD,
copilot_agent_type=AgentType.PUBLISHED,
custom_power_platform_cloud=None
)
# Create client and agent
client = CopilotClient(settings=settings, token=token)
agent = CopilotStudioAgent(client=client)
# Run a query
result = await agent.run("What is the capital of Italy?")
print(result)
asyncio.run(main())
Authentication
The package uses MSAL (Microsoft Authentication Library) for authentication with interactive flows when needed. Ensure your App Registration has:
- API Permissions: Power Platform API permissions (https://api.powerplatform.com/.default)
- Redirect URIs: Configured appropriately for your authentication method
- Public Client Flows: Enabled if using interactive authentication
Examples
For more comprehensive examples, see the Copilot Studio examples which demonstrate:
- Basic non-streaming and streaming execution
- Explicit settings and manual token acquisition
- Different authentication patterns
- Error handling and troubleshooting