Eduard van Valkenburg
6138487888
* 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>
Python Samples
This directory contains samples demonstrating the capabilities of Microsoft Agent Framework for Python.
Structure
| Folder | Description |
|---|---|
01-get-started/ |
Progressive tutorial: hello agent → hosting |
02-agents/ |
Deep-dive by concept: tools, middleware, providers, orchestrations |
03-workflows/ |
Workflow patterns: sequential, concurrent, state, declarative |
04-hosting/ |
Deployment: Azure Functions, Durable Tasks, A2A |
05-end-to-end/ |
Full applications, evaluation, demos |
Getting Started
Start with 01-get-started/ and work through the numbered files:
- 01_hello_agent.py — Create and run your first agent
- 02_add_tools.py — Add function tools with
@tool - 03_multi_turn.py — Multi-turn conversations with
AgentThread - 04_memory.py — Agent memory with
ContextProvider - 05_first_workflow.py — Build a workflow with executors and edges
- 06_host_your_agent.py — Host your agent via A2A
Prerequisites
pip install agent-framework --pre
Environment Variables
Samples call load_dotenv() to automatically load environment variables from a .env file in the python/ directory. This is a convenience for local development and testing.
For local development, set up your environment using any of these methods:
Option 1: Using a .env file (recommended for local development):
- Copy
.env.exampleto.envin thepython/directory:cp .env.example .env - Edit
.envand set your values (API keys, endpoints, etc.)
Option 2: Export environment variables directly:
export AZURE_AI_PROJECT_ENDPOINT="your-foundry-project-endpoint"
export AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"
Option 3: Using env_file_path parameter (for per-client configuration):
All client classes (e.g., OpenAIChatClient, AzureOpenAIResponsesClient) support an env_file_path parameter to load environment variables from a specific file:
from agent_framework.openai import OpenAIChatClient
# Load from a custom .env file
client = OpenAIChatClient(env_file_path="path/to/custom.env")
This allows different clients to use different configuration files if needed.
For the getting-started samples, you'll need at minimum:
AZURE_AI_PROJECT_ENDPOINT="your-foundry-project-endpoint"
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o"
Note for production: In production environments, set environment variables through your deployment platform (e.g., Azure App Settings, Kubernetes ConfigMaps/Secrets) rather than using .env files. The load_dotenv() call in samples will have no effect when a .env file is not present, allowing environment variables to be loaded from the system.
For Azure authentication, run az login before running samples.
Note on XML tags
Some sample files include XML-style snippet tags (for example <snippet_name> and </snippet_name>). These are used by our documentation tooling and can be ignored or removed when you use the samples outside this repository.
Additional Resources
- Agent Framework Documentation
- AGENTS.md — Structure documentation for maintainers
- SAMPLE_GUIDELINES.md — Coding conventions for samples