mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Python: [BREAKING] Replace Hosted*Tool classes with tool methods (#3634)
* Replace Hosted*Tool classes with client static factory methods * fixed failing test * mypy fix * mypy fix 2 * declarative mypy fix * addressed comments * ToolProtocol removal * fixed test * agents mypy fix * fix failing tests * mypy fix * addressed comments * fixed tests * addressed comments + added factory method overrides for azureai v2 client * mypy fix * added kwargs to azureai tool methods * fixed in test * _sessions fix * test fix
This commit is contained in:
Giles Odigwe
committed by
GitHub
Unverified
GitHub
parent
d249473a6d
commit
7a88af0aef
@@ -15,7 +15,7 @@ This folder contains examples demonstrating different ways to create and use age
|
||||
| [`azure_ai_with_bing_grounding.py`](azure_ai_with_bing_grounding.py) | Shows how to use Bing Grounding search with Azure AI agents to search the web for current information and provide grounded responses with citations. Requires a Bing connection configured in your Azure AI project. |
|
||||
| [`azure_ai_with_bing_custom_search.py`](azure_ai_with_bing_custom_search.py) | Shows how to use Bing Custom Search with Azure AI agents to search custom search instances and provide responses with relevant results. Requires a Bing Custom Search connection and instance configured in your Azure AI project. |
|
||||
| [`azure_ai_with_browser_automation.py`](azure_ai_with_browser_automation.py) | Shows how to use Browser Automation with Azure AI agents to perform automated web browsing tasks and provide responses based on web interactions. Requires a Browser Automation connection configured in your Azure AI project. |
|
||||
| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the `HostedCodeInterpreterTool` with Azure AI agents to write and execute Python code for mathematical problem solving and data analysis. |
|
||||
| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use `AzureAIClient.get_code_interpreter_tool()` with Azure AI agents to write and execute Python code for mathematical problem solving and data analysis. |
|
||||
| [`azure_ai_with_code_interpreter_file_generation.py`](azure_ai_with_code_interpreter_file_generation.py) | Shows how to retrieve file IDs from code interpreter generated files using both streaming and non-streaming approaches. |
|
||||
| [`azure_ai_with_code_interpreter_file_download.py`](azure_ai_with_code_interpreter_file_download.py) | Shows how to download files generated by code interpreter using the OpenAI containers API. |
|
||||
| [`azure_ai_with_content_filtering.py`](azure_ai_with_content_filtering.py) | Shows how to enable content filtering (RAI policy) on Azure AI agents using `RaiConfig`. Requires creating an RAI policy in Azure AI Foundry portal first. |
|
||||
@@ -23,8 +23,8 @@ This folder contains examples demonstrating different ways to create and use age
|
||||
| [`azure_ai_with_existing_conversation.py`](azure_ai_with_existing_conversation.py) | Demonstrates how to use an existing conversation created on the service side with Azure AI agents. Shows two approaches: specifying conversation ID at the client level and using AgentThread with an existing conversation ID. |
|
||||
| [`azure_ai_with_application_endpoint.py`](azure_ai_with_application_endpoint.py) | Demonstrates calling the Azure AI application-scoped endpoint. |
|
||||
| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIClient` settings, including project endpoint, model deployment, and credentials rather than relying on environment variable defaults. |
|
||||
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
|
||||
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. |
|
||||
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use `AzureAIClient.get_file_search_tool()` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
|
||||
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent using `AzureAIClient.get_mcp_tool()`. |
|
||||
| [`azure_ai_with_local_mcp.py`](azure_ai_with_local_mcp.py) | Shows how to integrate local Model Context Protocol (MCP) tools with Azure AI agents. |
|
||||
| [`azure_ai_with_response_format.py`](azure_ai_with_response_format.py) | Shows how to use structured outputs (response format) with Azure AI agents using Pydantic models to enforce specific response schemas. |
|
||||
| [`azure_ai_with_runtime_json_schema.py`](azure_ai_with_runtime_json_schema.py) | Shows how to use structured outputs (response format) with Azure AI agents using a JSON schema to enforce specific response schemas. |
|
||||
@@ -32,12 +32,12 @@ This folder contains examples demonstrating different ways to create and use age
|
||||
| [`azure_ai_with_search_context_semantic.py`](../../context_providers/azure_ai_search/azure_ai_with_search_context_semantic.py) | Shows how to use AzureAISearchContextProvider with semantic mode. Fast hybrid search with vector + keyword search and semantic ranking for RAG. Best for simple queries where speed is critical. |
|
||||
| [`azure_ai_with_sharepoint.py`](azure_ai_with_sharepoint.py) | Shows how to use SharePoint grounding with Azure AI agents to search through SharePoint content and answer user questions with proper citations. Requires a SharePoint connection configured in your Azure AI project. |
|
||||
| [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
|
||||
| [`azure_ai_with_image_generation.py`](azure_ai_with_image_generation.py) | Shows how to use the `ImageGenTool` with Azure AI agents to generate images based on text prompts. |
|
||||
| [`azure_ai_with_image_generation.py`](azure_ai_with_image_generation.py) | Shows how to use `AzureAIClient.get_image_generation_tool()` with Azure AI agents to generate images based on text prompts. |
|
||||
| [`azure_ai_with_memory_search.py`](azure_ai_with_memory_search.py) | Shows how to use memory search functionality with Azure AI agents for conversation persistence. Demonstrates creating memory stores and enabling agents to search through conversation history. |
|
||||
| [`azure_ai_with_microsoft_fabric.py`](azure_ai_with_microsoft_fabric.py) | Shows how to use Microsoft Fabric with Azure AI agents to query Fabric data sources and provide responses based on data analysis. Requires a Microsoft Fabric connection configured in your Azure AI project. |
|
||||
| [`azure_ai_with_openapi.py`](azure_ai_with_openapi.py) | Shows how to integrate OpenAPI specifications with Azure AI agents using dictionary-based tool configuration. Demonstrates using external REST APIs for dynamic data lookup. |
|
||||
| [`azure_ai_with_reasoning.py`](azure_ai_with_reasoning.py) | Shows how to enable reasoning for a model that supports it. |
|
||||
| [`azure_ai_with_web_search.py`](azure_ai_with_web_search.py) | Shows how to use the `HostedWebSearchTool` with Azure AI agents to perform web searches and retrieve up-to-date information from the internet. |
|
||||
| [`azure_ai_with_web_search.py`](azure_ai_with_web_search.py) | Shows how to use `AzureAIClient.get_web_search_tool()` with Azure AI agents to perform web searches and retrieve up-to-date information from the internet. |
|
||||
|
||||
## Environment Variables
|
||||
|
||||
|
||||
@@ -17,7 +17,9 @@ Shows both streaming and non-streaming responses with function tools.
|
||||
"""
|
||||
|
||||
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
|
||||
# see samples/getting_started/tools/function_tool_with_approval.py
|
||||
# and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
@tool(approval_mode="never_require")
|
||||
def get_weather(
|
||||
location: Annotated[str, Field(description="The location to get the weather for.")],
|
||||
|
||||
@@ -27,7 +27,9 @@ Each method returns a Agent that can be used for conversations.
|
||||
"""
|
||||
|
||||
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
|
||||
# see samples/getting_started/tools/function_tool_with_approval.py
|
||||
# and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
@tool(approval_mode="never_require")
|
||||
def get_weather(
|
||||
location: Annotated[str, Field(description="The location to get the weather for.")],
|
||||
|
||||
@@ -18,7 +18,9 @@ while subsequent calls with `get_agent()` reuse the latest agent version.
|
||||
"""
|
||||
|
||||
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
|
||||
# see samples/getting_started/tools/function_tool_with_approval.py
|
||||
# and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
@tool(approval_mode="never_require")
|
||||
def get_weather(
|
||||
location: Annotated[str, Field(description="The location to get the weather for.")],
|
||||
|
||||
@@ -2,8 +2,8 @@
|
||||
|
||||
import asyncio
|
||||
|
||||
from agent_framework import ChatResponse, HostedCodeInterpreterTool
|
||||
from agent_framework.azure import AzureAIProjectAgentProvider
|
||||
from agent_framework import ChatResponse
|
||||
from agent_framework.azure import AzureAIClient, AzureAIProjectAgentProvider
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
from openai.types.responses.response import Response as OpenAIResponse
|
||||
from openai.types.responses.response_code_interpreter_tool_call import ResponseCodeInterpreterToolCall
|
||||
@@ -11,22 +11,26 @@ from openai.types.responses.response_code_interpreter_tool_call import ResponseC
|
||||
"""
|
||||
Azure AI Agent Code Interpreter Example
|
||||
|
||||
This sample demonstrates using HostedCodeInterpreterTool with AzureAIProjectAgentProvider
|
||||
This sample demonstrates using get_code_interpreter_tool() with AzureAIProjectAgentProvider
|
||||
for Python code execution and mathematical problem solving.
|
||||
"""
|
||||
|
||||
|
||||
async def main() -> None:
|
||||
"""Example showing how to use the HostedCodeInterpreterTool with AzureAIProjectAgentProvider."""
|
||||
"""Example showing how to use the code interpreter tool with AzureAIProjectAgentProvider."""
|
||||
|
||||
async with (
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
code_interpreter_tool = client.get_code_interpreter_tool()
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="MyCodeInterpreterAgent",
|
||||
instructions="You are a helpful assistant that can write and execute Python code to solve problems.",
|
||||
tools=HostedCodeInterpreterTool(),
|
||||
tools=[code_interpreter_tool],
|
||||
)
|
||||
|
||||
query = "Use code to get the factorial of 100?"
|
||||
|
||||
+15
-8
@@ -9,9 +9,8 @@ from agent_framework import (
|
||||
AgentResponseUpdate,
|
||||
Annotation,
|
||||
Content,
|
||||
HostedCodeInterpreterTool,
|
||||
)
|
||||
from agent_framework.azure import AzureAIProjectAgentProvider
|
||||
from agent_framework.azure import AzureAIClient, AzureAIProjectAgentProvider
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
|
||||
"""
|
||||
@@ -119,17 +118,21 @@ async def download_container_files(file_contents: list[Annotation | Content], ag
|
||||
|
||||
|
||||
async def non_streaming_example() -> None:
|
||||
"""Example of downloading files from non-streaming response using CitationAnnotation."""
|
||||
"""Example of downloading files from non-streaming response using Annotation."""
|
||||
print("=== Non-Streaming Response Example ===")
|
||||
|
||||
async with (
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
code_interpreter_tool = client.get_code_interpreter_tool()
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="V2CodeInterpreterFileAgent",
|
||||
instructions="You are a helpful assistant that can write and execute Python code to create files.",
|
||||
tools=HostedCodeInterpreterTool(),
|
||||
tools=[code_interpreter_tool],
|
||||
)
|
||||
|
||||
print(f"User: {QUERY}\n")
|
||||
@@ -154,8 +157,8 @@ async def non_streaming_example() -> None:
|
||||
if annotations_found:
|
||||
print(f"SUCCESS: Found {len(annotations_found)} file annotation(s)")
|
||||
|
||||
# Download the container files
|
||||
downloaded_paths = await download_container_files(annotations_found, agent)
|
||||
# Download the container files (cast to Sequence for type compatibility)
|
||||
downloaded_paths = await download_container_files(list(annotations_found), agent)
|
||||
|
||||
if downloaded_paths:
|
||||
print("\nDownloaded files available at:")
|
||||
@@ -166,17 +169,21 @@ async def non_streaming_example() -> None:
|
||||
|
||||
|
||||
async def streaming_example() -> None:
|
||||
"""Example of downloading files from streaming response using HostedFileContent."""
|
||||
"""Example of downloading files from streaming response using Content with type='hosted_file'."""
|
||||
print("\n=== Streaming Response Example ===")
|
||||
|
||||
async with (
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
code_interpreter_tool = client.get_code_interpreter_tool()
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="V2CodeInterpreterFileAgentStreaming",
|
||||
instructions="You are a helpful assistant that can write and execute Python code to create files.",
|
||||
tools=HostedCodeInterpreterTool(),
|
||||
tools=[code_interpreter_tool],
|
||||
)
|
||||
|
||||
print(f"User: {QUERY}\n")
|
||||
|
||||
+12
-5
@@ -4,9 +4,8 @@ import asyncio
|
||||
|
||||
from agent_framework import (
|
||||
AgentResponseUpdate,
|
||||
HostedCodeInterpreterTool,
|
||||
)
|
||||
from agent_framework.azure import AzureAIProjectAgentProvider
|
||||
from agent_framework.azure import AzureAIClient, AzureAIProjectAgentProvider
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
|
||||
"""
|
||||
@@ -31,10 +30,14 @@ async def non_streaming_example() -> None:
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
code_interpreter_tool = client.get_code_interpreter_tool()
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="V2CodeInterpreterFileAgent",
|
||||
name="CodeInterpreterFileAgent",
|
||||
instructions="You are a helpful assistant that can write and execute Python code to create files.",
|
||||
tools=HostedCodeInterpreterTool(),
|
||||
tools=[code_interpreter_tool],
|
||||
)
|
||||
|
||||
print(f"User: {QUERY}\n")
|
||||
@@ -67,10 +70,14 @@ async def streaming_example() -> None:
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
code_interpreter_tool = client.get_code_interpreter_tool()
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="V2CodeInterpreterFileAgentStreaming",
|
||||
instructions="You are a helpful assistant that can write and execute Python code to create files.",
|
||||
tools=HostedCodeInterpreterTool(),
|
||||
tools=[code_interpreter_tool],
|
||||
)
|
||||
|
||||
print(f"User: {QUERY}\n")
|
||||
|
||||
+3
-1
@@ -17,7 +17,9 @@ This sample demonstrates usage of AzureAIProjectAgentProvider with existing conv
|
||||
"""
|
||||
|
||||
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
|
||||
# see samples/getting_started/tools/function_tool_with_approval.py
|
||||
# and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
@tool(approval_mode="never_require")
|
||||
def get_weather(
|
||||
location: Annotated[str, Field(description="The location to get the weather for.")],
|
||||
|
||||
@@ -18,7 +18,9 @@ settings rather than relying on environment variable defaults.
|
||||
"""
|
||||
|
||||
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/getting_started/tools/function_tool_with_approval.py and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
|
||||
# see samples/getting_started/tools/function_tool_with_approval.py
|
||||
# and samples/getting_started/tools/function_tool_with_approval_and_threads.py.
|
||||
@tool(approval_mode="never_require")
|
||||
def get_weather(
|
||||
location: Annotated[str, Field(description="The location to get the weather for.")],
|
||||
|
||||
@@ -4,8 +4,7 @@ import asyncio
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
from agent_framework import Content, HostedFileSearchTool
|
||||
from agent_framework.azure import AzureAIProjectAgentProvider
|
||||
from agent_framework.azure import AzureAIClient, AzureAIProjectAgentProvider
|
||||
from azure.ai.agents.aio import AgentsClient
|
||||
from azure.ai.agents.models import FileInfo, VectorStore
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
@@ -45,8 +44,9 @@ async def main() -> None:
|
||||
vector_store = await agents_client.vector_stores.create_and_poll(file_ids=[file.id], name="my_vectorstore")
|
||||
print(f"Created vector store, vector store ID: {vector_store.id}")
|
||||
|
||||
# 2. Create file search tool with uploaded resources
|
||||
file_search_tool = HostedFileSearchTool(inputs=[Content.from_hosted_vector_store(vector_store_id=vector_store.id)])
|
||||
# 2. Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
file_search_tool = client.get_file_search_tool(vector_store_ids=[vector_store.id])
|
||||
|
||||
# 3. Create an agent with file search capabilities using the provider
|
||||
agent = await provider.create_agent(
|
||||
@@ -55,7 +55,7 @@ async def main() -> None:
|
||||
"You are a helpful assistant that can search through uploaded employee files "
|
||||
"to answer questions about employees."
|
||||
),
|
||||
tools=file_search_tool,
|
||||
tools=[file_search_tool],
|
||||
)
|
||||
|
||||
# 4. Simulate conversation with the agent
|
||||
|
||||
@@ -3,8 +3,8 @@
|
||||
import asyncio
|
||||
from typing import Any
|
||||
|
||||
from agent_framework import AgentResponse, AgentThread, HostedMCPTool, Message, SupportsAgentRun
|
||||
from agent_framework.azure import AzureAIProjectAgentProvider
|
||||
from agent_framework import AgentResponse, AgentThread, Message, SupportsAgentRun
|
||||
from agent_framework.azure import AzureAIClient, AzureAIProjectAgentProvider
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
|
||||
"""
|
||||
@@ -65,14 +65,19 @@ async def run_hosted_mcp_without_approval() -> None:
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
# Create MCP tool using instance method
|
||||
mcp_tool = client.get_mcp_tool(
|
||||
name="Microsoft Learn MCP",
|
||||
url="https://learn.microsoft.com/api/mcp",
|
||||
approval_mode="never_require",
|
||||
)
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="MyLearnDocsAgent",
|
||||
instructions="You are a helpful assistant that can help with Microsoft documentation questions.",
|
||||
tools=HostedMCPTool(
|
||||
name="Microsoft Learn MCP",
|
||||
url="https://learn.microsoft.com/api/mcp",
|
||||
approval_mode="never_require",
|
||||
),
|
||||
tools=[mcp_tool],
|
||||
)
|
||||
|
||||
query = "How to create an Azure storage account using az cli?"
|
||||
@@ -91,14 +96,19 @@ async def run_hosted_mcp_with_approval_and_thread() -> None:
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
# Create MCP tool using instance method
|
||||
mcp_tool = client.get_mcp_tool(
|
||||
name="api-specs",
|
||||
url="https://gitmcp.io/Azure/azure-rest-api-specs",
|
||||
approval_mode="always_require",
|
||||
)
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="MyApiSpecsAgent",
|
||||
instructions="You are a helpful agent that can use MCP tools to assist users.",
|
||||
tools=HostedMCPTool(
|
||||
name="api-specs",
|
||||
url="https://gitmcp.io/Azure/azure-rest-api-specs",
|
||||
approval_mode="always_require",
|
||||
),
|
||||
tools=[mcp_tool],
|
||||
)
|
||||
|
||||
thread = agent.get_new_thread()
|
||||
|
||||
@@ -5,8 +5,7 @@ import tempfile
|
||||
from pathlib import Path
|
||||
from urllib import request as urllib_request
|
||||
|
||||
from agent_framework import HostedImageGenerationTool
|
||||
from agent_framework.azure import AzureAIProjectAgentProvider
|
||||
from agent_framework.azure import AzureAIClient, AzureAIProjectAgentProvider
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
|
||||
"""
|
||||
@@ -28,22 +27,21 @@ async def main() -> None:
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
# Create image generation tool using instance method
|
||||
image_gen_tool = client.get_image_generation_tool(
|
||||
model="gpt-image-1",
|
||||
size="1024x1024",
|
||||
output_format="png",
|
||||
quality="low",
|
||||
background="opaque",
|
||||
)
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="ImageGenAgent",
|
||||
instructions="Generate images based on user requirements.",
|
||||
tools=[
|
||||
HostedImageGenerationTool(
|
||||
options={
|
||||
"model_id": "gpt-image-1",
|
||||
"image_size": "1024x1024",
|
||||
"media_type": "png",
|
||||
},
|
||||
additional_properties={
|
||||
"quality": "low",
|
||||
"background": "opaque",
|
||||
},
|
||||
)
|
||||
],
|
||||
tools=[image_gen_tool],
|
||||
)
|
||||
|
||||
query = "Generate an image of Microsoft logo."
|
||||
|
||||
@@ -79,22 +79,22 @@ async def example_with_thread_persistence_in_memory() -> None:
|
||||
thread = agent.get_new_thread()
|
||||
|
||||
# First conversation
|
||||
query1 = "What's the weather like in Tokyo?"
|
||||
print(f"User: {query1}")
|
||||
result1 = await agent.run(query1, thread=thread, options={"store": False})
|
||||
print(f"Agent: {result1.text}")
|
||||
first_query = "What's the weather like in Tokyo?"
|
||||
print(f"User: {first_query}")
|
||||
first_result = await agent.run(first_query, thread=thread, options={"store": False})
|
||||
print(f"Agent: {first_result.text}")
|
||||
|
||||
# Second conversation using the same thread - maintains context
|
||||
query2 = "How about London?"
|
||||
print(f"\nUser: {query2}")
|
||||
result2 = await agent.run(query2, thread=thread, options={"store": False})
|
||||
print(f"Agent: {result2.text}")
|
||||
second_query = "How about London?"
|
||||
print(f"\nUser: {second_query}")
|
||||
second_result = await agent.run(second_query, thread=thread, options={"store": False})
|
||||
print(f"Agent: {second_result.text}")
|
||||
|
||||
# Third conversation - agent should remember both previous cities
|
||||
query3 = "Which of the cities I asked about has better weather?"
|
||||
print(f"\nUser: {query3}")
|
||||
result3 = await agent.run(query3, thread=thread, options={"store": False})
|
||||
print(f"Agent: {result3.text}")
|
||||
third_query = "Which of the cities I asked about has better weather?"
|
||||
print(f"\nUser: {third_query}")
|
||||
third_result = await agent.run(third_query, thread=thread, options={"store": False})
|
||||
print(f"Agent: {third_result.text}")
|
||||
print("Note: The agent remembers context from previous messages in the same thread.\n")
|
||||
|
||||
|
||||
@@ -121,10 +121,10 @@ async def example_with_existing_thread_id() -> None:
|
||||
# Start a conversation and get the thread ID
|
||||
thread = agent.get_new_thread()
|
||||
|
||||
query1 = "What's the weather in Paris?"
|
||||
print(f"User: {query1}")
|
||||
result1 = await agent.run(query1, thread=thread)
|
||||
print(f"Agent: {result1.text}")
|
||||
first_query = "What's the weather in Paris?"
|
||||
print(f"User: {first_query}")
|
||||
first_result = await agent.run(first_query, thread=thread)
|
||||
print(f"Agent: {first_result.text}")
|
||||
|
||||
# The thread ID is set after the first response
|
||||
existing_thread_id = thread.service_thread_id
|
||||
@@ -134,19 +134,19 @@ async def example_with_existing_thread_id() -> None:
|
||||
print("\n--- Continuing with the same thread ID in a new agent instance ---")
|
||||
|
||||
# Create a new agent instance from the same provider
|
||||
agent2 = await provider.create_agent(
|
||||
second_agent = await provider.create_agent(
|
||||
name="BasicWeatherAgent",
|
||||
instructions="You are a helpful weather agent.",
|
||||
tools=get_weather,
|
||||
)
|
||||
|
||||
# Create a thread with the existing ID
|
||||
thread = agent2.get_new_thread(service_thread_id=existing_thread_id)
|
||||
thread = second_agent.get_new_thread(service_thread_id=existing_thread_id)
|
||||
|
||||
query2 = "What was the last city I asked about?"
|
||||
print(f"User: {query2}")
|
||||
result2 = await agent2.run(query2, thread=thread)
|
||||
print(f"Agent: {result2.text}")
|
||||
second_query = "What was the last city I asked about?"
|
||||
print(f"User: {second_query}")
|
||||
second_result = await second_agent.run(second_query, thread=thread)
|
||||
print(f"Agent: {second_result.text}")
|
||||
print("Note: The agent continues the conversation from the previous thread by using thread ID.\n")
|
||||
|
||||
|
||||
|
||||
@@ -2,15 +2,14 @@
|
||||
|
||||
import asyncio
|
||||
|
||||
from agent_framework import HostedWebSearchTool
|
||||
from agent_framework.azure import AzureAIProjectAgentProvider
|
||||
from agent_framework.azure import AzureAIClient, AzureAIProjectAgentProvider
|
||||
from azure.identity.aio import AzureCliCredential
|
||||
|
||||
"""
|
||||
Azure AI Agent With Web Search
|
||||
|
||||
This sample demonstrates basic usage of AzureAIProjectAgentProvider to create an agent
|
||||
that can perform web searches using the HostedWebSearchTool.
|
||||
that can perform web searches using get_web_search_tool().
|
||||
|
||||
Pre-requisites:
|
||||
- Make sure to set up the AZURE_AI_PROJECT_ENDPOINT and AZURE_AI_MODEL_DEPLOYMENT_NAME
|
||||
@@ -25,10 +24,15 @@ async def main() -> None:
|
||||
AzureCliCredential() as credential,
|
||||
AzureAIProjectAgentProvider(credential=credential) as provider,
|
||||
):
|
||||
# Create a client to access hosted tool factory methods
|
||||
client = AzureAIClient(credential=credential)
|
||||
# Create web search tool using instance method
|
||||
web_search_tool = client.get_web_search_tool()
|
||||
|
||||
agent = await provider.create_agent(
|
||||
name="WebsearchAgent",
|
||||
instructions="You are a helpful assistant that can search the web",
|
||||
tools=[HostedWebSearchTool()],
|
||||
tools=[web_search_tool],
|
||||
)
|
||||
|
||||
query = "What's the weather today in Seattle?"
|
||||
|
||||
Reference in New Issue
Block a user