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* PR2: Wire context provider pipeline and update all internal consumers - Replace AgentThread with AgentSession across all packages - Replace ContextProvider with BaseContextProvider across all packages - Replace context_provider param with context_providers (Sequence) - Replace thread= with session= in run() signatures - Replace get_new_thread() with create_session() - Add get_session(service_session_id) to agent interface - DurableAgentThread -> DurableAgentSession - Remove _notify_thread_of_new_messages from WorkflowAgent - Wire before_run/after_run context provider pipeline in RawAgent - Auto-inject InMemoryHistoryProvider when no providers configured * fix: update all tests for context provider pipeline, fix lazy-loaders, remove old test files * refactor: update all sample files for context provider pipeline (AgentThread→AgentSession, ContextProvider→BaseContextProvider) * fix: update remaining ag-ui references (client docstring, getting_started sample) * fix: make get_session service_session_id keyword-only to avoid confusion with session_id * refactor: rename _RunContext.thread_messages to session_messages * refactor: remove _threads.py, _memory.py, and old provider files; migrate devui to use plain message lists * rename: remove _new_ prefix from test files * refactor: rewrite SlidingWindowChatMessageStore as SlidingWindowHistoryProvider(InMemoryHistoryProvider) * fix: read full history from session state directly instead of reaching into provider internals * fix: update stale .pyi stubs, sample imports, and README references for new provider types * fix: remove stale message_store, _notify_thread_of_new_messages, and session_id.key references in samples * refactor: merge context_providers and sessions sample folders into sessions, remove aggregate_context_provider * refactor: UserInfoMemory stores state in session.state instead of instance attributes * feat: add Pydantic BaseModel support to session state serialization Pydantic models stored in session.state are now automatically serialized via model_dump() and restored via model_validate() during to_dict()/from_dict() round-trips. Models are auto-registered on first serialization; use register_state_type() for cold-start deserialization. Also export register_state_type as a public API. * fix mem0 * Update sample README links and descriptions for session terminology - Replace 'thread' with 'session' in sample descriptions across all READMEs - Update file links for renamed samples (mem0_sessions, redis_sessions, etc.) - Fix Threads section → Sessions section in main samples/README.md - Update tools, middleware, workflows, durabletask, azure_functions READMEs - Update architecture diagrams in concepts/tools/README.md - Update migration guides (autogen, semantic-kernel) * Fix broken Redis README link to renamed sample * Fix Mem0 OSS client search: pass scoping params as direct kwargs AsyncMemory (OSS) expects user_id/agent_id/run_id as direct kwargs, while AsyncMemoryClient (Platform) expects them in a filters dict. Adds tests for both client types. Port of fix from #3844 to new Mem0ContextProvider. * Fix rebase issues: restore missing _conversation_state.py and checkpoint decode logic - Add back _conversation_state.py (encode/decode_chat_messages) lost in rebase - Fix on_checkpoint_restore to decode cache/conversation with decode_chat_messages - Fix on_checkpoint_restore to use decode_checkpoint_value for pending requests - Add tests/workflow/__init__.py for relative import support - Fix test_agent_executor checkpoint selection (checkpoints[1] not superstep) * Add STORES_BY_DEFAULT ClassVar to skip redundant InMemoryHistoryProvider injection Chat clients that store history server-side by default (OpenAI Responses API, Azure AI Agent) now declare STORES_BY_DEFAULT = True. The agent checks this during auto-injection and skips InMemoryHistoryProvider unless the user explicitly sets store=False. * Fix broken markdown links in azure_ai and redis READMEs * Fix getting-started samples to use session API instead of removed thread/ContextProvider API * updates to workflow as agent * fix group chat import * Rename Thread→Session throughout, fix service_session_id propagation, remove stale AGUIThread - Fix: Propagate conversation_id from ChatResponse back to session.service_session_id in both streaming and non-streaming paths in _agents.py - Rename AgentThreadException → AgentSessionException - Remove stale AGUIThread from ag_ui lazy-loader - Rename use_service_thread → use_service_session in ag-ui package - Rename test functions from *_thread_* to *_session_* - Rename sample files from *_thread* to *_session* - Update docstrings and comments: thread → session - Update _mcp.py kwargs filter: add 'session' alongside 'thread' - Fix ContinuationToken docstring example: thread=thread → session=session - Fix _clients.py docstring: 'Agent threads' → 'Agent sessions' * Fix broken markdown links after thread→session file renames * fix azure ai test
125 lines
4.3 KiB
Python
125 lines
4.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""Sample agent using Azure OpenAI Responses API for Agent Framework DevUI.
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This agent uses the Responses API which supports:
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- PDF file uploads
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- Image uploads
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- Audio inputs
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- And other multimodal content
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The Chat Completions API (AzureOpenAIChatClient) does NOT support PDF uploads.
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Use this agent when you need to process documents or other file types.
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Required environment variables:
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- AZURE_OPENAI_ENDPOINT: Your Azure OpenAI endpoint
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- AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME: Deployment name for Responses API
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(falls back to AZURE_OPENAI_CHAT_DEPLOYMENT_NAME if not set)
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- AZURE_OPENAI_API_KEY: Your API key (or use Azure CLI auth)
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"""
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import logging
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import os
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from typing import Annotated
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from agent_framework import Agent, tool
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from agent_framework.azure import AzureOpenAIResponsesClient
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logger = logging.getLogger(__name__)
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# Get deployment name - try responses-specific env var first, fall back to chat deployment
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_deployment_name = os.environ.get(
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"AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME",
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os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", ""),
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)
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# Get endpoint - try responses-specific env var first, fall back to default
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_endpoint = os.environ.get(
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"AZURE_OPENAI_RESPONSES_ENDPOINT",
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os.environ.get("AZURE_OPENAI_ENDPOINT", ""),
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)
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def analyze_content(
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query: Annotated[str, "What to analyze or extract from the uploaded content"],
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) -> str:
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"""Analyze uploaded content based on the user's query.
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This is a placeholder - the actual analysis is done by the model
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when processing the uploaded files.
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"""
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return f"Analyzing content for: {query}"
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# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production; see samples/02-agents/tools/function_tool_with_approval.py and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
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@tool(approval_mode="never_require")
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def summarize_document(
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length: Annotated[str, "Desired summary length: 'brief', 'medium', or 'detailed'"] = "medium",
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) -> str:
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"""Generate a summary of the uploaded document."""
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return f"Generating {length} summary of the document..."
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@tool(approval_mode="never_require")
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def extract_key_points(
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max_points: Annotated[int, "Maximum number of key points to extract"] = 5,
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) -> str:
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"""Extract key points from the uploaded document."""
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return f"Extracting up to {max_points} key points..."
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# Agent using Azure OpenAI Responses API (supports PDF uploads!)
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agent = Agent(
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name="AzureResponsesAgent",
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description="An agent that can analyze PDFs, images, and other documents using Azure OpenAI Responses API",
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instructions="""
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You are a helpful document analysis assistant. You can:
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1. Analyze uploaded PDF documents and extract information
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2. Summarize document contents
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3. Answer questions about uploaded files
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4. Extract key points and insights
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When a user uploads a file, carefully analyze its contents and provide
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helpful, accurate information based on what you find.
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For PDFs, you can read and understand the text, tables, and structure.
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For images, you can describe what you see and extract any text.
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""",
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client=AzureOpenAIResponsesClient(
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deployment_name=_deployment_name,
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endpoint=_endpoint,
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api_version="2025-03-01-preview", # Required for Responses API
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),
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tools=[summarize_document, extract_key_points],
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)
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def main():
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"""Launch the Azure Responses agent in DevUI."""
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from agent_framework_devui import serve
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logging.basicConfig(level=logging.INFO, format="%(message)s")
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logger.info("=" * 60)
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logger.info("Starting Azure Responses Agent")
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logger.info("=" * 60)
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logger.info("")
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logger.info("This agent uses the Azure OpenAI Responses API which supports:")
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logger.info(" - PDF file uploads")
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logger.info(" - Image uploads")
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logger.info(" - Audio inputs")
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logger.info("")
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logger.info("Try uploading a PDF and asking questions about it!")
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logger.info("")
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logger.info("Required environment variables:")
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logger.info(" - AZURE_OPENAI_ENDPOINT")
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logger.info(" - AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME")
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logger.info(" - AZURE_OPENAI_API_KEY (or use Azure CLI auth)")
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logger.info("")
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serve(entities=[agent], port=8090, auto_open=True)
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if __name__ == "__main__":
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main()
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