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1e350ea22f
* 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
322 lines
11 KiB
Python
322 lines
11 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""Tests for conversation store implementation."""
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from typing import cast
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import pytest
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from openai.types.conversations import InputFileContent, InputImageContent, InputTextContent
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from agent_framework_devui._conversations import InMemoryConversationStore
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@pytest.mark.asyncio
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async def test_create_conversation():
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"""Test creating a conversation."""
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store = InMemoryConversationStore()
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conversation = store.create_conversation(metadata={"agent_id": "test_agent"})
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assert conversation.id.startswith("conv_")
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assert conversation.object == "conversation"
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assert conversation.metadata == {"agent_id": "test_agent"}
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@pytest.mark.asyncio
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async def test_get_conversation():
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"""Test retrieving a conversation."""
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store = InMemoryConversationStore()
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# Create conversation
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created = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Retrieve it
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retrieved = store.get_conversation(created.id)
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assert retrieved is not None
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assert retrieved.id == created.id
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assert retrieved.metadata == {"agent_id": "test_agent"}
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@pytest.mark.asyncio
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async def test_get_conversation_not_found():
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"""Test retrieving non-existent conversation."""
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store = InMemoryConversationStore()
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conversation = store.get_conversation("conv_nonexistent")
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assert conversation is None
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@pytest.mark.asyncio
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async def test_update_conversation():
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"""Test updating conversation metadata."""
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store = InMemoryConversationStore()
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# Create conversation
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created = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Update metadata
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updated = store.update_conversation(created.id, metadata={"agent_id": "new_agent", "session_id": "sess_123"})
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assert updated.id == created.id
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assert updated.metadata == {"agent_id": "new_agent", "session_id": "sess_123"}
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@pytest.mark.asyncio
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async def test_delete_conversation():
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"""Test deleting a conversation."""
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store = InMemoryConversationStore()
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# Create conversation
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created = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Delete it
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result = store.delete_conversation(created.id)
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assert result.id == created.id
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assert result.deleted is True
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assert result.object == "conversation.deleted"
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# Verify it's gone
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assert store.get_conversation(created.id) is None
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@pytest.mark.asyncio
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async def test_get_session():
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"""Test getting AgentSession for execution."""
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store = InMemoryConversationStore()
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# Create conversation
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conversation = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Get session
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session = store.get_session(conversation.id)
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assert session is not None
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# AgentSession should have session_id
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assert hasattr(session, "session_id")
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@pytest.mark.asyncio
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async def test_get_session_not_found():
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"""Test getting session for non-existent conversation."""
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store = InMemoryConversationStore()
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session = store.get_session("conv_nonexistent")
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assert session is None
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@pytest.mark.asyncio
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async def test_list_conversations_by_metadata():
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"""Test filtering conversations by metadata."""
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store = InMemoryConversationStore()
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# Create multiple conversations
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_conv1 = store.create_conversation(metadata={"agent_id": "agent1"})
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_conv2 = store.create_conversation(metadata={"agent_id": "agent2"})
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conv3 = store.create_conversation(metadata={"agent_id": "agent1", "session_id": "sess_1"})
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# Filter by agent_id
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results = await store.list_conversations_by_metadata({"agent_id": "agent1"})
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assert len(results) == 2
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assert all(cast(dict[str, str], c.metadata).get("agent_id") == "agent1" for c in results if c.metadata)
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# Filter by agent_id and session_id
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results = await store.list_conversations_by_metadata({"agent_id": "agent1", "session_id": "sess_1"})
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assert len(results) == 1
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assert results[0].id == conv3.id
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@pytest.mark.asyncio
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async def test_add_items():
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"""Test adding items to conversation."""
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store = InMemoryConversationStore()
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# Create conversation
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conversation = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Add items
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items = [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}]
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conv_items = await store.add_items(conversation.id, items=items)
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assert len(conv_items) == 1
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# Message is a ConversationItem type - check standard OpenAI fields
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assert conv_items[0].type == "message"
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assert conv_items[0].role == "user"
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assert conv_items[0].status == "completed"
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assert len(conv_items[0].content) == 1
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assert conv_items[0].content[0].type == "text"
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text_content = cast(InputTextContent, conv_items[0].content[0])
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assert text_content.text == "Hello"
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@pytest.mark.asyncio
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async def test_list_items():
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"""Test listing conversation items."""
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store = InMemoryConversationStore()
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# Create conversation
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conversation = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Add items
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items = [
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{"role": "user", "content": [{"type": "text", "text": "Hello"}]},
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{"role": "assistant", "content": [{"type": "text", "text": "Hi there"}]},
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]
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await store.add_items(conversation.id, items=items)
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# List items
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retrieved_items, has_more = await store.list_items(conversation.id)
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assert len(retrieved_items) >= 2 # At least the items we added
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assert has_more is False
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@pytest.mark.asyncio
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async def test_list_items_pagination():
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"""Test pagination when listing items."""
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store = InMemoryConversationStore()
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# Create conversation
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conversation = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Add multiple items
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items = [{"role": "user", "content": [{"type": "text", "text": f"Message {i}"}]} for i in range(5)]
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await store.add_items(conversation.id, items=items)
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# List with limit
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retrieved_items, has_more = await store.list_items(conversation.id, limit=3)
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assert len(retrieved_items) == 3
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assert has_more is True
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@pytest.mark.asyncio
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async def test_list_items_converts_function_calls():
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"""Test that list_items properly converts function calls to ResponseFunctionToolCallItem."""
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from agent_framework import Message
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store = InMemoryConversationStore()
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# Create conversation
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conversation = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Simulate messages from agent execution with function calls
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messages = [
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Message(role="user", contents=[{"type": "text", "text": "What's the weather in SF?"}]),
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Message(
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role="assistant",
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contents=[
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{
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"type": "function_call",
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"name": "get_weather",
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"arguments": '{"city": "San Francisco"}',
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"call_id": "call_test123",
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}
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],
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),
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Message(
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role="tool",
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contents=[
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{
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"type": "function_result",
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"call_id": "call_test123",
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"result": '{"temperature": 65, "condition": "sunny"}',
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}
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],
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),
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Message(role="assistant", contents=[{"type": "text", "text": "The weather is sunny, 65°F"}]),
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]
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# Add messages to internal storage
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store._conversations[conversation.id]["messages"].extend(messages)
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# List conversation items
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items, has_more = await store.list_items(conversation.id)
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# Verify we got the right number and types of items
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assert len(items) == 4, f"Expected 4 items, got {len(items)}"
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assert has_more is False
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# Check item types
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assert items[0].type == "message", "First item should be a message"
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assert items[0].role == "user"
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assert len(items[0].content) == 1
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text_content_0 = cast(InputTextContent, items[0].content[0])
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assert text_content_0.text == "What's the weather in SF?"
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assert items[1].type == "function_call", "Second item should be a function_call"
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assert items[1].call_id == "call_test123"
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assert items[1].name == "get_weather"
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assert items[1].arguments == '{"city": "San Francisco"}'
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assert items[1].status == "completed"
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assert items[2].type == "function_call_output", "Third item should be a function_call_output"
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assert items[2].call_id == "call_test123"
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assert items[2].output == '{"temperature": 65, "condition": "sunny"}'
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assert items[2].status == "completed"
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assert items[3].type == "message", "Fourth item should be a message"
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assert items[3].role == "assistant"
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assert len(items[3].content) == 1
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text_content_3 = cast(InputTextContent, items[3].content[0])
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assert text_content_3.text == "The weather is sunny, 65°F"
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# CRITICAL: Ensure no empty message items
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for item in items:
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if item.type == "message":
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assert len(item.content) > 0, f"Message item {item.id} has empty content!"
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@pytest.mark.asyncio
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async def test_list_items_handles_images_and_files():
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"""Test that list_items properly converts data content (images/files) to OpenAI types."""
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from agent_framework import Message
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store = InMemoryConversationStore()
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# Create conversation
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conversation = store.create_conversation(metadata={"agent_id": "test_agent"})
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# Simulate message with image and file
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messages = [
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Message(
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role="user",
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contents=[
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{"type": "text", "text": "Check this image and PDF"},
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{"type": "data", "uri": "data:image/png;base64,iVBORw0KGgo=", "media_type": "image/png"},
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{"type": "data", "uri": "data:application/pdf;base64,JVBERi0=", "media_type": "application/pdf"},
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],
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),
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]
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# Add messages to internal storage
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store._conversations[conversation.id]["messages"].extend(messages)
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# List items
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items, has_more = await store.list_items(conversation.id)
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assert len(items) == 1
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assert items[0].type == "message"
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assert items[0].role == "user"
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assert len(items[0].content) == 3
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# Check content types
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assert items[0].content[0].type == "text"
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text_content = cast(InputTextContent, items[0].content[0])
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assert text_content.text == "Check this image and PDF"
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assert items[0].content[1].type == "input_image"
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image_content = cast(InputImageContent, items[0].content[1])
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assert image_content.image_url == "data:image/png;base64,iVBORw0KGgo="
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assert image_content.detail == "auto"
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assert items[0].content[2].type == "input_file"
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file_content = cast(InputFileContent, items[0].content[2])
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assert file_content.file_url == "data:application/pdf;base64,JVBERi0="
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