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Manohar Paturi (ManoharPaturi) wants to merge 1 commit into from
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Python: let the DevUI item endpoint resolve list item ids #9863

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189 changes: 107 additions & 82 deletions python/packages/devui/agent_framework_devui/_conversations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@

from __future__ import annotations

import re
import time
import uuid
from abc import ABC, abstractmethod
Expand Down Expand Up @@ -414,89 +415,10 @@ async def list_items(
items: list[ConversationItem] = []
af_messages = stored_messages

# Convert each AgentFramework Message to appropriate ConversationItem type(s)
# A single Message may produce multiple ConversationItems
# (e.g., a message with both text and a function call)
for i, msg in enumerate(af_messages):
item_id = f"item_{i}"
role_str = msg.role if hasattr(msg.role, "value") else str(msg.role)
role = cast(MessageRole, role_str) # Safe: Agent Framework roles match OpenAI roles

# Process each content item in the message
# A single Message may produce multiple ConversationItems
# (e.g., a message with both text and a function call)
message_contents: list[TextContent | ResponseOutputRefusal | ResponseInputImage | ResponseInputFile] = []
function_calls: list[ResponseFunctionToolCallItem] = []
function_results: list[ResponseFunctionToolCallOutputItem] = []

for content in msg.contents:
content_type = getattr(content, "type", None)
mapped_content = self._to_openai_content(content)

if mapped_content is not None:
message_contents.append(mapped_content)
elif content_type == "function_call":
# Function call - create separate ConversationItem
call_id = getattr(content, "call_id", None)
name = getattr(content, "name", "")
arguments = getattr(content, "arguments", "")

if call_id and name:
function_calls.append(
ResponseFunctionToolCallItem(
id=f"{item_id}_call_{call_id}",
call_id=call_id,
name=name,
arguments=arguments,
type="function_call",
status="completed",
)
)

elif content_type == "function_result":
# Function result - create separate ConversationItem
call_id = getattr(content, "call_id", None)
# Output is stored in the 'result' field of FunctionResultContent
result_value = getattr(content, "result", None)
# Convert result to string (it could be dict, list, or other types)
if result_value is None:
output = ""
elif isinstance(result_value, str):
output = result_value
else:
import json

try:
output = json.dumps(result_value)
except (TypeError, ValueError):
output = str(result_value)

if call_id:
function_results.append(
ResponseFunctionToolCallOutputItem(
id=f"{item_id}_result_{call_id}",
call_id=call_id,
output=output,
type="function_call_output",
status="completed",
)
)

# Create ConversationItems based on what we found
# If message has text/images/files, create a Message item
if message_contents:
message = OpenAIMessage(
id=item_id,
type="message",
role=role,
content=message_contents, # type: ignore
status="completed",
)
items.append(message)

# Add function call items
items.extend(function_calls)

# Add function result items
items.extend(function_results)
items.extend(self._items_from_message(index=i, msg=msg))

# Include checkpoints from checkpoint storage as conversation items
checkpoint_storage = conv_data.get("checkpoint_storage")
Expand Down Expand Up @@ -744,6 +666,92 @@ def _to_openai_content(

return None

def _items_from_message(self, *, index: int, msg: Message) -> list[ConversationItem]:
"""Build the ConversationItems for one stored message.

The list endpoint and item retrieval must agree on ids and content, so
both go through this conversion: ``item_<index>`` for text, image, and
file content, plus ``item_<index>_call_<call id>`` and
``item_<index>_result_<call id>`` for tool traffic.
"""
item_id = f"item_{index}"
role_str = msg.role if hasattr(msg.role, "value") else str(msg.role)
role = cast(MessageRole, role_str) # Safe: Agent Framework roles match OpenAI roles

message_contents: list[TextContent | ResponseOutputRefusal | ResponseInputImage | ResponseInputFile] = []
function_calls: list[ResponseFunctionToolCallItem] = []
function_results: list[ResponseFunctionToolCallOutputItem] = []

for content in msg.contents:
content_type = getattr(content, "type", None)
mapped_content = self._to_openai_content(content)

if mapped_content is not None:
message_contents.append(mapped_content)
elif content_type == "function_call":
# Function call - create separate ConversationItem
call_id = getattr(content, "call_id", None)
name = getattr(content, "name", "")
arguments = getattr(content, "arguments", "")

if call_id and name:
function_calls.append(
ResponseFunctionToolCallItem(
id=f"{item_id}_call_{call_id}",
call_id=call_id,
name=name,
arguments=arguments,
type="function_call",
status="completed",
)
)

elif content_type == "function_result":
# Function result - create separate ConversationItem
call_id = getattr(content, "call_id", None)
# Output is stored in the 'result' field of FunctionResultContent
result_value = getattr(content, "result", None)
# Convert result to string (it could be dict, list, or other types)
if result_value is None:
output = ""
elif isinstance(result_value, str):
output = result_value
else:
import json

try:
output = json.dumps(result_value)
except (TypeError, ValueError):
output = str(result_value)

if call_id:
function_results.append(
ResponseFunctionToolCallOutputItem(
id=f"{item_id}_result_{call_id}",
call_id=call_id,
output=output,
type="function_call_output",
status="completed",
)
)

built: list[ConversationItem] = []
# If message has text/images/files, create a Message item
if message_contents:
built.append(
OpenAIMessage(
id=item_id,
type="message",
role=role,
content=message_contents, # type: ignore
status="completed",
)
)

built.extend(function_calls)
built.extend(function_results)
return built

async def get_item(self, conversation_id: str, item_id: str) -> ConversationItem | None:
"""Get a specific conversation item by ID.

Expand All @@ -756,6 +764,23 @@ async def get_item(self, conversation_id: str, item_id: str) -> ConversationItem
if item:
return item

# List ids are positional per message. Resolve them by rebuilding the
# addressed message's items with the same conversion the list uses, so
# every id the list endpoint returns is retrievable by id.
list_id_match = re.fullmatch(r"item_(\d+)(?:_(?:call|result)_.+)?", item_id)
if list_id_match:
conv_data = self._conversations.get(conversation_id)
if not conv_data:
return None
stored_messages = conv_data["messages"]
index = int(list_id_match.group(1))
if index >= len(stored_messages):
return None
for candidate in self._items_from_message(index=index, msg=stored_messages[index]):
if candidate.id == item_id:
return candidate
return None

# If not found and ID is a checkpoint, load from checkpoint storage
if item_id.startswith("checkpoint_"):
checkpoint_id = item_id[len("checkpoint_") :] # Remove "checkpoint_" prefix
Expand Down
27 changes: 27 additions & 0 deletions python/packages/devui/tests/devui/test_conversations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -158,6 +158,33 @@ async def test_add_items():
assert text_content.text == "Hello"


@pytest.mark.asyncio
async def test_get_item_resolves_ids_returned_by_list_items():
"""Every id the items list returns must be retrievable by id."""
store = InMemoryConversationStore()
conversation = store.create_conversation(metadata={"agent_id": "test_agent"})
added = await store.add_items(
conversation.id,
items=[
{"role": "user", "content": [{"type": "text", "text": "Hello"}]},
{"role": "assistant", "content": [{"type": "text", "text": "Hi there"}]},
],
)

listed, _ = await store.list_items(conversation.id)
assert [item.id for item in listed] == ["item_0", "item_1"]

for listed_item in listed:
retrieved = await store.get_item(conversation.id, listed_item.id)
assert retrieved is not None
assert retrieved.id == listed_item.id
assert retrieved.type == listed_item.type

# Ids minted by the add path stay retrievable, and an unknown index is not.
assert await store.get_item(conversation.id, added[0].id) is not None
assert await store.get_item(conversation.id, "item_99") is None


@pytest.mark.asyncio
async def test_add_and_list_items_preserves_marked_refusal_text():
store = InMemoryConversationStore()
Expand Down
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