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Python: [Breaking] Simplified Content types to a single class with classmethod constructors. (#3252)

Browse Source 
* ported Content to a new model

* fixed linting

* fixes

* fixed data format handling

* fix for 3.10 mypy

* fix

* fix int test
This commit is contained in:
Eduard van Valkenburg
2026-01-20 23:09:39 +01:00
committed by GitHub
Unverified
parent 73761aa4a3
commit 83e6229c11
132 changed files with 3949 additions and 4741 deletions
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python/packages/devui/agent_framework_devui/_conversations.py
+1 -1
View File
@@ -321,7 +321,7 @@ class InMemoryConversationStore(ConversationStore):
# Convert ChatMessage contents to OpenAI TextContent format
message_content = []
for content_item in msg.contents:
if hasattr(content_item, "type") and content_item.type == "text":
if content_item.type == "text":
# Extract text from TextContent object
text_value = getattr(content_item, "text", "")
message_content.append(TextContent(type="text", text=text_value))
python/packages/devui/agent_framework_devui/_executor.py
+13 -19
View File
@@ -7,7 +7,7 @@ import logging
from collections.abc import AsyncGenerator
from typing import Any
from agent_framework import AgentProtocol
from agent_framework import AgentProtocol, Content
from agent_framework._workflows._events import RequestInfoEvent
from ._conversations import ConversationStore, InMemoryConversationStore
@@ -602,7 +602,7 @@ class AgentFrameworkExecutor:
"""
# Import Agent Framework types
try:
from agent_framework import ChatMessage, DataContent, Role, TextContent
from agent_framework import ChatMessage, Role
except ImportError:
# Fallback to string extraction if Agent Framework not available
return self._extract_user_message_fallback(input_data)
@@ -613,14 +613,12 @@ class AgentFrameworkExecutor:
# Handle OpenAI ResponseInputParam (List[ResponseInputItemParam])
if isinstance(input_data, list):
return self._convert_openai_input_to_chat_message(input_data, ChatMessage, TextContent, DataContent, Role)
return self._convert_openai_input_to_chat_message(input_data, ChatMessage, Role)
# Fallback for other formats
return self._extract_user_message_fallback(input_data)
def _convert_openai_input_to_chat_message(
self, input_items: list[Any], ChatMessage: Any, TextContent: Any, DataContent: Any, Role: Any
) -> Any:
def _convert_openai_input_to_chat_message(self, input_items: list[Any], ChatMessage: Any, Role: Any) -> Any:
"""Convert OpenAI ResponseInputParam to Agent Framework ChatMessage.
Processes text, images, files, and other content types from OpenAI format
@@ -629,14 +627,12 @@ class AgentFrameworkExecutor:
Args:
input_items: List of OpenAI ResponseInputItemParam objects (dicts or objects)
ChatMessage: ChatMessage class for creating chat messages
TextContent: TextContent class for text content
DataContent: DataContent class for data/media content
Role: Role enum for message roles
Returns:
ChatMessage with converted content
"""
contents = []
contents: list[Content] = []
# Process each input item
for item in input_items:
@@ -649,7 +645,7 @@ class AgentFrameworkExecutor:
# Handle both string content and list content
if isinstance(message_content, str):
contents.append(TextContent(text=message_content))
contents.append(Content.from_text(text=message_content))
elif isinstance(message_content, list):
for content_item in message_content:
# Handle dict content items
@@ -658,7 +654,7 @@ class AgentFrameworkExecutor:
if content_type == "input_text":
text = content_item.get("text", "")
contents.append(TextContent(text=text))
contents.append(Content.from_text(text=text))
elif content_type == "input_image":
image_url = content_item.get("image_url", "")
@@ -676,7 +672,7 @@ class AgentFrameworkExecutor:
media_type = "image/png"
else:
media_type = "image/png"
contents.append(DataContent(uri=image_url, media_type=media_type))
contents.append(Content.from_uri(uri=image_url, media_type=media_type))
elif content_type == "input_file":
# Handle file input
@@ -710,7 +706,7 @@ class AgentFrameworkExecutor:
# Assume file_data is base64, create data URI
data_uri = f"data:{media_type};base64,{file_data}"
contents.append(
DataContent(
Content.from_uri(
uri=data_uri,
media_type=media_type,
additional_properties=additional_props,
@@ -718,7 +714,7 @@ class AgentFrameworkExecutor:
)
elif file_url:
contents.append(
DataContent(
Content.from_uri(
uri=file_url,
media_type=media_type,
additional_properties=additional_props,
@@ -728,21 +724,19 @@ class AgentFrameworkExecutor:
elif content_type == "function_approval_response":
# Handle function approval response (DevUI extension)
try:
from agent_framework import FunctionApprovalResponseContent, FunctionCallContent
request_id = content_item.get("request_id", "")
approved = content_item.get("approved", False)
function_call_data = content_item.get("function_call", {})
# Create FunctionCallContent from the function_call data
function_call = FunctionCallContent(
function_call = Content.from_function_call(
call_id=function_call_data.get("id", ""),
name=function_call_data.get("name", ""),
arguments=function_call_data.get("arguments", {}),
)
# Create FunctionApprovalResponseContent with correct signature
approval_response = FunctionApprovalResponseContent(
approval_response = Content.from_function_approval_response(
approved, # positional argument
id=request_id, # keyword argument 'id', NOT 'request_id'
function_call=function_call, # FunctionCallContent object
@@ -764,7 +758,7 @@ class AgentFrameworkExecutor:
# If no contents found, create a simple text message
if not contents:
contents.append(TextContent(text=""))
contents.append(Content.from_text(text=""))
chat_message = ChatMessage(role=Role.USER, contents=contents)
python/packages/devui/agent_framework_devui/_mapper.py
+27 -33
View File
@@ -12,7 +12,7 @@ from datetime import datetime
from typing import Any, Union
from uuid import uuid4
from agent_framework import ChatMessage, TextContent
from agent_framework import ChatMessage, Content
from openai.types.responses import (
Response,
ResponseContentPartAddedEvent,
@@ -92,7 +92,7 @@ def _serialize_content_recursive(value: Any) -> Any:
if isinstance(value, (list, tuple)):
serialized = [_serialize_content_recursive(item) for item in value]
# For single-item lists containing text Content, extract just the text
# This handles the MCP case where result = [TextContent(text="Hello")]
# This handles the MCP case where result = [Content.from_text(text="Hello")]
# and we want output = "Hello" not output = '[{"type": "text", "text": "Hello"}]'
if len(serialized) == 1 and isinstance(serialized[0], dict) and serialized[0].get("type") == "text":
return serialized[0].get("text", "")
@@ -127,18 +127,18 @@ class MessageMapper:
# Register content type mappers for all 12 Agent Framework content types
self.content_mappers = {
"TextContent": self._map_text_content,
"TextReasoningContent": self._map_reasoning_content,
"FunctionCallContent": self._map_function_call_content,
"FunctionResultContent": self._map_function_result_content,
"ErrorContent": self._map_error_content,
"UsageContent": self._map_usage_content,
"DataContent": self._map_data_content,
"UriContent": self._map_uri_content,
"HostedFileContent": self._map_hosted_file_content,
"HostedVectorStoreContent": self._map_hosted_vector_store_content,
"FunctionApprovalRequestContent": self._map_approval_request_content,
"FunctionApprovalResponseContent": self._map_approval_response_content,
"text": self._map_text_content,
"text_reasoning": self._map_reasoning_content,
"function_call": self._map_function_call_content,
"function_result": self._map_function_result_content,
"error": self._map_error_content,
"usage": self._map_usage_content,
"data": self._map_data_content,
"uri": self._map_uri_content,
"hosted_file": self._map_hosted_file_content,
"hosted_vector_store": self._map_hosted_vector_store_content,
"function_approval_request": self._map_approval_request_content,
"function_approval_response": self._map_approval_response_content,
}
async def convert_event(self, raw_event: Any, request: AgentFrameworkRequest) -> Sequence[Any]:
@@ -603,7 +603,7 @@ class MessageMapper:
return events
# Check if we're streaming text content
has_text_content = any(isinstance(content, TextContent) for content in update.contents)
has_text_content = any(content.type == "text" for content in update.contents)
# Check if we're in an executor context with an existing item
executor_id = context.get("current_executor_id")
@@ -647,10 +647,8 @@ class MessageMapper:
# Process each content item
for content in update.contents:
content_type = content.__class__.__name__
# Special handling for TextContent to use proper delta events
if content_type == "TextContent" and "current_message_id" in context:
if content.type == "text" and "current_message_id" in context:
# Stream text content via proper delta events
events.append(
ResponseTextDeltaEvent(
@@ -663,9 +661,9 @@ class MessageMapper:
sequence_number=self._next_sequence(context),
)
)
elif content_type in self.content_mappers:
elif content.type in self.content_mappers:
# Use existing mappers for other content types
mapped_events = await self.content_mappers[content_type](content, context)
mapped_events = await self.content_mappers[content.type](content, context)
if mapped_events is not None: # Handle None returns (e.g., UsageContent)
if isinstance(mapped_events, list):
events.extend(mapped_events)
@@ -676,7 +674,7 @@ class MessageMapper:
events.append(await self._create_unknown_content_event(content, context))
# Don't increment content_index for text deltas within the same part
if content_type != "TextContent":
if content.type != "text":
context["content_index"] = context.get("content_index", 0) + 1
except Exception as e:
@@ -708,10 +706,8 @@ class MessageMapper:
for message in messages:
if hasattr(message, "contents") and message.contents:
for content in message.contents:
content_type = content.__class__.__name__
if content_type in self.content_mappers:
mapped_events = await self.content_mappers[content_type](content, context)
if content.type in self.content_mappers:
mapped_events = await self.content_mappers[content.type](content, context)
if mapped_events is not None: # Handle None returns (e.g., UsageContent)
if isinstance(mapped_events, list):
events.extend(mapped_events)
@@ -726,9 +722,7 @@ class MessageMapper:
# Add usage information if present
usage_details = getattr(response, "usage_details", None)
if usage_details:
from agent_framework import UsageContent
usage_content = UsageContent(details=usage_details)
usage_content = Content.from_usage(usage_details=usage_details)
await self._map_usage_content(usage_content, context)
# Note: _map_usage_content returns None - it accumulates usage for final Response.usage
@@ -1421,11 +1415,11 @@ class MessageMapper:
Returns:
None - no event emitted (usage goes in final Response.usage)
"""
# Extract usage from UsageContent.details (UsageDetails object)
details = getattr(content, "details", None)
total_tokens = getattr(details, "total_token_count", 0) or 0
prompt_tokens = getattr(details, "input_token_count", 0) or 0
completion_tokens = getattr(details, "output_token_count", 0) or 0
# Extract usage from UsageContent.usage_details (UsageDetails object)
details = content.usage_details or {}
total_tokens = details.get("total_token_count", 0)
prompt_tokens = details.get("input_token_count", 0)
completion_tokens = details.get("output_token_count", 0)
# Accumulate for final Response.usage
request_id = context.get("request_id", "default")