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chore: import upstream snapshot with attribution
2026-07-13 13:22:28 +08:00

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# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
#
# SPDX-License-Identifier: Apache-2.0
from unittest.mock import ANY
import pytest
from openai.types import Reasoning, ResponseFormatText
from openai.types.responses import (
FunctionTool,
Response,
ResponseCompletedEvent,
ResponseContentPartAddedEvent,
ResponseContentPartDoneEvent,
ResponseCreatedEvent,
ResponseFunctionCallArgumentsDeltaEvent,
ResponseFunctionCallArgumentsDoneEvent,
ResponseFunctionToolCall,
ResponseInProgressEvent,
ResponseOutputItemAddedEvent,
ResponseOutputItemDoneEvent,
ResponseOutputMessage,
ResponseOutputText,
ResponseReasoningItem,
ResponseTextConfig,
ResponseTextDeltaEvent,
ResponseTextDoneEvent,
ResponseUsage,
)
from openai.types.responses.response_usage import InputTokensDetails, OutputTokensDetails
from haystack.components.generators.chat.openai_responses import (
_convert_chat_message_to_responses_api_format,
_convert_response_chunk_to_streaming_chunk,
_convert_streaming_chunks_to_chat_message,
)
from haystack.dataclasses import (
ChatMessage,
ChatRole,
FileContent,
ImageContent,
ReasoningContent,
StreamingChunk,
TextContent,
ToolCall,
ToolCallDelta,
ToolCallResult,
)
@pytest.fixture
def openai_responses_streaming_chunks_with_tool_call():
return [
StreamingChunk(
content="",
meta={
"received_at": ANY,
"response": {
"id": "resp_095b57053855eac100690491f4e22c8196ac124365e8c70424",
"created_at": 1761907188.0,
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"output": [],
"tools": [
{
"name": "weather",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
},
"strict": False,
"type": "function",
"description": "useful to determine the weather in a given location",
}
],
"reasoning": {"effort": "medium", "generate_summary": None, "summary": None},
"usage": None,
},
"sequence_number": 0,
"type": "response.created",
},
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=0,
start=True,
reasoning=ReasoningContent(
reasoning_text="",
extra={
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"summary": [],
"type": "reasoning",
},
),
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=0,
reasoning=ReasoningContent(
reasoning_text="",
extra={
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"summary": [],
"type": "reasoning",
},
),
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=1,
tool_calls=[
ToolCallDelta(
index=1,
tool_name="weather",
arguments=None,
id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
extra={
"arguments": "",
"call_id": "call_OZZXFm7SLb4F3Xg8a9XVVCvv",
"id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"name": "weather",
"status": "in_progress",
"type": "function_call",
},
)
],
start=True,
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=1,
tool_calls=[
ToolCallDelta(
index=1,
tool_name=None,
arguments='{"city":"Paris"}',
id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
extra={
"item_id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"output_index": 1,
"sequence_number": 5,
"type": "response.function_call_arguments.delta",
"obfuscation": "PySUcQ59ZZRkOm",
},
)
],
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"arguments": '{"city":"Paris"}',
"item_id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"name": "weather",
"output_index": 1,
"sequence_number": 10,
"type": "response.function_call_arguments.done",
},
index=1,
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"response": {
"id": "resp_095b57053855eac100690491f4e22c8196ac124365e8c70424",
"created_at": 1761907188.0,
"metadata": {},
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"output": [
{
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"summary": [],
"type": "reasoning",
},
{
"arguments": '{"city":"Paris"}',
"call_id": "call_OZZXFm7SLb4F3Xg8a9XVVCvv",
"name": "weather",
"type": "function_call",
"id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"status": "completed",
},
],
"tools": [
{
"name": "weather",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
"additionalProperties": False,
},
"strict": False,
"type": "function",
"description": "useful to determine the weather in a given location",
}
],
"top_p": 1.0,
"reasoning": {"effort": "medium", "generate_summary": None, "summary": None},
"usage": {
"input_tokens": 62,
"input_tokens_details": {"cached_tokens": 0},
"output_tokens": 83,
"output_tokens_details": {"reasoning_tokens": 64},
"total_tokens": 145,
},
"store": True,
},
"sequence_number": 12,
"type": "response.completed",
},
finish_reason="tool_calls",
),
]
class TestConversionToStreamingChunks:
def test_convert_streaming_chunks_to_chat_message_with_tool_call_empty_reasoning(
self, openai_responses_streaming_chunks_with_tool_call
):
chat_message = _convert_streaming_chunks_to_chat_message(openai_responses_streaming_chunks_with_tool_call)
assert chat_message == ChatMessage(
_role="assistant",
_content=[
ReasoningContent(
reasoning_text="",
extra={
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"summary": [],
"type": "reasoning",
},
),
ToolCall(
tool_name="weather",
arguments={"city": "Paris"},
id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
extra={"call_id": "call_OZZXFm7SLb4F3Xg8a9XVVCvv"},
),
],
_name=None,
_meta={
"id": "resp_095b57053855eac100690491f4e22c8196ac124365e8c70424",
"created_at": 1761907188.0,
"metadata": {},
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"tools": [
{
"name": "weather",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
"additionalProperties": False,
},
"strict": False,
"type": "function",
"description": "useful to determine the weather in a given location",
}
],
"top_p": 1.0,
"reasoning": {"effort": "medium", "generate_summary": None, "summary": None},
"usage": {
"input_tokens": 62,
"input_tokens_details": {"cached_tokens": 0},
"output_tokens": 83,
"output_tokens_details": {"reasoning_tokens": 64},
"total_tokens": 145,
},
"store": True,
},
)
def test_convert_only_text(self):
openai_chunks = [
ResponseCreatedEvent(
response=Response(
id="resp_0a8811e62a95217b00690c5ff62c14819596eae387d116f285",
created_at=1762418678.0,
metadata={},
model="gpt-5-mini-2025-08-07",
object="response",
output=[],
parallel_tool_calls=True,
temperature=1.0,
tool_choice="auto",
tools=[],
top_p=1.0,
background=False,
reasoning=Reasoning(effort="medium", generate_summary=None, summary=None),
service_tier="auto",
status="in_progress",
text=ResponseTextConfig(format=ResponseFormatText(type="text"), verbosity="medium"),
top_logprobs=0,
truncation="disabled",
prompt_cache_retention=None,
store=True,
),
sequence_number=0,
type="response.created",
),
ResponseInProgressEvent(
response=Response(
id="resp_0a8811e62a95217b00690c5ff62c14819596eae387d116f285",
created_at=1762418678.0,
metadata={},
model="gpt-5-mini-2025-08-07",
object="response",
output=[],
parallel_tool_calls=True,
temperature=1.0,
tool_choice="auto",
tools=[],
top_p=1.0,
background=False,
reasoning=Reasoning(effort="medium", generate_summary=None, summary=None),
service_tier="auto",
status="in_progress",
text=ResponseTextConfig(format=ResponseFormatText(type="text"), verbosity="medium"),
top_logprobs=0,
truncation="disabled",
prompt_cache_retention=None,
store=True,
),
sequence_number=1,
type="response.in_progress",
),
ResponseOutputItemAddedEvent(
item=ResponseReasoningItem(
id="rs_0a8811e62a95217b00690c5ff70a308195a8207d7eb43f1d5b", summary=[], type="reasoning"
),
output_index=0,
sequence_number=2,
type="response.output_item.added",
),
ResponseOutputItemDoneEvent(
item=ResponseReasoningItem(
id="rs_0a8811e62a95217b00690c5ff70a308195a8207d7eb43f1d5b", summary=[], type="reasoning"
),
output_index=0,
sequence_number=3,
type="response.output_item.done",
),
ResponseOutputItemAddedEvent(
item=ResponseOutputMessage(
id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
content=[],
role="assistant",
status="in_progress",
type="message",
),
output_index=1,
sequence_number=4,
type="response.output_item.added",
),
ResponseContentPartAddedEvent(
content_index=0,
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
output_index=1,
part=ResponseOutputText(annotations=[], text="", type="output_text", logprobs=[]),
sequence_number=5,
type="response.content_part.added",
),
ResponseTextDeltaEvent(
content_index=0,
delta="Germany",
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
logprobs=[],
output_index=1,
sequence_number=6,
type="response.output_text.delta",
obfuscation="EV5gCoyiD",
),
ResponseTextDeltaEvent(
content_index=0,
delta=":",
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
logprobs=[],
output_index=1,
sequence_number=7,
type="response.output_text.delta",
obfuscation="EkdNXp1EE2Cgj8z",
),
ResponseTextDeltaEvent(
content_index=0,
delta=" Berlin",
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
logprobs=[],
output_index=1,
sequence_number=8,
type="response.output_text.delta",
obfuscation="1eS0q9aye",
),
ResponseTextDeltaEvent(
content_index=0,
delta="\n",
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
logprobs=[],
output_index=1,
sequence_number=9,
type="response.output_text.delta",
obfuscation="H9Ict3F41DwGS4a",
),
ResponseTextDeltaEvent(
content_index=0,
delta="France",
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
logprobs=[],
output_index=1,
sequence_number=10,
type="response.output_text.delta",
obfuscation="4vxrblWURx",
),
ResponseTextDeltaEvent(
content_index=0,
delta=":",
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
logprobs=[],
output_index=1,
sequence_number=11,
type="response.output_text.delta",
obfuscation="B1CMJsNGhhqIz5K",
),
ResponseTextDeltaEvent(
content_index=0,
delta=" Paris",
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
logprobs=[],
output_index=1,
sequence_number=12,
type="response.output_text.delta",
obfuscation="ojbz89bS7j",
),
ResponseTextDoneEvent(
content_index=0,
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
logprobs=[],
output_index=1,
sequence_number=13,
text="Germany: Berlin\nFrance: Paris",
type="response.output_text.done",
),
ResponseContentPartDoneEvent(
content_index=0,
item_id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
output_index=1,
part=ResponseOutputText(
annotations=[], text="Germany: Berlin\nFrance: Paris", type="output_text", logprobs=[]
),
sequence_number=14,
type="response.content_part.done",
),
ResponseOutputItemDoneEvent(
item=ResponseOutputMessage(
id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
content=[
ResponseOutputText(
annotations=[], text="Germany: Berlin\nFrance: Paris", type="output_text", logprobs=[]
)
],
role="assistant",
status="completed",
type="message",
),
output_index=1,
sequence_number=15,
type="response.output_item.done",
),
ResponseCompletedEvent(
response=Response(
id="resp_0a8811e62a95217b00690c5ff62c14819596eae387d116f285",
created_at=1762418678.0,
error=None,
incomplete_details=None,
instructions=None,
metadata={},
model="gpt-5-mini-2025-08-07",
object="response",
output=[
ResponseReasoningItem(
id="rs_0a8811e62a95217b00690c5ff70a308195a8207d7eb43f1d5b", summary=[], type="reasoning"
),
ResponseOutputMessage(
id="msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
content=[
ResponseOutputText(
annotations=[],
text="Germany: Berlin\nFrance: Paris",
type="output_text",
logprobs=[],
)
],
role="assistant",
status="completed",
type="message",
),
],
parallel_tool_calls=True,
temperature=1.0,
tool_choice="auto",
tools=[],
top_p=1.0,
background=False,
reasoning=Reasoning(effort="medium", generate_summary=None, summary=None),
safety_identifier=None,
service_tier="default",
status="completed",
text=ResponseTextConfig(format=ResponseFormatText(type="text"), verbosity="medium"),
top_logprobs=0,
truncation="disabled",
usage=ResponseUsage(
input_tokens=15,
input_tokens_details=InputTokensDetails(cached_tokens=0),
output_tokens=77,
output_tokens_details=OutputTokensDetails(reasoning_tokens=64),
total_tokens=92,
),
prompt_cache_retention=None,
store=True,
),
sequence_number=16,
type="response.completed",
),
]
streaming_chunks = []
for chunk in openai_chunks:
streaming_chunk = _convert_response_chunk_to_streaming_chunk(chunk, previous_chunks=streaming_chunks)
streaming_chunks.append(streaming_chunk)
assert streaming_chunks == [
StreamingChunk(
content="",
meta={
"received_at": ANY,
"response": {
"id": "resp_0a8811e62a95217b00690c5ff62c14819596eae387d116f285",
"created_at": 1762418678.0,
"metadata": {},
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"output": [],
"parallel_tool_calls": True,
"temperature": 1.0,
"tool_choice": "auto",
"tools": [],
"top_p": 1.0,
"background": False,
"reasoning": {"effort": "medium", "generate_summary": None, "summary": None},
"service_tier": "auto",
"status": "in_progress",
"text": {"format": {"type": "text"}, "verbosity": "medium"},
"top_logprobs": 0,
"truncation": "disabled",
"prompt_cache_retention": None,
"store": True,
},
"sequence_number": 0,
"type": "response.created",
},
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"response": {
"id": "resp_0a8811e62a95217b00690c5ff62c14819596eae387d116f285",
"created_at": 1762418678.0,
"metadata": {},
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"output": [],
"parallel_tool_calls": True,
"temperature": 1.0,
"tool_choice": "auto",
"tools": [],
"top_p": 1.0,
"background": False,
"reasoning": {"effort": "medium", "generate_summary": None, "summary": None},
"service_tier": "auto",
"status": "in_progress",
"text": {"format": {"type": "text"}, "verbosity": "medium"},
"top_logprobs": 0,
"truncation": "disabled",
"prompt_cache_retention": None,
"store": True,
},
"sequence_number": 1,
"type": "response.in_progress",
},
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=0,
start=True,
reasoning=ReasoningContent(
reasoning_text="",
extra={
"id": "rs_0a8811e62a95217b00690c5ff70a308195a8207d7eb43f1d5b",
"summary": [],
"type": "reasoning",
},
),
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=0,
reasoning=ReasoningContent(
reasoning_text="",
extra={
"id": "rs_0a8811e62a95217b00690c5ff70a308195a8207d7eb43f1d5b",
"summary": [],
"type": "reasoning",
},
),
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"item": {
"id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"content": [],
"role": "assistant",
"status": "in_progress",
"type": "message",
},
"output_index": 1,
"sequence_number": 4,
"type": "response.output_item.added",
},
index=1,
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"content_index": 0,
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"output_index": 1,
"part": {"annotations": [], "text": "", "type": "output_text", "logprobs": []},
"sequence_number": 5,
"type": "response.content_part.added",
},
index=1,
),
StreamingChunk(
content="Germany",
meta={
"content_index": 0,
"delta": "Germany",
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"logprobs": [],
"output_index": 1,
"sequence_number": 6,
"type": "response.output_text.delta",
"obfuscation": "EV5gCoyiD",
"received_at": ANY,
},
index=1,
start=True,
),
StreamingChunk(
content=":",
meta={
"content_index": 0,
"delta": ":",
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"logprobs": [],
"output_index": 1,
"sequence_number": 7,
"type": "response.output_text.delta",
"obfuscation": "EkdNXp1EE2Cgj8z",
"received_at": ANY,
},
index=1,
),
StreamingChunk(
content=" Berlin",
meta={
"content_index": 0,
"delta": " Berlin",
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"logprobs": [],
"output_index": 1,
"sequence_number": 8,
"type": "response.output_text.delta",
"obfuscation": "1eS0q9aye",
"received_at": ANY,
},
index=1,
),
StreamingChunk(
content="\n",
meta={
"content_index": 0,
"delta": "\n",
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"logprobs": [],
"output_index": 1,
"sequence_number": 9,
"type": "response.output_text.delta",
"obfuscation": "H9Ict3F41DwGS4a",
"received_at": ANY,
},
index=1,
),
StreamingChunk(
content="France",
meta={
"content_index": 0,
"delta": "France",
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"logprobs": [],
"output_index": 1,
"sequence_number": 10,
"type": "response.output_text.delta",
"obfuscation": "4vxrblWURx",
"received_at": ANY,
},
index=1,
),
StreamingChunk(
content=":",
meta={
"content_index": 0,
"delta": ":",
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"logprobs": [],
"output_index": 1,
"sequence_number": 11,
"type": "response.output_text.delta",
"obfuscation": "B1CMJsNGhhqIz5K",
"received_at": ANY,
},
index=1,
),
StreamingChunk(
content=" Paris",
meta={
"content_index": 0,
"delta": " Paris",
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"logprobs": [],
"output_index": 1,
"sequence_number": 12,
"type": "response.output_text.delta",
"obfuscation": "ojbz89bS7j",
"received_at": ANY,
},
index=1,
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"content_index": 0,
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"logprobs": [],
"output_index": 1,
"sequence_number": 13,
"text": "Germany: Berlin\nFrance: Paris",
"type": "response.output_text.done",
},
index=1,
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"content_index": 0,
"item_id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"output_index": 1,
"part": {
"annotations": [],
"text": "Germany: Berlin\nFrance: Paris",
"type": "output_text",
"logprobs": [],
},
"sequence_number": 14,
"type": "response.content_part.done",
},
index=1,
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"item": {
"id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"content": [
{
"annotations": [],
"text": "Germany: Berlin\nFrance: Paris",
"type": "output_text",
"logprobs": [],
}
],
"role": "assistant",
"status": "completed",
"type": "message",
},
"output_index": 1,
"sequence_number": 15,
"type": "response.output_item.done",
},
index=1,
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"response": {
"id": "resp_0a8811e62a95217b00690c5ff62c14819596eae387d116f285",
"created_at": 1762418678.0,
"error": None,
"incomplete_details": None,
"instructions": None,
"metadata": {},
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"output": [
{
"id": "rs_0a8811e62a95217b00690c5ff70a308195a8207d7eb43f1d5b",
"summary": [],
"type": "reasoning",
},
{
"id": "msg_0a8811e62a95217b00690c5ff88f6c8195b037e57d327a1ee0",
"content": [
{
"annotations": [],
"text": "Germany: Berlin\nFrance: Paris",
"type": "output_text",
"logprobs": [],
}
],
"role": "assistant",
"status": "completed",
"type": "message",
},
],
"parallel_tool_calls": True,
"temperature": 1.0,
"tool_choice": "auto",
"tools": [],
"top_p": 1.0,
"background": False,
"reasoning": {"effort": "medium", "generate_summary": None, "summary": None},
"safety_identifier": None,
"service_tier": "default",
"status": "completed",
"text": {"format": {"type": "text"}, "verbosity": "medium"},
"top_logprobs": 0,
"truncation": "disabled",
"usage": {
"input_tokens": 15,
"input_tokens_details": {"cached_tokens": 0},
"output_tokens": 77,
"output_tokens_details": {"reasoning_tokens": 64},
"total_tokens": 92,
},
"prompt_cache_retention": None,
"store": True,
},
"sequence_number": 16,
"type": "response.completed",
},
finish_reason="stop",
),
]
def test_convert_only_function_call(self):
chunks = [
ResponseCreatedEvent(
response=Response(
id="resp_095b57053855eac100690491f4e22c8196ac124365e8c70424",
created_at=1761907188.0,
metadata={},
model="gpt-5-mini-2025-08-07",
object="response",
output=[],
parallel_tool_calls=True,
temperature=1.0,
tool_choice="auto",
tools=[
FunctionTool(
name="weather",
parameters={
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
"additionalProperties": False,
},
strict=False,
type="function",
description="useful to determine the weather in a given location",
)
],
reasoning=Reasoning(effort="medium", generate_summary=None, summary=None),
usage=None,
),
sequence_number=0,
type="response.created",
),
ResponseOutputItemAddedEvent(
item=ResponseReasoningItem(
id="rs_095b57053855eac100690491f54e308196878239be3ba6133c", summary=[], type="reasoning"
),
output_index=0,
sequence_number=2,
type="response.output_item.added",
),
ResponseOutputItemDoneEvent(
item=ResponseReasoningItem(
id="rs_095b57053855eac100690491f54e308196878239be3ba6133c", summary=[], type="reasoning"
),
output_index=0,
sequence_number=3,
type="response.output_item.done",
),
ResponseOutputItemAddedEvent(
item=ResponseFunctionToolCall(
arguments="",
call_id="call_OZZXFm7SLb4F3Xg8a9XVVCvv",
name="weather",
type="function_call",
id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
status="in_progress",
),
output_index=1,
sequence_number=4,
type="response.output_item.added",
),
ResponseFunctionCallArgumentsDeltaEvent(
delta='{"city":',
item_id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
output_index=1,
sequence_number=5,
type="response.function_call_arguments.delta",
obfuscation="PySUcQ59ZZRkOm",
),
ResponseFunctionCallArgumentsDeltaEvent(
delta='"Paris"}',
item_id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
output_index=1,
sequence_number=8,
type="response.function_call_arguments.delta",
obfuscation="INeMDAi1uAj",
),
ResponseFunctionCallArgumentsDoneEvent(
arguments='{"city":"Paris"}',
item_id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
name="weather", # added name here because pydantic complains otherwise API returns a none here
output_index=1,
sequence_number=10,
type="response.function_call_arguments.done",
),
ResponseCompletedEvent(
response=Response(
id="resp_095b57053855eac100690491f4e22c8196ac124365e8c70424",
created_at=1761907188.0,
metadata={},
model="gpt-5-mini-2025-08-07",
object="response",
output=[
ResponseReasoningItem(
id="rs_095b57053855eac100690491f54e308196878239be3ba6133c", summary=[], type="reasoning"
),
ResponseFunctionToolCall(
arguments='{"city":"Paris"}',
call_id="call_OZZXFm7SLb4F3Xg8a9XVVCvv",
name="weather",
type="function_call",
id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
status="completed",
),
],
parallel_tool_calls=True,
temperature=1.0,
tool_choice="auto",
tools=[
FunctionTool(
name="weather",
parameters={
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
"additionalProperties": False,
},
strict=False,
type="function",
description="useful to determine the weather in a given location",
)
],
top_p=1.0,
reasoning=Reasoning(effort="medium", generate_summary=None, summary=None),
usage=ResponseUsage(
input_tokens=62,
input_tokens_details=InputTokensDetails(cached_tokens=0),
output_tokens=83,
output_tokens_details=OutputTokensDetails(reasoning_tokens=64),
total_tokens=145,
),
store=True,
),
sequence_number=12,
type="response.completed",
),
]
streaming_chunks = []
for chunk in chunks:
streaming_chunk = _convert_response_chunk_to_streaming_chunk(chunk, previous_chunks=streaming_chunks)
streaming_chunks.append(streaming_chunk)
assert streaming_chunks == [
# TODO Unneeded streaming chunk
StreamingChunk(
content="",
meta={
"received_at": ANY,
"response": {
"id": "resp_095b57053855eac100690491f4e22c8196ac124365e8c70424",
"created_at": 1761907188.0,
"metadata": {},
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"output": [],
"parallel_tool_calls": True,
"temperature": 1.0,
"tool_choice": "auto",
"tools": [
{
"name": "weather",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
"additionalProperties": False,
},
"strict": False,
"type": "function",
"description": "useful to determine the weather in a given location",
}
],
"reasoning": {"effort": "medium", "generate_summary": None, "summary": None},
"usage": None,
},
"sequence_number": 0,
"type": "response.created",
},
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=0,
start=True,
reasoning=ReasoningContent(
reasoning_text="",
extra={
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"summary": [],
"type": "reasoning",
},
),
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=0,
reasoning=ReasoningContent(
reasoning_text="",
extra={
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"summary": [],
"type": "reasoning",
},
),
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=1,
tool_calls=[
ToolCallDelta(
index=1,
tool_name="weather",
arguments=None,
id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
extra={
"arguments": "",
"call_id": "call_OZZXFm7SLb4F3Xg8a9XVVCvv",
"id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"name": "weather",
"status": "in_progress",
"type": "function_call",
},
)
],
start=True,
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=1,
tool_calls=[
ToolCallDelta(
index=1,
tool_name=None,
arguments='{"city":',
id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
extra={
"item_id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"output_index": 1,
"sequence_number": 5,
"type": "response.function_call_arguments.delta",
"obfuscation": "PySUcQ59ZZRkOm",
},
)
],
),
StreamingChunk(
content="",
meta={"received_at": ANY},
index=1,
tool_calls=[
ToolCallDelta(
index=1,
tool_name=None,
arguments='"Paris"}',
id="fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
extra={
"item_id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"output_index": 1,
"sequence_number": 8,
"type": "response.function_call_arguments.delta",
"obfuscation": "INeMDAi1uAj",
},
)
],
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"arguments": '{"city":"Paris"}',
"item_id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"name": "weather",
"output_index": 1,
"sequence_number": 10,
"type": "response.function_call_arguments.done",
},
index=1,
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"response": {
"id": "resp_095b57053855eac100690491f4e22c8196ac124365e8c70424",
"created_at": 1761907188.0,
"metadata": {},
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"output": [
{
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"summary": [],
"type": "reasoning",
},
{
"arguments": '{"city":"Paris"}',
"call_id": "call_OZZXFm7SLb4F3Xg8a9XVVCvv",
"name": "weather",
"type": "function_call",
"id": "fc_095b57053855eac100690491f6a224819680e2f9c7cbc5a531",
"status": "completed",
},
],
"parallel_tool_calls": True,
"temperature": 1.0,
"tool_choice": "auto",
"tools": [
{
"name": "weather",
"parameters": {
"type": "object",
"properties": {"city": {"type": "string"}},
"required": ["city"],
"additionalProperties": False,
},
"strict": False,
"type": "function",
"description": "useful to determine the weather in a given location",
}
],
"top_p": 1.0,
"reasoning": {"effort": "medium", "generate_summary": None, "summary": None},
"usage": {
"input_tokens": 62,
"input_tokens_details": {"cached_tokens": 0},
"output_tokens": 83,
"output_tokens_details": {"reasoning_tokens": 64},
"total_tokens": 145,
},
"store": True,
},
"sequence_number": 12,
"type": "response.completed",
},
finish_reason="tool_calls",
),
]
class TestResponseToChatMessage:
def test_convert_system_message(self):
message = ChatMessage.from_system("You are good assistant")
assert _convert_chat_message_to_responses_api_format(message) == [
{"role": "system", "content": "You are good assistant"}
]
def test_convert_user_message(self):
message = ChatMessage.from_user("I have a question")
assert _convert_chat_message_to_responses_api_format(message) == [
{"role": "user", "content": [{"type": "input_text", "text": "I have a question"}]}
]
def test_convert_multimodal_user_message(self, base64_image_string):
message = ChatMessage.from_user(
content_parts=[
TextContent("I have a question"),
ImageContent(base64_image=base64_image_string, detail="auto"),
]
)
assert message.to_openai_dict_format() == {
"role": "user",
"content": [
{"type": "text", "text": "I have a question"},
{
"type": "image_url",
"image_url": {"url": f"data:image/png;base64,{base64_image_string}", "detail": "auto"},
},
],
}
# image content only should be supported as well
message = ChatMessage.from_user(content_parts=[ImageContent(base64_image=base64_image_string, detail="auto")])
assert message.to_openai_dict_format() == {
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url": f"data:image/png;base64,{base64_image_string}", "detail": "auto"},
}
],
}
def test_convert_user_message_with_file_content(self, base64_pdf_string):
message = ChatMessage.from_user(
content_parts=[FileContent(base64_data=base64_pdf_string, mime_type="application/pdf", filename="test.pdf")]
)
assert _convert_chat_message_to_responses_api_format(message) == [
{
"role": "user",
"content": [
{
"type": "input_file",
"filename": "test.pdf",
"file_data": f"data:application/pdf;base64,{base64_pdf_string}",
}
],
}
]
def test_convert_user_message_with_file_content_no_filename(self, base64_pdf_string):
message = ChatMessage.from_user(
content_parts=[FileContent(base64_data=base64_pdf_string, mime_type="application/pdf")]
)
assert _convert_chat_message_to_responses_api_format(message) == [
{
"role": "user",
"content": [
{
"type": "input_file",
"filename": "filename",
"file_data": f"data:application/pdf;base64,{base64_pdf_string}",
}
],
}
]
def test_convert_assistant_message(self):
message = ChatMessage.from_assistant(text="I have an answer", meta={"finish_reason": "stop"})
assert _convert_chat_message_to_responses_api_format(message) == [
{"role": "assistant", "content": "I have an answer"}
]
def test_convert_assistant_message_w_tool_call(self):
chat_message = ChatMessage(
_role=ChatRole.ASSISTANT,
_content=[
TextContent(text="I need to use the functions.weather tool."),
ReasoningContent(
reasoning_text="I need to use the functions.weather tool.",
extra={"id": "rs_0d13efdd", "type": "reasoning"},
),
ToolCall(
tool_name="weather",
arguments={"location": "Berlin"},
id="fc_0d13efdd",
extra={"call_id": "call_a82vwFAIzku9SmBuQuecQSRq"},
),
],
_name=None,
# some keys are removed to keep the test concise
_meta={
"id": "resp_0d13efdd97aa4",
"created_at": 1761148307.0,
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"parallel_tool_calls": True,
"temperature": 1.0,
"tool_choice": "auto",
"tools": [
{
"name": "weather",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"],
"additionalProperties": False,
},
"strict": False,
"type": "function",
"description": "A tool to get the weather",
}
],
"top_p": 1.0,
"reasoning": {"effort": "low", "summary": "detailed"},
"usage": {"input_tokens": 59, "output_tokens": 19, "total_tokens": 78},
"store": True,
},
)
responses_api_format = _convert_chat_message_to_responses_api_format(chat_message)
assert responses_api_format == [
{
"id": "rs_0d13efdd",
"type": "reasoning",
"summary": [{"text": "I need to use the functions.weather tool.", "type": "summary_text"}],
},
{
"type": "function_call",
"name": "weather",
"arguments": '{"location": "Berlin"}',
"id": "fc_0d13efdd",
"call_id": "call_a82vwFAIzku9SmBuQuecQSRq",
},
{"content": "I need to use the functions.weather tool.", "role": "assistant"},
]
def test_convert_assistant_message_reasoning_strips_invalid_streaming_fields(self):
chat_message = ChatMessage(
_role=ChatRole.ASSISTANT,
_content=[
ReasoningContent(
reasoning_text="Let me think.",
extra={
"id": "rs_abc",
"type": "reasoning",
"encrypted_content": "enc123",
"status": "completed",
"item_id": "some_item",
"output_index": 0,
"summary_index": 1,
"event_id": "ev_xyz",
"sequence_number": 42,
},
)
],
)
result = _convert_chat_message_to_responses_api_format(chat_message)
assert result == [
{
"id": "rs_abc",
"type": "reasoning",
"encrypted_content": "enc123",
"status": "completed",
"summary": [{"text": "Let me think.", "type": "summary_text"}],
}
]
def test_convert_tool_message(self):
tool_call_result = ChatMessage(
_role=ChatRole.TOOL,
_content=[
ToolCallResult(
result="result",
origin=ToolCall(
id="fc_0d13efdd",
tool_name="weather",
arguments={"location": "Berlin"},
extra={"call_id": "call_a82vwFAIzku9SmBuQuecQSRq"},
),
error=False,
)
],
)
assert _convert_chat_message_to_responses_api_format(tool_call_result) == [
{
"call_id": "call_a82vwFAIzku9SmBuQuecQSRq",
"output": [{"type": "input_text", "text": "result"}],
"type": "function_call_output",
}
]
def test_convert_tool_message_list_with_image(self, base64_image_string):
tool_result = [
TextContent(text="first result"),
ImageContent(base64_image=base64_image_string, mime_type="image/png"),
]
message = ChatMessage.from_tool(
tool_result=tool_result,
origin=ToolCall(
tool_name="mytool", arguments={}, id="123", extra={"call_id": "call_a82vwFAIzku9SmBuQuecQSRq"}
),
error=False,
)
assert _convert_chat_message_to_responses_api_format(message) == [
{
"call_id": "call_a82vwFAIzku9SmBuQuecQSRq",
"output": [
{"type": "input_text", "text": "first result"},
{"type": "input_image", "image_url": f"data:image/png;base64,{base64_image_string}"},
],
"type": "function_call_output",
}
]
def test_convert_tool_message_list_with_file(self, base64_pdf_string):
tool_result = [
TextContent(text="first result"),
FileContent(base64_data=base64_pdf_string, mime_type="application/pdf", filename="guide.pdf"),
]
message = ChatMessage.from_tool(
tool_result=tool_result,
origin=ToolCall(
tool_name="mytool", arguments={}, id="123", extra={"call_id": "call_a82vwFAIzku9SmBuQuecQSRq"}
),
error=False,
)
assert _convert_chat_message_to_responses_api_format(message) == [
{
"call_id": "call_a82vwFAIzku9SmBuQuecQSRq",
"output": [
{"type": "input_text", "text": "first result"},
{
"type": "input_file",
"filename": "guide.pdf",
"file_data": f"data:application/pdf;base64,{base64_pdf_string}",
},
],
"type": "function_call_output",
}
]
def test_convert_invalid(self):
message = ChatMessage(_role=ChatRole.ASSISTANT, _content=[])
with pytest.raises(ValueError):
_convert_chat_message_to_responses_api_format(message)
message = ChatMessage(
_role=ChatRole.USER,
_content=[
TextContent(text="I have an answer"),
ToolCallResult(
result="I have another answer",
origin=ToolCall(id="123", tool_name="mytool", arguments={"a": 1}),
error=False,
),
],
)
with pytest.raises(ValueError):
_convert_chat_message_to_responses_api_format(message)
def test_convert_streaming_chunks_to_chat_message_preserves_encrypted_content(self):
"""Test that encrypted_content in reasoning extra is preserved during streaming conversion."""
chunks = [
StreamingChunk(
content="",
meta={"received_at": ANY},
index=0,
start=True,
reasoning=ReasoningContent(
reasoning_text="",
extra={
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"type": "reasoning",
"encrypted_content": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...", # Simulated encrypted reasoning
"status": "in_progress",
},
),
),
StreamingChunk(
content="",
meta={
"received_at": ANY,
"response": {
"id": "resp_095b57053855eac100690491f4e22c8196ac124365e8c70424",
"created_at": 1761907188.0,
"model": "gpt-5-mini-2025-08-07",
"object": "response",
"output": [
{
"id": "rs_095b57053855eac100690491f54e308196878239be3ba6133c",
"type": "reasoning",
"encrypted_content": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
"status": "completed",
}
],
},
"sequence_number": 16,
"type": "response.completed",
},
finish_reason="stop",
),
]
message = _convert_streaming_chunks_to_chat_message(chunks)
# Verify reasoning content exists and has the correct structure
assert message.reasoning is not None
assert message.reasoning.reasoning_text == ""
# Verify encrypted_content is preserved along with id and type
assert message.reasoning.extra.get("id") == "rs_095b57053855eac100690491f54e308196878239be3ba6133c"
assert message.reasoning.extra.get("type") == "reasoning"
assert message.reasoning.extra.get("encrypted_content") == "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
assert message.reasoning.extra.get("status") == "in_progress"
def test_encrypted_content_preserved_through_full_streaming_pipeline(self):
"""
Feeds real OpenAI event objects through the full pipeline:
_convert_response_chunk_to_streaming_chunk → _convert_streaming_chunks_to_chat_message
to verify encrypted_content survives end-to-end.
"""
REASONING_ID = "rs_abc123"
ENCRYPTED = "eyJhbGciOiJIUzI1NiJ9.encrypted_reasoning"
openai_events = [
# reasoning item starts — encrypted_content not yet available
ResponseOutputItemAddedEvent(
item=ResponseReasoningItem(id=REASONING_ID, summary=[], type="reasoning", status="in_progress"),
output_index=0,
sequence_number=0,
type="response.output_item.added",
),
# reasoning item finishes — encrypted_content is now populated
ResponseOutputItemDoneEvent(
item=ResponseReasoningItem(
id=REASONING_ID, summary=[], type="reasoning", encrypted_content=ENCRYPTED, status="completed"
),
output_index=0,
sequence_number=1,
type="response.output_item.done",
),
]
streaming_chunks = []
for event in openai_events:
chunk = _convert_response_chunk_to_streaming_chunk(event, previous_chunks=streaming_chunks)
streaming_chunks.append(chunk)
# The done chunk must carry reasoning so encrypted_content reaches the assembly step
done_chunk = streaming_chunks[1]
assert done_chunk.reasoning is not None, (
"response.output_item.done for reasoning must produce a StreamingChunk with reasoning set; "
"without this, encrypted_content is silently dropped before assembly"
)
assert done_chunk.reasoning.extra.get("encrypted_content") == ENCRYPTED
message = _convert_streaming_chunks_to_chat_message(streaming_chunks)
assert message.reasoning is not None
assert message.reasoning.extra.get("id") == REASONING_ID
assert message.reasoning.extra.get("encrypted_content") == ENCRYPTED, (
"encrypted_content was dropped — the response.output_item.done event for reasoning items "
"must be handled in _convert_response_chunk_to_streaming_chunk"
)
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