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fix: update _convert_streaming_chunks_to_chat_message to handle tool calls with empty arguments (#9639)

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* Update util function

* Add a new test

* PR comments
This commit is contained in:
Amna Mubashar 2025-07-23 13:28:05 +02:00 committed by GitHub
parent 59403de1f0
commit 8e792a3d12
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2 changed files with 102 additions and 2 deletions
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4
haystack/components/generators/utils.py
View File

@ -104,11 +104,11 @@ def _convert_streaming_chunks_to_chat_message(chunks: List[StreamingChunk]) -> C
for key in sorted_keys:
tool_call_dict = tool_call_data[key]
try:
arguments = json.loads(tool_call_dict["arguments"])
arguments = json.loads(tool_call_dict.get("arguments", "{}")) if tool_call_dict.get("arguments") else {}
tool_calls.append(ToolCall(id=tool_call_dict["id"], tool_name=tool_call_dict["name"], arguments=arguments))
except json.JSONDecodeError:
logger.warning(
"OpenAI returned a malformed JSON string for tool call arguments. This tool call "
"The LLM provider returned a malformed JSON string for tool call arguments. This tool call "
"will be skipped. To always generate a valid JSON, set `tools_strict` to `True`. "
"Tool call ID: {_id}, Tool name: {_name}, Arguments: {_arguments}",
_id=tool_call_dict["id"],

100
test/components/generators/test_utils.py
View File

@ -506,6 +506,106 @@ def test_convert_streaming_chunk_to_chat_message_empty_tool_call_delta():
assert result.meta["finish_reason"] == "tool_calls"
def test_convert_streaming_chunk_to_chat_message_with_empty_tool_call_arguments():
chunks = [
# Message start with input tokens
StreamingChunk(
content="",
meta={
"type": "message_start",
"message": {
"id": "msg_123",
"type": "message",
"role": "assistant",
"content": [],
"model": "claude-sonnet-4-20250514",
"stop_reason": None,
"stop_sequence": None,
"usage": {"input_tokens": 25, "output_tokens": 0},
},
},
index=0,
tool_calls=[],
tool_call_result=None,
start=True,
finish_reason=None,
),
# Initial text content
StreamingChunk(
content="",
meta={"type": "content_block_start", "index": 0, "content_block": {"type": "text", "text": ""}},
index=1,
tool_calls=[],
tool_call_result=None,
start=True,
finish_reason=None,
),
StreamingChunk(
content="Let me check",
meta={"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": "Let me check"}},
index=2,
tool_calls=[],
tool_call_result=None,
start=False,
finish_reason=None,
),
StreamingChunk(
content=" the weather",
meta={"type": "content_block_delta", "index": 0, "delta": {"type": "text_delta", "text": " the weather"}},
index=3,
tool_calls=[],
tool_call_result=None,
start=False,
finish_reason=None,
),
# Tool use content
StreamingChunk(
content="",
meta={
"type": "content_block_start",
"index": 1,
"content_block": {"type": "tool_use", "id": "toolu_123", "name": "weather", "input": {}},
},
index=5,
tool_calls=[ToolCallDelta(index=1, id="toolu_123", tool_name="weather", arguments=None)],
tool_call_result=None,
start=True,
finish_reason=None,
),
StreamingChunk(
content="",
meta={"type": "content_block_delta", "index": 1, "delta": {"type": "input_json_delta", "partial_json": ""}},
index=7,
tool_calls=[ToolCallDelta(index=1, id=None, tool_name=None, arguments="")],
tool_call_result=None,
start=False,
finish_reason=None,
),
# Final message delta
StreamingChunk(
content="",
meta={
"type": "message_delta",
"delta": {"stop_reason": "tool_use", "stop_sequence": None},
"usage": {"completion_tokens": 40},
},
index=8,
tool_calls=[],
tool_call_result=None,
start=False,
finish_reason="tool_calls",
),
]
message = _convert_streaming_chunks_to_chat_message(chunks=chunks)
assert message.texts == ["Let me check the weather"]
assert len(message.tool_calls) == 1
assert message.tool_calls[0].arguments == {}
assert message.tool_calls[0].id == "toolu_123"
assert message.tool_calls[0].tool_name == "weather"
def test_print_streaming_chunk_content_only():
chunk = StreamingChunk(
content="Hello, world!",