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acfac42107
Update _normalize_stream_tool_calls to preserve provider_specific_fields (including thought_signature) from streaming tool call chunks. Also preserve provider_specific_fields from delta in invoke_llm_stream. This ensures Gemini's thought_signature is round-tripped correctly: 1. LiteLLM extracts thought_signature from Gemini response 2. It's preserved in Message/ToolCall entities (via SDK changes) 3. _convert_messages includes it in the next request Also add unit tests for provider_specific_fields round-tripping. Fixes: langbot-app/LangBot#1899
173 lines
6.3 KiB
Python
173 lines
6.3 KiB
Python
"""Unit tests for provider_specific_fields round-trip in LiteLLMRequester.
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This tests the fix for GitHub issue #1899: Gemini requires thought_signature
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to be preserved across tool call rounds for function calls to work correctly.
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"""
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import langbot_plugin.api.entities.builtin.provider.message as provider_message
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from langbot.pkg.provider.modelmgr.requesters.litellmchat import LiteLLMRequester
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def _make_requester() -> LiteLLMRequester:
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# _convert_messages and _normalize_stream_tool_calls do not touch instance config.
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return LiteLLMRequester.__new__(LiteLLMRequester)
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def test_convert_messages_preserves_tool_call_provider_specific_fields():
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"""Tool calls should retain provider_specific_fields through _convert_messages."""
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req = _make_requester()
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msg = provider_message.Message(
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role='assistant',
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content=None,
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tool_calls=[
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provider_message.ToolCall(
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id='call_123',
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type='function',
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function=provider_message.FunctionCall(
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name='search',
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arguments='{"query": "test"}',
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),
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provider_specific_fields={
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'thought_signature': 'c2tpcF90aG91Z2h0X3NpZ25hdHVyZQ==',
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},
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),
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],
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)
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out = req._convert_messages([msg])
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assert len(out) == 1
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assert out[0]['tool_calls'] is not None
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assert len(out[0]['tool_calls']) == 1
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tc = out[0]['tool_calls'][0]
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assert tc['id'] == 'call_123'
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assert tc['function']['name'] == 'search'
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assert 'provider_specific_fields' in tc
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assert tc['provider_specific_fields']['thought_signature'] == 'c2tpcF90aG91Z2h0X3NpZ25hdHVyZQ=='
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def test_convert_messages_preserves_message_provider_specific_fields():
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"""Messages should retain provider_specific_fields through _convert_messages."""
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req = _make_requester()
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msg = provider_message.Message(
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role='assistant',
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content='Hello',
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provider_specific_fields={
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'thought_signatures': ['sig1', 'sig2'],
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},
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)
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out = req._convert_messages([msg])
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assert len(out) == 1
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assert 'provider_specific_fields' in out[0]
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assert out[0]['provider_specific_fields']['thought_signatures'] == ['sig1', 'sig2']
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def test_normalize_stream_tool_calls_preserves_provider_specific_fields():
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"""Streaming tool calls should retain provider_specific_fields."""
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req = _make_requester()
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tool_call_state: dict[int, dict] = {}
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# Simulate first chunk with id and type
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raw_tool_calls_1 = [
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{
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'index': 0,
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'id': 'call_abc',
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'type': 'function',
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'function': {
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'name': 'get_weather',
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'arguments': '',
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},
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'provider_specific_fields': {
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'thought_signature': 'dGVzdF9zaWduYXR1cmU=',
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},
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},
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]
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result_1 = req._normalize_stream_tool_calls(raw_tool_calls_1, tool_call_state)
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assert result_1 is not None
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assert len(result_1) == 1
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assert result_1[0]['provider_specific_fields']['thought_signature'] == 'dGVzdF9zaWduYXR1cmU='
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# Simulate second chunk without provider_specific_fields (should be retained from state)
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raw_tool_calls_2 = [
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{
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'index': 0,
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'function': {
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'arguments': '{"city": "Tokyo"}',
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},
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},
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]
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result_2 = req._normalize_stream_tool_calls(raw_tool_calls_2, tool_call_state)
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assert result_2 is not None
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assert len(result_2) == 1
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# Should retain the provider_specific_fields from the first chunk
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assert result_2[0]['provider_specific_fields']['thought_signature'] == 'dGVzdF9zaWduYXR1cmU='
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assert result_2[0]['function']['arguments'] == '{"city": "Tokyo"}'
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def test_normalize_stream_tool_calls_merges_function_level_psf():
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"""Function-level provider_specific_fields should be merged into tool-level."""
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req = _make_requester()
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tool_call_state: dict[int, dict] = {}
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raw_tool_calls = [
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{
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'index': 0,
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'id': 'call_xyz',
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'type': 'function',
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'function': {
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'name': 'search',
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'arguments': '{}',
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'provider_specific_fields': {
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'thought_signature': 'ZnVuY19sZXZlbF9zaWc=',
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},
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},
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},
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]
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result = req._normalize_stream_tool_calls(raw_tool_calls, tool_call_state)
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assert result is not None
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assert result[0]['provider_specific_fields']['thought_signature'] == 'ZnVuY19sZXZlbF9zaWc='
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def test_tool_call_roundtrip_through_message_entity():
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"""Full round-trip: LiteLLM response dict -> Message entity -> _convert_messages."""
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# Simulate what LiteLLM returns for a Gemini tool call response
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message_data = {
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'role': 'assistant',
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'content': None,
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'tool_calls': [
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{
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'id': 'call_gemini_123',
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'type': 'function',
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'function': {
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'name': 'web_search',
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'arguments': '{"query": "test"}',
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},
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'provider_specific_fields': {
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'thought_signature': 'Z2VtaW5pX3NpZ25hdHVyZQ==',
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},
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},
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],
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'provider_specific_fields': {
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'thought_signatures': ['Z2VtaW5pX3NpZ25hdHVyZQ=='],
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},
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}
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# Parse into Message entity (this is what invoke_llm does)
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msg = provider_message.Message(**message_data)
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# Verify the entity has the fields
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assert msg.tool_calls is not None
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assert len(msg.tool_calls) == 1
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assert msg.tool_calls[0].provider_specific_fields is not None
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assert msg.tool_calls[0].provider_specific_fields['thought_signature'] == 'Z2VtaW5pX3NpZ25hdHVyZQ=='
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assert msg.provider_specific_fields is not None
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assert msg.provider_specific_fields['thought_signatures'] == ['Z2VtaW5pX3NpZ25hdHVyZQ==']
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# Convert back to dict for LiteLLM (this is what _convert_messages does)
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req = _make_requester()
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out = req._convert_messages([msg])
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# Verify the fields are preserved in the output
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assert out[0]['tool_calls'][0]['provider_specific_fields']['thought_signature'] == 'Z2VtaW5pX3NpZ25hdHVyZQ=='
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assert out[0]['provider_specific_fields']['thought_signatures'] == ['Z2VtaW5pX3NpZ25hdHVyZQ==']
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