fix(litellmchat): preserve provider_specific_fields for Gemini thought_signature (#2265)

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

tests/unit_tests/provider/test_provider_specific_fields.py

@@ -0,0 +1,172 @@
+"""Unit tests for provider_specific_fields round-trip in LiteLLMRequester.
+
+This tests the fix for GitHub issue #1899: Gemini requires thought_signature
+to be preserved across tool call rounds for function calls to work correctly.
+"""
+
+import langbot_plugin.api.entities.builtin.provider.message as provider_message
+
+from langbot.pkg.provider.modelmgr.requesters.litellmchat import LiteLLMRequester
+
+
+def _make_requester() -> LiteLLMRequester:
+    # _convert_messages and _normalize_stream_tool_calls do not touch instance config.
+    return LiteLLMRequester.__new__(LiteLLMRequester)
+
+
+def test_convert_messages_preserves_tool_call_provider_specific_fields():
+    """Tool calls should retain provider_specific_fields through _convert_messages."""
+    req = _make_requester()
+    msg = provider_message.Message(
+        role='assistant',
+        content=None,
+        tool_calls=[
+            provider_message.ToolCall(
+                id='call_123',
+                type='function',
+                function=provider_message.FunctionCall(
+                    name='search',
+                    arguments='{"query": "test"}',
+                ),
+                provider_specific_fields={
+                    'thought_signature': 'c2tpcF90aG91Z2h0X3NpZ25hdHVyZQ==',
+                },
+            ),
+        ],
+    )
+    out = req._convert_messages([msg])
+    assert len(out) == 1
+    assert out[0]['tool_calls'] is not None
+    assert len(out[0]['tool_calls']) == 1
+
+    tc = out[0]['tool_calls'][0]
+    assert tc['id'] == 'call_123'
+    assert tc['function']['name'] == 'search'
+    assert 'provider_specific_fields' in tc
+    assert tc['provider_specific_fields']['thought_signature'] == 'c2tpcF90aG91Z2h0X3NpZ25hdHVyZQ=='
+
+
+def test_convert_messages_preserves_message_provider_specific_fields():
+    """Messages should retain provider_specific_fields through _convert_messages."""
+    req = _make_requester()
+    msg = provider_message.Message(
+        role='assistant',
+        content='Hello',
+        provider_specific_fields={
+            'thought_signatures': ['sig1', 'sig2'],
+        },
+    )
+    out = req._convert_messages([msg])
+    assert len(out) == 1
+    assert 'provider_specific_fields' in out[0]
+    assert out[0]['provider_specific_fields']['thought_signatures'] == ['sig1', 'sig2']
+
+
+def test_normalize_stream_tool_calls_preserves_provider_specific_fields():
+    """Streaming tool calls should retain provider_specific_fields."""
+    req = _make_requester()
+    tool_call_state: dict[int, dict] = {}
+
+    # Simulate first chunk with id and type
+    raw_tool_calls_1 = [
+        {
+            'index': 0,
+            'id': 'call_abc',
+            'type': 'function',
+            'function': {
+                'name': 'get_weather',
+                'arguments': '',
+            },
+            'provider_specific_fields': {
+                'thought_signature': 'dGVzdF9zaWduYXR1cmU=',
+            },
+        },
+    ]
+    result_1 = req._normalize_stream_tool_calls(raw_tool_calls_1, tool_call_state)
+    assert result_1 is not None
+    assert len(result_1) == 1
+    assert result_1[0]['provider_specific_fields']['thought_signature'] == 'dGVzdF9zaWduYXR1cmU='
+
+    # Simulate second chunk without provider_specific_fields (should be retained from state)
+    raw_tool_calls_2 = [
+        {
+            'index': 0,
+            'function': {
+                'arguments': '{"city": "Tokyo"}',
+            },
+        },
+    ]
+    result_2 = req._normalize_stream_tool_calls(raw_tool_calls_2, tool_call_state)
+    assert result_2 is not None
+    assert len(result_2) == 1
+    # Should retain the provider_specific_fields from the first chunk
+    assert result_2[0]['provider_specific_fields']['thought_signature'] == 'dGVzdF9zaWduYXR1cmU='
+    assert result_2[0]['function']['arguments'] == '{"city": "Tokyo"}'
+
+
+def test_normalize_stream_tool_calls_merges_function_level_psf():
+    """Function-level provider_specific_fields should be merged into tool-level."""
+    req = _make_requester()
+    tool_call_state: dict[int, dict] = {}
+
+    raw_tool_calls = [
+        {
+            'index': 0,
+            'id': 'call_xyz',
+            'type': 'function',
+            'function': {
+                'name': 'search',
+                'arguments': '{}',
+                'provider_specific_fields': {
+                    'thought_signature': 'ZnVuY19sZXZlbF9zaWc=',
+                },
+            },
+        },
+    ]
+    result = req._normalize_stream_tool_calls(raw_tool_calls, tool_call_state)
+    assert result is not None
+    assert result[0]['provider_specific_fields']['thought_signature'] == 'ZnVuY19sZXZlbF9zaWc='
+
+
+def test_tool_call_roundtrip_through_message_entity():
+    """Full round-trip: LiteLLM response dict -> Message entity -> _convert_messages."""
+    # Simulate what LiteLLM returns for a Gemini tool call response
+    message_data = {
+        'role': 'assistant',
+        'content': None,
+        'tool_calls': [
+            {
+                'id': 'call_gemini_123',
+                'type': 'function',
+                'function': {
+                    'name': 'web_search',
+                    'arguments': '{"query": "test"}',
+                },
+                'provider_specific_fields': {
+                    'thought_signature': 'Z2VtaW5pX3NpZ25hdHVyZQ==',
+                },
+            },
+        ],
+        'provider_specific_fields': {
+            'thought_signatures': ['Z2VtaW5pX3NpZ25hdHVyZQ=='],
+        },
+    }
+
+    # Parse into Message entity (this is what invoke_llm does)
+    msg = provider_message.Message(**message_data)
+
+    # Verify the entity has the fields
+    assert msg.tool_calls is not None
+    assert len(msg.tool_calls) == 1
+    assert msg.tool_calls[0].provider_specific_fields is not None
+    assert msg.tool_calls[0].provider_specific_fields['thought_signature'] == 'Z2VtaW5pX3NpZ25hdHVyZQ=='
+    assert msg.provider_specific_fields is not None
+    assert msg.provider_specific_fields['thought_signatures'] == ['Z2VtaW5pX3NpZ25hdHVyZQ==']
+
+    # Convert back to dict for LiteLLM (this is what _convert_messages does)
+    req = _make_requester()
+    out = req._convert_messages([msg])
+
+    # Verify the fields are preserved in the output
+    assert out[0]['tool_calls'][0]['provider_specific_fields']['thought_signature'] == 'Z2VtaW5pX3NpZ25hdHVyZQ=='
+    assert out[0]['provider_specific_fields']['thought_signatures'] == ['Z2VtaW5pX3NpZ25hdHVyZQ==']