68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
from src.llm.backend import CompletionResult, ToolCallResult
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from src.llm.history_adapters import (
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AnthropicHistoryAdapter,
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GeminiHistoryAdapter,
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OpenAIHistoryAdapter,
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)
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def test_anthropic_history_adapter_preserves_thinking_blocks() -> None:
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adapter = AnthropicHistoryAdapter()
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result = CompletionResult(
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content="Done",
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thinking_blocks=[
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{
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"type": "thinking",
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"thinking": "private reasoning",
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"signature": "sig_123",
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}
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],
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tool_calls=[
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ToolCallResult(id="tool_1", name="search", input={"query": "honcho"})
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],
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)
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message = adapter.format_assistant_tool_message(result)
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assert message["role"] == "assistant"
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assert message["content"][0]["type"] == "thinking"
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assert message["content"][1] == {"type": "text", "text": "Done"}
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assert message["content"][2]["type"] == "tool_use"
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def test_gemini_history_adapter_preserves_thought_signature() -> None:
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adapter = GeminiHistoryAdapter()
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result = CompletionResult(
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content="Calling a tool",
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tool_calls=[
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ToolCallResult(
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id="tool_1",
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name="search",
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input={"query": "honcho"},
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thought_signature="sig_abc",
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)
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],
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)
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message = adapter.format_assistant_tool_message(result)
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assert message["role"] == "model"
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assert message["parts"][1]["thought_signature"] == "sig_abc"
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def test_openai_history_adapter_preserves_reasoning_details() -> None:
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adapter = OpenAIHistoryAdapter()
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result = CompletionResult(
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content="Calling a tool",
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reasoning_details=[{"type": "reasoning", "content": "step 1"}],
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tool_calls=[
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ToolCallResult(id="tool_1", name="search", input={"query": "honcho"})
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],
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)
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message = adapter.format_assistant_tool_message(result)
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assert message["role"] == "assistant"
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assert message["reasoning_details"] == [{"type": "reasoning", "content": "step 1"}]
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assert message["tool_calls"][0]["function"]["name"] == "search"
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