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fcf9230257
- **Description:** Add function call support in Sparkllm chat model. Related documents https://www.xfyun.cn/doc/spark/Web.html#_2-function-call%E8%AF%B4%E6%98%8E - @baskaryan --------- Co-authored-by: ccurme <chester.curme@gmail.com>
86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
from langchain_core.messages import (
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AIMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain_core.output_parsers.openai_tools import (
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parse_tool_call,
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)
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from langchain_community.chat_models.sparkllm import (
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convert_dict_to_message,
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convert_message_to_dict,
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)
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def test__convert_dict_to_message_human() -> None:
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message_dict = {"role": "user", "content": "foo"}
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result = convert_dict_to_message(message_dict)
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expected_output = HumanMessage(content="foo")
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assert result == expected_output
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def test__convert_dict_to_message_ai() -> None:
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message_dict = {"role": "assistant", "content": "foo"}
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result = convert_dict_to_message(message_dict)
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expected_output = AIMessage(content="foo")
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assert result == expected_output
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def test__convert_dict_to_message_other_role() -> None:
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message_dict = {"role": "system", "content": "foo"}
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result = convert_dict_to_message(message_dict)
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expected_output = SystemMessage(content="foo")
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assert result == expected_output
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def test__convert_dict_to_message_function_call() -> None:
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raw_function_calls = [
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{
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"function": {
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"name": "get_current_weather",
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"arguments": '{"location": "Boston", "unit": "fahrenheit"}',
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},
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"type": "function",
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}
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]
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message_dict = {
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"role": "assistant",
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"content": "foo",
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"tool_calls": raw_function_calls,
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}
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result = convert_dict_to_message(message_dict)
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tool_calls = [
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parse_tool_call(raw_tool_call, return_id=True)
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for raw_tool_call in raw_function_calls
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]
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expected_output = AIMessage(
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content="foo",
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additional_kwargs={"tool_calls": raw_function_calls},
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tool_calls=tool_calls,
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invalid_tool_calls=[],
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)
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assert result == expected_output
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def test__convert_message_to_dict_human() -> None:
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message = HumanMessage(content="foo")
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result = convert_message_to_dict(message)
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expected_output = {"role": "user", "content": "foo"}
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assert result == expected_output
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def test__convert_message_to_dict_ai() -> None:
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message = AIMessage(content="foo")
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result = convert_message_to_dict(message)
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expected_output = {"role": "assistant", "content": "foo"}
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assert result == expected_output
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def test__convert_message_to_dict_system() -> None:
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message = SystemMessage(content="foo")
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result = convert_message_to_dict(message)
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expected_output = {"role": "system", "content": "foo"}
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assert result == expected_output
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