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## Description make DashScope models support Partial Mode for text continuation. For text continuation in ChatTongYi, it supports text continuation with a prefix by adding a "partial" argument in AIMessage. The document is [Partial Mode ](https://help.aliyun.com/zh/model-studio/user-guide/partial-mode?spm=a2c4g.11186623.help-menu-2400256.d_1_0_0_8.211e5b77KMH5Pn&scm=20140722.H_2862210._.OR_help-T_cn~zh-V_1). The API example is: ```py import os import dashscope messages = [{ "role": "user", "content": "请对“春天来了，大地”这句话进行续写，来表达春天的美好和作者的喜悦之情" }, { "role": "assistant", "content": "春天来了，大地", "partial": True }] response = dashscope.Generation.call( api_key=os.getenv("DASHSCOPE_API_KEY"), model='qwen-plus', messages=messages, result_format='message', ) print(response.output.choices[0].message.content) ``` --------- Co-authored-by: Chester Curme <chester.curme@gmail.com>
113 lines
3.5 KiB
Python
113 lines
3.5 KiB
Python
from langchain_core.messages import (
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AIMessage,
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FunctionMessage,
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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.tongyi 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, # type: ignore[arg-type]
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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_dict_to_message_partial_mode() -> None:
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message_dict = {"role": "assistant", "content": "foo", "partial": True}
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result = convert_dict_to_message(message_dict)
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expected_output = AIMessage(content="foo", additional_kwargs={"partial": True})
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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_ai_partial_mode() -> None:
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message = AIMessage(content="foo", additional_kwargs={"partial": True})
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result = convert_message_to_dict(message)
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expected_output = {"role": "assistant", "content": "foo", "partial": True}
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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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def test__convert_message_to_dict_tool() -> None:
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message = FunctionMessage(name="foo", content="bar")
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result = convert_message_to_dict(message)
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expected_output = {
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"role": "tool",
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"tool_call_id": "",
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"content": "bar",
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"name": "foo",
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}
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assert result == expected_output
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