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94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
"""Unit tests for LiteLLMRequester._convert_messages.
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Focus: the content-part normalization that (a) converts image_base64 parts to
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the OpenAI image_url shape and (b) drops non-image file parts (file_base64 /
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file_url) which OpenAI-compatible chat models reject. The latter is essential
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for Voice/File attachments — including ones replayed from conversation history —
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since the agent consumes their bytes via the sandbox, not the model payload.
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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 does not touch instance config, so bypass __init__.
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return LiteLLMRequester.__new__(LiteLLMRequester)
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def test_convert_messages_drops_file_base64_part():
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req = _make_requester()
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msg = provider_message.Message(
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role='user',
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content=[
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provider_message.ContentElement.from_text('analyze this audio'),
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provider_message.ContentElement.from_file_base64('data:audio/wav;base64,AAAA', 'voice.wav'),
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],
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)
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out = req._convert_messages([msg])
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parts = out[0]['content']
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types = [p.get('type') for p in parts]
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assert 'file_base64' not in types
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assert types == ['text']
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assert parts[0]['text'] == 'analyze this audio'
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def test_convert_messages_drops_file_url_part():
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req = _make_requester()
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msg = provider_message.Message(
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role='user',
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content=[
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provider_message.ContentElement.from_text('here is a doc'),
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provider_message.ContentElement.from_file_url('http://example.com/report.xlsx', 'report.xlsx'),
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],
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)
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out = req._convert_messages([msg])
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types = [p.get('type') for p in out[0]['content']]
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assert types == ['text']
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def test_convert_messages_keeps_image_and_converts_to_image_url():
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req = _make_requester()
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msg = provider_message.Message(
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role='user',
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content=[
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provider_message.ContentElement.from_text('look'),
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provider_message.ContentElement.from_image_base64('data:image/png;base64,AAAA'),
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],
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)
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out = req._convert_messages([msg])
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parts = out[0]['content']
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types = [p.get('type') for p in parts]
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# image is preserved and reshaped to the OpenAI image_url form
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assert types == ['text', 'image_url']
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img_part = parts[1]
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assert img_part['image_url'] == {'url': 'data:image/png;base64,AAAA'}
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assert 'image_base64' not in img_part
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def test_convert_messages_mixed_history_strips_only_files():
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req = _make_requester()
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# Simulate replayed history: an old voice turn + a current text turn.
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history_voice = provider_message.Message(
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role='user',
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content=[
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provider_message.ContentElement.from_text('old audio turn'),
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provider_message.ContentElement.from_file_base64('data:audio/wav;base64,BBBB', 'voice.wav'),
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],
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)
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current = provider_message.Message(
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role='user',
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content=[provider_message.ContentElement.from_text('now do the csv')],
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)
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out = req._convert_messages([history_voice, current])
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assert [p.get('type') for p in out[0]['content']] == ['text']
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assert [p.get('type') for p in out[1]['content']] == ['text']
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def test_convert_messages_plain_string_content_untouched():
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req = _make_requester()
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msg = provider_message.Message(role='user', content='just text')
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out = req._convert_messages([msg])
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assert out[0]['content'] == 'just text'
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