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feat: openai_api_compatible support config stream_mode_delimiter (#2190)
Co-authored-by: wanggang <wanggy01@servyou.com.cn> Co-authored-by: Chenhe Gu <guchenhe@gmail.com>
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@ -224,7 +224,7 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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entity.model_properties[ModelPropertyKey.MODE] = LLMMode.COMPLETION.value
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else:
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raise ValueError(f"Unknown completion type {credentials['completion_type']}")
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return entity
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# validate_credentials method has been rewritten to use the requests library for compatibility with all providers following OpenAI's API standard.
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@ -343,32 +343,44 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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)
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)
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for chunk in response.iter_lines(decode_unicode=True, delimiter='\n\n'):
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# delimiter for stream response, need unicode_escape
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import codecs
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delimiter = credentials.get("stream_mode_delimiter", "\n\n")
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delimiter = codecs.decode(delimiter, "unicode_escape")
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for chunk in response.iter_lines(decode_unicode=True, delimiter=delimiter):
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if chunk:
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decoded_chunk = chunk.strip().lstrip('data: ').lstrip()
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chunk_json = None
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try:
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chunk_json = json.loads(decoded_chunk)
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# stream ended
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except json.JSONDecodeError as e:
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logger.error(f"decoded_chunk error,delimiter={delimiter},decoded_chunk={decoded_chunk}")
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yield create_final_llm_result_chunk(
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index=chunk_index + 1,
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message=AssistantPromptMessage(content=""),
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finish_reason="Non-JSON encountered."
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)
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break
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if not chunk_json or len(chunk_json['choices']) == 0:
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continue
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choice = chunk_json['choices'][0]
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finish_reason = chunk_json['choices'][0].get('finish_reason')
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chunk_index += 1
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if 'delta' in choice:
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delta = choice['delta']
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if delta.get('content') is None or delta.get('content') == '':
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continue
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if finish_reason is not None:
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yield create_final_llm_result_chunk(
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index=chunk_index,
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message=AssistantPromptMessage(content=choice.get('text', '')),
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finish_reason=finish_reason
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)
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else:
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continue
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assistant_message_tool_calls = delta.get('tool_calls', None)
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# assistant_message_function_call = delta.delta.function_call
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@ -387,24 +399,22 @@ class OAIAPICompatLargeLanguageModel(_CommonOAI_API_Compat, LargeLanguageModel):
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full_assistant_content += delta.get('content', '')
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elif 'text' in choice:
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if choice.get('text') is None or choice.get('text') == '':
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choice_text = choice.get('text', '')
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if choice_text == '':
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continue
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# transform assistant message to prompt message
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assistant_prompt_message = AssistantPromptMessage(
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content=choice.get('text', '')
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)
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full_assistant_content += choice.get('text', '')
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assistant_prompt_message = AssistantPromptMessage(content=choice_text)
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full_assistant_content += choice_text
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else:
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continue
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# check payload indicator for completion
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if chunk_json['choices'][0].get('finish_reason') is not None:
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if finish_reason is not None:
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yield create_final_llm_result_chunk(
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index=chunk_index,
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message=assistant_prompt_message,
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finish_reason=chunk_json['choices'][0]['finish_reason']
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finish_reason=finish_reason
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)
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else:
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yield LLMResultChunk(
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@ -75,3 +75,12 @@ model_credential_schema:
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value: llm
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default: '4096'
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type: text-input
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- variable: stream_mode_delimiter
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label:
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zh_Hans: 流模式返回结果的分隔符
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en_US: Delimiter for streaming results
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show_on:
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- variable: __model_type
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value: llm
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default: '\n\n'
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type: text-input
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@ -12,6 +12,7 @@ from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAI
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Using Together.ai's OpenAI-compatible API as testing endpoint
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"""
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def test_validate_credentials():
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model = OAIAPICompatLargeLanguageModel()
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@ -34,6 +35,7 @@ def test_validate_credentials():
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}
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)
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def test_invoke_model():
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model = OAIAPICompatLargeLanguageModel()
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@ -65,9 +67,47 @@ def test_invoke_model():
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assert isinstance(response, LLMResult)
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assert len(response.message.content) > 0
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def test_invoke_stream_model():
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model = OAIAPICompatLargeLanguageModel()
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response = model.invoke(
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model='mistralai/Mixtral-8x7B-Instruct-v0.1',
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credentials={
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'api_key': os.environ.get('TOGETHER_API_KEY'),
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'endpoint_url': 'https://api.together.xyz/v1/',
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'mode': 'chat',
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'stream_mode_delimiter': '\\n\\n'
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},
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prompt_messages=[
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SystemPromptMessage(
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content='You are a helpful AI assistant.',
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),
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UserPromptMessage(
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content='Who are you?'
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)
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],
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model_parameters={
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'temperature': 1.0,
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'top_k': 2,
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'top_p': 0.5,
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},
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stop=['How'],
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stream=True,
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user="abc-123"
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)
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assert isinstance(response, Generator)
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for chunk in response:
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assert isinstance(chunk, LLMResultChunk)
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assert isinstance(chunk.delta, LLMResultChunkDelta)
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assert isinstance(chunk.delta.message, AssistantPromptMessage)
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def test_invoke_stream_model_without_delimiter():
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model = OAIAPICompatLargeLanguageModel()
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response = model.invoke(
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model='mistralai/Mixtral-8x7B-Instruct-v0.1',
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credentials={
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@ -100,6 +140,7 @@ def test_invoke_stream_model():
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assert isinstance(chunk.delta, LLMResultChunkDelta)
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assert isinstance(chunk.delta.message, AssistantPromptMessage)
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# using OpenAI's ChatGPT-3.5 as testing endpoint
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def test_invoke_chat_model_with_tools():
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model = OAIAPICompatLargeLanguageModel()
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@ -126,22 +167,22 @@ def test_invoke_chat_model_with_tools():
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parameters={
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state e.g. San Francisco, CA"
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},
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"unit": {
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"type": "string",
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"enum": [
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"celsius",
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"fahrenheit"
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]
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}
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"location": {
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"type": "string",
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"description": "The city and state e.g. San Francisco, CA"
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},
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"unit": {
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"type": "string",
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"enum": [
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"celsius",
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"fahrenheit"
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]
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}
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},
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"required": [
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"location"
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"location"
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]
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}
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}
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),
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],
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model_parameters={
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@ -156,6 +197,7 @@ def test_invoke_chat_model_with_tools():
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assert isinstance(result.message, AssistantPromptMessage)
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assert len(result.message.tool_calls) > 0
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def test_get_num_tokens():
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model = OAIAPICompatLargeLanguageModel()
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