c3c25db48c
- Use cases related to LLM call: https://cinnamon-ai.atlassian.net/browse/AUR-388?focusedCommentId=34873 - Sample usages: `test_llms_chat_models.py` and `test_llms_completion_models.py`: ```python from kotaemon.llms.chats.openai import AzureChatOpenAI model = AzureChatOpenAI( openai_api_base="https://test.openai.azure.com/", openai_api_key="some-key", openai_api_version="2023-03-15-preview", deployment_name="gpt35turbo", temperature=0, request_timeout=60, ) output = model("hello world") ``` For the LLM-call component, I decide to wrap around Langchain's LLM models and Langchain's Chat models. And set the interface as follow: - Completion LLM component: ```python class CompletionLLM: def run_raw(self, text: str) -> LLMInterface: # Run text completion: str in -> LLMInterface out def run_batch_raw(self, text: list[str]) -> list[LLMInterface]: # Run text completion in batch: list[str] in -> list[LLMInterface] out # run_document and run_batch_document just reuse run_raw and run_batch_raw, due to unclear use case ``` - Chat LLM component: ```python class ChatLLM: def run_raw(self, text: str) -> LLMInterface: # Run chat completion (no chat history): str in -> LLMInterface out def run_batch_raw(self, text: list[str]) -> list[LLMInterface]: # Run chat completion in batch mode (no chat history): list[str] in -> list[LLMInterface] out def run_document(self, text: list[BaseMessage]) -> LLMInterface: # Run chat completion (with chat history): list[langchain's BaseMessage] in -> LLMInterface out def run_batch_document(self, text: list[list[BaseMessage]]) -> list[LLMInterface]: # Run chat completion in batch mode (with chat history): list[list[langchain's BaseMessage]] in -> list[LLMInterface] out ``` - The LLMInterface is as follow: ```python @dataclass class LLMInterface: text: list[str] completion_tokens: int = -1 total_tokens: int = -1 prompt_tokens: int = -1 logits: list[list[float]] = field(default_factory=list) ```
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from unittest.mock import patch
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from langchain.llms import AzureOpenAI as AzureOpenAILC, OpenAI as OpenAILC
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from kotaemon.llms.completions.openai import AzureOpenAI, OpenAI
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from kotaemon.llms.base import LLMInterface
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_openai_completion_response = {
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"id": "cmpl-7qyNoIo6gRSCJR0hi8o3ZKBH4RkJ0",
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"object": "sample text_completion",
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"created": 1392751226,
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"model": "gpt-35-turbo",
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"choices": [
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{"text": "completion", "index": 0, "finish_reason": "length", "logprobs": None}
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],
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"usage": {"completion_tokens": 20, "prompt_tokens": 2, "total_tokens": 22},
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}
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@patch(
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"openai.api_resources.completion.Completion.create",
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side_effect=lambda *args, **kwargs: _openai_completion_response,
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)
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def test_azureopenai_model(openai_completion):
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model = AzureOpenAI(
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openai_api_base="https://test.openai.azure.com/",
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openai_api_key="some-key",
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openai_api_version="2023-03-15-preview",
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deployment_name="gpt35turbo",
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temperature=0,
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request_timeout=60,
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)
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assert isinstance(
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model.agent, AzureOpenAILC
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), "Agent not wrapped in Langchain's AzureOpenAI"
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output = model(["hello world"])
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assert isinstance(output, list), "Output for batch is not a list"
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assert isinstance(output[0], LLMInterface), "Output for text is not LLMInterface"
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openai_completion.assert_called()
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output = model("hello world")
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assert isinstance(output, LLMInterface), "Output for single text is not LLMInterface"
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@patch(
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"openai.api_resources.completion.Completion.create",
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side_effect=lambda *args, **kwargs: _openai_completion_response,
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)
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def test_openai_model(openai_completion):
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model = OpenAI(
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openai_api_base="https://test.openai.azure.com/",
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openai_api_key="some-key",
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openai_api_version="2023-03-15-preview",
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deployment_name="gpt35turbo",
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temperature=0,
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request_timeout=60,
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)
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assert isinstance(
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model.agent, OpenAILC
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), "Agent is not wrapped in Langchain's OpenAI"
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output = model(["hello world"])
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assert isinstance(output, list), "Output for batch is not a list"
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assert isinstance(output[0], LLMInterface), "Output for text is not LLMInterface"
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openai_completion.assert_called()
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output = model("hello world")
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assert isinstance(output, LLMInterface), "Output for single text is not LLMInterface"
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