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) ```
79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
from unittest.mock import patch
|
|
|
|
from langchain.chat_models import AzureChatOpenAI as AzureChatOpenAILC
|
|
from langchain.schema.messages import (
|
|
SystemMessage,
|
|
HumanMessage,
|
|
AIMessage,
|
|
)
|
|
|
|
from kotaemon.llms.chats.openai import AzureChatOpenAI
|
|
from kotaemon.llms.base import LLMInterface
|
|
|
|
|
|
_openai_chat_completion_response = {
|
|
"id": "chatcmpl-7qyuw6Q1CFCpcKsMdFkmUPUa7JP2x",
|
|
"object": "chat.completion",
|
|
"created": 1692338378,
|
|
"model": "gpt-35-turbo",
|
|
"choices": [
|
|
{
|
|
"index": 0,
|
|
"finish_reason": "stop",
|
|
"message": {
|
|
"role": "assistant",
|
|
"content": "Hello! How can I assist you today?",
|
|
},
|
|
}
|
|
],
|
|
"usage": {"completion_tokens": 9, "prompt_tokens": 10, "total_tokens": 19},
|
|
}
|
|
|
|
|
|
@patch(
|
|
"openai.api_resources.chat_completion.ChatCompletion.create",
|
|
side_effect=lambda *args, **kwargs: _openai_chat_completion_response,
|
|
)
|
|
def test_azureopenai_model(openai_completion):
|
|
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,
|
|
)
|
|
assert isinstance(
|
|
model.agent, AzureChatOpenAILC
|
|
), "Agent not wrapped in Langchain's AzureChatOpenAI"
|
|
|
|
# test for str input - stream mode
|
|
output = model("hello world")
|
|
assert isinstance(output, LLMInterface), "Output for single text is not LLMInterface"
|
|
openai_completion.assert_called()
|
|
|
|
# test for list[str] input - batch mode
|
|
output = model(["hello world"])
|
|
assert isinstance(output, list), "Output for batch string is not a list"
|
|
assert isinstance(output[0], LLMInterface), "Output for text is not LLMInterface"
|
|
openai_completion.assert_called()
|
|
|
|
# test for list[message] input - stream mode
|
|
messages = [
|
|
SystemMessage(content="You are a philosohper"),
|
|
HumanMessage(content="What is the meaning of life"),
|
|
AIMessage(content="42"),
|
|
HumanMessage(content="What is the meaning of 42"),
|
|
]
|
|
|
|
output = model(messages)
|
|
assert isinstance(output, LLMInterface), "Output for single text is not LLMInterface"
|
|
openai_completion.assert_called()
|
|
|
|
# test for list[list[message]] input - batch mode
|
|
output = model([messages])
|
|
assert isinstance(output, list), "Output for batch string is not a list"
|
|
assert isinstance(output[0], LLMInterface), "Output for text is not LLMInterface"
|
|
openai_completion.assert_called()
|
|
|