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) ```
57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
from abc import abstractmethod
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from theflow.base import Composable
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class BaseComponent(Composable):
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"""Base class for component
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A component is a class that can be used to compose a pipeline. To use the
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component, you should implement the following methods:
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- run_raw: run on raw input
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- run_batch_raw: run on batch of raw input
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- run_document: run on document
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- run_batch_document: run on batch of documents
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- is_document: check if input is document
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- is_batch: check if input is batch
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"""
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@abstractmethod
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def run_raw(self, *args, **kwargs):
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...
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@abstractmethod
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def run_batch_raw(self, *args, **kwargs):
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...
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@abstractmethod
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def run_document(self, *args, **kwargs):
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...
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@abstractmethod
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def run_batch_document(self, *args, **kwargs):
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...
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@abstractmethod
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def is_document(self, *args, **kwargs) -> bool:
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...
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@abstractmethod
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def is_batch(self, *args, **kwargs) -> bool:
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...
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def run(self, *args, **kwargs):
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"""Run the component."""
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is_document = self.is_document(*args, **kwargs)
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is_batch = self.is_batch(*args, **kwargs)
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if is_document and is_batch:
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return self.run_batch_document(*args, **kwargs)
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elif is_document and not is_batch:
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return self.run_document(*args, **kwargs)
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elif not is_document and is_batch:
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return self.run_batch_raw(*args, **kwargs)
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else:
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return self.run_raw(*args, **kwargs)
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