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| """ | |
| This module contains functions for generating responses using LLMs. | |
| """ | |
| import enum | |
| import logging | |
| from random import sample | |
| from typing import List | |
| from uuid import uuid4 | |
| from firebase_admin import firestore | |
| import gradio as gr | |
| from leaderboard import db | |
| from model import ContextWindowExceededError | |
| from model import Model | |
| from model import supported_models | |
| logging.basicConfig() | |
| logger = logging.getLogger(__name__) | |
| logger.setLevel(logging.INFO) | |
| def get_history_collection(category: str): | |
| if category == Category.SUMMARIZE.value: | |
| return db.collection("arena-summarization-history") | |
| if category == Category.TRANSLATE.value: | |
| return db.collection("arena-translation-history") | |
| def create_history(category: str, model_name: str, instruction: str, | |
| prompt: str, response: str): | |
| doc_id = uuid4().hex | |
| doc = { | |
| "id": doc_id, | |
| "model": model_name, | |
| "instruction": instruction, | |
| "prompt": prompt, | |
| "response": response, | |
| "timestamp": firestore.SERVER_TIMESTAMP | |
| } | |
| doc_ref = get_history_collection(category).document(doc_id) | |
| doc_ref.set(doc) | |
| class Category(enum.Enum): | |
| SUMMARIZE = "Summarize" | |
| TRANSLATE = "Translate" | |
| # TODO(#31): Let the model builders set the instruction. | |
| def get_instruction(category: str, model: Model, source_lang: str, | |
| target_lang: str): | |
| if category == Category.SUMMARIZE.value: | |
| return model.summarize_instruction | |
| if category == Category.TRANSLATE.value: | |
| return model.translate_instruction.format(source_lang=source_lang, | |
| target_lang=target_lang) | |
| def get_responses(prompt: str, category: str, source_lang: str, | |
| target_lang: str): | |
| if not category: | |
| raise gr.Error("Please select a category.") | |
| if category == Category.TRANSLATE.value and (not source_lang or | |
| not target_lang): | |
| raise gr.Error("Please select source and target languages.") | |
| models: List[Model] = sample(list(supported_models), 2) | |
| responses = [] | |
| for model in models: | |
| instruction = get_instruction(category, model, source_lang, target_lang) | |
| try: | |
| # TODO(#1): Allow user to set configuration. | |
| response = model.completion(instruction, prompt) | |
| create_history(category, model.name, instruction, prompt, response) | |
| responses.append(response) | |
| except ContextWindowExceededError as e: | |
| logger.exception("Context window exceeded for model %s.", model.name) | |
| raise gr.Error( | |
| "The prompt is too long. Please try again with a shorter prompt." | |
| ) from e | |
| except Exception as e: | |
| logger.exception("Failed to get response from model %s.", model.name) | |
| raise gr.Error("Failed to get response. Please try again.") from e | |
| model_names = [model.name for model in models] | |
| # It simulates concurrent stream response generation. | |
| max_response_length = max(len(response) for response in responses) | |
| for i in range(max_response_length): | |
| yield [response[:i + 1] for response in responses | |
| ] + model_names + [instruction] | |
| yield responses + model_names + [instruction] | |