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""" |
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Copyright (c) 2023, salesforce.com, inc. |
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All rights reserved. |
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SPDX-License-Identifier: Apache License 2.0 |
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For full license text, see the LICENSE file in the repo root or https://www.apache.org/licenses/LICENSE-2.0 |
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""" |
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import os |
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os.environ["OPENAI_API_KEY"] = "" |
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from langchain.chains.llm import LLMChain |
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from langchain.prompts import PromptTemplate |
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from langchain.chat_models import ChatOpenAI |
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import json |
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from utils import open_json, save_json, open_jsonl |
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from collections import defaultdict |
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class EvaluateDialogs(object): |
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""" Evaluate Dialogs based on OpenAI. To run this: |
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pip install openai |
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pip install langchain |
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""" |
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def __init__(self): |
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self.data_dir = "/Users/jianguozhang/TOD-Family/TOD-Studio/open-source/" |
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self.excluded_datasets = ['MetaLWOZ', "MuDoCo", "SalesBot", "HDSA-Dialog", "MULTIWOZ2_2"] |
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self.quality_agent_prompt = PromptTemplate( |
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input_variables=["dialog"], |
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template=""" |
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Hi AI, I plan to train a language model for response generation. Please analyze the following dialogue and evaluate it based on the criteria provided. Assign a score from 1 (poor) to 5 (excellent) for each category. We're looking for a critical assessment, and higher scores should only be given to truly exceptional examples. The criteria for evaluation are: Understanding, Relevance, Completeness, Correctness, and Coherence. |
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After your assessment, provide an overall score for the dialogue along with a concise summary of your evaluation. The overall score should also be on a scale of 1 (poor) to 5 (excellent) and should represent a holistic assessment of the dialogue. |
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Please present your evaluation and comment into the following format: |
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{{ |
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"Understanding": _, |
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"Relevance": _, |
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"Completeness": _, |
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"Correctness": _, |
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"Coherence": _, |
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"Overall": {{"score": _, "comment": _}} |
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}} |
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Please replace each underscore (_) with the appropriate score. For the 'Overall' field, provide the score and a concise comment. Regarding to the comment, it should not only summarize the dialogue's quality but also highlight any issues or shortcomings you may have identified in the dialogue. |
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Below is the dialog: |
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{dialog} |
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Evaluate the dialog now. |
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""" |
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) |
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self.quality_chain = LLMChain(llm=ChatOpenAI(temperature=0.2, model_name="gpt-3.5-turbo"), prompt=self.quality_agent_prompt) |
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def run_openai_evaluation(self, dialog): |
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res = self.quality_chain.run(dialog=dialog) |
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try: |
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res = json.loads(res) |
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except: |
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res = str(res) |
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return res |
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def tod(self): |
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""" |
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Evaluate TOD dialogues |
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:return: |
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""" |
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folder_name = "Task-Oriented-Dialogues--OpenAI" |
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folder_path = os.path.join(self.data_dir, folder_name) |
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dataset_names = os.listdir(folder_path) |
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print(dataset_names) |
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print() |
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for dataset_name in dataset_names: |
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if not os.path.isdir(os.path.join(folder_path, dataset_name)): |
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continue |
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data = open_json(os.path.join(folder_path, dataset_name, "train.json")) |
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f_writer = open(os.path.join(folder_path, dataset_name, "train_quality_scores.json"), "w") |
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print("Start processing: {} #total dialogs: {}".format(dataset_name, len(data))) |
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for index, item in enumerate(data): |
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output = defaultdict(dict) |
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output["source"] = item["source"] |
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output["quality score"] = self.run_openai_evaluation(item["dialog"]) |
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json.dump(output, f_writer) |
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f_writer.write("\n") |
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if index % 10 == 0 or index + 1 == len(data): |
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f_writer.flush() |
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def run(self): |
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self.tod() |
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process = EvaluateDialogs() |
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process.run() |
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