import time import openai import pandas as pd from tqdm import tqdm from scipy.sparse import csr_matrix from typing import Mapping, List, Tuple, Any, Union, Callable from bertopic.representation._base import BaseRepresentation from bertopic.representation._utils import ( retry_with_exponential_backoff, truncate_document, validate_truncate_document_parameters, ) DEFAULT_CHAT_PROMPT = """You will extract a short topic label from given documents and keywords. Here are two examples of topics you created before: # Example 1 Sample texts from this topic: - Traditional diets in most cultures were primarily plant-based with a little meat on top, but with the rise of industrial style meat production and factory farming, meat has become a staple food. - Meat, but especially beef, is the worst food in terms of emissions. - Eating meat doesn't make you a bad person, not eating meat doesn't make you a good one. Keywords: meat beef eat eating emissions steak food health processed chicken topic: Environmental impacts of eating meat # Example 2 Sample texts from this topic: - I have ordered the product weeks ago but it still has not arrived! - The website mentions that it only takes a couple of days to deliver but I still have not received mine. - I got a message stating that I received the monitor but that is not true! - It took a month longer to deliver than was advised... Keywords: deliver weeks product shipping long delivery received arrived arrive week topic: Shipping and delivery issues # Your task Sample texts from this topic: [DOCUMENTS] Keywords: [KEYWORDS] Based on the information above, extract a short topic label (three words at most) in the following format: topic: """ DEFAULT_SYSTEM_PROMPT = "You are an assistant that extracts high-level topics from texts." class OpenAI(BaseRepresentation): r"""Using the OpenAI API to generate topic labels based on one of their Completion of ChatCompletion models. For an overview see: https://platform.openai.com/docs/models Arguments: client: A `openai.OpenAI` client model: Model to use within OpenAI, defaults to `"gpt-4o-mini"`. generator_kwargs: Kwargs passed to `openai.Completion.create` for fine-tuning the output. prompt: The prompt to be used in the model. If no prompt is given, `self.default_prompt_` is used instead. NOTE: Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt to decide where the keywords and documents need to be inserted. system_prompt: The system prompt to be used in the model. If no system prompt is given, `self.default_system_prompt_` is used instead. delay_in_seconds: The delay in seconds between consecutive prompts in order to prevent RateLimitErrors. exponential_backoff: Retry requests with a random exponential backoff. A short sleep is used when a rate limit error is hit, then the requests is retried. Increase the sleep length if errors are hit until 10 unsuccessful requests. If True, overrides `delay_in_seconds`. nr_docs: The number of documents to pass to OpenAI if a prompt with the `["DOCUMENTS"]` tag is used. diversity: The diversity of documents to pass to OpenAI. Accepts values between 0 and 1. A higher values results in passing more diverse documents whereas lower values passes more similar documents. doc_length: The maximum length of each document. If a document is longer, it will be truncated. If None, the entire document is passed. tokenizer: The tokenizer used to calculate to split the document into segments used to count the length of a document. * If tokenizer is 'char', then the document is split up into characters which are counted to adhere to `doc_length` * If tokenizer is 'whitespace', the document is split up into words separated by whitespaces. These words are counted and truncated depending on `doc_length` * If tokenizer is 'vectorizer', then the internal CountVectorizer is used to tokenize the document. These tokens are counted and truncated depending on `doc_length` * If tokenizer is a callable, then that callable is used to tokenize the document. These tokens are counted and truncated depending on `doc_length` Usage: To use this, you will need to install the openai package first: `pip install openai` Then, get yourself an API key and use OpenAI's API as follows: ```python import openai from bertopic.representation import OpenAI from bertopic import BERTopic # Create your representation model client = openai.OpenAI(api_key=MY_API_KEY) representation_model = OpenAI(client, delay_in_seconds=5) # Use the representation model in BERTopic on top of the default pipeline topic_model = BERTopic(representation_model=representation_model) ``` You can also use a custom prompt: ```python prompt = "I have the following documents: [DOCUMENTS] \nThese documents are about the following topic: '" representation_model = OpenAI(client, prompt=prompt, delay_in_seconds=5) ``` To choose a model: ```python representation_model = OpenAI(client, model="gpt-4o-mini", delay_in_seconds=10) ``` """ def __init__( self, client, model: str = "gpt-4o-mini", prompt: str = None, system_prompt: str = None, generator_kwargs: Mapping[str, Any] = {}, delay_in_seconds: float = None, exponential_backoff: bool = False, nr_docs: int = 4, diversity: float = None, doc_length: int = None, tokenizer: Union[str, Callable] = None, **kwargs, ): self.client = client self.model = model if prompt is None: self.prompt = DEFAULT_CHAT_PROMPT else: self.prompt = prompt if system_prompt is None: self.system_prompt = DEFAULT_SYSTEM_PROMPT else: self.system_prompt = system_prompt self.default_prompt_ = DEFAULT_CHAT_PROMPT self.default_system_prompt_ = DEFAULT_SYSTEM_PROMPT self.delay_in_seconds = delay_in_seconds self.exponential_backoff = exponential_backoff self.nr_docs = nr_docs self.diversity = diversity self.doc_length = doc_length self.tokenizer = tokenizer validate_truncate_document_parameters(self.tokenizer, self.doc_length) self.prompts_ = [] self.generator_kwargs = generator_kwargs if self.generator_kwargs.get("model"): self.model = generator_kwargs.get("model") del self.generator_kwargs["model"] if self.generator_kwargs.get("prompt"): del self.generator_kwargs["prompt"] if not self.generator_kwargs.get("stop"): self.generator_kwargs["stop"] = "\n" def extract_topics( self, topic_model, documents: pd.DataFrame, c_tf_idf: csr_matrix, topics: Mapping[str, List[Tuple[str, float]]], ) -> Mapping[str, List[Tuple[str, float]]]: """Extract topics. Arguments: topic_model: A BERTopic model documents: All input documents c_tf_idf: The topic c-TF-IDF representation topics: The candidate topics as calculated with c-TF-IDF Returns: updated_topics: Updated topic representations """ # Extract the top n representative documents per topic repr_docs_mappings, _, _, _ = topic_model._extract_representative_docs( c_tf_idf, documents, topics, 500, self.nr_docs, self.diversity ) # Generate using OpenAI's Language Model updated_topics = {} for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose): truncated_docs = [truncate_document(topic_model, self.doc_length, self.tokenizer, doc) for doc in docs] prompt = self._create_prompt(truncated_docs, topic, topics) self.prompts_.append(prompt) # Delay if self.delay_in_seconds: time.sleep(self.delay_in_seconds) messages = [ {"role": "system", "content": self.system_prompt}, {"role": "user", "content": prompt}, ] kwargs = { "model": self.model, "messages": messages, **self.generator_kwargs, } if self.exponential_backoff: response = chat_completions_with_backoff(self.client, **kwargs) else: response = self.client.chat.completions.create(**kwargs) # Check whether content was actually generated # Addresses #1570 for potential issues with OpenAI's content filter # Addresses #2176 for potential issues when openAI returns a None type object if response and hasattr(response.choices[0].message, "content"): label = response.choices[0].message.content.strip().replace("topic: ", "") else: label = "No label returned" updated_topics[topic] = [(label, 1)] return updated_topics def _create_prompt(self, docs, topic, topics): keywords = list(zip(*topics[topic]))[0] # Use the Default Chat Prompt if self.prompt == DEFAULT_CHAT_PROMPT: prompt = self.prompt.replace("[KEYWORDS]", ", ".join(keywords)) prompt = self._replace_documents(prompt, docs) # Use a custom prompt that leverages keywords, documents or both using # custom tags, namely [KEYWORDS] and [DOCUMENTS] respectively else: prompt = self.prompt if "[KEYWORDS]" in prompt: prompt = prompt.replace("[KEYWORDS]", ", ".join(keywords)) if "[DOCUMENTS]" in prompt: prompt = self._replace_documents(prompt, docs) return prompt @staticmethod def _replace_documents(prompt, docs): to_replace = "" for doc in docs: to_replace += f"- {doc}\n" prompt = prompt.replace("[DOCUMENTS]", to_replace) return prompt def chat_completions_with_backoff(client, **kwargs): return retry_with_exponential_backoff( client.chat.completions.create, errors=(openai.RateLimitError,), )(**kwargs)