import time import pandas as pd from tqdm import tqdm from scipy.sparse import csr_matrix from typing import Mapping, List, Tuple, Union, Callable from bertopic.representation._base import BaseRepresentation from bertopic.representation._utils import truncate_document DEFAULT_PROMPT = """ This is a list of texts where each collection of texts describe a topic. After each collection of texts, the name of the topic they represent is mentioned as a short-highly-descriptive title --- Topic: 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 word 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 name: Environmental impacts of eating meat --- Topic: 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 name: Shipping and delivery issues --- Topic: Sample texts from this topic: [DOCUMENTS] Keywords: [KEYWORDS] Topic name:""" class Cohere(BaseRepresentation): """ Use the Cohere API to generate topic labels based on their generative model. Find more about their models here: https://docs.cohere.ai/docs Arguments: client: A `cohere.Client` model: Model to use within Cohere, defaults to `"xlarge"`. 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. delay_in_seconds: The delay in seconds between consecutive prompts in order to prevent RateLimitErrors. 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 trunctated 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 cohere first: `pip install cohere` Then, get yourself an API key and use Cohere's API as follows: ```python import cohere from bertopic.representation import Cohere from bertopic import BERTopic # Create your representation model co = cohere.Client(my_api_key) representation_model = Cohere(co) # 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]. What topic do they contain?" representation_model = Cohere(co, prompt=prompt) ``` """ def __init__(self, client, model: str = "xlarge", prompt: str = None, delay_in_seconds: float = None, nr_docs: int = 4, diversity: float = None, doc_length: int = None, tokenizer: Union[str, Callable] = None ): self.client = client self.model = model self.prompt = prompt if prompt is not None else DEFAULT_PROMPT self.default_prompt_ = DEFAULT_PROMPT self.delay_in_seconds = delay_in_seconds self.nr_docs = nr_docs self.diversity = diversity self.doc_length = doc_length self.tokenizer = tokenizer self.prompts_ = [] 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: Not used documents: Not used c_tf_idf: Not used topics: The candidate topics as calculated with c-TF-IDF Returns: updated_topics: Updated topic representations """ # Extract the top 4 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 Cohere'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) request = self.client.generate(model=self.model, prompt=prompt, max_tokens=50, num_generations=1, stop_sequences=["\n"]) label = request.generations[0].text.strip() updated_topics[topic] = [(label, 1)] + [("", 0) for _ in range(9)] 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_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