import pandas as pd from tqdm import tqdm from scipy.sparse import csr_matrix from llama_cpp import Llama from typing import Mapping, List, Tuple, Any, Union, Callable from bertopic.representation._base import BaseRepresentation from bertopic.representation._utils import truncate_document DEFAULT_PROMPT = """ Q: I have a topic that contains the following documents: [DOCUMENTS] The topic is described by the following keywords: '[KEYWORDS]'. Based on the above information, can you give a short label of the topic? A: """ class LlamaCPP(BaseRepresentation): """ A llama.cpp implementation to use as a representation model. Arguments: model: Either a string pointing towards a local LLM or a `llama_cpp.Llama` object. 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. pipeline_kwargs: Kwargs that you can pass to the `llama_cpp.Llama` when it is called such as `max_tokens` to be generated. 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 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 a llama.cpp, first download the LLM: ```bash wget https://huggingface.co/TheBloke/zephyr-7B-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q4_K_M.gguf ``` Then, we can now use the model the model with BERTopic in just a couple of lines: ```python from bertopic import BERTopic from bertopic.representation import LlamaCPP # Use llama.cpp to load in a 4-bit quantized version of Zephyr 7B Alpha representation_model = LlamaCPP("zephyr-7b-alpha.Q4_K_M.gguf") # Create our BERTopic model topic_model = BERTopic(representation_model=representation_model, verbose=True) ``` If you want to have more control over the LLMs parameters, you can run it like so: ```python from bertopic import BERTopic from bertopic.representation import LlamaCPP from llama_cpp import Llama # Use llama.cpp to load in a 4-bit quantized version of Zephyr 7B Alpha llm = Llama(model_path="zephyr-7b-alpha.Q4_K_M.gguf", n_gpu_layers=-1, n_ctx=4096, stop="Q:") representation_model = LlamaCPP(llm) # Create our BERTopic model topic_model = BERTopic(representation_model=representation_model, verbose=True) ``` """ def __init__(self, model: Union[str, Llama], prompt: str = None, pipeline_kwargs: Mapping[str, Any] = {}, nr_docs: int = 4, diversity: float = None, doc_length: int = None, tokenizer: Union[str, Callable] = None ): if isinstance(model, str): self.model = Llama(model_path=model, n_gpu_layers=-1, stop="Q:") elif isinstance(model, Llama): self.model = model else: raise ValueError("Make sure that the model that you" "pass is either a string referring to a" "local LLM or a ` llama_cpp.Llama` object.") self.prompt = prompt if prompt is not None else DEFAULT_PROMPT self.default_prompt_ = DEFAULT_PROMPT self.pipeline_kwargs = pipeline_kwargs 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 topic representations and return a single label Arguments: topic_model: A BERTopic model 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 ) updated_topics = {} for topic, docs in tqdm(repr_docs_mappings.items(), disable=not topic_model.verbose): # Prepare prompt 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) # Extract result from generator and use that as label topic_description = self.model(prompt, **self.pipeline_kwargs)['choices'] topic_description = [(description["text"].replace(prompt, ""), 1) for description in topic_description] if len(topic_description) < 10: topic_description += [("", 0) for _ in range(10-len(topic_description))] updated_topics[topic] = topic_description return updated_topics def _create_prompt(self, docs, topic, topics): keywords = ", ".join(list(zip(*topics[topic]))[0]) # Use the default prompt and replace keywords if self.prompt == DEFAULT_PROMPT: prompt = self.prompt.replace("[KEYWORDS]", keywords) # Use a prompt that leverages either keywords or documents in # a custom location else: prompt = self.prompt if "[KEYWORDS]" in prompt: prompt = prompt.replace("[KEYWORDS]", keywords) if "[DOCUMENTS]" in prompt: to_replace = "" for doc in docs: to_replace += f"- {doc}\n" prompt = prompt.replace("[DOCUMENTS]", to_replace) return prompt