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Add BERTopic.

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戒酒的李白
2025-08-12 19:01:20 +08:00
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LLMTopicDetection_BERTopic/bertopic/representation/_cohere.py
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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, validate_truncate_document_parameters
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:"""
DEFAULT_SYSTEM_PROMPT = "You are an assistant that extracts high-level topics from texts."
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.
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.
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 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 = "command-r",
prompt: str = None,
system_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.system_prompt = system_prompt if system_prompt is not None else DEFAULT_SYSTEM_PROMPT
self.default_prompt_ = DEFAULT_PROMPT
self.default_system_prompt_ = DEFAULT_SYSTEM_PROMPT
self.delay_in_seconds = delay_in_seconds
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_ = []
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.chat(
model=self.model,
preamble=self.system_prompt,
message=prompt,
max_tokens=50,
stop_sequences=["\n"],
)
label = request.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