6B. LLM & Generative AI

As we have seen in the previous section, the topics that you get from BERTopic can be fine-tuned using a number of approaches. Here, we are going to focus on text generation Large Language Models such as ChatGPT, GPT-4, and open-source solutions.

Using these techniques, we can further fine-tune topics to generate labels, summaries, poems of topics, and more. To do so, we first generate a set of keywords and documents that describe a topic best using BERTopic's c-TF-IDF calculate. Then, these candidate keywords and documents are passed to the text generation model and asked to generate output that fits the topic best.

A huge benefit of this is that we can describe a topic with only a few documents and we therefore do not need to pass all documents to the text generation model. Not only speeds this the generation of topic labels up significantly, you also do not need a massive amount of credits when using an external API, such as Cohere or OpenAI.

Prompt Engineering

In most of the examples below, we use certain tags to customize our prompts. There are currently two tags, namely "[KEYWORDS]" and "[DOCUMENTS]". These tags indicate where in the prompt they are to be replaced with a topics keywords and top 4 most representative documents respectively. For example, if we have the following prompt:

prompt = """
I have topic that contains the following documents: \n[DOCUMENTS]
The topic is described by the following keywords: [KEYWORDS]

Based on the above information, can you give a short label of the topic?
"""

then that will be rendered as follows:

"""
I have a topic that contains the following documents:
- Our videos are also made possible by your support on patreon.co.
- If you want to help us make more videos, you can do so on patreon.com or get one of our posters from our shop.
- If you want to help us make more videos, you can do so there.
- And if you want to support us in our endeavor to survive in the world of online video, and make more videos, you can do so on patreon.com.

The topic is described by the following keywords: videos video you our support want this us channel patreon make on we if facebook to patreoncom can for and more watch

Based on the above information, can you give a short label of the topic?
"""

Tip 1

You can access the default prompts of these models with representation_model.default_prompt_. The prompts that were generated after training can be accessed with topic_model.representation_model.prompts_.

Selecting Documents

By default, four of the most representative documents will be passed to [DOCUMENTS]. These documents are selected by calculating their similarity (through c-TF-IDF representations) with the main c-TF-IDF representation of the topics. The four best matching documents per topic are selected.

To increase the number of documents passed to [DOCUMENTS], we can use the nr_docs parameter which is accessible in all LLMs on this page. Using this value allows you to select the top n most representative documents instead. If you have a long enough context length, then you could even give the LLM dozens of documents.

However, some of these documents might be very similar to one another and might be near duplicates. They will not provide much additional information about the content of the topic. Instead, we can use the diversity parameter in each LLM to only select documents that are sufficiently diverse. It takes values between 0 and 1 but a value of 0.1 already does wonders!

Truncating Documents

We can truncate the input documents in [DOCUMENTS] in order to reduce the number of tokens that we have in our input prompt. To do so, all text generation modules have two parameters that we can tweak:

• 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 tokenized the document. These tokens are counted and truncated depending on doc_length

This means that the definition of doc_length changes depending on what constitutes a token in the tokenizer parameter. If a token is a character, then doc_length refers to max length in characters. If a token is a word, then doc_length refers to the max length in words.

Let's illustrate this with an example. In the code below, we will use tiktoken to count the number of tokens in each document and limit them to 100 tokens. All documents that have more than 100 tokens will be truncated.

We start by installing the relevant packages:

pip install tiktoken openai

Then, we use bertopic.representation.OpenAI to represent our topics with nicely written labels. We specify that documents that we put in the prompt cannot exceed 100 tokens each. Since we will put 4 documents in the prompt, they will total roughly 400 tokens:

import openai
import tiktoken
from bertopic.representation import OpenAI
from bertopic import BERTopic

# Tokenizer
tokenizer= tiktoken.encoding_for_model("gpt-3.5-turbo")

# Create your representation model
client = openai.OpenAI(api_key="sk-...")
representation_model = OpenAI(
    client,
    model="gpt-3.5-turbo",
    delay_in_seconds=2,
    chat=True,
    nr_docs=4,
    doc_length=100,
    tokenizer=tokenizer
)

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

🤗 Transformers

Nearly every week, there are new and improved models released on the 🤗 Model Hub that, with some creativity, allow for further fine-tuning of our c-TF-IDF based topics. These models range from text generation to zero-classification. In BERTopic, wrappers around these methods are created as a way to support whatever might be released in the future.

Using a GPT-like model from the huggingface hub is rather straightforward:

from bertopic.representation import TextGeneration
from bertopic import BERTopic

# Create your representation model
representation_model = TextGeneration('gpt2')

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

GPT2, however, is not the most accurate model out there on HuggingFace models. You can get much better results with a flan-T5 like model:

from transformers import pipeline
from bertopic.representation import TextGeneration

prompt = "I have a topic described by the following keywords: [KEYWORDS]. Based on the previous keywords, what is this topic about?"

# Create your representation model
generator = pipeline('text2text-generation', model='google/flan-t5-base')
representation_model = TextGeneration(generator)

As can be seen from the example above, if you would like to use a text2text-generation model, you will to pass a transformers.pipeline with the "text2text-generation" parameter. Moreover, you can use a custom prompt and decide where the keywords should be inserted by using the [KEYWORDS] or documents with the [DOCUMENTS] tag.

Mistral (GGUF)

We can go a step further with open-source Large Language Models (LLMs) that have shown to match the performance of closed-source LLMs like ChatGPT.

In this example, we will show you how to use Zephyr, a fine-tuning version of Mistral 7B. Mistral 7B outperforms other open-source LLMs at a much smaller scale and is a worthwhile solution for use cases such as topic modeling. We want to keep inference as fast as possible and a relatively small model helps with that. Zephyr is a fine-tuned version of Mistral 7B that was trained on a mix of publicly available and synthetic datasets using Direct Preference Optimization (DPO).

To use Zephyr in BERTopic, we will first need to install and update a couple of packages that can handle quantized versions of Zephyr:

pip install ctransformers[cuda]
pip install --upgrade git+https://github.com/huggingface/transformers

Instead of loading in the full model, we can instead load a quantized model which is a compressed version of the original model:

from ctransformers import AutoModelForCausalLM
from transformers import AutoTokenizer, pipeline

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
model = AutoModelForCausalLM.from_pretrained(
    "TheBloke/zephyr-7B-alpha-GGUF",
    model_file="zephyr-7b-alpha.Q4_K_M.gguf",
    model_type="mistral",
    gpu_layers=50,
    hf=True
)
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")

# Pipeline
generator = pipeline(
    model=model, tokenizer=tokenizer,
    task='text-generation',
    max_new_tokens=50,
    repetition_penalty=1.1
)

This Zephyr model requires a specific prompt template in order to work:

prompt = """<|system|>You are a helpful, respectful and honest assistant for labeling topics..</s>
<|user|>
I have a topic that contains the following documents:
[DOCUMENTS]

The topic is described by the following keywords: '[KEYWORDS]'.

Based on the information about the topic above, please create a short label of this topic. Make sure you to only return the label and nothing more.</s>
<|assistant|>"""

After creating this prompt template, we can create our representation model to be used in BERTopic:

from bertopic.representation import TextGeneration

# Text generation with Zephyr
zephyr = TextGeneration(generator, prompt=prompt)
representation_model = {"Zephyr": zephyr}

# Topic Modeling
topic_model = BERTopic(representation_model=representation_model, verbose=True)

Llama (Manual Quantization)

Full Llama Tutorial:

Open-source LLMs are starting to become more and more popular. Here, we will go through a minimal example of using Llama 2 together with BERTopic.

Note

Although this is an example of the older Llama 2 model, you can use the code below for any Llama variant.

First, we need to load in our Llama model:

from torch import bfloat16
import transformers

# set quantization configuration to load large model with less GPU memory
# this requires the `bitsandbytes` library
bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,  # 4-bit quantization
    bnb_4bit_quant_type='nf4',  # Normalized float 4
    bnb_4bit_use_double_quant=True,  # Second quantization after the first
    bnb_4bit_compute_dtype=bfloat16  # Computation type
)

# Llama Tokenizer
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)

# Llama Model
model = transformers.AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    quantization_config=bnb_config,
    device_map='auto',
)
model.eval()

# Our text generator
generator = transformers.pipeline(
    model=model, tokenizer=tokenizer,
    task='text-generation',
    temperature=0.1,
    max_new_tokens=500,
    repetition_penalty=1.1
)

After doing so, we will need to define a prompt that works with both Llama as well as BERTopic:

# System prompt describes information given to all conversations
system_prompt = """
<s>[INST] <<SYS>>
You are a helpful, respectful and honest assistant for labeling topics.
<</SYS>>
"""

# Example prompt demonstrating the output we are looking for
example_prompt = """
I have a topic that contains the following documents:
- 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.

The topic is described by the following keywords: 'meat, beef, eat, eating, emissions, steak, food, health, processed, chicken'.

Based on the information about the topic above, please create a short label of this topic. Make sure you to only return the label and nothing more.

[/INST] Environmental impacts of eating meat
"""

# Our main prompt with documents ([DOCUMENTS]) and keywords ([KEYWORDS]) tags
main_prompt = """
[INST]
I have a topic that contains the following documents:
[DOCUMENTS]

The topic is described by the following keywords: '[KEYWORDS]'.

Based on the information about the topic above, please create a short label of this topic. Make sure you to only return the label and nothing more.
[/INST]
"""

prompt = system_prompt + example_prompt + main_prompt

Three pieces of the prompt were created:

• system_prompt helps us guide the model during a conversation. For example, we can say that it is a helpful assistant that is specialized in labeling topics.
• example_prompt gives an example of a correctly labeled topic to guide Llama
• main_prompt contains the main question we are going to ask it, namely to label a topic. Note that it uses the [DOCUMENTS] and [KEYWORDS] to provide the most relevant documents and keywords as additional context

After having generated our prompt template, we can start running our topic model:

from bertopic.representation import TextGeneration
from bertopic import BERTopic

# Text generation with Llama
llama2 = TextGeneration(generator, prompt=prompt)
representation_model = {
    "Llama2": llama2,
}

# Create our BERTopic model
topic_model = BERTopic(representation_model=representation_model,  verbose=True)

llama.cpp

An amazing framework for using LLMs for inference is llama.cpp which has python bindings that we can use in BERTopic. To start with, we first need to install llama-cpp-python:

pip install llama-cpp-python

or using the following for hardware acceleration:

CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python

Note

There are a number of installation options depending on your hardware and OS. Make sure that you select the correct one to optimize your performance.

After installation, you need to download your LLM locally before we use it in BERTopic, like so:

wget https://huggingface.co/TheBloke/zephyr-7B-alpha-GGUF/resolve/main/zephyr-7b-alpha.Q4_K_M.gguf

Finally, we can now use the model with BERTopic in just a couple of lines:

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:

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)

Note

The default template that is being used uses a "Q: ... A: ... " type of structure which is why the stop is set at "Q:". The default template is:

"""
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:
"""

OpenAI

Instead of using a language model from 🤗 transformers, we can use external APIs instead that do the work for you. Here, we can use OpenAI to extract our topic labels from the candidate documents and keywords. To use this, you will need to install openai first:

pip install openai

Then, get yourself an API key and use OpenAI's API as follows:

import openai
from bertopic.representation import OpenAI
from bertopic import BERTopic

# Create your representation model
client = openai.OpenAI(api_key="sk-...")
representation_model = OpenAI(client)

# 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:

prompt = "I have the following documents: [DOCUMENTS] \nThese documents are about the following topic: '"
representation_model = OpenAI(client, prompt=prompt)

GPT-4o

To choose a specific model from OpenAI's offering:

representation_model = OpenAI(client, model="gpt-4o-mini", delay_in_seconds=10)

Prompting with their models is very satisfying and is customizable as follows:

prompt = """
I have a topic that contains the following documents:
[DOCUMENTS]
The topic is described by the following keywords: [KEYWORDS]

Based on the information above, extract a short topic label in the following format:
topic: <topic label>
"""

Note

Whenever you create a custom prompt, it is important to add

Based on the information above, extract a short topic label in the following format:
topic: <topic label>

at the end of your prompt as BERTopic extracts everything that comes after topic:. Having said that, if topic: is not in the output, then it will simply extract the entire response, so feel free to experiment with the prompts.

Summarization

Due to the structure of the prompts in OpenAI's chat models, we can extract different types of topic representations from their GPT models. Instead of extracting a topic label, we can instead ask it to extract a short description of the topic instead:

summarization_prompt = """
I have a topic that is described by the following keywords: [KEYWORDS]
In this topic, the following documents are a small but representative subset of all documents in the topic:
[DOCUMENTS]

Based on the information above, please give a description of this topic in the following format:
topic: <description>
"""

representation_model = OpenAI(client, model="gpt-4o-mini", prompt=summarization_prompt, nr_docs=5, delay_in_seconds=3)

The above is not constrained to just creating a short description or summary of the topic, we can extract labels, keywords, poems, example documents, extensitive descriptions, and more using this method! If you want to have multiple representations of a single topic, it might be worthwhile to also check out multi-aspect topic modeling with BERTopic.

Ollama

To use Ollama within BERTopic, it is advised to use the openai package as it allows to pass through a model using the url on which the model is running.

You will first need to install openai:

pip install openai

After installation, usage is straightforward and you can select any model that you have prepared in your ollama model list. You can see all models by running ollama list.

Select one from the list and you can use it in BERTopic as follows:

import openai
from bertopic.representation import OpenAI
from bertopic import BERTopic

client = openai.OpenAI(
    base_url = 'http://localhost:11434/v1', #wherever ollama is running
    api_key='ollama', # required, but unused
)


# Create your representation model
representation_model = OpenAI(client, model='phi3:14b-medium-128k-instruct-q4_K_M')

# Create your BERTopic model
topic_model = BERTopic(representation_model=representation_model,  verbose=True)

LiteLLM

An amazing framework to simplify connecting to external LLMs, is LiteLLM. This package allows you to connect to OpenAI, Cohere, Anthropic, etc. all within one package. This makes iteration and testing out different models a breeze!

o start with, we first need to install litellm:

pip install litellm

After installation, usage is straightforward and you can select any model found in their docs. Let's show an example with OpenAI:

import os
from bertopic import BERTopic
from bertopic.representation import LiteLLM

# set ENV variables
os.environ["OPENAI_API_KEY"] = "MY_KEY"

# Create your representation model
representation_model = LiteLLM(model="gpt-4o-mini")

# Create our BERTopic model
topic_model = BERTopic(representation_model=representation_model,  verbose=True)

LangChain

Langchain is a package that helps users with chaining large language models. In BERTopic, we can leverage this package in order to more efficiently combine external knowledge. Here, this external knowledge are the most representative documents in each topic.

To use langchain, you will need to install the langchain package first. Additionally, you will need an underlying LLM to support langchain, like openai:

pip install langchain, openai

Then, you can create your chain as follows:

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
chain = load_qa_chain(OpenAI(temperature=0, openai_api_key=my_openai_api_key), chain_type="stuff")

Finally, you can pass the chain to BERTopic as follows:

from bertopic.representation import LangChain

# Create your representation model
representation_model = LangChain(chain)

# 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:

prompt = "What are these documents about? Please give a single label."
representation_model = LangChain(chain, prompt=prompt)

Note

The prompt does not make use of [KEYWORDS] and [DOCUMENTS] tags as the documents are already used within langchain's load_qa_chain.

Cohere

Instead of using a language model from 🤗 transformers, we can use external APIs instead that do the work for you. Here, we can use Cohere to extract our topic labels from the candidate documents and keywords. 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:

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:

prompt = """
I have 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?
"""
representation_model = Cohere(co, prompt=prompt)