can you create a flow chart or diagram that I can use in my blog jekyl markdown side for this:

## How It Works ### 1. Data Cleaning and Preprocessing Before analysis begins, it is critical to clean the raw textual data to ensure high-quality results. Using BERTopic’s vectorizer and tokenizer, responses were preprocessed to remove noise such as: - Non-alphanumeric characters - Excessive white spaces - Common stop words The tokenizer splits the text into smaller, meaningful units (tokens), while the vectorizer converts these tokens into numerical embeddings, capturing the semantic meaning of the text. By integrating these preprocessing steps, we ensured that the clustering and topic modeling stages were both accurate and efficient. --- ### 2. Embedding and Clustering The cleaned text is transformed into high-dimensional embeddings using advanced models such as SentenceTransformers. These embeddings capture the relationships between textual elements, enabling more nuanced understanding. The embeddings are then clustered using algorithms like HDBSCAN, which groups similar responses together. This clustering process creates distinct themes that reflect the underlying structure of the data. BERTopic’s modular nature allowed for the customization of both the embedding and clustering stages, making it adaptable to the specific needs of the consultation analysis. --- ### 3. Topic Extraction and Representation Once clusters are formed, BERTopic identifies the most representative keywords for each cluster. These keywords act as labels for the themes, offering interpretable insights into the dominant topics within the data. This step ensures that policymakers can easily identify the key areas of interest in the consultation. --- ### 4. Summarization with Large Language Models To provide concise summaries for each topic: 1. Representative documents from each cluster are selected. 2. These documents, along with the identified keywords, are fed into a large language model like GPT. 3. The model generates detailed, human-readable summaries that are compiled into PDF reports for each topic. This stage ensures that policymakers receive actionable insights directly derived from the raw data, packaged in an easy-to-understand format. --- ### 5. Deployment on Vertex AI and Google Colab Enterprise The entire pipeline was implemented and scaled using Vertex AI, a Google Cloud platform designed for deploying machine learning workflows. Google Colab Enterprise was used for experimentation and rapid prototyping, leveraging its powerful compute resources and seamless integration with Vertex AI. This setup allowed for efficient handling of the large BBC public consultation dataset with over 100,000 free-text responses, ensuring reliability, scalability, and cost-effectiveness throughout the process.