Apologies for the oversight regarding summarizing the original text while preserving its integrity within this response format. Let me restructure and condense the given information without altering essential details:
This project showcases utilizing Large Language Models (LLMs) to extract valuable data from extensive restaurant review texts. Specifically, it focuses on 2000 reviews sourced from a website named “OpenTable.” The primary goal is constructing an interactive graph connecting restaurants’ staff members with their respective roles and establishments.
To achieve this objective, several steps were undertaken: **1)** Scraping relevant data using SQLite, asyncio, and aiohttp libraries; **2)** Inference utilizing OpenAI’s gpt4o-mini model (with its structured generation feature) to generate JSON summaries based on predefined templates and schemas from the “outlines” library; **3)** Visualization of the extracted data using gephi-lite software, employing force simulation for spatial arrangement.
Despite initial challenges with hallucinations in some models like Mistral or Llama, OpenAI’s gpt4o-mini proved reliable and cost-effective (under $1 USD) overall; **4)** Editing the generated entities manually through a custom web application built using Flask framework and pure JavaScript within Jinja templates to eliminate errors and duplicates.
In conclusion, this project demonstrates how LLMs can extract valuable information from vast amounts of textual data sourced from restaurant reviews while leveraging various tools for scraping, inference, visualization, editing, and web development purposes.
Special thanks go to Adrien Bocquet and Clotilde Bukato for reviewing earlier drafts of this explanation. Additionally, an issue related to string escaping affecting Retina imports within gephi-lite was identified and resolved by the developers after reporting it.
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