How I Built a LinkedIn Summarizer with LlamaIndex to Save Time

Sometimes, the most exciting projects are the ones that exceed your initial expectations. Which is exactly what happened to me when I decided to build a tool to summarize LinkedIn profiles using LlamaIndex and chatGPT. What started out as a simple idea quickly evolved into a challenging but rewarding exploration of web scraping, API requests, and the intricacies of large language model (LLM) processing.

The Initial vision:

My vision was simple: A minimalist web app that could quickly provide a concise summary of a LinkedIn profile, complete with the user’s profile picture. It would be great to just clicking a button or insert a URL and instantly get the key highlights of someone’s professional journey as a reminder before any meeting or coffee chat.

Idea for LinkedIn Summarizer

The reality check:

My initial approach was naive. I assumed I could use a few straightforward web scraping techniques to extract profile data off of LinkedIn and get right into working on the meat of the project with LlamaIndex and ChatGPT. However, LinkedIn’s security measures put a stop to that idea.

LinkedIn like many other platforms use APIs to control how their data is accessed. With that in mind I realized I had to access the data correctly, I’d need to work with the LinkedIn API, which requires authentication and adherence to their terms of service.

While I am comfortable working with APIs and did sign up for the LinkedIns API developers process, I wanted to focus on proving the core concept’s viability. I wanted to get the LLM summarization working and play around with Agents since everyone has been talking about them non-stopped recently. So I opted for a simpler, but temporary, solution: Downloading my own profile as a PDF to have a sample case that I know very well.

LlamaIndex and ChatGPT in Action:

With my LinkedIn PDF, I was finally able to turn to LlamaIndex and ChatGPT to summarize my LinkedIn post but also to create seperate tools for different purposes like my education, years of employment, and volunteer work.

Results for Education

I created specific tools and prompts to guide the LLM to extract the relevant information when presented with a query by implementing the tools components that I learned in the meetup I attended Deep Learning Adventures. Something that was cool that I learned is that you’ll be told what tools Llama uses which made figuring out when things went wrong happening.

Challenges

One of the biggest challenges I encountered was the LLM’s tendency to incorporate extraneous information since it didn’t really know better. Because the PDF contained ads and recommended users, the LLM sometimes interpreted the elements as part of my profile. Leading to inaccurate summaries and required me to refine my prompts, data processing, and the source data.

Sometimes it was simple such as changing the description to specifically only use information related to the main profile.

Another limitation was the inherent nature of LLMs. While they can generate impressive summaries, they aren’t infallible. They can misinterpret information, omit information that I might find valuable, or even hallucinate. This highlights how this likely won’t entirely replace my need to ever look at a LinkedIn profile but will likely be a helpful tool as a refresher right before I meet with someone again.

Despite all these challenges, after some adjustments it worked reasonably well for my proof of concept and the potential to use this as a tool.

Moving Forward: Iteration and improvement

I plan on testing this tool in the real-world. Next week I have some meetings with people I’ve just met and I would want to use this as a supplementary resource to know a bit about their background and refer back to it in the moment without having to scroll too much (trust me people’s profiles can get really long really fast)

It’s important to note that I still plan on reading each persons profiles so I can better understand their backgrounds. This tool is meant to augment my work, not replace it.

My next steps involve streamlining the process to accept LinkedIn URLs directly, eliminating the need for PDF downloads. I also want to improve the presentation of the summarized data, making it more visually appealing and easier to digest. Ultimately I want to migrate the project from a Jupyter notebook to a web application.

Take aways and Reflections

This project reinforced the importance of iterative development. Building a functional tool requires a willingness to experiment, adapt, and learn form mistakes. The initial vision is just the startingg point the real work lies in refining and improving the concept through practical application.

I’m excited about the potential of LLMs to enhance productivity and automate tedious tasks.

I’m really enjoying working on project that have real-world applications. It’s rewarding to see how technology can be used to solve practical problems and improve our daily lives.

I’d love to hear your experiences with LLMs and automations as well as if you have any projects you’re working on? Have you used similar tools to enhance your productivity? Share your thoughts!