Simple Linkedin Bio Search and Summary App

This is a simple app developed as part of learning LLM development experience, where one could simply input in the frontend web app on a person of interest and retireve a likely Linkedin page with a short summary and three interesting facts included.

Disclaimer: Accuracy and quickness of results are not guaranteed as this is just a simple exploratory development project.

Sample frontend

Using Barrack Obama as example

API used

Please note that the following API requires the use of API key to work and are not free.

• Proxycurl for scraping Linkedin Profile page (https://nubela.co/proxycurl/).
• Tavily Search for performing efficient, quick and persistent search based on input (https://docs.tavily.com/docs/welcome).
• Langchain's ChatOpenAi to chat with OpenAI's GPT-3.5-Turbo model (https://api.python.langchain.com/en/latest/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).

Environment file to edit

Please create an .env file with the following parameters. PYTHONPATH is required to be filled to ensure successful folder imports in project.

OPENAI_API_KEY=<YOUR API KEY>
PROXYCURL_API_KEY=<YOUR API KEY>
TAVILY_API_KEY=<YOUR API KEY>
PYTHONPATH=<Absolute path to the directory where this project is cloned>

# Optional if you are not using LangSmith for tracking llm utilisation related metrics
LANGCHAIN_API_KEY=<YOUR API KEY>
LANGCHAIN_TRACING_V2=true
LANGCHAIN_PROJECT=<YOUR PROJECT NAME>

For more information on Langsmith, refer to https://www.langchain.com/langsmith

Installation and execution

Please use Anaconda distribution to install the necessary libraries with the following command

conda env create -f environment.yml

Upon installation and environment exectuion, do run the following command to spin up the flask app, which is then accessible via http://localhost:5000 by default.

python <Path to project repo>/app.py

Programming languages/tools involved

• Python
• Flask
• Langchain
  • ChatOpenAI
  • PromptTemplate
  • PydanticOutputParser
  • Harrison Chase' ReAct prompt template from Langchain Hub
• HTML/CSS

Acknowledgement and Credits

The codebase developed are in reference to Udemy course titled "LangChain- Develop LLM powered applications with LangChain" available via https://www.udemy.com/course/langchain.