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What will you learn?

In this use case, you’ll learn how to gather information from LinkedIn profiles using Tavily’s Search and Extract functionality. We’ll teach you how to structure your search parameters to generate concise and readable answers to your provided query, and how to properly use the Tavily Python SDK to retrieve the desired information effectively and efficiently. Once the search, extract, and answer process have been completed, we’ll demonstrate how to parse the raw extracted content into usable profile data.

How does it work?

Through use of the include_domains and include_raw_content parameters, our system searches for LinkedIn profiles that match the provided query and extracts the profile content to provide complete insights into professional experiences, information and skills.

Include Raw Content

By including the include_raw_content parameter, Tavily’s Search and Extract functionality are used in tandem to provide the most accurate and complete result.

Include Answer

Utilizing the include_answer parameter provides an LLM-generated response to the provided query, making it easy to access and parse concise and relevant information quickly with detailed sources.
To achieve the proper functionality when searching LinkedIn URLs, be sure to specify search_depth = advanced in your use of Tavily Search.

Getting Started

We have prepared a Jupyter Notebook outlining the contents of this tutorial
First create an account and get your free API key.

Get your Tavily API key

Next, use the Tavily Python SDK to create the workflow.
1

Install the Tavily Python SDK

Shell
2

Import the necessary libraries

Python
3

Instantiate the Tavily Client

Python
4

Execute the candidate leads search and extract

Python
5

Function to parse LinkedIn profile raw content into key info

Python
6

Execute the candidate leads search and extract

Python
7

Output

Shell