> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tavily.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LinkedIn Profile Search

> Discover and analyze LinkedIn profiles with Tavily's APIs

## 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.

<Tip>
  To achieve the proper functionality when searching LinkedIn URLs, be sure
  to specify `search_depth = advanced` in your use of Tavily Search.
</Tip>

## Getting Started

> We have prepared a [Jupyter Notebook](https://github.com/tavily-ai/tavily-tutorials/blob/main/linkedin-profile-search.ipynb) outlining the contents of this tutorial

First create an account and get your free API key.

<Card title="Get your Tavily API key" icon="key" href="https://app.tavily.com" horizontal />

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

<Steps>
  <Step title="Install the Tavily Python SDK">
    ```bash Shell theme={null}
    uv venv
    uv pip install tavily-python python-dotenv ipykernel
    ```
  </Step>

  <Step title="Import the necessary libraries">
    ```python Python theme={null}
    import getpass
    import os

    if not os.environ.get("TAVILY_API_KEY"):
        os.environ["TAVILY_API_KEY"] = getpass.getpass("TAVILY_API_KEY:\n")

    TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
    ```
  </Step>

  <Step title="Instantiate the Tavily Client">
    ```python Python theme={null}
    from tavily import TavilyClient

    tavily_client = TavilyClient()
    ```
  </Step>

  <Step title="Execute the candidate leads search and extract">
    ```python Python theme={null}
    response = tavily_client.search(
        query="Who are the C-Suite employees at OpenAI?",
        search_depth="advanced",
        include_answer="advanced",
        include_raw_content=True,
        include_domains=["linkedin.com/in"],
        max_results=10,
    )
    ```
  </Step>

  <Step title="Function to parse LinkedIn profile raw content into key info">
    ```python Python theme={null}
    import re

    def extract_profile_data(raw_content):
        profile_data = {
            "location": None,
            "education": [],
            "skills": [],
            "work_experience": []
        }

        # Check if raw_content is None or empty
        if raw_content is None or raw_content.strip() == "":
            return profile_data

        # Extract Location
        location_match = re.search(r'\n(.*?)\n\d+ connections', raw_content)
        if location_match:
            profile_data["location"] = location_match.group(1).strip()

        # Extract Work Experience Section
        experience_match = re.search(r'Experience:\n(.*?)\n\nEducation:', raw_content, re.DOTALL)
        if experience_match:
            experience_text = experience_match.group(1)

            # Extract Company Names and Date Ranges
            experience_entries = re.findall(r'(.+?) \(https://www\.linkedin\.com/company/.*?\)\n(.*?)\n', experience_text)

            for company, date_range in experience_entries:
                profile_data["work_experience"].append({
                    "company": company.strip(),
                    "date_range": date_range.strip()
                })

        # Extract Education
        education_match = re.search(r'Education:\n(.*?)\n\nSkills:', raw_content, re.DOTALL)
        if education_match:
            education_text = education_match.group(1)
            education_entries = re.findall(r'(.+?)\n(.*?)\n(\w+ \d{4} - \w+ \d{4}|N/A - Present|\w+ \d{4} - \w+ \d{4})\nGrade: (.*?)\nActivities and societies: (.*?)\n', education_text)

            for institution, program, date_range, grade, activities in education_entries:
                profile_data["education"].append({
                    "institution": institution.strip(),
                    "program": program.strip(),
                    "date_range": date_range.strip(),
                    "grade": grade.strip(),
                    "activities": activities.strip()
                })

        # Extract Skills
        skills_match = re.search(r'Skills:\n(.*?)\n\n', raw_content, re.DOTALL)
        if skills_match:
            skills_text = skills_match.group(1)
            profile_data["skills"] = [skill.strip() for skill in skills_text.split('\n') if skill.strip()]

        return profile_data

    ```
  </Step>

  <Step title="Execute the candidate leads search and extract">
    ```python Python theme={null}
    profiles = []

    for profile in response["results"]:
        profile_data = extract_profile_data(profile["raw_content"])
        profile_data["name"] = profile["title"]
        profile_data["url"] = profile["url"]
        profiles.append(profile_data)

    print(response["answer"])
    print(profiles)
    ```
  </Step>

  <Step title="Output">
    ```json Shell theme={null}
     # LLM-generated answer
     OpenAI's C-Suite includes several key executives. Sam Altman serves as the CEO, while Mira Murati holds the position of Chief Technology Officer (CTO). Brad Lightcap is the Chief Operating Officer (COO) of the company. Greg Brockman, one of the co-founders, holds the roles of President and Chairman. While not explicitly mentioned as C-Suite, Wojciech Zaremba is another co-founder who plays a significant role in the organization. The leadership team also includes Srinivas Narayanan as a Vice President, though his specific C-Suite title is not clear. These individuals collectively form the core executive team responsible for steering OpenAI's strategic direction and operations.

     # Profile data
     [
         {
             "name": "Hannah Wong - OpenAI"
             "url": "https://www.linkedin.com/in/hannah-wong-66b78612",
             "location": "San Francisco Bay Area, United States",
             "education": [
                 {
                 "institution": "University of Washington",
                 "program": "Bachelor of Arts, Communications, Women Studies",
                 "date_range": "N/A - Present",
                 "grade": "Cum Laude",
                 "activities": "Association of Women in Communication, University Chorale"
                 }
             ],
             "skills": [
                 "Media Relations; Product Launch; Crisis Communications; Corporate Communications; Event Planning; Creative Writing; Reputation Management; Social Media Communications; Project Management; Team Leadership; Teamwork; Mentoring; Executive Communications; Product Reviews; Influencers; Global Communications; Management"
             ],
             "work_experience": [
                 {
                 "company": "Chief Communications Officer at OpenAI",
                 "date_range": "Aug 2024 - Present"
                 },
                 {
                 "company": "VP, Communications at OpenAI",
                 "date_range": "Jun 2023 - Aug 2024"
                 },
                 {
                 "company": "Director, Head of Public Relations at OpenAI",
                 "date_range": "Feb 2021 - Jun 2023"
                 },
                 {
                 "company": "Senior PR Manager, Apple Pay, Apple Card, iCloud at Apple",
                 "date_range": "Jan 2019 - Nov 2020"
                 },
                 {
                 "company": "PR Manager, iPad at Apple",
                 "date_range": "Nov 2014 - Dec 2018"
                 },
                 {
                 "company": "PR Manager, Consumer Momentum at Apple",
                 "date_range": "Oct 2013 - Oct 2014"
                 },
                 {
                 "company": "Corporate Communications at Gap Inc.",
                 "date_range": "Dec 2012 - May 2013"
                 },
                 {
                 "company": "Account Supervisor, Xbox LIVE, Charles Schwab, Twitter at Edelman",
                 "date_range": "Jun 2009 - Oct 2011"
                 },
                 {
                 "company": "Senior Account Executive, Xbox LIVE at Edelman",
                 "date_range": "Apr 2008 - Jun 2009"
                 },
                 {
                 "company": "Account Executive, Xbox at Edelman",
                 "date_range": "Apr 2007 - Mar 2008"
                 },
                 {
                 "company": "Assistant Account Executive at Edelman",
                 "date_range": "Apr 2006 - Mar 2007"
                 }
             ]
         },
         ...
     ]
    ```
  </Step>
</Steps>