This project provides a practical, hands-on learning experience for building a generative AI application using LangChain. It targets developers and researchers interested in understanding and implementing AI-driven social networking tools, specifically for generating personalized icebreakers based on LinkedIn and Twitter data.

How It Works

The application leverages LangChain to orchestrate data retrieval and generation. It scrapes LinkedIn and Twitter profiles using external APIs, processes the collected information, and then utilizes an LLM (via OpenAI) to craft customized icebreaker messages. This approach allows for a structured and modular development process, demonstrating LangChain's capabilities in chaining different data sources and AI models.

Quick Start & Requirements

• Install dependencies: pipenv install
• Run the Flask server: pipenv run app.py
• Run tests: pipenv run pytest .
• Requires API keys for OpenAI, Scrapin.io, Tavily, and Twitter.
• LangSmith API key is required if LangChain tracing is enabled.
• Project uses paid API services.

Highlighted Details

• Demonstrates LangChain integration for data scraping and LLM-based content generation.
• Focuses on practical application development for social networking.
• Includes unit tests for validation.

Maintenance & Community

No specific information on contributors, sponsorships, or community channels is provided in the README.

Licensing & Compatibility

The repository does not explicitly state a license.

Limitations & Caveats

The project relies on several paid third-party APIs (LinkedIn scraping, Twitter API, OpenAI), which incur costs and may have rate limits. Enabling LangChain tracing requires a valid LangSmith API key, and the application will error without it.