🚀 Advanced LangChain Workflows

This repository demonstrates various advanced LangChain workflows for structured AI-driven outputs, utilizing Google Gemini, OpenAI, and Anthropic models. The examples cover sequential and parallel processing, conditional chains, and structured data generation.

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📁 Repository Structure

File	Description
simple_chain.py	Implements a basic LangChain pipeline using Google Gemini to generate structured facts about a given topic.
sequential_chain.py	Demonstrates a sequential pipeline where one model generates a detailed report, and another summarizes it into five key points.
parallel_chains.py	Uses parallel chains to simultaneously generate notes and quiz questions from a text, then merges them into a structured document.
conditional_chains.py	Implements a conditional chain that classifies feedback sentiment and generates appropriate responses based on sentiment analysis.

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🔧 Tech Stack

• LangChain — AI workflow orchestration
• Google Gemini (PaLM) — LLM inference via langchain-google-genai
• OpenAI GPT — Alternative LLM backend
• Anthropic Claude — Used for advanced AI processing
• Pydantic — Structured validation for output enforcement
• Python-dotenv — Environment variable management

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🚀 Quick Start

1. Clone this repository:

   git clone https://github.com/<your-username>/advanced-langchain-workflows.git
   cd advanced-langchain-workflows

2. Create & activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # macOS/Linux
   venv\Scripts\activate     # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Add your API keys to a .env file:

   GOOGLE_API_KEY=your_gemini_api_key_here
   OPENAI_API_KEY=your_openai_api_key_here
   

5. Run any example script:

   python simple_chain.py
   python sequential_chain.py
   python parallel_chains.py
   python conditional_chains.py

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📄 License

MIT © 2025

📫 Contact

Questions or feedback? Email haseebulhassan1172003@gmail.com.