🦜️🔗 LangChain: How to build AI-driven Applications Tutorial

⚡ Build context-aware reasoning applications ⚡

🚀 Introduction

This repository is a part of LangChain tutorial. It demonstrates the integration of OpenAI model using the LangChain framework. It includes several examples to illustrate the capabilities of LangChain in creating context-aware applications.

📚 Prerequisites

Before starting, ensure you have the following:

• Python (3.x recommended) installed on your system.
• OpenAI API key (Follow these instructions to get your OpenAI API key)

🛠 Installation

1. Clone the Repository: First, clone this repository to your local machine using your preferred method.

2. Create a Virtual Environment:

• Navigate to the project directory in your terminal.
• Create a virtual environment:

python3 -m venv venv

• Activate the virtual environment:
  • On Windows: venv\Scripts\activate
  • On MacOS/Linux: source venv/bin/activate

1. Install Requirements:

• Ensure your virtual environment is active.
• Install the required packages using:

pip install -r requirements.txt

4. Set Up OpenAI API Key:

• Obtain an OpenAI API key (Follow these instructions to get your OpenAI API key.).
• Create a .env file in the root of the project directory.
• Add your API key to the file:

OPENAI_API_KEY=your_api_key_here

Running the Examples

This repository includes several example scripts showcasing different features of LangChain with OpenAI:

1. Simple Interaction (simple_interaction.py):

• Demonstrates a basic interaction with OpenAI's API.

• Run the script with:

python simple_interaction.py

2. Multiple Prompts (multiple_prompts.py):

• Shows how to send multiple prompts to the AI model.

• Run with:

python multiple_prompts.py

3. Prompt Templating (prompt_templating.py):

• Illustrates dynamic data injection in prompts.

• Execute the script using:

python prompt_templating.py

4. Output Parser (output_parser.py):

• Shows how to parse and refine LLM responses.

• Execute with:

python output_parser.py

5. Chain Integration (chain_integration.py):

• Demonstrates how to streamline processes using LangChain's syntax.

• Run using:

python chain_integration.py