Basic LangChain OpenAI Chat Example

This folder contains a basic example demonstrating how to use LangChain with OpenAI's chat models without Retrieval Augmented Generation (RAG).

Overview

The example shows how to:

• Set up a basic chat interaction with OpenAI's models through LangChain
• Load environment variables securely
• Create and invoke a chat model
• Handle and display model responses

Files

• chat_model_basic.py: Basic implementation of chat functionality using LangChain and OpenAI

Prerequisites

• Python 3.x
• Required packages:
  • langchain
  • langchain-openai
  • python-dotenv

Setup

1. Install the required packages:

pip install langchain langchain-openai python-dotenv

2. Create a .env file in the root directory with your OpenAI API key:

OPENAI_API_KEY=your_api_key_here

Usage

Run the example script:

python chat_model_basic.py

The script will:

1. Load your OpenAI API key from the environment
2. Initialize a chat model (gpt-4o-mini)
3. Send a query about Retrieval Augmented Generation in Cybersecurity
4. Display both the full response object and its content

Notes

• The example uses the "gpt-4o-mini" model, but you can modify this to use other OpenAI models
• Make sure to keep your API key secure and never commit it to version control
• This is a basic example without RAG capabilities - see other examples for more advanced features

Additional Resources

• LangChain Chat Model Documentation
• OpenAI Chat Model Documentation

Files

• chat_model_basic.py: Basic implementation of chat functionality using LangChain and OpenAI
• embeddings.py: Demonstrates how to generate text embeddings using OpenAI's embedding models

Examples

Chat Model Example

Basic chat interaction with OpenAI models through LangChain.

Embeddings Example

The embeddings.py script shows how to:

• Generate embeddings for text input using OpenAI's API
• Use the "text-embedding-3-small" model
• Process and display embedding vectors

Example usage:

python embeddings.py

Streamlit Interface

The chatbot example includes a Streamlit web interface that provides:

• A clean, interactive chat interface
• Real-time streaming responses
• Persistent chat history within the session
• Easy-to-use input field for questions

To run the Streamlit interface:

streamlit run chatbot_example.py

Security Considerations

API Key Management

• API keys are loaded from environment variables using python-dotenv
• Never hardcode API keys in your source code
• Add .env to your .gitignore file

Best Practices

• Regularly rotate your API keys
• Monitor API usage for unusual patterns
• Implement rate limiting for production deployments
• Validate and sanitize user inputs

Part 1Project Structure

• chat_model_basic.py: Basic implementation of chat functionality using LangChain and OpenAI
• embeddings.py: Demonstrates how to generate text embeddings using OpenAI's embedding models
• chatbot_example.py: Streamlit interface for the chatbot example
• .env: Environment variables file for API keys
• requirements.txt: List of required packages