LangChain Prompts

A comprehensive collection of LangChain prompt engineering examples and conversational AI implementations built using Python and LangChain.

This repository demonstrates core prompt management techniques, chat history handling, message orchestration, and reusable prompt templates that serve as the foundation for production-grade LLM applications.

Overview

Modern AI applications rely heavily on effective prompt engineering and conversation management. This repository provides practical implementations of LangChain prompt components, helping developers understand how to build scalable and maintainable AI systems.

The examples included here focus on real-world use cases such as dynamic prompt generation, conversation memory, chat history management, and structured message handling.

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Topics Covered

Prompt Templates

• Dynamic prompt creation using ChatPromptTemplate
• Variable injection into prompts
• Reusable prompt structures
• Domain-specific prompt customization

Message Management

• System Messages
• Human Messages
• AI Messages
• Multi-turn conversation handling

Message Placeholders

• Dynamic chat history insertion
• Context-aware conversations
• Stateful prompt generation

Chat History

• Storing conversation history
• Loading historical conversations
• Maintaining conversational context

Interactive Chatbots

• Gemini-powered chatbot implementation
• Continuous conversation flow
• Context retention across interactions

Prompt Utilities

• Prompt generation workflows
• UI-driven prompt creation
• Template management techniques

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Repository Structure

LangChain_Prompts/
│
├── chat_prompt_template.py
├── messages.py
├── message_placeholder.py
├── chatbot.py
├── updated_chatbot_msg.py
├── prompt_generator.py
├── prompt_ui.py
├── chat_history.txt
├── requirements.txt
└── template.json

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Technologies Used

• Python
• LangChain
• Google Gemini
• Prompt Engineering
• Conversational AI
• Large Language Models (LLMs)

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Installation

Clone the repository:

git clone <repository-url>
cd LangChain_Prompts

Create a virtual environment:

python -m venv venv

Activate the environment:

Windows

venv\Scripts\activate

Linux/macOS

source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

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Environment Variables

Create a .env file and configure your API credentials:

GOOGLE_API_KEY=your_api_key

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Use Cases

• Learning LangChain prompt fundamentals
• Building conversational AI systems
• Understanding chat memory mechanisms
• Implementing prompt engineering workflows
• Developing context-aware AI assistants

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Ongoing Development

This repository is actively maintained and continuously expanded with new LangChain concepts, prompt engineering techniques, and conversational AI implementations.

Upcoming additions include:

• Few-Shot Prompting
• Prompt Chaining
• Output Parsers
• LangChain Memory
• RAG Pipelines
• AI Agents and Tools
• LangGraph Workflows
• Production AI Design Patterns

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Author

Bhupendra Shivhare

AI Engineer | Machine Learning Practitioner | Generative AI Enthusiast

Focused on building practical AI solutions, educational content, and end-to-end implementations using LangChain, LLMs, and modern AI frameworks.