This repository provides example implementations of chatbots using the Langchain framework and Streamlit for the user interface. It targets developers looking to build interactive, context-aware, and data-connected conversational AI applications. The benefit is a collection of ready-to-run examples demonstrating various Langchain capabilities.

How It Works

The project showcases several chatbot archetypes, including basic conversational agents, context-aware bots, internet-enabled bots, document-aware bots, SQL database interaction bots, and website-scraping bots. These examples leverage Langchain's modular components to chain together LLM calls, data retrieval, and specific tool integrations, simplifying the development of complex chatbot functionalities.

Quick Start & Requirements

• Primary install / run command: streamlit run Home.py
• Prerequisites: Python, Streamlit, Langchain. Docker is also supported.
• Links: Streamlit App

Highlighted Details

• Demonstrates context-aware chatbots that maintain conversation history.
• Includes examples of chatbots with internet access for real-time information.
• Features chatbots capable of querying custom documents and SQL databases.
• Showcases chatbots that can interact with website content.

Maintenance & Community

The project welcomes contributions via Pull Requests and plans to add more examples over time.

Licensing & Compatibility

The repository does not explicitly state a license. Users should verify compatibility for commercial or closed-source use.

Limitations & Caveats

The project is presented as a collection of examples, and specific performance benchmarks or advanced error handling are not detailed. Users may need to configure API keys for underlying LLM services.