AI-based-Chatbot-using-Langchain

A chatbot made using mainly Streamlit and Langchain.

This repository contains a conversational chatbot built using the LangChain framework, designed to handle a variety of natural language processing (NLP) tasks efficiently.

Features

• Conversational AI: The chatbot leverages LangChain to handle dynamic, multi-turn conversations with a memory-augmented model.
• Customizable Chain: Easily modify or extend the chain logic to integrate different models, databases, or workflows.
• Pre-built Language Models: Supports various language models like OpenAI GPT, allowing for high-quality text generation.
• Retrieval-Augmented Generation (RAG): The chatbot can perform RAG-style interactions, pulling information from external knowledge sources (e.g., Pinecone, Elasticsearch).
• OpenAI Integration: Seamlessly integrates with OpenAI's language models for flexible and intelligent responses.
• Tool Support: Can utilize external APIs or tools (like search engines or knowledge bases) to enhance conversation.

Technologies Used

• LangChain: Core library that enables chain-based workflows for NLP applications.
• OpenAI GPT API: For advanced language understanding and generation.
• Pinecone/FAISS/Elasticsearch (Optional): Retrieval-based augmentation for improved response relevance.
• Python: Core programming language used for chatbot logic.
• Streamlit : For deploying a user-friendly interface for the chatbot.

Installation

1. Clone the repository:

   git clone https://github.com/snehaapratap/AI-based-Chatbot-using-Langchain.git
   cd langchain-chatbot

2. Install required dependencies:

   pip install -r requirements.txt

3. Set up environment variables for API keys (e.g., OpenAI API, Pinecone):

   export OPENAI_API_KEY=your-openai-api-key

4. Run the chatbot:

   streamlit run main.py

Customization

You can modify the chatbot's behavior by editing the chain logic within the chatbot.py file. Some areas for customization include:

• Changing the language model.
• Modifying how the chatbot handles memory or retrieval.
• Adding new tools or APIs to expand capabilities.

Usage

Once the chatbot is up and running, you can interact with it via the terminal or through a web interface (if integrated with Streamlit). The chatbot is designed to:

• Hold meaningful, multi-turn conversations.
• Fetch relevant information when prompted.
• Assist with tasks such as question answering, summarization, and more.

Future Enhancements

• Improved Memory Handling: Implementing long-term memory to track context over extended sessions.
• Fine-Tuning: Training on custom datasets to improve domain-specific performance.
• Expanded Tool Integration: Adding more external APIs for enhanced conversational capabilities.

Contribution

Feel free to open issues or pull requests if you'd like to contribute to improving the chatbot!!