🚀 AI-Powered Customer Support Chatbot using Langchain and Pinecone

An intelligent customer support chatbot that leverages LangChain and Pinecone to provide efficient and context-aware responses using Retrieval-Augmented Generation (RAG). The chatbot processes user queries, retrieves relevant documents, and generates human-like responses, making it ideal for automated customer service solutions.

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✨ Features

✅ Retrieval-Augmented Generation (RAG) for enhanced accuracy
✅ Pinecone Vector Database for fast and scalable search
✅ LangChain Integration for intelligent reasoning
✅ FastAPI Backend for seamless API interaction
✅ Streamlit UI for an interactive chat interface
✅ Token-based Query Processing for optimized performance
✅ Efficient Document Chunking & Embedding for better retrieval
✅ Support for Large Datasets using vector-based search

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🛠️ Tech Stack

• Python 🐍
• LangChain (for retrieval-based responses)
• Pinecone (vector database for similarity search)
• ChatOpenAI (for conversational AI-based responses)
• Streamlit (for front-end interface)
• OpenAI Embeddings (for embedding generation)

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⚡ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/your-username/customer-support-chatbot-using-langchain.git
cd customer-support-chatbot-using-langchain

2️⃣ Create a Virtual Environment

python -m venv venv
source venv/bin/activate  # For Linux/macOS
venv\Scripts\activate  # For Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Set Environment Variables

Create a .env file and add your API keys:

PINECONE_API_KEY=your_pinecone_api_key
OPENAI_API_KEY=your_openai_api_key

5️⃣ Run the Chatbot

streamlit run app.py

📌 Usage

1️⃣ Upload or integrate your customer support knowledge base. 2️⃣ The chatbot processes user queries and retrieves relevant answers. 3️⃣ Responses are generated using LLMs, ensuring context-awareness. 4️⃣ The chat history is stored for better conversation tracking.

💡 Ideal for: Customer support automation, FAQ handling, and AI-powered assistance.

🚀 Future Improvements

• ✨ Multi-turn conversation handling
• 🔍 Enhanced retrieval accuracy with fine-tuning
• 🌐 Multilingual support for diverse customer interactions
• 📊 Analytics dashboard for chatbot performance monitoring

🤝 Contributing

Contributions are welcome! Feel free to fork the repository, submit pull requests, or open issues.

📜 License

This project is licensed under the MIT License.

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