/chatbot-using-RAG

chat bot using google gemini

Primary LanguagePython

Customer Support Automation with LangChain

This project leverages LangChain technology to automate the process of retrieving and generating responses to customer queries based on a knowledge base or FAQs. LangChain empowers the application to effectively handle natural language and provide efficient, personalized customer support.

Model Architecture

Overview

The architecture integrates various components to streamline customer support interactions:

  1. Document Loading and Processing:

    • Fetches documents (FAQs/knowledge base) and splits them into smaller chunks for efficient processing.

  2. LangChain Integration:

    • Trains a LangChain model on the processed documents, enhancing its ability to understand the context and nuances of customer queries.
    • Fine-tunes the model to align with your specific customer support domain and language requirements.

  3. Retrieval and Generation:

    • Utilizes the trained LangChain model to retrieve relevant information from the knowledge base based on user queries.
    • Generates human-quality responses tailored to address specific customer needs effectively.

  4. User Interface Integration:

    • Provides a responsive web interface using Streamlit for seamless user interaction.
    • Accepts user queries via text input and displays generated responses using Markdown.

Detailed Architecture

Here’s a detailed breakdown of each architectural component:

  1. Document Loading and Processing

    • Component:
      • WebBaseLoader
      • RecursiveCharacterTextSplitter
    • Description:
      • WebBaseLoader fetches documents from a specified URL (knowledge base or FAQ).
      • RecursiveCharacterTextSplitter segments documents into smaller chunks for efficient processing.

  2. LangChain Integration

    • Description:
      • Trains a LangChain model on the processed documents to understand the content and relationships within.
      • Fine-tunes the model with domain-specific data to improve accuracy in customer query understanding and response generation.

  3. Retrieval and Generation

    • Component:
      • LangChain Model
    • Description:
      • Retrieves relevant information from the knowledge base using the trained LangChain model.
      • Generates natural language responses that are contextually appropriate and informative.

  4. User Interface Integration

    • Component:
      • Streamlit (st)
    • Description:
      • Provides a user-friendly web interface for customer interaction.
      • Users can input queries via st.text_input.
      • Responses are displayed using st.markdown, ensuring clarity and readability.

Example Usage

  1. Document Loading and Processing:

    • Use WebBaseLoader to fetch documents from a specified URL.
    • RecursiveCharacterTextSplitter segments documents into manageable chunks.

  2. LangChain Integration:

    • Train a LangChain model on the fetched documents.
    • Fine-tune the model for your specific customer support domain.

  3. Retrieval and Generation:

    • Utilize the trained LangChain model to fetch information and generate responses.

  4. User Interface Integration:

    • Build an interactive UI using Streamlit.
    • Allow users to input queries with st.text_input.
    • Display responses using st.markdown.