grazz7/RAG_Chatbot

Project Overview

This respository was created as part for an internship report, containing the source codes and respurces in developing the Retrieval-Augmented Generation(RAG) banking chatbot.

RAG allows the chatbot to answer questions based on the knowledge base instead of only using the Large Language Model (LLM).

Objectives

• Develop a chatbot capable of answering questions based on public dataset(s)
• Implement a Retrieval-Augmented Generation (RAG) pipeline
• Generate embeddings for efficient document retrieval
• Build a vector database
• Improve response accuracy by providing relevant context to the language model
• Implement advanced requirements
• Evaluate the chatbot's performance

Installation

Virtual environment:
python -m venv venv
venv\Scripts\activate

Libraries:
pip install -r requirements.txt

Run the chatbot:
streamlit run app.py

How it works

The chatbot follows a Retrieval-Augmented Generation (RAG) workflow:

1. Documents are collected and preprocessed.
2. The documents are split into smaller text chunks.
3. Each chunk is converted into vector embeddings.
4. The embeddings are stored in a vector database.
5. When a user submits a query:
   • The query is embedded into a vector.
   • The vector database retrieves the most relevant document chunks.
   • The retrieved context is combined with the user's query.
   • The LLM generates an appropriate response using the retrieved information.

Requirements

• Python 3.10 or above
• LangChain
• Ollama
• Vector database (ChromaDB)
• Sentence Transformer / Embedding model
• python-dotenv
• Other dependencies listed in requirements.txt

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license: mit Datasets: Bank Faqs, customer support conversations