https://github.com/shubh-bharadwaj/rag-chatbot
https://github.com/shubh-bharadwaj/rag-chatbot
chatbot generative-ai langchain python3 retrieval-augmented-generation
Last synced: 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/shubh-bharadwaj/rag-chatbot
- Owner: Shubh-Bharadwaj
- License: mit
- Created: 2024-11-05T05:01:47.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-05T05:27:37.000Z (over 1 year ago)
- Last Synced: 2025-02-16T08:14:59.810Z (over 1 year ago)
- Topics: chatbot, generative-ai, langchain, python3, retrieval-augmented-generation
- Language: Python
- Homepage:
- Size: 70.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 🤖📚 RAG Chatbot Project
Welcome to the Retrieval-Augmented Generation (RAG) Chatbot project! This project is designed to build a chatbot that can respond accurately to queries using information from your documents and data sources, rather than solely relying on pre-trained knowledge.
**🚀 Project Overview**
This project explores two main topics:
**- Retrieval Augmented Generation (RAG):** This powerful application retrieves contextual documents from an external dataset to enhance response quality and relevance.
**- Chatbot Development:** Build a custom chatbot that generates responses based on the information within your specific documents and data sources.
**đź“‚ Project Structure**
**- Data Loading:** Scripts and utilities for loading documents using LangChain’s loaders.
**- Data Splitting:** Code to handle document splitting and preprocessing.
**- Vector Store:** Scripts for embedding documents and storing vectors for efficient retrieval.
**- Retrieval:** Code for querying and accessing relevant data from the vector store.
**- Chatbot:** Main logic for conversational flow and handling user interactions.
**🛠️ Technologies & Tools Used**
**- LangChain:** Framework for creating LLM applications with powerful data handling capabilities.
**- Python Libraries:** For building the chatbot and managing data flow.
**- Vector Stores:** Essential for embeddings and effective retrieval of contextual data.
## Note
The chatbot is designed to interact with _machinelearning-lecture01.pdf_, answering questions based on its introductory machine learning content. However, you can also upload your own documents to personalize the chatbot’s responses to your unique data sources.