https://github.com/insafhamdi/automate-a-retrieval-augmented-ai-chatbot-for-car-assistance
https://github.com/insafhamdi/automate-a-retrieval-augmented-ai-chatbot-for-car-assistance
chatbot chroma gpt35 langchain langchain-python llm ndcg-evaluation rag rag-implementation rouge-metric streamlit
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/insafhamdi/automate-a-retrieval-augmented-ai-chatbot-for-car-assistance
- Owner: insafhamdi
- Created: 2024-10-06T11:31:20.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-10-17T11:19:59.000Z (7 months ago)
- Last Synced: 2024-10-20T01:20:09.930Z (7 months ago)
- Topics: chatbot, chroma, gpt35, langchain, langchain-python, llm, ndcg-evaluation, rag, rag-implementation, rouge-metric, streamlit
- Language: HTML
- Homepage:
- Size: 10.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AutoMate: Context-Aware Chatbot for Car Manuals
## Overview
**AutoMate** is a context-aware chatbot designed to integrate with car manuals, providing drivers with real-time guidance and support. By leveraging Large Language Models *(LLMs)* and Retrieval Augmented Generation *(RAG)*, AutoMate can effectively answer queries related to vehicle warning messages, their meanings, and recommended actions.
The chatbot aims to enhance the driving experience by delivering information in a user-friendly manner, with potential integration with text-to-speech software to read responses aloud.
## Project Description
As a proof of concept, we utilize pages from the car manual for the MG ZS. This manual contains crucial information about various car warning messages and their associated actions.
## Model Used
AutoMate employs *OpenAI GPT-3.5* for generating responses. The model has been fine-tuned to understand and provide context-aware answers based on the specific content of the car manual.
## Features
- **Context-Aware Responses:** AutoMate retrieves relevant information from the car manual using RAG and generates concise answers based on user queries.
- **Integration with Text-to-Speech:** Future enhancements will allow the chatbot's responses to be read aloud, improving accessibility for drivers.
- **User-Friendly Interface:** The chatbot provides a simple interaction model for users to query warning messages and receive guidance.
## RAG Architecture
The architecture of Retrieval Augmented Generation (RAG) combines information retrieval with language generation. Below is a diagram that illustrates this architecture:
## Technologies Used
- **LangChain:** A framework for building applications with LLMs, facilitating easy retrieval and generation of context-aware responses.
- **OpenAI API:** Used for accessing the LLM to generate responses based on the retrieved information from the car manual.
- **Chroma Vectorstore:** A vector store for storing and retrieving documents related to the car manual.
- **Streamlit:** A framework for creating interactive web applications to facilitate user interaction with the chatbot.