https://github.com/ysskrishna/ai-support-bot
A full-stack chatbot application that uses RAGS to interact intelligently with users based on custom-loaded knowledgebases. It supports dynamic dataset loading for seamless updates. The chatbot’s language model is evaluated on relevance, accuracy, coherence, completeness, creativity, tone, and alignment with intent, ensuring high-quality chats
https://github.com/ysskrishna/ai-support-bot
chatgpt chroma chromadb docker docker-compose fastapi generative-ai langchain openai openai-chatgpt rag react retrieval-augmented-generation tailwindcss vector-database ysskrishna
Last synced: 3 months ago
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A full-stack chatbot application that uses RAGS to interact intelligently with users based on custom-loaded knowledgebases. It supports dynamic dataset loading for seamless updates. The chatbot’s language model is evaluated on relevance, accuracy, coherence, completeness, creativity, tone, and alignment with intent, ensuring high-quality chats
- Host: GitHub
- URL: https://github.com/ysskrishna/ai-support-bot
- Owner: ysskrishna
- License: agpl-3.0
- Created: 2024-08-04T07:37:39.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-12T18:16:16.000Z (about 1 year ago)
- Last Synced: 2026-01-03T13:20:19.706Z (6 months ago)
- Topics: chatgpt, chroma, chromadb, docker, docker-compose, fastapi, generative-ai, langchain, openai, openai-chatgpt, rag, react, retrieval-augmented-generation, tailwindcss, vector-database, ysskrishna
- Language: Python
- Homepage:
- Size: 3.75 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# AI Support Bot with Custom Knowledgebase Integration
A full-stack chatbot application that uses RAGS to interact intelligently with users based on custom-loaded knowledgebases. It supports dynamic dataset loading for seamless updates. The chatbot’s language model is evaluated on relevance, accuracy, coherence, completeness, creativity, tone, and alignment with intent, ensuring high-quality, user-focused interactions.
## Techstack used
- React
- Tailwindcss
- FastAPI
- ChromaDB
- Langchain
- OpenAI
- Docker
## Flowchart
This diagram illustrates the high level components involved and thier interaction
## Demo
https://github.com/user-attachments/assets/49a82013-c936-4f13-8739-646039b77d55
## Project Configuration
Before running the project, make sure to adjust the following configuration files:
### Backend Configuration
- Adjust the `.env` file located in the backend folder if any environment variables need modification.
## Start Containers
To start the project, use Docker Compose to build and run the containers:
```
docker compose up --build
```
### Frontend URL
Once the containers are running, you can access the frontend application at:
```
http://localhost:5173/
```
### Backend URL
Once the containers are running, you can access the backend application at:
```
http://localhost:8081/
```
## Pending Improvements
- Add SQL/NoSQL DB to store the user queries and generated reponses
https://github.com/pixegami/langchain-rag-tutorial