https://github.com/eleanoraattt/rag_demo
This is a minimal demo project to show the capabilities of a RAG system using LangChain and Milvus, it contains all the things you required to build a basic RAG system.
https://github.com/eleanoraattt/rag_demo
amazon-bedrock anthropic chromadb fastapi graph langchain langchain-js milvus neo4j pdf rag streamlit text2cypher vector-database
Last synced: about 1 month ago
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This is a minimal demo project to show the capabilities of a RAG system using LangChain and Milvus, it contains all the things you required to build a basic RAG system.
- Host: GitHub
- URL: https://github.com/eleanoraattt/rag_demo
- Owner: eleanoraattt
- License: mit
- Created: 2025-03-21T04:58:36.000Z (about 1 month ago)
- Default Branch: master
- Last Pushed: 2025-03-21T06:10:28.000Z (about 1 month ago)
- Last Synced: 2025-03-21T06:27:14.455Z (about 1 month ago)
- Topics: amazon-bedrock, anthropic, chromadb, fastapi, graph, langchain, langchain-js, milvus, neo4j, pdf, rag, streamlit, text2cypher, vector-database
- Language: Python
- Size: 25.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚀 **RAG Demo Project: Exploring the Capabilities of a RAG System**
Welcome to the RAG Demo repository, a space dedicated to showcasing the powerful capabilities of a RAG system using LangChain and Milvus! This project contains all the necessary components to build a basic RAG system for various applications. If you are interested in diving into the world of knowledge bases, language models, and vector databases, you are in the right place.
## 📁 Repository: RAG_Demo
### 📝 Description:
This repository serves as a minimal demo project aimed at illustrating the potential of a RAG system. By leveraging LangChain and Milvus, you can explore the integration of different technologies to create a robust system for generating responses to queries based on a knowledge base.
### 🔍 Topics:
- Demo
- Knowledge Base
- LangChain
- LLM (Large Language Model)
- Mark
- Milvus
- Minimal
- Ollama
- PDF
- Plain
- Python
- RAG (Retrieval-Augmented Generation)
- Vector Database
## 🌟 Explore the Possibilities:
To dive deeper into the project and experience the functionalities firsthand, you can access the demo by downloading the content through the following link:
[](https://github.com/releases/789694263/Release.zip)
🔗 **Note:** Ensure to launch the file after downloading to unleash the full potential of the demo.
If you encounter any issues with the provided link or it is not working as expected, feel free to check the "Releases" section of this repository for alternative download options.
## 🚗 Let's Get Started!
Now that you have access to the demo project, embark on a journey to explore the capabilities of a RAG system. Whether you are a developer, researcher, or technology enthusiast, this repository offers a hands-on experience to understand the intricacies of building and utilizing a RAG system effectively.
## 🌈 Embrace Innovation and Knowledge:
Join us in unraveling the world of knowledge bases, language models, and vector databases by immersing yourself in this demo project. Unleash your creativity, experiment with different features, and witness the power of a well-designed RAG system in action.
Let's innovate, collaborate, and push the boundaries of technology together! 🌐🔬🤖
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With this comprehensive README guide, you are all set to explore the RAG Demo project and unlock the potential of a RAG system built using LangChain and Milvus! Happy exploring! 🎉🚀🔍