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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.

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RAG DEMO

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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.

Before Start

First, make a copy of .env.sample and rename it to .env, and change any fields need to be changed

Then:

  1. Setup the Milvus as the vector database
    1. See folder Milvus
  2. Setup the Ollama for the document tokenization and interaction
    1. See Setup - OllamaEmbeddings
    2. See Ollama
  3. Prep the documents used for RAG and the vector DB
  4. Copy all the documents to the Documents folder under the project root
  5. Run python prep_doc.py to prepare the documents for the RAG system
  6. You can run python milvus_search.py to verify all the documents has been loaded to the vector DB

Start the demo

Run python main.py and type anything you want to ask the RAG system

Screenshots

Ask RAG a random question

a2f22b53e37ee6fb92e43bd67d735e20

Vector Database

fb20250642869db7e0e8dd406774fdd3 680b5c08190bfca80f51d1aeef6edff9

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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.

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