/RAG_LLM

RAG agent created using Open Source Mistral LLM

Primary LanguagePython

Retrieval Augmented Generation

Playing around with RAG - LLM's exploring certain use cases.

  • Open Source LLM model used : Mistral
  • Vector Data Store used : Chroma DB
  • Embeddings Function : Ollama Nomic-Embed-Text
  • Data used : AEM Guide

To Setup the project :

  1. pip install langchain-community
  2. pip install chromadb(If build fails for chromadb-hnswlib, you need to sudo apt-get install libfuse I think it was. lib something something atlease)
  3. Setup Ollama -> curl -fsSL https://ollama.com/install.sh | sh
  4. ollama serve
  5. ollama pull nomic-embed-text
  6. ollama pull mistral

To Run the project :

  1. Change the path of data in data_loader.py/load_documents()(DATA_PATH)
  2. Run data_loader.py
  3. Run query_data.py
  4. Enter your queries, get your replies

Test Run of the Project

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