Retrieval Augmented Generation (RAG)

Overview

ref: https://github.com/chatchat-space/Langchain-Chatchat/blob/master/README_en.md

My Pick

• LlamaEdge

Raw

• Basic RAG
• RAG with LlamaIndex - Nvidia CUDA + WSL + Word documents + Local LLM
• LlamaHub 🦙

Examples

Mistral-7B-Instruct Multiple-PDF Chatbot with Langchain & Streamlit

Fixed notebook is Chat_with_MultiplePDFs_Mistral_7B_Instruct1.ipynb

• Youtube: https://www.youtube.com/watch?v=tqpXvPzteT4

• Colab: https://colab.research.google.com/drive/11sf5LAF5EC1M0cDh-pUyowvS7EflwlMH?usp=sharing

• ⚠️ This notebook didn't use GPU. To support GPU will need.

  !CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python
  

  and

  llm = LlamaCpp(
    streaming = True,
    model_path="./mistral-7b-instruct-v0.1.Q4_K_M.gguf",
    temperature=0.75,
    top_p=1,
    verbose=True,
    n_ctx=4096,
    n_gpu_layers=30,
    n_threads=2,
    n_batch=521,
  )