Simple RAG implementation using python-llama-cpp

DISCLAIMER: I did this project just for personal use and learning purposes, focusing on understanding basic RAG, embedding models, and running local AI. Therefore, I used LLM-generated code for this specific project, as the coding itself was not the objective. For the same reason, it has a lot of rough edges, but it works. Since I didn’t find an updated, straightforward way to do this anywhere else, I decided to post it, as I felt it might be useful to someone else.

• Github link: GCaggianese/simple_python-llama-cpp_RAG
• Codeberg link: GCaggianese/simple_python-llama-cpp_RAG

Pre-requisites:

System Requirements:

• Python 3.8+
• C Compiler (GCC, Clang, etc.)
• Ninja

Install Python requirements.txt

pip install -r requirements.txt

For Nvidia CUDA support:

• The easiest way to install python-llama-cpp with CUDA (12.4) support is using

  CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python \
    --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124 \
    --force-reinstall --no-cache-dir
  

  As explained in Github: llama-cpp-python.

  • Notice the --force-reinstall --no-cache-dir, those are mandatory as if not it will likely reinstall from cache without CUDA support.

• This step should be done AFTER installing the requirements.txt.

GNU Guix

If you’re using GNU Guix you’ll need not only the Nonguix channel but also guix-hpc-non-free. Whith those channels, having the nvidia-service-type and the cuda-toolkit@12.4.0, you’ll be able to run it on the GPU.

Docs directory

Create a docs directory for the documents, you’ll put here the stuff you want the AI to retrieve context from.

mkdir docs

Usage:

Note: if you use envrc it will create a venv directory and activate a Python it’s venv. I suggest only using this .envrc only if you’re using GNU Guix as it adds some environment variables to make it work there.

1. Install all the Python requirements.
2. Run python rag.py
   • It will now download the models (granite-3.3-2b-instruct, all-MiniLM-L6-v2).
   • Then those will be loaded on your CPU/GPU.
   • You’ll see the interactive prompt.
   • IMPORTANT: It may fail on your hardware as I’m using a n_ctx=8196 with 4000 tokens, that may be way too much for some hardware (not to mention the model itself is not that small).
3. Ask!

Examples:

Tested with:

• OS: GNU Guix
• Kernel: Linux 6.16.12
• CPU: 11th Gen Intel(R) Core(TM) i7-11800H (16) @ 4.60 GHz
• GPU: NVIDIA GeForce RTX 3060 Ti Driver Version: 580.82.09 CUDA Version: 13.0 CUDA ToolKit Version: 12.4.0

Asking questions about Simple Linear Regression (SLR) from Wikipedia

1. Install pandoc and wget This is only needed for downloading the webpage into a txt and not needed for the RAG itself, if you already have a .txt or a .pdf just go ahead and use those ;)

2. Download the page and convert it into a .txt (this is not actually needed but will clean the document and help the model to find meaningful context).

   wget https://en.wikipedia.org/wiki/Simple_linear_regression
   pandoc -f html -i Simple_linear_regression -o slr.txt
   mv slr.txt docs
   rm Simple_Linear_Regression
   

3. Run: python rag.py

Output:

❯ python rag.py
llama_context: n_ctx_per_seq (8196) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
Interactive mode – type '/bye' or Ctrl-D to quit.

🤖 >

• The model is loaded and waiting for questions.

Ask!

🤖 > which method is common stipulated to be used in SLR?
Question: which method is common stipulated to be used in SLR?
Answer:  The common and stipulated method used in Simple Linear Regression (SLR) is the Ordinary Least Squares (OLS) method.
This method estimates the coefficients of the linear equation that best fits the data according to a defined criterion, which in this case is minimizing the sum of squared residuals (the differences between observed values and values predicted by the model).
Time taken: 1.24 seconds

Context used:

Doc 1: In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample points with one independent variable and one ...

Doc 2: In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample points with one independent variable and one ...