local-rag-llamaindex

Local RAG with LlamaIndex

A local Retrieval-Augmented Generation chatbot running entirely on Jetson. Uses the jetson-containers Docker environment, ChromaDB as the vector store, and a quantized Llama2-7b (MLC q4f16) model for inference.

Hardware: reComputer Jetson with ≥16GB RAM Prerequisites: JetPack 5.1+, Docker installed and running

Execution model

Run one phase at a time. After each phase:

• Relay all output lines to the user.
• If output contains [STOP] → stop immediately, consult the failure decision tree.
• If output ends with [OK] → tell the user "Phase N complete" and proceed to the next phase.

Phase 1 — setup (~2 min)

Clone jetson-containers and install its Python tooling.

git clone --depth=1 https://github.com/dusty-nv/jetson-containers
cd jetson-containers
pip install -r requirements.txt

Expected: pip install completes without errors. [OK]

Phase 2 — clone (~1 min)

Clone the RAG project into the jetson-containers data directory.

cd jetson-containers/data
git clone https://github.com/Seeed-Projects/RAG_based_on_Jetson.git

Expected: RAG_based_on_Jetson/ directory created. [OK]

Phase 3 — model (varies, depends on network)

Install git-lfs and clone the quantized Llama2-7b model weights.

sudo apt-get install git-lfs
git lfs install
cd jetson-containers/data/RAG_based_on_Jetson
git clone https://huggingface.co/JiahaoLi/llama2-7b-MLC-q4f16-jetson-containers

Expected: model directory llama2-7b-MLC-q4f16-jetson-containers/ populated with weight files. [OK]

Phase 4 — run (inside Docker container)

Launch the MLC container, install dependencies inside it, then start the RAG app.

# From the jetson-containers root:
cd jetson-containers
./run.sh $(./autotag mlc)

Once inside the container shell:

cd data/RAG_based_on_Jetson/
pip install -r requirements.txt
pip install chromadb==0.3.29
python3 RAG.py

Note: pip install chromadb==0.3.29 may print dependency conflict warnings — these are ignorable as long as the install completes and RAG.py starts successfully.