Build Your Own Jarvis — RAG Assistant (Cookbook)

This mini-cookbook spins up a personal AI assistant that:

• runs a self-hosted LLM via Ollama (default: llama3),
• ingests your local ./data files into a vector database (Chroma by default, Pinecone optional),
• answers questions using retrieval‑augmented generation (RAG),
• exposes a simple Streamlit chat UI.

Designed to be finished in ~40 mins for an assignment/demo.

0) Prereqs

• Python 3.10+
• Ollama installed & running → https://ollama.com (then run ollama pull llama3)
• (Optional) Pinecone account + API key if you prefer cloud vector DB.

1) Setup

python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt

2) Add your knowledge

Drop PDFs/TXTs/MD/DOCs into ./data. (You can add more later.)

3) Configure (optional)

Create .env in the project root if needed:

# Use Pinecone instead of local Chroma (True/False). Defaults to False.
USE_PINECONE=False

# When USE_PINECONE=True
PINECONE_API_KEY=YOUR_KEY
PINECONE_ENV=YOUR_ENV   # e.g., us-east-1-aws
PINECONE_INDEX_NAME=jarvis-index

# LLM model to use with Ollama
OLLAMA_MODEL=llama3
SYSTEM_PROMPT=You are Jarvis, a concise, helpful assistant for my files. Cite file names when relevant.

4) Ingest

python ingest.py

This reads ./data, builds embeddings, and populates Chroma or Pinecone.

5) Run the chat UI

streamlit run app.py

Open the local URL and start chatting.

6) Notes