In this notebook we will look into building Basic RAG Pipeline with LlamaIndex. The pipeline has following steps.

1. Setup LLM and Embedding Model.

1. Download Data.

1. Load Data.

1. Index Data.

1. Create Query Engine.

1. Querying.

#Installation

Setup LLM and Embedding model

We will use juglow latest released Haijun 3 Opus models

!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'



--2024-03-08 06:51:30-- https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.108.133, 185.199.110.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 75042 (73K) [text/plain] Saving to: ‘data/paul_graham/paul_graham_essay.txt’ data/paul_graham/pa 100%[===================>] 73.28K --.-KB/s in 0.002s 2024-03-08 06:51:30 (34.6 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

from llama_index.core import (

SimpleDirectoryReader,

VectorStoreIndex,

)

#Load Data