---
datasets:
- chromadb/paul_graham_essay
language:
- en
tags:
- RAG
- Retrieval Augmented Generation
- llama-index
---
# Summary:
  Retrieval Augmented Generation (RAG) is a technique to specialize a language model with a specific knowledge domain by feeding in relevant data so that it can give better answers.
# How does RAG works?
    1. Ready/ Preprocess your input data i.e. tokenization & vectorization
    2. Feed the processed data to the Language Model.
    3. Indexing the stored data that matches the context of the query.
# Implementing RAG with llama-index
### 1. Load relevant data and build an index
    from llama_index import VectorStoreIndex, SimpleDirectoryReader
    documents = SimpleDirectoryReader("data").load_data()
    index = VectorStoreIndex.from_documents(documents)
### 2. Query your data
    query_engine = index.as_query_engine()
    response = query_engine.query("What did the author do growing up?")
    print(response)
# My application of RAG on ChatGPT
Check RAG.ipynb