metadata

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