| --- |
| 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 |
