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