Wikipedia RAG System in Python - Beginner Tutorial with LlamaIndex

Key Concepts

• Retrieval Augmented Generation (RAG)
• Llama Index
• Streamlit
• Vector embeddings
• OpenAI API
• Wikipedia reader
• Vector store index
• Query engine
• Cached resources

Implementation of a RAG System with Llama Index and Streamlit

Overview

The video demonstrates how to build a Wikipedia-powered RAG system in Python using Llama Index and Streamlit. The system retrieves relevant information from Wikipedia articles based on user queries and generates answers based on the retrieved context. Llama Index simplifies the process by automating tasks like data processing and embedding generation.

Step-by-Step Process

1. Installation:
   • Install necessary packages using pip or uv: streamlit, llama-index, and optionally python-dotenv.
   • Install Llama Index dependencies: llama-index-embeddings-openai, llama-index-llms-openai, and llama-index-readers-wikipedia.
   • Obtain an OpenAI API key from the OpenAI developer platform.
2. API Key Loading:
   • Store the OpenAI API key in an .env file in the format openai_api_key=YOUR_API_KEY.
   • Load the API key into the environment using the load_dotenv() function from the dotenv package.
3. Import Libraries:
   • Import necessary libraries from Llama Index:
     • OpenAI (from llama_index.llms.openai) for using OpenAI's language models.
     • OpenAIEmbedding (from llama_index.embeddings.openai) for generating embeddings.
     • WikipediaReader (from llama_index.readers.wikipedia) for reading Wikipedia articles.
     • VectorStoreIndex, StorageContext, and load_index_from_storage (from llama_index.core) for managing the vector store index.
   • Import streamlit as st for building the web application.
   • Import os for accessing environment variables.
4. Define Index Directory:
   • Define a constant index_directory to store the vector store index (e.g., wiki_rag).
5. Define Wikipedia Pages:
   • Create a list of strings called pages containing the titles of the Wikipedia articles to use as the knowledge base.
   • Example: pages = ["Convolutional neural network", "Recurrent neural network", "Artificial neural network"]
6. Create Cached Index Resource:
   • Define a function get_index() decorated with @st.cache_resource to cache the index.
   • Inside get_index():
     • Check if the index directory exists using os.path.isdir(index_directory).
     • If the directory exists:
       • Load the storage context using StorageContext.from_defaults(persist_dir=index_directory).
       • Load the index from storage using load_index_from_storage(storage_context).
       • Return the loaded index.
     • If the directory does not exist:
       • Create a WikipediaReader instance.
       • Load data from Wikipedia using WikipediaReader.load_data(pages=pages, auto_suggest=False).
       • Create an OpenAIEmbedding instance with the desired embedding model (e.g., "text-embedding-3-small").
       • Create a VectorStoreIndex from the loaded documents, providing the embedding model.
       • Persist the storage context to disk using index.storage_context.persist(persist_dir=index_directory).
       • Return the created index.
7. Create Cached Query Engine Resource:
   • Define a function get_query_engine() decorated with @st.cache_resource to cache the query engine.
   • Inside get_query_engine():
     • Get the index using index = get_index().
     • Create an OpenAI instance with the desired language model (e.g., "gpt-4-0613") and temperature (e.g., 0 for strictness).
     • Return the query engine using index.as_query_engine(llm=llm, similarity_top_k=3). similarity_top_k specifies the number of relevant items to retrieve as context.
8. Build Streamlit User Interface:
   • Define a main() function.
   • Set the title of the application using st.title("Wikipedia RAG Application").
   • Create a text input field for the user's question using question = st.text_input("Ask a question").
   • Create a submit button using button = st.button("Submit").
   • If the button is clicked and a question is entered (if button and question):
     • Display a spinner animation while processing using with st.spinner("Thinking...").
     • Get the query engine using qa = get_query_engine().
     • Query the engine using response = qa.query(question).
     • Display the answer using st.subheader("Answer") and st.write(response.response).
     • Display the retrieved context using st.subheader("Retrieved Context").
     • Iterate through the source nodes in response.source_nodes and display the content of each node using st.markdown(source_node.get_content()).
9. Run the Application:
   • Use the standard python idiom if __name__ == "__main__": main()
   • Run the Streamlit application using streamlit run main.py.

Examples and Use Cases

• The video uses AI and machine learning-related Wikipedia articles as an example knowledge base.
• The system can be adapted to other domains by providing different Wikipedia pages or other data sources.
• Example questions:
  • "What can you tell me about CNNs?"
  • "What can you tell me about RNNs?"
  • "What can you tell me about XLSTMs?"

Key Arguments and Perspectives

• Llama Index simplifies the development of RAG systems by automating complex tasks.
• Retrieving context from external sources like Wikipedia can improve the accuracy and relevance of answers.
• Using a cached query engine can improve performance by avoiding redundant computations.

Notable Quotes

• "In llama index we don't have to specify anything manually. We don't need to do anything manually. We just need to say what we want to have done and then basically llama index does it for us behind the scenes."
• "This is what I mean by llama index is extremely simple to use. You don't need to handle any data processing. Everything's already done for you in the proper format behind the scenes."

Technical Terms and Concepts

• Retrieval Augmented Generation (RAG): A technique that combines information retrieval with text generation to improve the quality of generated text.
• Llama Index: A Python framework for building applications that use large language models (LLMs) to access and reason about data.
• Streamlit: A Python library for creating interactive web applications.
• Vector embeddings: Numerical representations of text that capture semantic meaning.
• Vector store index: A data structure that stores vector embeddings and allows for efficient similarity search.
• Query engine: A component that takes a user query and retrieves relevant information from the vector store index.
• Cached resources: Resources that are stored in memory to improve performance by avoiding redundant computations.

Logical Connections

• The video starts by introducing the concept of RAG and the tools used (Llama Index and Streamlit).
• It then walks through the step-by-step process of building the RAG system, explaining each step in detail.
• The video concludes with a demonstration of the system and a summary of the key takeaways.

Synthesis/Conclusion

The video provides a practical guide to building a Wikipedia-powered RAG system using Llama Index and Streamlit. Llama Index simplifies the process by automating tasks like data processing and embedding generation, while Streamlit allows for easy creation of a user interface. The resulting system can answer questions based on context retrieved from Wikipedia, providing more accurate and relevant responses. The key takeaway is that Llama Index significantly reduces the complexity of building RAG systems, making them accessible to a wider range of developers.