LLamaIndex and HuggingFace | Ultimate Guide
AI Summary
Summary: Introduction to Llama Index Framework
- Data Integration with Llama Index
- Data is ubiquitous on the internet in various forms.
- Integrating a large language model (LLM) with a data source is crucial.
- Llama Index is a framework for this integration.
- Implementation Guide
- The guide covers implementing a chat with PDF using Llama Index and Hugging Face.
- Hugging Face is used as an open-source alternative to OpenAI’s models, which require payment.
- Installation Steps
- Install Llama Index, P PDF, and Lang Chain Community.
- Data and Indexing Process
- Data is in the form of a PDF book on Japanese culture (iigi).
- Data is loaded, parsed into smaller segments (nodes), and converted into vectors.
- Vectors are stored in a vector database, forming an index.
- Persisting Indexes
- Indexes are stored in a persistent directory (DB) to avoid re-indexing the same document.
- Code Implementation
- Import necessary modules and log in to Hugging Face.
- Define service context with LLM and embedding model.
- Create or load index based on the existence of the persistent directory.
- Save the index in the persistent directory.
- Query Engine
- User prompts are converted into vectors and matched with relevant documents from the index.
- The LLM generates a response based on the relevant context.
- Running the Code
- Execute the Python script to log in, create/load index, and run the query engine.
- Example prompts include asking for lessons from the book or the author’s name.
- Future Videos
- More videos in the series will cover advanced topics like custom retrievers and response synthesizers.