Why LlamaIndex?

LlamaIndex is chosen for this project because it provides a streamlined and efficient way to build document-based question-answering applications. Here’s why LlamaIndex is a better fit than LangChain for this use case:

• Specialized for Document Understanding: LlamaIndex is specifically designed for working with document data, including indexing, querying, and retrieving answers based on document content.

• Simplified Indexing: LlamaIndex’s VectorStoreIndex class simplifies the process of creating vector indices, making it easier to handle large datasets.

• Built-in Storage: LlamaIndex includes a robust storage mechanism, making it easy to persist and load indices, preserving data between sessions.

• Direct Integration with Hugging Face: LlamaIndex offers seamless integration with Hugging Face models, making it straightforward to use large language models like Meta-Llama.

While LangChain is a great library for building more general-purpose AI applications, LlamaIndex offers a more specialized and user-friendly approach for document-based Q&A tasks, making it ideal for this project.