Llama Index is a Large Language Model framework designed to build LLM applications efficiently. It offers tools for document indexing by creating smaller nodes from large data chunks, creating embeddings with service context, and providing a storage context to store embeddings without redundancy. Additionally, it enhances the retrieval process by searching the index with the help of a query engine to find the most relevant documents.
By passing the prompt and retrieved context to the LLM, responses can be generated from the specified information provided in the source of the pipeline. This process is almost similar to the Langchain framework. However, while Langchain is a more general-purpose and flexible framework, Llama Index is specifically designed for building search and retrieval applications. It is optimized for efficiency and speed, making it ideal for applications requiring fast data lookup and retrieval.
In short, Llama Index is like a super smart librarian.
In the next module, we will create a project i.e., Chat with our own data that uses a PDF data as a data source and an embedding model in the service context to generate numerical embeddings, which are then stored in a vector store. We will utilize a "persist directory" to efficiently store the generated embeddings without redundancy. For instance, if we upload the same PDF twice, the persist directory will store the information in the same index, avoiding excess storage use. As in the previous project, we will define a retriever and an LLM to generate responses using a prompt and the context retrieved from the query engine.
Link to notebook: here
