----------------------
Hmm, I don't know enough to give you a confident answer yet. The speed of generating an index can depend on various factors such as the size and complexity of the documents, the chunk size, the specific hardware and software environment, and the specific implementation of the indexing algorithm.
However, the LlamaIndex documentation provides some guidance on how to create and optimize indexes. For example, you can customize the chunk size and chunk overlap when creating an index, which can potentially affect the speed of index generation. Here is an example of how to create a VectorStoreIndex with a custom chunk size:
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core import Settings
documents = SimpleDirectoryReader("./data").load_data()
Settings.chunk_size = 512
Settings.chunk_overlap = 50
index = VectorStoreIndex.from_documents(
documents,
)
query_engine = index.as_query_engine(similarity_top_k=4)
(Source:
LlamaIndex Documentation ())
For more detailed information on optimizing LlamaIndex, you may want to refer to the
LlamaIndex documentation ().
----------------------
Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
----------------------