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LlamaIndex provides a class called
VectorStoreIndex for creating an index with vector stores. This class can be used with multiple vector stores. Here is an example of how to create an instance of
VectorStoreIndex from a pre-existing vector store:
new_index_instance = VectorStoreIndex.from_vector_store(
vector_store=new_vector_store
)
For multi-modal use cases, LlamaIndex provides a built-in
MultiModalVectorStoreIndex that supports building separate vector stores for image and text embedding vector stores.
from llama_index import MultiModalVectorStoreIndex
# Assuming you have image and text embedding vector stores
multi_modal_index = MultiModalVectorStoreIndex(image_vector_store, text_vector_store)
Please note that the above code is a pseudo-code and may not work directly. You need to replace
image_vector_store and
text_vector_store with your actual vector stores.
For more details, you can refer to the following sources:
- VectorStoreIndex ()
- MultiModalVectorStoreIndex ()
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