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The
VectorStoreIndex in LlamaIndex is responsible for creating the embeddings for your text. When you create an index using
VectorStoreIndex, it takes your documents (or nodes) and splits them up into nodes. It then creates
vector embeddings of the text of every node, ready to be queried by an LLM. This is what is meant when we say it "embeds your text".
The process of generating embeddings involves using an API from your LLM and can take a long time, especially if you have a lot of text, since it involves many round-trip API calls.
Here is an example of how you might create a
VectorStoreIndex:
from llama_index.core.schema import TextNode
from llama_index.core import VectorStoreIndex, StorageContext
nodes = [
TextNode(
text="The Shawshank Redemption",
metadata={
"author": "Stephen King",
"theme": "Friendship",
},
),
# ... more nodes ...
]
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context, embed_model=embed_model)In this example, the
VectorStoreIndex is created with a list of
TextNode objects, a
StorageContext, and an embedding model (
embed_model). The
VectorStoreIndex will use the embedding model to generate embeddings for the text of each node.
For more details, you can refer to the
LlamaIndex documentation ().
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