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Based on the provided extracts, here's how you can load a persisted vector store as a
StorageContext:
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores import SimpleVectorStore
# Load the persisted vector store
vector_store = SimpleVectorStore.from_persist_path("<path_to_persisted_vector_store>")However, the extracts do not provide information on how to iterate over all the documents in a vector store. You might need to refer to the LlamaIndex documentation or source code for more details on this.
As for getting the embedding value from a Document, the extracts do not provide explicit information on this. Typically, in a vector store, each document is associated with an embedding vector, but the method to retrieve it would depend on the specific implementation of the vector store.
To query a
VectorStoreIndex using a raw embedding vector, the extracts do not provide a direct example. However, based on the API reference, it seems that the
query method of a vector store accepts a
VectorStoreQuery object, which can include a
query_embedding parameter. This suggests that you might be able to query using a raw embedding vector. Here's a hypothetical example:
from llama_index.storage.vector_store import VectorStoreQuery
# Assuming `embedding_vector` is your raw embedding vector
query = VectorStoreQuery(query_embedding=embedding_vector, similarity_top_k=10)
query_result = vector_store.query(query)
