vector index store initialization issue

[rahulkrprajapati]

I wanted to initialzie an empty VectorStoreIndex by providing just a storage_context.

 self.base_index = VectorStoreIndex(
                    nodes=None, storage_context=self.storage_context
                )

But I'm getting an error:

An error occurred: One of nodes, objects, or index_struct must be provided.

Considering I would be inserting the nodes later on what would be the correct way of initialisation and reloading of indices?

[dosubot[bot]]

🤖

Hey @rahulkrprajapati, great to see you back! Hope you're doing well.

To initialize an empty VectorStoreIndex with only a storage_context, you can use the from_vector_store class method. This method takes a VectorStore object and an optional ServiceContext object as arguments. It creates a StorageContext object from the VectorStore and then initializes a VectorStoreIndex object with this StorageContext and an empty list of nodes.

Here is an example of how you can do this:

vector_store = ...  # Your VectorStore object
service_context = ...  # Your ServiceContext object (optional)

index = VectorStoreIndex.from_vector_store(vector_store, service_context)

Please note that the VectorStore object you provide must store text. If it doesn't, the from_vector_store method will raise a ValueError.

After initializing the VectorStoreIndex, you can insert nodes using the insert_nodes method. This method takes a sequence of BaseNode objects and optionally additional arguments for insertion.

Here is an example of how you can insert nodes:

nodes = [...]  # Your list of BaseNode objects

index.insert_nodes(nodes)

This is supported by the following code in the provided context:

@classmethod
def from_vector_store(
    cls,
    vector_store: VectorStore,
    service_context: Optional[ServiceContext] = None,
    **kwargs: Any,
) -> "VectorStoreIndex":
    if not vector_store.stores_text:
        raise ValueError(
            "Cannot initialize from a vector store that does not store text."
        )

    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    return cls(
        nodes=[], service_context=service_context, storage_context=storage_context
    )

def insert_nodes(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None:
    """Insert nodes.
NOTE: overrides BaseIndex.insert_nodes.
        VectorStoreIndex only stores nodes in document store
        if vector store does not store text
    """
    self._insert(nodes, **insert_kwargs)
    self._storage_context.index_store.add_index_struct(self._index_struct)

You can find more details in the source code.

Sources

• llama-index-legacy/llama_index/legacy/indices/vector_store/base.py

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[rahulkrprajapati]

@dosu-bot I don't have a vector store defined yet. But I want the load the doc store and index store as below

edis_client = redis.Redis(
                    host=self.config.get("UPSTASH_REDIS_HOST"),
                    port=self.config.get("UPSTASH_REDIS_PORT"),
                    password=self.config.get("UPSTASH_REDIS_PASSWORD"),
                    ssl=True,
                )
              docstore = RedisDocumentStore.from_redis_client(
                    redis_client=self.redis_client,
                    namespace=self.namespace
                )
                storage_context = StorageContext.from_defaults(
                    docstore=self.docstore,
                    index_store=RedisIndexStore.from_redis_client(
                        redis_client=self.redis_client,
                        namespace=self.namespace
                    ),
                )
                base_index = VectorStoreIndex(
                    nodes=None, storage_context=self.storage_context
                )
                self.base_retriever = self.base_index.as_retriever(
                    similarity_top_k=self.similarity_top_k
                )
                retriever = AutoMergingRetriever(
                    self.base_retriever, self.storage_context, verbose=True
                )
                postprocessor = SentenceTransformerRerank(
                    model="cross-encoder/ms-marco-MiniLM-L-2-v2",
                    top_n=similarity_top_k_reranker,
                )

and later on I also want to add new documents to the index store as:

base_index.insert_nodes(leaf_nodes)

[dosubot[bot]]

🤖

Based on the error message you're encountering, it seems like you're trying to initialize a VectorStoreIndex without providing any of the nodes, objects, or index_struct parameters. However, you can initialize an empty VectorStoreIndex with just a storage_context and then add documents to it later.

Here's how you can do it:

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
import pinecone

# init pinecone
pinecone.init(api_key="<api_key>", environment="<environment>")
pinecone.create_index(
    "quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)

# construct vector store and customize storage context
storage_context = StorageContext.from_defaults(
    vector_store=PineconeVectorStore(pinecone.Index("quickstart"))
)

# Initialize an empty VectorStoreIndex
index = VectorStoreIndex(storage_context=storage_context)

In the above code, we're initializing Pinecone, which is a vector database, and using it as the vector store for our VectorStoreIndex. You can replace Pinecone with any other vector store that you prefer.

To add new documents to the index store, you can use the from_documents method:

from llama_index.core import SimpleDirectoryReader

# Load documents
documents = SimpleDirectoryReader(
    "../../examples/data/paul_graham"
).load_data()

# Add documents to the index
index.from_documents(documents)

In this code, we're loading documents from a directory and adding them to the index using the from_documents method.

Please replace <api_key> and <environment> with your actual Pinecone API key and environment. Also, replace "../../examples/data/paul_graham" with the actual path to your documents.

You can find more details about initializing a VectorStoreIndex and adding documents to it in the VectorStoreIndex guide and the VectorStoreIndex source code.

I hope this helps! If you have any other questions or run into any issues, feel free to ask.

Sources

• docs/module_guides/indexing/vector_store_index.md
• llama-index-core/llama_index/core/indices/vector_store/base.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.