VectorStoreIndex has empty index because its vectorstore stores_text attribute is true

[seeifsalem]

Hello, I noticed that in VectorStoreIndex when we initiate an instance using VectorStoreIndex.from_vector_store(vector_store = vector_store) and that the vector_store instance has stores_text = True, we end up with an empty index structure. It's due to this part of the code.

def _add_nodes_to_index(
       self,
       index_struct: IndexDict,
       nodes: Sequence[BaseNode],
       show_progress: bool = False,
       **insert_kwargs: Any,
   ) -> None:
       """Add document to index."""
       if not nodes:
           return

       for nodes_batch in iter_batch(nodes, self._insert_batch_size):
           nodes_batch = self._get_node_with_embedding(nodes_batch, show_progress)
           new_ids = self._vector_store.add(nodes_batch, **insert_kwargs)

           if not self._vector_store.stores_text or self._store_nodes_override:
               # NOTE: if the vector store doesn't store text,
               # we need to add the nodes to the index struct and document store
               for node, new_id in zip(nodes_batch, new_ids):
                   # NOTE: remove embedding from node to avoid duplication
                   node_without_embedding = node.copy()
                   node_without_embedding.embedding = None

                   index_struct.add_node(node_without_embedding, text_id=new_id)
                   self._docstore.add_documents(
                       [node_without_embedding], allow_update=True
                   )
           else:
               # NOTE: if the vector store keeps text,
               # we only need to add image and index nodes
               for node, new_id in zip(nodes_batch, new_ids):
                   if isinstance(node, (ImageNode, IndexNode)):
                       # NOTE: remove embedding from node to avoid duplication
                       node_without_embedding = node.copy()
                       node_without_embedding.embedding = None

                       index_struct.add_node(node_without_embedding, text_id=new_id)
                       self._docstore.add_documents(
                           [node_without_embedding], allow_update=True
                       )

In this code extracted from the VectorStoreIndex class, you can see it but I don't understand the reasoning behind and I find myself having an empty index every time because I use the ChromaVectorStore. Any idea on the reason behind this? Does this mean that my indexing is done somewhere else? My main goal is to understand how the indexing is done in my code and how I can choose my indexing methods.
This is even more peculiar, since the from_vector_store() method only accepts vector stores that store text

[dosubot[bot]]

Hey @seeifsalem! I'm here to help you with any bugs, questions, or contributions you have for the repository. Let's tackle this issue together!

The design where only ImageNode and IndexNode types are added to the index structure when the vector store keeps text (stores_text=True) is intended to avoid redundancy and duplication of data. When the vector store already stores the text, there is no need to add text nodes to the index structure again. Instead, only nodes that contain non-textual data, such as images (ImageNode) or references to other indices (IndexNode), are added to the index structure. This ensures that the index remains efficient and avoids unnecessary duplication of text data.

Here is a breakdown of the indexing process in this case:

1. Batch Processing: Nodes are processed in batches to optimize performance.
2. Embedding Generation: For each batch, embeddings are generated for the nodes.
3. Node Addition: Nodes are added to the vector store, and new IDs are generated for them.
4. Conditional Indexing: If the vector store keeps text, only ImageNode and IndexNode types are added to the index structure. The embedding is removed from these nodes to avoid duplication.
5. Document Store Update: The document store is updated with the nodes that are added to the index structure.

The relevant code snippet demonstrates this process:

if not self._vector_store.stores_text or self._store_nodes_override:
    for node, new_id in zip(nodes_batch, new_ids):
        node_without_embedding = node.copy()
        node_without_embedding.embedding = None
        index_struct.add_node(node_without_embedding, text_id=new_id)
        self._docstore.add_documents([node_without_embedding], allow_update=True)
else:
    for node, new_id in zip(nodes_batch, new_ids):
        if isinstance(node, (ImageNode, IndexNode)):
            node_without_embedding = node.copy()
            node_without_embedding.embedding = None
            index_struct.add_node(node_without_embedding, text_id=new_id)
            self._docstore.add_documents([node_without_embedding], allow_update=True)

This code ensures that only ImageNode and IndexNode types are added to the index structure when the vector store keeps text, thereby maintaining efficiency and avoiding redundancy ^[1][2].

Regarding your use of ChromaVectorStore, it handles text storage by storing embeddings, metadata, and documents within a ChromaDB collection. The add method in ChromaVectorStore is responsible for adding nodes to the index by extracting embeddings, metadata, and documents from the nodes and storing them in the ChromaDB collection ^[3][4].

If you find that your index is empty, it is likely because the nodes being added are not of type ImageNode or IndexNode. To address this, you may need to adjust your indexing strategy or ensure that the nodes you are adding fit the criteria for being included in the index structure.

To continue talking to Dosu, mention @dosu.

[dosubot[bot]]

When your index_struct is empty, the type of index being used is EmptyIndexStruct. This is confirmed by checking the type of index_struct in your EmptyIndex instance:

empty_index = EmptyIndex(service_context=mock_service_context)
assert isinstance(empty_index.index_struct, EmptyIndexStruct)

To optimize retrieval in your pipeline, ensure that the EmptyIndex is correctly implemented and used in contexts where an empty index is appropriate. The EmptyIndex is designed to handle cases with no data and supports pure LLM calls and certain properties like token counting and composition with other indices. It does not support operations like insertion or deletion of nodes, as indicated by the NotImplementedError in the _insert and _delete_node methods. This ensures that the EmptyIndex remains optimized for its intended use case.

For retrieval, the EmptyIndex behaves as expected when queried, returning no nodes:

retriever = empty_index.as_retriever()
nodes = retriever.retrieve("What is?")
assert len(nodes) == 0

This confirms that the retriever returns no nodes when the index is empty, which is the expected behavior ^[1][2][3].

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[seeifsalem]

I am a bit struggling to understand the difference between the VectorStoreIndex and the BaseIndexStroe class, if you can help with that it would be great

[dosubot[bot]]

The primary difference between the VectorStoreIndex and the BaseIndexStore classes lies in their roles and the data structures they use for storing and managing indexed nodes.

BaseIndexStore

• Role: Serves as an abstract base class for managing index structures.
• Key Methods:
  • index_structs() -> List[IndexStruct]: Retrieve a list of index structures.
  • add_index_struct(index_struct: IndexStruct) -> None: Add a new index structure.
  • delete_index_struct(key: str) -> None: Delete an index structure by its key.
  • get_index_struct(struct_id: Optional[str] = None) -> Optional[IndexStruct]: Retrieve a specific index structure by its ID.
  • persist(persist_path: str = DEFAULT_PERSIST_PATH, fs: Optional[fsspec.AbstractFileSystem] = None) -> None: Persist the index store to disk.
• Data Structure: Uses IndexDict for managing nodes ^[1].

VectorStoreIndex

• Role: Extends the BaseIndex class and is specifically designed to handle the storage and retrieval of embeddings using a vector store.
• Key Methods:
  • _add_nodes_to_index: Adds nodes to the index, handling embedding generation and storage.
  • from_vector_store(vector_store): Initializes the index from an existing vector store.
• Data Structure: Uses BasePydanticVectorStore for storing nodes and their embeddings. The index_struct_cls is set to IndexDict, but the primary storage mechanism is the BasePydanticVectorStore ^[2][3].

Key Differences

1. Storage Mechanism:

   • BaseIndexStore: Uses IndexDict for managing nodes.
   • VectorStoreIndex: Uses BasePydanticVectorStore for storing nodes and their embeddings.

2. Functionality:

   • BaseIndexStore: Provides a general framework for managing index structures with methods for adding, deleting, and retrieving index structures.
   • VectorStoreIndex: Focuses on handling embeddings and integrates with various vector stores for efficient storage and retrieval of embeddings.

3. Use Case:

   • BaseIndexStore: Suitable for general index management tasks.
   • VectorStoreIndex: Optimized for applications requiring embedding storage and retrieval, such as LLM applications.

Understanding these differences can help you choose the appropriate class based on your specific needs for indexing and retrieval in your application.

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[seeifsalem]

okay so now I am going back to my original question, what type of indexing is used then when I use VectorStoreIndex.from_vector_store(vector_store = chroma_vector_store) with chroma_vector_store, a previously initiated vector store of type ChromaVectorStore

[dosubot[bot]]

When you use VectorStoreIndex.from_vector_store(vector_store=chroma_vector_store) with chroma_vector_store, the type of indexing being used is a VectorStoreIndex.

In this context, the indexing is done by storing the embeddings in a Chroma vector store. The process involves initializing a Chroma client, creating a collection in Chroma to store the data, and then assigning Chroma as the vector_store in a StorageContext. This setup ensures optimized retrieval by leveraging the efficient storage and retrieval capabilities of the Chroma vector store.

The VectorStoreIndex works by taking your documents, splitting them into nodes, and creating vector embeddings of the text in each node. These embeddings are numerical representations of the semantics or meaning of your text. When you query the index, your query is also turned into a vector embedding, and a mathematical operation is performed to rank all the stored embeddings by their semantic similarity to the query. The most similar embeddings are then returned as the corresponding chunks of text, a process known as "top-k semantic retrieval."

Additionally, LlamaIndex supports more dynamic and expressive forms of retrieval beyond top-k semantic search by using metadata filters. Given a natural language query, the system can infer a set of metadata filters and the appropriate query string to pass to the vector database. This allows for retrieval that can combine filtering on metadata tags with semantic search within the filtered set.

Here is a relevant code snippet:

import chromadb
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext

# initialize client
db = chromadb.PersistentClient(path="./chroma_db")

# get collection
chroma_collection = db.get_or_create_collection("quickstart")

# assign chroma as the vector_store to the context
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# load your index from stored vectors
index = VectorStoreIndex.from_vector_store(
    vector_store, storage_context=storage_context
)

# create a query engine
query_engine = index.as_query_engine()
response = query_engine.query("What is llama2?")
print(response)

This setup ensures that your indexing is optimized for retrieval by leveraging the capabilities of the Chroma vector store ^[1][2][3].

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