[Question]: Is it possible to speed up index.storage_context.persist() on the minor index updates

[LuninaPolina]

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Question

Hello! Thank you for your great project!

I've built a huge vector store index and saved it to the persist directory via index.storage_context.persist(persist_dir=persist_dir) In my project, I load and do minor updates to the index (insert or delete 5-10 nodes) and want to update it in the persist_dir.

However, this takes a lot of time and I wonder - is it possible to make index saving process faster considering that the index almost didn't change and I probably need to rewrite only a small portion of the saved data. Or may be there are other techniques that will allow me to run persist() faster.

Will really appreciate any advice.

[dosubot[bot]]

@LuninaPolina Hello! I'm here to help you with any issues or questions you have. Let's solve this together!

Yes, it is possible to speed up index.storage_context.persist() on minor index updates by persisting only the components that have changed. The current implementation persists all components (docstore, index_store, graph_store, property_graph_store, and vector_stores) every time persist() is called, which can be inefficient for minor updates.

To optimize this, you can modify the persist() method to include flags or checks that determine whether each component has been modified and needs to be persisted. Here is an example of how you might implement this:

class StorageContext:
    # Existing code...

    def persist(
        self,
        persist_dir: Union[str, os.PathLike] = DEFAULT_PERSIST_DIR,
        docstore_fname: str = DOCSTORE_FNAME,
        index_store_fname: str = INDEX_STORE_FNAME,
        vector_store_fname: str = VECTOR_STORE_FNAME,
        image_store_fname: str = IMAGE_STORE_FNAME,
        graph_store_fname: str = GRAPH_STORE_FNAME,
        pg_graph_store_fname: str = PG_FNAME,
        fs: Optional[fsspec.AbstractFileSystem] = None,
        persist_docstore: bool = True,
        persist_index_store: bool = True,
        persist_graph_store: bool = True,
        persist_property_graph_store: bool = True,
        persist_vector_stores: bool = True,
    ) -> None:
        """Persist the storage context.

        Args:
            persist_dir (str): directory to persist the storage context
            persist_docstore (bool): whether to persist the docstore
            persist_index_store (bool): whether to persist the index store
            persist_graph_store (bool): whether to persist the graph store
            persist_property_graph_store (bool): whether to persist the property graph store
            persist_vector_stores (bool): whether to persist the vector stores
        """
        if fs is not None:
            persist_dir = str(persist_dir)  # NOTE: doesn't support Windows here
            docstore_path = concat_dirs(persist_dir, docstore_fname)
            index_store_path = concat_dirs(persist_dir, index_store_fname)
            graph_store_path = concat_dirs(persist_dir, graph_store_fname)
            pg_graph_store_path = concat_dirs(persist_dir, pg_graph_store_fname)
        else:
            persist_dir = Path(persist_dir)
            docstore_path = str(persist_dir / docstore_fname)
            index_store_path = str(persist_dir / index_store_fname)
            graph_store_path = str(persist_dir / graph_store_fname)
            pg_graph_store_path = str(persist_dir / pg_graph_store_fname)

        if persist_docstore:
            self.docstore.persist(persist_path=docstore_path, fs=fs)
        if persist_index_store:
            self.index_store.persist(persist_path=index_store_path, fs=fs)
        if persist_graph_store:
            self.graph_store.persist(persist_path=graph_store_path, fs=fs)
        if self.property_graph_store and persist_property_graph_store:
            self.property_graph_store.persist(persist_path=pg_graph_store_path, fs=fs)
        if persist_vector_stores:
            # save each vector store under its namespace
            for vector_store_name, vector_store in self.vector_stores.items():
                if fs is not None:
                    vector_store_path = concat_dirs(
                        str(persist_dir),
                        f"{vector_store_name}{NAMESPACE_SEP}{vector_store_fname}",
                    )
                else:
                    vector_store_path = str(
                        Path(persist_dir)
                        / f"{vector_store_name}{NAMESPACE_SEP}{vector_store_fname}"
                    )
                vector_store.persist(persist_path=vector_store_path, fs=fs)

With this implementation, you can call persist() with specific flags to persist only the components that have been updated, thus speeding up the process for minor updates. For example:

index.storage_context.persist(persist_docstore=False, persist_index_store=True, persist_graph_store=False, persist_property_graph_store=False, persist_vector_stores=False)

Additionally, if you are working with a large number of documents and need to frequently update the index, you can consider scheduling the indexing process to run periodically (e.g., once per day) and only add information for files that have changed. This approach can save significant time and resources, especially when dealing with extensive document collections ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[LuninaPolina]

@dosu my question is about updating all the components considering that the change in them is minor, e.g. only 5-10 nodes have been added or removed

[dosubot[bot]]

To efficiently update all components in index.storage_context.persist() when only 5-10 nodes have been added or removed, you can use the insert method to add new documents to your existing index without re-indexing everything. Here is how you can do it:

from llama_index.legacy.indices.vector_store.base import VectorStoreIndex

# Assuming `index` is your existing index and `new_documents` is the list of new nodes
for doc in new_documents:
    index.insert(doc)

# Persist the updated index to disk
index.storage_context.persist(persist_dir="<persist_dir>")

This approach allows you to efficiently update your index by only adding the new nodes and then persisting the changes, avoiding the need to re-index all data. The persist method in the StorageContext class ensures that all components, including document stores, index stores, and vector stores, are saved correctly to the specified directory. The StorageContext class handles the persistence of each component individually, ensuring that the updates are efficiently managed and stored ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.

[LuninaPolina]

@dosu the persist process itself is slow since I need to update all index files on the disk

[dosubot[bot]]

To speed up the persist process in index.storage_context.persist(), you can use a more efficient storage backend like Chroma, which is designed for handling large-scale vector data efficiently. Here’s how you can set it up:

1. Install Chroma:

   pip install chromadb

2. Use Chroma as the vector store in your StorageContext:

   import chromadb
   from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
   from llama_index.vector_stores.chroma import ChromaVectorStore
   from llama_index.core import StorageContext
   
   # Load some documents
   documents = SimpleDirectoryReader("./data").load_data()
   
   # Initialize Chroma client, setting path to save data
   db = chromadb.PersistentClient(path="./chroma_db")
   
   # Create 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)
   
   # Create your index
   index = VectorStoreIndex.from_documents(
       documents, storage_context=storage_context
   )
   
   # Persist the index
   index.storage_context.persist(persist_dir="<persist_dir>")

By using Chroma, you leverage a more optimized storage solution that can handle large-scale data more efficiently, potentially speeding up the persist process. Additionally, ensure that your StorageContext is properly configured to handle multiple vector stores and other components efficiently, as shown in the StorageContext class implementation. This includes setting appropriate paths and handling file systems correctly to avoid bottlenecks during the persist operation ^[1][2][3].

To continue talking to Dosu, mention @dosu.