persist Qdrant or load Qdrant on Windows

[Alnasser0]

Hello. How do I persist Qdrant? Every time I run my code the embedding is created from scratch, and it takes long time to finish but I want to load the one currently on disk. I am currently using Qdrant Windows binary, so Qdrant is running in the background in CMD.

Here is my code:

import logging
from typing import List, Any
from llama_index.core import VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core.schema import BaseNode
from llama_index.core import Settings
from qdrant_client import QdrantClient, AsyncQdrantClient
from pathlib import Path

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

def create_load_index(
    nodes: List[BaseNode],
    embed_model: Any,
    collection_name: str = "PEDro_qdrant_test",
    host: str = "localhost",
    port: int = 6333,
) -> VectorStoreIndex:

    logger.info("Initializing Qdrant client and index setup.")
    client = QdrantClient(host=host, port=port)
    aclient = AsyncQdrantClient(host=host, port=port)

    vector_store = QdrantVectorStore(
        collection_name=collection_name,
        client=client,
        aclient=aclient,
        enable_hybrid=True
    )
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    Settings.chunk_size = 512

    try:
        # First, attempt to load from storage
        logger.info("Attempting to load index from storage.")
        index = load_index_from_storage(
            storage_context, 
            embed_model=embed_model  # Add embed_model here
        )
        logger.info("Index successfully loaded from existing storage.")
        return index
    
    except Exception as e:
        logger.warning(f"Could not load existing index. Error: {e}")
        
        # If loading fails, check if collection exists and create if needed
        if not client.collection_exists(collection_name):
            logger.info("Collection does not exist. Creating new index.")
            index = VectorStoreIndex( 
                nodes=nodes, 
                storage_context=storage_context, 
                embed_model=embed_model, 
                show_progress=True 
            )
        else:
            logger.info("Collection exists but index load failed. Recreating index.")
            index = VectorStoreIndex( 
                nodes=nodes, 
                storage_context=storage_context, 
                embed_model=embed_model, 
                show_progress=True 
            )
        logger.info("New index created and stored.")
        storage_context.persist()
        logger.info("Index persisted.")
        return index

[dosubot[bot]]

To persist and load your Qdrant index on Windows, you need to ensure that your storage_context is correctly configured to save and load the index data. Here are a few steps and considerations to help you achieve this:

1. Persisting the Index:

   • After creating or updating your index, call the persist method on your storage_context to save the index data to disk. Make sure you specify a persist_dir where the data should be stored.
   • Example:

     storage_context.persist(persist_dir="path/to/persist_dir")

2. Loading the Index:

   • When loading the index, ensure that the storage_context is initialized with the same persist_dir used during persistence.
   • Example:

     storage_context = StorageContext.from_defaults(
         vector_store=vector_store,
         persist_dir="path/to/persist_dir"
     )
     index = load_index_from_storage(storage_context, embed_model=embed_model)

3. Handling Exceptions:

   • Use a try-except block to handle any exceptions that might occur during the loading process. This will allow you to log errors and take corrective actions if needed.

4. Windows Compatibility:

   • Ensure that the path specified in persist_dir is accessible and writable on your Windows system. Avoid using special characters or spaces in the directory path.

By following these steps, you should be able to persist your Qdrant index and load it efficiently on Windows, avoiding the need to recreate embeddings from scratch each time.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Bug Report | Other}

[Alnasser0]

@dosu can you help me refactor my code to resolve this issue?

[dosubot[bot]]

To refactor your code for persisting and loading the Qdrant index efficiently, you can follow these steps:

1. Specify a persist_dir for your StorageContext to ensure the index is saved and loaded from the same location.
2. Use the persist method after creating or updating the index to save it to disk.

Here's a refactored version of your code:

import logging
from typing import List, Any
from llama_index.core import VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core.schema import BaseNode
from llama_index.core import Settings
from qdrant_client import QdrantClient, AsyncQdrantClient

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

def create_load_index(
    nodes: List[BaseNode],
    embed_model: Any,
    collection_name: str = "PEDro_qdrant_test",
    host: str = "localhost",
    port: int = 6333,
    persist_dir: str = "path/to/persist_dir"  # Specify your persist directory
) -> VectorStoreIndex:

    logger.info("Initializing Qdrant client and index setup.")
    client = QdrantClient(host=host, port=port)
    aclient = AsyncQdrantClient(host=host, port=port)

    vector_store = QdrantVectorStore(
        collection_name=collection_name,
        client=client,
        aclient=aclient,
        enable_hybrid=True
    )
    storage_context = StorageContext.from_defaults(
        vector_store=vector_store,
        persist_dir=persist_dir  # Use the persist directory
    )
    Settings.chunk_size = 512

    try:
        # Attempt to load from storage
        logger.info("Attempting to load index from storage.")
        index = load_index_from_storage(
            storage_context, 
            embed_model=embed_model
        )
        logger.info("Index successfully loaded from existing storage.")
        return index
    
    except Exception as e:
        logger.warning(f"Could not load existing index. Error: {e}")
        
        # Check if collection exists and create if needed
        if not client.collection_exists(collection_name):
            logger.info("Collection does not exist. Creating new index.")
        else:
            logger.info("Collection exists but index load failed. Recreating index.")
        
        index = VectorStoreIndex( 
            nodes=nodes, 
            storage_context=storage_context, 
            embed_model=embed_model, 
            show_progress=True 
        )
        logger.info("New index created and stored.")
        storage_context.persist()  # Persist the index
        logger.info("Index persisted.")
        return index

Make sure to replace "path/to/persist_dir" with the actual path where you want to store your index data. This setup will help you persist and load the index efficiently, avoiding the need to recreate embeddings from scratch each time.

To continue talking to Dosu, mention @dosu.

---

^{Help Dosu learn! Give it feedback: Great Response | Irrelevant Answer | Incorrect Sources | Too Verbose | Hallucination | Bug Report | Other}