[Bug]: Metadata length (236) is close to chunk size (256). Resulting chunks are less than 50 tokens. Consider increasing the chunk size or decreasing the size of your metadata to avoid this.

[sarathsurpur]

Bug Description

When ingesting the text chunks in to locally hosted qdrant db it throws this warnings message, and number of text chunks obtained increased exponentially. And its taking forever to ingest the data in to vector db.

Version

0.9.19

Steps to Reproduce

create text chunks with 256 token size of and ingesting this data in to vector db throws this message

Relevant Logs/Tracbacks

No response

[dosubot]

To resolve the issue of receiving a warning about metadata length being close to chunk size when ingesting text chunks into a Qdrant database, you should consider reducing the size of the metadata or increasing the chunk size. This can help prevent the exponential increase in the number of text chunks and excessive ingestion time.

Here is a potential approach to address this issue:

1. Reduce Metadata Size: Ensure that the metadata associated with each text chunk is as minimal as possible. Only include essential information in the metadata.

2. Increase Chunk Size: If the text chunks are too small, consider increasing the chunk size before ingestion.

Here is an example of how you might adjust the chunk size before ingestion:

def split_text_into_chunks(text, chunk_size):
    """Split text into larger chunks of specified size."""
    return [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]

# Example usage
text = "Your long text here..."
chunk_size = 1000  # Increase the chunk size as needed
text_chunks = split_text_into_chunks(text, chunk_size)

# Ingest each chunk into Qdrant
for chunk in text_chunks:
    document = Document(
        id_=generate_unique_id(),  # Ensure each document has a unique ID
        text=chunk,
        metadata={"your": "metadata"}  # Include only essential metadata
    )
    # Ingest document into Qdrant
    qdrant_client.upload_documents(collection_name="your_collection", documents=[document])

Additionally, you can leverage the QdrantVectorStore class from the LlamaIndex integration to manage the ingestion process more efficiently. This class provides methods to handle batch uploads, parallel processing, and retries, which can help improve ingestion performance.

Here is an example of how to use the QdrantVectorStore class:

from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client

client = qdrant_client.QdrantClient()

vector_store = QdrantVectorStore(
    collection_name="your_collection",
    client=client,
    batch_size=64,  # Adjust batch size as needed
    parallel=1,  # Adjust parallel processes as needed
    max_retries=3  # Adjust max retries as needed
)

# Example usage
text = "Your long text here..."
chunk_size = 1000  # Increase the chunk size as needed
text_chunks = split_text_into_chunks(text, chunk_size)

nodes = [TextNode(id_=generate_unique_id(), text=chunk, metadata={"your": "metadata"}) for chunk in text_chunks]

# Add nodes to the vector store
vector_store.add(nodes)

Moreover, you can use the TokenTextSplitter class from LlamaIndex to handle the chunking process more effectively, especially when dealing with metadata. This class ensures that the metadata length is taken into account when splitting the text into chunks.

Here is an example of how to use the TokenTextSplitter class:

from llama_index.core.node_parser.text.token import TokenTextSplitter

# Initialize the TokenTextSplitter with appropriate chunk size and overlap
splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=100)

# Example usage
text = "Your long text here..."
metadata_str = "Your metadata here..."
text_chunks = splitter.split_text_metadata_aware(text, metadata_str)

# Ingest each chunk into Qdrant
for chunk in text_chunks:
    document = Document(
        id_=generate_unique_id(),  # Ensure each document has a unique ID
        text=chunk,
        metadata={"your": "metadata"}  # Include only essential metadata
    )
    # Ingest document into Qdrant
    qdrant_client.upload_documents(collection_name="your_collection", documents=[document])

By reducing the size of each text chunk, minimizing the metadata, and using the QdrantVectorStore and TokenTextSplitter classes for efficient ingestion, you can mitigate the warning and improve the ingestion performance ^[1][2][3].

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[logan-markewich]

either decrease the amount of text you are putting into metadata,

document.metadata = {} (or some smaller subset)

or set the metadata excludes on your input nodes/documents.

document.excluded_llm_metadata_keys = ["key1", ...]
document.excluded_embed_metadata_keys = ["key1", ...]

Its taking forever beasuse its probably creating a ton of nodes 😓