Unstructured

Unstructured is a library designed to help preprocess, structure unstructured text documents for downstream machine learning tasks.

Qdrant can be used as an ingestion destination in Unstructured.

Setup

Install Unstructured with the qdrant extra.

pip install "unstructured[qdrant]"

Usage

Depending on the use case you can prefer the command line or using it within your application.

CLI

EMBEDDING_PROVIDER=${EMBEDDING_PROVIDER:-"langchain-huggingface"}

unstructured-ingest \
  local \
  --input-path example-docs/book-war-and-peace-1225p.txt \
  --output-dir local-output-to-qdrant \
  --strategy fast \
  --chunk-elements \
  --embedding-provider "$EMBEDDING_PROVIDER" \
  --num-processes 2 \
  --verbose \
  qdrant \
  --collection-name "test" \
  --location "http://localhost:6333" \
  --batch-size 80

For a full list of the options the CLI accepts, run unstructured-ingest <upstream connector> qdrant --help

Programmatic usage

from unstructured.ingest.connector.local import SimpleLocalConfig
from unstructured.ingest.connector.qdrant import (
    QdrantWriteConfig,
    SimpleQdrantConfig,
)
from unstructured.ingest.interfaces import (
    ChunkingConfig,
    EmbeddingConfig,
    PartitionConfig,
    ProcessorConfig,
    ReadConfig,
)
from unstructured.ingest.runner import LocalRunner
from unstructured.ingest.runner.writers.base_writer import Writer
from unstructured.ingest.runner.writers.qdrant import QdrantWriter

def get_writer() -> Writer:
    return QdrantWriter(
        connector_config=SimpleQdrantConfig(
            location="http://localhost:6333",
            collection_name="test",
        ),
        write_config=QdrantWriteConfig(batch_size=80),
    )

if __name__ == "__main__":
    writer = get_writer()
    runner = LocalRunner(
        processor_config=ProcessorConfig(
            verbose=True,
            output_dir="local-output-to-qdrant",
            num_processes=2,
        ),
        connector_config=SimpleLocalConfig(
            input_path="example-docs/book-war-and-peace-1225p.txt",
        ),
        read_config=ReadConfig(),
        partition_config=PartitionConfig(),
        chunking_config=ChunkingConfig(chunk_elements=True),
        embedding_config=EmbeddingConfig(provider="langchain-huggingface"),
        writer=writer,
        writer_kwargs={},
    )
    runner.run()

Next steps