> ## Documentation Index
> Fetch the complete documentation index at: https://unstructured-53-kapa-ai.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Astra

> Batch process all your records using `unstructured-ingest` to store structured outputs and embeddings locally on your filesystem and upload those to a Astra DB index.

First you’ll need to install the Astra dependencies as shown here.

```bash
pip install "unstructured[astra]"

```

## Run Locally

The upstream connector can be any of the ones supported, but for convenience here, showing a sample command using the upstream local connector.

<CodeGroup>
  ```bash Shell
  #!/usr/bin/env bash

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

  unstructured-ingest \
    local \
    --input-path example-docs/book-war-and-peace-1p.txt \
    --output-dir local-output-to-astra \
    --strategy fast \
    --chunk-elements \
    --embedding-provider "$EMBEDDING_PROVIDER" \
    --num-processes 2 \
    --verbose \
    astra \
    --token "$ASTRA_DB_TOKEN" \
    --api-endpoint "$ASTRA_DB_ENDPOINT" \
    --collection-name "$COLLECTION_NAME" \
    --embedding-dimension "$EMBEDDING_DIMENSION"
  ```

  ```python Python
  import os

  from unstructured.ingest.connector.astra import (
      AstraAccessConfig,
      AstraWriteConfig,
      SimpleAstraConfig,
  )
  from unstructured.ingest.connector.local import SimpleLocalConfig
  from unstructured.ingest.interfaces import (
      ChunkingConfig,
      EmbeddingConfig,
      PartitionConfig,
      ProcessorConfig,
      ReadConfig,
  )
  from unstructured.ingest.runner import LocalRunner
  from unstructured.ingest.runner.writers.astra import (
      AstraWriter,
  )
  from unstructured.ingest.runner.writers.base_writer import Writer


  def get_writer() -> Writer:
      return AstraWriter(
          connector_config=SimpleAstraConfig(
              access_config=AstraAccessConfig(
                  token=os.getenv("ASTRA_DB_TOKEN"), api_endpoint=os.getenv("ASTRA_DB_ENDPOINT")
              ),
              collection_name="test_collection",
              embedding_dimension=384,
          ),
          write_config=AstraWriteConfig(batch_size=80),
      )


  if __name__ == "__main__":
      writer = get_writer()
      runner = LocalRunner(
          processor_config=ProcessorConfig(
              verbose=True,
              output_dir="local-output-to-astra",
              num_processes=2,
          ),
          connector_config=SimpleLocalConfig(
              input_path="example-docs/book-war-and-peace-1p.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()
  ```
</CodeGroup>

For a full list of the options the CLI accepts check `unstructured-ingest <upstream connector> astra --help`.

NOTE: Keep in mind that you will need to have all the appropriate extras and dependencies for the file types of the documents contained in your data storage platform if you’re running this locally. You can find more information about this in the [installation guide](/open-source/installation/overview).