SQLite - Unstructured

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Connect SQLite to your preprocessing pipeline, and use the Unstructured Ingest CLI or the Unstructured Ingest Python library to batch process all your documents and store structured outputs locally on your filesystem. The requirements are as follows.

A SQLite instance. Download and install SQLite.
A SQLite database. Create a database.
The path to the database’s .db file.
A table in the database. Create a table. The table’s schema must match the schema of the documents that Unstructured produces. Unstructured cannot provide a schema that is guaranteed to work in all circumstances. This is because these schemas will vary based on your source files’ types; how you want Unstructured to partition, chunk, and generate embeddings; any custom post-processing code that you run; and other factors. You can adapt the following table schema example for your own needs:
SQLite
CREATE TABLE elements ( id TEXT PRIMARY KEY, record_id TEXT, element_id TEXT, text TEXT, embeddings TEXT, parent_id TEXT, page_number INTEGER, is_continuation INTEGER, orig_elements TEXT, partitioner_type TEXT );
See also:
- CREATE TABLE for SQLite
- Unstructured document elements and metadata

You might also need to install additional dependencies, depending on your needs. Learn more. The following environment variables:

SQLITE_DB_PATH - The path to the database’s .db file, represented by --database (CLI) or database (Python).

Now call the Unstructured Ingest CLI or the Unstructured Ingest Python library. The destination connector can be any of the ones supported. This example uses the local destination connector. This example sends data to Unstructured for processing by default. To process data locally instead, see the instructions at the end of this page.

#!/usr/bin/env bash

# Chunking and embedding are optional.

unstructured-ingest \
  sqlite \
    --database-path $SQLITE_DB_PATH \
    --table-name elements \
    --id-column id \
    --download-dir $LOCAL_FILE_DOWNLOAD_DIR \
    --chunking-strategy by_title \
    --embedding-provider huggingface \
    --output-dir $LOCAL_FILE_OUTPUT_DIR \
    --partition-by-api \
    --api-key $UNSTRUCTURED_API_KEY \
    --partition-endpoint $UNSTRUCTURED_API_URL

import os

from unstructured_ingest.pipeline.pipeline import Pipeline
from unstructured_ingest.interfaces import ProcessorConfig

from unstructured_ingest.processes.connectors.sql.sqlite import (
    SQLiteIndexerConfig,
    SQLiteDownloaderConfig,
    SQLiteConnectionConfig,
    SQLiteAccessConfig
)

from unstructured_ingest.processes.partitioner import PartitionerConfig
from unstructured_ingest.processes.chunker import ChunkerConfig
from unstructured_ingest.processes.embedder import EmbedderConfig

from unstructured_ingest.processes.connectors.local import (
    LocalConnectionConfig,
    LocalUploaderConfig
)

# Chunking and embedding are optional.

if __name__ == "__main__":
    Pipeline.from_configs(
        context=ProcessorConfig(),
        indexer_config=SQLiteIndexerConfig(
            table_name="elements",
            id_column="id"
        ),
        downloader_config=SQLiteDownloaderConfig(download_dir=os.getenv("LOCAL_FILE_DOWNLOAD_DIR")),
        source_connection_config=SQLiteConnectionConfig(
            access_config=SQLiteAccessConfig(),
            database_path=os.getenv("SQLITE_DB_PATH")
        ),
        partitioner_config=PartitionerConfig(
            partition_by_api=True,
            api_key=os.getenv("UNSTRUCTURED_API_KEY"),
            partition_endpoint=os.getenv("UNSTRUCTURED_API_URL"),
            additional_partition_args={
                "split_pdf_page": True,
                "split_pdf_allow_failed": True,
                "split_pdf_concurrency_level": 15
            }
        ),
        chunker_config=ChunkerConfig(chunking_strategy="by_title"),
        embedder_config=EmbedderConfig(embedding_provider="huggingface"),
        destination_connection_config=LocalConnectionConfig(),
        uploader_config=LocalUploaderConfig(output_dir=os.getenv("LOCAL_FILE_OUTPUT_DIR"))
    ).run()

For the Unstructured Ingest CLI and the Unstructured Ingest Python library, you can use the --partition-by-api option (CLI) or partition_by_api (Python) parameter to specify where files are processed:

To do local file processing, omit --partition-by-api (CLI) or partition_by_api (Python), or explicitly specify partition_by_api=False (Python). Local file processing does not use an Unstructured API key or API URL, so you can also omit the following, if they appear:
- --api-key $UNSTRUCTURED_API_KEY (CLI) or api_key=os.getenv("UNSTRUCTURED_API_KEY") (Python)
- --partition-endpoint $UNSTRUCTURED_API_URL (CLI) or partition_endpoint=os.getenv("UNSTRUCTURED_API_URL") (Python)
- The environment variables UNSTRUCTURED_API_KEY and UNSTRUCTURED_API_URL
To send files to the Unstructured Partition Endpoint for processing, specify --partition-by-api (CLI) or partition_by_api=True (Python). Unstructured also requires an Unstructured API key and API URL, by adding the following:
- --api-key $UNSTRUCTURED_API_KEY (CLI) or api_key=os.getenv("UNSTRUCTURED_API_KEY") (Python)
- --partition-endpoint $UNSTRUCTURED_API_URL (CLI) or partition_endpoint=os.getenv("UNSTRUCTURED_API_URL") (Python)
- The environment variables UNSTRUCTURED_API_KEY and UNSTRUCTURED_API_URL, representing your API key and API URL, respectively.
You must specify the API URL only if you are not using the default API URL for Unstructured Ingest, for example, if you are using a version of the Unstructured API that is hosted on your own compute infrastructure.The default API URL for Unstructured Ingest is https://api.unstructuredapp.io/general/v0/general, which is the API URL for the Unstructured Partition Endpoint.If you do not have an API key, get one now.If the Unstructured API is hosted on your own compute infrastructure, the process for generating Unstructured API keys, and the Unstructured API URL that you use, are different. For details, contact Unstructured Sales at sales@unstructured.io.