KDB.AI

Batch process all your records to store structured outputs in KDB.AI.

The requirements are as follows.

• A KDB.AI Cloud or server instance. Sign Up for KDB.AI Cloud: Starter Edition. Set up KDB.AI Server.

• The instance’s endpoint URL. Get the KDB.AI Cloud endpoint URL. Get the KDB.AI Server endpoint URL.

• An API key. Create the API key.

• The name of the target table to access. Create the table.

  KDB.AI requires the target table to have a defined schema before Unstructured can write to the table. The recommended table schema for Unstructured contains the fields id, element_id, document, metadata, and embeddings, as follows. This example code demonstrates the use of the KDB.AI Client for Python to create a table with this recommended schema, along with creating a vector index that contains 3072 dimensions:

  Python

  import kdbai_client as kdbai
  import os
  
  session = kdbai.Session(
      endpoint=os.getenv("KDBAI_ENDPOINT"),
      api_key=os.getenv("KDBAI_API_KEY")
  )
  
  db = session.database("default")
  
  schema = [
      {
          "name": "id",
          "type": "str"
      },
      {
          "name": "element_id",
          "type": "str"
      },
      {
          "name": "document",
          "type": "str"
      },
      {
          "name": "metadata", 
          "type": "general"
      },
      {
          "name": "embeddings",
          "type": "float32s"
      }
  ]
  
  indexes = [ 
      {
          "name": "vectorIndex",
          "type": "flat", 
          "params": {
              "dims": 3072,
              "metric": "L2"
          },
          "column": "embeddings"
      }
  ]
  
  table = db.create_table(
      table=os.getenv("KDBAI_TABLE"),
      schema=schema,
      indexes=indexes
  )
  
  print(f"The table named '{table.name}' now exists.")
  

The KDB.AI connector dependencies:

pip install "unstructured-ingest[kdbai]"

You might also need to install additional dependencies, depending on your needs. Learn more.

The following environment variables:

• KDBAI_ENDPOINT - The KDB.AI instance’s endpoint URL, represented by --endpoint (CLI) or endpoint (Python).
• KDBAI_API_KEY - The KDB.AI API key, represented by --api-key (CLI) or api_key (Python).
• KDBAI_TABLE - The name of the target table, represented by --table-name (CLI) or table_name (Python).

Now call the Unstructured CLI or Python. The source connector can be any of the ones supported. This example uses the local source connector:

This example sends files to Unstructured API services for processing by default. To process files locally instead, see the instructions at the end of this page.

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 Unstructured API services for processing, specify --partition-by-api (CLI) or partition_by_api=True (Python).

  Unstructured API services 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.

  Get an API key and API URL.