> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.
# IBM watsonx.data
The IBM watsonx.data destination connector relies on an Apache Iceberg-based catalog within the watsonx.data data store instance.
Apache Iceberg is suitable for managed data storage and cataloging, but not for embedding storage or semantic similarity
queries. For embedding storage and semantic similarity queries, Unstructured recommends that you use the following destination connectors
instead:
* [Astra DB](/open-source/ingestion/destination-connectors/astradb)
* [Milvus](/open-source/ingestion/destination-connectors/milvus) on IBM watsonx.data
Batch process all your records to store structured outputs in IBM watsonx.data.
The requirements are as follows.
* An [IBM Cloud account](https://cloud.ibm.com/login). [Create an IBM Cloud account](https://cloud.ibm.com/registration) if you do not already have one.
* An API key for the IBM Cloud account. If you do not have one already, create one as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. In the top navigation bar, click **Manage** and then, under **Security and access**, click **Access (IAM)**.
3. On the sidebar, under **Manage identities**, click **API keys**.
4. With the **View** list showing **My IBM Cloud API keys**, click **Create**.
5. Enter some **Name** and an optional **Description** for the API key.
6. Leave **Leaked action** set to **Disable the leaked key** and **Session management** set to **No**.
7. Click **Create**.
8. Click **Copy** or **Download** to copy or save the API key to a secure location. You won't be able to access this API key from this screen again.
* An IBM Cloud Object Storage (COS) instance in the account, and a bucket within that instance. If you do not have them already,
create them as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. Click **Create resource**.
4. With **IBM Cloud catalog** selected, search for and select **Object Storage**.
5. Complete the on-screen instructions to finish creating the COS instance.
6. With the COS instance's settings page shown, on the **Buckets** tab, click **Create bucket**.
7. Complete the on-screen instructions to finish creating the bucket.
* The name, region, and public endpoint for the target bucket within the target Cloud Object Storage (COS) instance. To get these:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Storage**, and then click the target COS instance.
4. On the **Buckets** tab, click the target bucket.
5. On the **Configuration** tab, note the following:
* Under **Bucket details**, note the **Bucket name**. This is the bucket's name.
* Under **Bucket details** section, note the value inside of the parentheses inside **Location**, for example `us-east`. This is the bucket's region.
* Under **Endpoints**, note the value of **Public**, for example `s3.us-east.cloud-object-storage.appdomain.cloud`. (Ignore the values of
**Private** and **Direct**). This is the bucket's public endpoint.
* An HMAC access key ID and secret access key for the target Cloud Object Storage (COS) instance. If you do not have them already,
get or create them as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Storage**, and then click the target COS instance.
4. On the **Service credentials** tab, if there is a credential that you want to use in the list, expand the credential, and copy the following values to a secure location:
* `access_key_id` under `cos_hmac_keys`, which represents the HMAC access key ID.
* `secret_access_key` under `cos_hmac_keys`, which represents the HMAC secret access key.
After you have copied the preceding values, you have completed this procedure.
5. If there is not a credential that you want to use, or there are no credentials at all, click **New Credential**.
6. Enter some **Name** for the credential.
7. For **Role**, select at least **Writer**, leave **Select Service ID** set to **Auto Generated**,
switch on **Include HMAC Credential**, and then click **Add**.
8. In the list of credentials, expand the credential, and copy the following values to a secure location:
* `access_key_id` under `cos_hmac_keys`, which represents the HMAC access key ID.
* `secret_access_key` under `cos_hmac_keys`, which represents the HMAC secret access key.
* An IBM watsonx.data data store instance in the IBM Cloud account. If you do not have one already, create one as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. Click **Create resource**.
4. With **IBM Cloud catalog** selected, search for and select **watsonx.data**.
5. Complete the on-screen instructions to finish creating the watsonx.data data store instance.
* An Apache Iceberg-based catalog within the watsonx.data data store instance. If you do not have one already, create one as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Databases**, and then click the target watsonx.data data store instance.
4. Click **Open web console**.
5. If prompted, log in to the web console.
6. On the sidebar, click **Infrastructure manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the
top navigation bar.
7. Click **Add component**.
8. Under **Storage**, click **IBM Cloud Object Storage**, and then click **Next**.
9. Complete the on-screen instructions to finish creating the Iceberg catalog. This includes providing the following settings:
If you select **Discover COS instance**, you must provide the following settings:
* The name of the target COS instance.
* The name of the target storage object (such as the target bucket) within the COS instance.
* Some display name for the component.
* After you provide this information, do the following:
a. Check the box labelled **Associate Catalog**.
b. For **Catalog type**, select **Apache Iceberg**.
c. Enter some **Catalog name**.
d. Click **Associate**.
If you select **Register my own**, you must provide the following settings:
* Some display name for the component.
* The name of the target bucket within the target Cloud Object Storage (COS) instance that you noted earlier.
* The region for the target bucket, which you noted earlier.
* The public endpoint for the target bucket, which you noted earlier. For this screen only, be sure to prefix the public endpoint with `https://`.
* The HMAC access key ID for the target COS instance, which you noted earlier.
* The HMAC secret access key for the target COS instance, which you noted earlier.
* After you provide this information, do the following:
a. Check the box labelled **Associate Catalog**.
b. For **Catalog type**, select **Apache Iceberg**.
c. Enter some **Catalog name**.
d. Click **Associate**.
10. On the sidebar, click **Infrastructure manager**. Make sure the catalog is associated with the appropriate engines. If it is not, rest your mouse
on an unassociated target engine, click the **Manage associations** icon, check the box next to the target catalog's name, and then
click **Save and restart engine**.
To create an engine if one is not already shown, click **Add component**, and follow the on-screen to add an appropriate engine from the list of available **Engines**
(for example, an **IBM Presto** engine).
* The catalog name and metastore REST endpoint for the target Iceberg catalog. To get this:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Databases**, and then click the target watsonx.data data store instance.
4. Click **Open web console**.
5. If prompted, log in to the web console.
6. On the sidebar, click **Infrastructure manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the
top navigation bar.
7. In the **Catalogs** section, click the target Iceberg catalog.
8. On the **Details** tab, note the value of **Name** representing the catalog name, and **Metastore REST endpoint** representing the metastore REST endpoint. (Ignore the **Metastore Thrift endpoint** value.)
* A namespace (also known as a schema) and a table in the target catalog. If you do not have these already, create them as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Databases**, and then click the target watsonx.data data store instance.
4. Click **Open web console**.
5. If prompted, log in to the web console.
6. On the sidebar, click **Data manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the
top navigation bar.
7. On the **Browse data** tab, under **Catalogs associated**, click the target catalog.
8. Click the ellipses, and then click **Create schema**.
9. Enter some **Name** for the schema, and then click **Create**.
10. On the sidebar, click **Query workspace**.
11. In the SQL editor, enter and run a table creation statement such as the following one that uses
[Presto SQL](https://prestodb.io/docs/current/connector/iceberg.html) syntax, replacing `` with the name of the target
catalog and `` with the name of the target schema, with at minimum the fields `id`, `type`, `text`, `element_id`, and `record_id`:
```sql theme={null}
CREATE TABLE ..elements (
id varchar,
type varchar,
text varchar,
element_id varchar,
record_id varchar,
)
WITH (
delete_mode = 'copy-on-write',
format = 'PARQUET',
format_version = '2'
)
```
For objects in the `metadata` field that Unstructured produces and that you want to store in an Apache Iceberg table in IBM watsonx.data,
you must create fields in your table schema that follows Unstructured's `metadata` field naming convention. For example, if Unstructured
produces a `metadata` field with the following child objects:
```json theme={null}
"metadata": {
"is_extracted": "true",
"coordinates": {
"points": [
[
134.20055555555555,
241.36027777777795
],
[
134.20055555555555,
420.0269444444447
],
[
529.7005555555555,
420.0269444444447
],
[
529.7005555555555,
241.36027777777795
]
],
"system": "PixelSpace",
"layout_width": 1654,
"layout_height": 2339
},
"filetype": "application/pdf",
"languages": [
"eng"
],
"page_number": 1,
"image_base64": "aaa...bbb...111",
"image_mime_type": "image/jpeg",
"filename": "realestate.pdf",
"data_source": {
"url": "file:///home/etl/node/downloads/00000000-0000-0000-0000-000000000001/7458635f-realestate.pdf",
"record_locator": {
"protocol": "file",
"remote_file_path": "file:///home/etl/node/downloads/00000000-0000-0000-0000-000000000001/7458635f-realestate.pdf"
}
},
"entities": {
"items": [
{
"entity": "HOME FOR FUTURE",
"type": "ORGANIZATION"
},
{
"entity": "221 Queen Street, Melbourne VIC 3000",
"type": "LOCATION"
}
],
"relationships": [
{
"from": "HOME FOR FUTURE",
"relationship": "based_in",
"to": "221 Queen Street, Melbourne VIC 3000"
}
]
}
}
```
You could create corresponding fields in your table schema with the following field names and data types:
| **Field name** | **Apache Iceberg data type** |
| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| `is_extracted` | `varchar` |
| `points` | `varchar` |
| `system` | `varchar` |
| `layout_width` | `bigint` |
| `layout_height` | `bigint` |
| `filetype` | `varchar` |
| `languages` | `array(varchar)` |
| `page_number` | `varchar` |
| `image_base64` | `varchar` |
| `image_mime_type` | `varchar` |
| `filename` | `varchar` |
| `url` | `varchar` |
| `record_locator` | `varchar` |
| `entities` | `row(items array(row(entity varchar, type varchar)), relationships array(row("from" varchar, relationship varchar, "to" varchar)))` |
Incoming elements that do not have matching column
names will be dropped upon record insertion. For example, if the incoming data has an element named `sent_from` and there is no
column named `sent_from` in the table, the `sent_from` element will be dropped upon record insertion. You should modify the preceding
sample table creation statement to add columns for any additional elements that you want to be included upon record
insertion.
To increase query performance, Iceberg uses [hidden partitioning](https://iceberg.apache.org/docs/latest/partitioning/) to
group similar rows together when writing. You can also
[explicitly define partitions](https://prestodb.io/docs/current/connector/iceberg.html#create-table) as part of the
preceding `CREATE TABLE` statement.
* The name of the target namespace (also known as a schema) within the target catalog, and name of the target table within that schema. To get these:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Databases**, and then click the target watsonx.data data store instance.
4. Click **Open web console**.
5. If prompted, log in to the web console.
6. On the sidebar, click **Data manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the
top navigation bar.
7. On the **Browse data** tab, expand the name of the target catalog, and note the names of the target schema and target table.
* The name of the column in the target table that uniquely identifies each of the records in the table.
* To improve performance, the target table should be set to regularly remove old metadata files. To do this, run the following Python script.
(You cannot use the preceding `CREATE TABLE` statement, or other SQL statements such as `ALTER TABLE`, to set this behavior.) To get the
values for the specified environment variables, see the preceding instructions.
```python theme={null}
# Improves performance by setting the target table to regularly remove
# old metadata files.
#
# First, install the following dependencies into your Python virtual
# environment:
#
# pip install requests pyiceberg pyarrow
#
# Then, set the following environment variables:
#
# IBM_IAM_API_KEY - An API key value for the target IBM Cloud account.
# IBM_ICEBERG_CATALOG_METASTORE_REST_ENDPOINT - The metastore REST endpoint
# value for the target Apache Iceberg catalog in the target IBM watsonx.data
# data store instance.
# IBM_COS_BUCKET_PUBLIC_ENDPOINT - The target IBM Cloud Object Storage (COS)
# instance’s endpoint value.
# IBM_COS_ACCESS_KEY - An HMAC access key ID for the target COS instance.
# IBM_COS_SECRET_ACCESS_KEY - The associated HMAC secret access key ID for the
# target HMAC access key.
# IBM_COS_BUCKET_REGION - The target COS instance’s region short ID.
# IBM_ICEBERG_CATALOG - The name of the target Iceberg catalog.
# IBM_ICEBERG_SCHEMA - The name of the target namespace (also known as a schema)
# in the target catalog.
# IBM_ICEBERG_TABLE - The name of the target table in the target schema.
#
# To get these values, see the Unstructured documentation for the
# IBM watsonx.data connector.
import os
import requests
from pyiceberg.catalog import load_catalog
def main():
# Get a bearer token for the target IBM Cloud account.
bearer_token = requests.post(
url="https://iam.cloud.ibm.com/identity/token",
headers={
"Content-Type": "application/x-www-form-urlencoded",
"Accept": "application/json"
},
data={
"grant_type": "urn:ibm:params:oauth:grant-type:apikey",
"apikey": os.getenv("IBM_IAM_API_KEY")
}
).json().get("access_token")
# Connect to the target Iceberg catalog.
catalog = load_catalog(
os.getenv("IBM_ICEBERG_CATALOG"),
**{
"type": "rest",
"uri": f"https://{os.getenv("IBM_ICEBERG_CATALOG_METASTORE_REST_ENDPOINT")}/mds/iceberg",
"token": bearer_token,
"warehouse": os.getenv("IBM_ICEBERG_CATALOG"),
"s3.endpoint": os.getenv("IBM_COS_BUCKET_PUBLIC_ENDPOINT"),
"s3.access-key-id": os.getenv("IBM_COS_ACCESS_KEY"),
"s3.secret-access-key": os.getenv("IBM_COS_SECRET_ACCESS_KEY"),
"s3.region": os.getenv("IBM_COS_BUCKET_REGION")
},
)
# Load the target table.
table = catalog.load_table(f"{os.getenv("IBM_ICEBERG_SCHEMA")}.{os.getenv("IBM_ICEBERG_TABLE")}")
# Set the target table's properties to remove old metadata files.
with table.transaction() as transaction:
transaction.set_properties(
{
"commit.manifest.min-count-to-merge": 10,
"commit.manifest-merge.enabled": True,
"write.metadata.previous-versions-max": 10,
"write.metadata.delete-after-commit.enabled": True,
}
)
# Confirm that the target table's properties were set as expected.
print(table.metadata.properties)
if __name__ == "__main__":
main()
```
The IBM watsonx.data connector dependencies:
```bash CLI, Python theme={null}
pip install "unstructured-ingest[ibm-watsonx-s3]"
```
You might also need to install additional dependencies, depending on your needs. [Learn more](/open-source/ingestion/ingest-dependencies).
The following environment variables:
* `IBM_IAM_API_KEY` - An API key for the target IBM Cloud account, represented by `--iam-api-key` (CLI) or `iam_api_key` (Python).
* `IBM_COS_ACCESS_KEY` - An HMAC access key ID for the target IBM Cloud Object Storage (COS) instance, represented by `--access-key-id` (CLI) or `access_key_id` (Python).
* `IBM_COS_SECRET_ACCESS_KEY` - The associated HMAC secret access key ID for the target HMAC access key, represented by `--secret-access-key` (CLI) or `secret_access_key` (Python).
* `IBM_ICEBERG_CATALOG_METASTORE_REST_ENDPOINT` - The metastore REST endpoint value for the target Apache Iceberg catalog in the target IBM watsonx.data data store instance, represented by `--iceberg-endpoint` (CLI) or `iceberg_endpoint` (Python). Do not include `https://` in this value.
* `IBM_COS_BUCKET_PUBLIC_ENDPOINT` - The target COS instance's endpoint value, represented by `--object-storage-endpoint` (CLI) or `object_storage_endpoint` (Python).
* `IBM_COS_BUCKET_REGION` - The target COS instance's region short ID, represented by `--object-storage-region` (CLI) or `object_storage_region` (Python).
* `IBM_ICEBERG_CATALOG` - The name of the target Iceberg catalog, represented by `--catalog` (CLI) or `catalog` (Python).
* `IBM_ICEBERG_SCHEMA` - The name of the target namespace (also known as a schema) in the target catalog, represented by `--namespace` (CLI) or `namespace` (Python).
* `IBM_ICEBERG_TABLE` - The name of the target table in the target schema, represented by `--table` (CLI) or `table` (Python).
* `IBM_ICEBERG_TABLE_UNIQUE_RECORD_COLUMN` - The name of the column that uniquely identifies each record in the target table, represented by `--record-id-key` (CLI) or `record_id_key` (Python). The default is `record_id`.
Additionally:
* `--max-retries-connection` (CLI) or `max_retries_connection` (Python) is an optional parameter that specifies the maximum number of retries when connecting to the catalog. Typically, an optimal setting is `15`. The default is `10`. If specified, it must be a number between `2` and `100`, inclusive.
* `--max-retries` (CLI) or `max_retries` (Python) is an optional parameter that specifies the number of times to retry uploading data. Typically, an optimal setting is `150`. The default is `50`. If specified, it must be a number between `2` and `500`, inclusive.
Now call the Unstructured Ingest CLI or Unstructured Ingest Python. The source connector can be any of the ones supported. This example uses the local source connector.
This example sends files to Unstructured for processing by default. To process files locally instead, see the instructions at the end of this page.
```bash CLI theme={null}
#!/usr/bin/env bash
# Chunking and embedding are optional.
unstructured-ingest \
local \
--input-path $LOCAL_FILE_INPUT_DIR \
--chunk-elements \
--embedding-provider huggingface \
--partition-by-api \
--api-key $UNSTRUCTURED_API_KEY\
--partition-endpoint $UNSTRUCTURED_API_URL \
--strategy hi_res \
--additional-partition-args="{\"split_pdf_page\":\"true\", \"split_pdf_allow_failed\":\"true\", \"split_pdf_concurrency_level\": 15}" \
ibm-watsonx-s3 \
--iam-api-key $IBM_IAM_API_KEY \
--access-key-id $IBM_COS_ACCESS_KEY \
--secret-access-key $IBM_COS_SECRET_ACCESS_KEY \
--iceberg-endpoint $IBM_ICEBERG_CATALOG_METASTORE_REST_ENDPOINT \
--object-storage-endpoint $IBM_COS_BUCKET_PUBLIC_ENDPOINT \
--object-storage-region $IBM_COS_BUCKET_REGION \
--catalog $IBM_ICEBERG_CATALOG \
--namespace $IBM_ICEBERG_SCHEMA \
--table $IBM_ICEBERG_TABLE \
--max-retries 50 \
--max-retries-connection 10 \
--record-id-key $IBM_ICEBERG_TABLE_UNIQUE_RECORD_COLUMN
```
```python Python Ingest theme={null}
import os
from unstructured_ingest.pipeline.pipeline import Pipeline
from unstructured_ingest.interfaces import ProcessorConfig
from unstructured_ingest.processes.connectors.local import (
LocalIndexerConfig,
LocalDownloaderConfig,
LocalConnectionConfig
)
from unstructured_ingest.processes.connectors.ibm_watsonx.ibm_watsonx_s3 import (
IbmWatsonxConnectionConfig,
IbmWatsonxAccessConfig,
IbmWatsonxUploadStagerConfig,
IbmWatsonxUploaderConfig
)
from unstructured_ingest.processes.partitioner import PartitionerConfig
from unstructured_ingest.processes.chunker import ChunkerConfig
from unstructured_ingest.processes.embedder import EmbedderConfig
# Chunking and embedding are optional.
if __name__ == "__main__":
Pipeline.from_configs(
context=ProcessorConfig(reprocess=True, clear_cache=True),
indexer_config=LocalIndexerConfig(input_path=os.getenv("LOCAL_FILE_INPUT_DIR")),
downloader_config=LocalDownloaderConfig(),
source_connection_config=LocalConnectionConfig(),
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=IbmWatsonxConnectionConfig(
access_config=IbmWatsonxAccessConfig(
iam_api_key=os.getenv("IBM_IAM_API_KEY"),
access_key_id=os.getenv("IBM_COS_ACCESS_KEY"),
secret_access_key=os.getenv("IBM_COS_SECRET_ACCESS_KEY")
),
iceberg_endpoint=os.getenv("IBM_ICEBERG_CATALOG_METASTORE_REST_ENDPOINT"),
object_storage_endpoint=os.getenv("IBM_COS_BUCKET_PUBLIC_ENDPOINT"),
object_storage_region=os.getenv("IBM_COS_BUCKET_REGION"),
catalog=os.getenv("IBM_ICEBERG_CATALOG")
),
stager_config=IbmWatsonxUploadStagerConfig(),
uploader_config=IbmWatsonxUploaderConfig(
namespace=os.getenv("IBM_ICEBERG_SCHEMA"),
table=os.getenv("IBM_ICEBERG_TABLE"),
max_retries=50,
max_retries_connection=10,
record_id_key=os.getenv("IBM_ICEBERG_TABLE_UNIQUE_RECORD_COLUMN")
)
).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 legacy [Unstructured Partition Endpoint](/api-reference/legacy-api/partition/overview) 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, which applies to **Let's Go**, **Pay-As-You-Go**, and **Business SaaS** accounts.
The default API URL for Unstructured Ingest is `https://api.unstructuredapp.io/general/v0/general`, which is the API URL for the legacy[Unstructured Partition Endpoint](/api-reference/legacy-api/partition/overview). However, you should always use the URL that was provided to you when your Unstructured account was created. If you do not have this URL, email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
If you do not have an API key, [get one now](/api-reference/legacy-api/partition/overview).
If you are using a **Business** account, the process
for generating Unstructured API keys, and the Unstructured API URL that you use, are different.
For instructions, see your Unstructured account administrator, or email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).