> ## 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.
# Milvus
Batch process all your records to store structured outputs in Milvus.
The requirements are as follows.
* For the [Unstructured UI](/ui/overview) or the [Unstructured API](/api-reference/overview), only Milvus cloud-based instances (such as Milvus on IBM watsonx.data, or Zilliz Cloud) are supported.
* For [Unstructured Ingest](/open-source/ingestion/overview), Milvus local and cloud-based instances are supported.
* For Milvus on IBM watsonx.data, you will need:
* An [IBM Cloud account](https://cloud.ibm.com/registration).
* An IBM watsonx.data [Lite plan](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-tutorial_prov_lite_1)
or [Enterprise plan](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-getting-started_1) within your IBM Cloud account.
* If you are provisioning a Lite plan, be sure to choose the **Generative AI** use case when prompted, as this is the only use case offered that includes Milvus.
* A [Milvus service instance in IBM watsonx.data](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-adding-milvus-service).
* If you are creating a Milvus service instance within a watsonx.data Lite plan, when you are prompted to choose a Milvus instance size, you can only select **Lite**. Because the Lite
Milvus instance size is recommended only for 384 dimensions, you should also use an embedding model that uses 384 dimensions only.
* If you are creating a Milvus service instance within a watsonx.data Enterprise plan, you can choose any available Milvus instance size. However, all Milvus instance sizes other than
**Custom** are recommended only for 384 dimensions, which means you should use an embedding model that uses 384 dimensions only.
The **Custom** Milvus instance size is recommended for any number of dimensions.
* The URI of the instance, which takes the format of `https://`, followed by the instance's **GRPC host**, followed by a colon and the **GRPC port**.
This takes the format of `https://:`. To get this information, do the following:
a. Sign in to your IBM Cloud account.
b. 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 title bar.
c. Expand **Databases**, and then click the name of the target **watsonx.data** plan.
d. Click **Open web console**.
e. On the sidebar, click **Infrastructure manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the title bar.
f. Click the target Milvus service instance.
g. On the **Details** tab, under **Type**, click **View connect details**.
h. Under **Service details**, expand **GRPC**, and note the value of **GRPC host** and **GRPC port**.
* The name of the [database](https://milvus.io/docs/manage_databases.md) in the instance.
* The name of the [collection](https://milvus.io/docs/manage-collections.md) in the database. Note the collection requirements at the end of this section.
* The username and password to access the instance.
* The username for Milvus on IBM watsonx.data is typically `ibmlhapikey`.
More recent versions of Milvus on IBM watsonx.data require `ibmlhapikey_` instead, where `` is
your IBMid, for example `me@example.com`. To get your IBMid, do the following:
1. Sign in to your IBM Cloud account.
2. In the title bar, click **Manage** and then, under **Security and access**, click **Access (IAM)**.
3. In the sidebar, expand **Manage identities**, and then click **Users**.
4. In the list of users, click your user name.
5. On the **User details** tab, in the **Details** tile, note the value of **IBMid**.
* The password for Milvus on IBM watsonx.data is in the form of an IBM Cloud user API key. To create an IBM Cloud user API key:
a. Sign in to your IBM Cloud account.
b. In the title bar, click **Manage** and then, under **Security and access**, click **Access (IAM)**.
c. On the sidebar, under **Manage identities**, click **API keys**. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the title bar.
d. Click **Create**.
e. Enter some **Name** for the API key.
f. Optionally, enter some **Description** for the API key.
g. For **Leaked action**, leave **Disable the leaked key** selected.
h. For **Session management**, leave **No** selected.
i. Click **Create**.
j. Click **Download** (or **Copy**), and then download the API key to a secure location (or paste the copied API key into a secure location). You won't be able to access this API key from this dialog again. If you lose this API key, you can create a new one (and you should then delete the old one).
* For Zilliz Cloud, you will need:
* A [Zilliz Cloud account](https://cloud.zilliz.com/signup).
* A [Zilliz Cloud cluster](https://docs.zilliz.com/docs/create-cluster).
* The URI of the cluster, also known as the cluster's *public endpoint*, which takes a format such as
`https://..-.cloud.zilliz.com`. To get this public endpoint value, do the following:
1. After you sign in to your Zilliz Cloud account, on the sidebar, in the list of available projects, select the project that contains the cluster.
2. On the sidebar, click **Clusters**.
3. Click the tile for the cluster.
4. On the **Cluster Details** tab, on the **Connect** subtab, copy the **Public Endpoint** value.
* The username and password to access the cluster, as follows:
1. After you sign in to your Zilliz Cloud account, on the sidebar, in the list of available projects, select the project that contains the cluster.
2. On the sidebar, click **Clusters**.
3. Click the tile for the cluster.
4. On the **Users** tab, copy the name of the user.
5. Next to the user's name, under **Actions**, click the ellipsis (three dots) icon, and then click **Reset Password**.
6. Enter a new password for the user, and then click **Confirm**. Copy this new password.
* The name of the [database](https://docs.zilliz.com/docs/database#create-database) in the instance.
* The name of the [collection](https://docs.zilliz.com/docs/manage-collections-console#create-collection) in the database.
The collection must have a defined schema before Unstructured can write to the collection. The minimum viable
schema for Unstructured contains only the fields `element_id`, `embeddings`, `record_id`, and `text`, as follows.
`type` is an optional field, but highly recommended. For settings for additional Unstructured-produced fields,
such as the ones within `metadata`, see the usage notes toward the end of this section and adapt them to your specific needs.
| Field Name | Field Type | Max Length | Dimension |
| -------------------------------- | ----------------- | ---------- | --------- |
| `element_id` (primary key field) | **VARCHAR** | `200` | -- |
| `embeddings` (vector field) | **FLOAT\_VECTOR** | -- | `384` |
| `record_id` | **VARCHAR** | `200` | -- |
| `text` | **VARCHAR** | `65536` | -- |
| `type` | **VARCHAR** | `200` | -- |
In the **Create Index** area for the collection, next to **Vector Fields**, click **Edit Index**. Make sure that for the
`embeddings` field, the **Field Type** is set to **FLOAT\_VECTOR** and the **Metric Type** is set to **Cosine**.
The number of dimensions for the `embeddings` field must match the number of dimensions for the embedding model that you plan to use.
Fields with a **VARCHAR** data type are limited to a maximum length of 65,535 characters. Attempting to exceed this character count
will cause Unstructured to throw errors when attempting to write to a Milvus collection, and the associated Unstructured job could fail.
For example, `metadata` fields that typically exceed these character counts include `image_base64` and `orig_elements`.
The `record_id`, `element_id`, and `id` fields are closely related, but each has a distinct purpose.
The `record_id` identifies the source document.
* For S3 and Azure Blob connectors, the record ID is a [Version 5 UUID](https://docs.python.org/3/library/uuid.html#uuid.uuid5) generated from the namespace and file path of the document. This ensures that the ID is deterministic and unique at the file level.
* For all other blob connectors, the record ID is a Version 4 UUID representing a random 32-character hexadecimal string.
* For SQL connectors, the record ID is generated from the table name and record ID of the database table.
The `element_id` identifies the specific element. Source connectors generate element ID in one of the following ways, depending on the source connector:
* A SHA-256 hash of the element's text, its position on the page, the page number it's on,
and the name of the related file. This is to ensure that the ID is deterministic and unique at the file level.
* A [Version 4 UUID](https://docs.python.org/3/library/uuid.html#uuid.uuid4) generated using random numbers.
Older connectors generate SHA-256 hashes for element IDs, while more modern connectors generate UUIDs. Going forward, older connectors will be converted to using UUIDs as well.
Each element from the same document contains that document's record ID; this enables Unstructured to identify all the elements generated from a given document.
If a source connector has been set to not reprocess all documents each time a workflow runs, Unstructured uses the record ID (along with the record version) to determine which documents are unchanged and should not be processed again.
The `id` represents the actual row that Unstructured writes into the destination location. The ID is a [Version 5 UUID](https://docs.python.org/3/library/uuid.html#uuid.uuid5) generated from the namespace, element ID and record ID of the source document. The ID is deterministic and unique at the row level.
Destination connectors process document updates in one of the following ways, depending on the connector:
* Use the record ID to identify and delete all elements from a given document, prior to writing new elements from that document into the destination.
* Use the ID to perform upsert operations without generating duplicate rows, ensuring that reprocessing documents is idempotent.
```mermaid theme={null}
flowchart LR
subgraph Source
subgraph Document
RID[Record ID]
end
end
subgraph el1[Element]
EID1[Element ID]
end
subgraph el2[Element]
EID2[Element ID]
end
subgraph el3[Element]
EID3[Element ID]
end
subgraph Destination
subgraph row1[Row]
ID1[ID]
end
subgraph row2[Row]
ID2[ID]
end
subgraph row3[Row]
ID3[ID]
end
end
RID --> el1
RID --> el2
RID --> el3
el1 --> row1
el2 --> row2
el3 --> row3
```
* For Milvus local, you will need:
* A [Milvus instance](https://milvus.io/docs/install-overview.md).
* The [URI](https://milvus.io/api-reference/pymilvus/v2.4.x/MilvusClient/Client/MilvusClient.md) of the instance.
* The name of the [database](https://milvus.io/docs/manage_databases.md) in the instance.
* The name of the [collection](https://milvus.io/docs/manage-collections.md) in the database.
Note the collection requirements at the end of this section.
* The [username and password, or token](https://milvus.io/docs/authenticate.md) to access the instance.
All Milvus instances require the target collection to have a defined schema before Unstructured can write to the collection. The minimum viable
schema for Unstructured contains only the fields `element_id`, `embeddings`, `record_id`, and `text`, as follows.
`type` is an optional field, but highly recommended.
This example code demonstrates the use of the
[Python SDK for Milvus](https://pypi.org/project/pymilvus/) to create a collection with this schema,
targeting Milvus on IBM watsonx.data. For the `MilvusClient` arguments to connect to other types of Milvus deployments, see your Milvus provider's documentation:
```python Python theme={null}
import os
from pymilvus import (
MilvusClient,
FieldSchema,
DataType,
CollectionSchema
)
DATABASE_NAME = "default"
COLLECTION_NAME = "my_collection"
client = MilvusClient(
uri="https://" +
os.getenv("MILVUS_USER") +
":" +
os.getenv("MILVUS_PASSWORD") +
"@" +
os.getenv("MILVUS_GRPC_HOST") +
":" +
os.getenv("MILVUS_GRPC_PORT"),
db_name=DATABASE_NAME
)
# IMPORTANT: The number of dimensions for the "embeddings" field that
# follows must match the number of dimensions for the embedding model
# that you plan to use.
fields = [
FieldSchema(name="element_id", dtype=DataType.VARCHAR, is_primary=True, max_length=200),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=384),
FieldSchema(name="record_id", dtype=DataType.VARCHAR, max_length=200),
FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=65535),
FieldSchema(name="type", dtype=DataType.VARCHAR,max_length=200, nullable=True) # Optional, but highly recommended.
]
schema = CollectionSchema(fields=fields)
client.create_collection(
collection_name=COLLECTION_NAME,
schema=schema,
using=DATABASE_NAME
)
index_params = client.prepare_index_params()
index_params.add_index(
field_name="embeddings",
metric_type="COSINE",
index_type="IVF_FLAT",
params={"nlist": 1024}
)
client.create_index(
collection_name=COLLECTION_NAME,
index_params=index_params
)
client.load_collection(collection_name=COLLECTION_NAME)
```
For objects in the `metadata` field that Unstructured produces and that you want to store in Milvus, you must create fields in your Milvus collection 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_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 Milvus collection schema by using the following field names, data types, and related settings for the
Python SDK for Milvus:
```python theme={null}
# ...
fields = [
# Define minimum viable required fields: "element_id", "embeddings", "record_id", and "text".
# "type" is an optional field, but highly recommended.
FieldSchema(name="element_id", dtype=DataType.VARCHAR, is_primary=True, max_length=200),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=1536),
FieldSchema(name="record_id", dtype=DataType.VARCHAR, max_length=200),
FieldSchema(name="text", dtype=DataType.VARCHAR, max_length=65535),
FieldSchema(name="type",dtype=DataType.VARCHAR,max_length=200, nullable=True),
# Define optional fields, such as these ones related to the "metadata" field.
FieldSchema(name="is_extracted", dtype=DataType.VARCHAR, max_length=5, nullable=True),
FieldSchema(name="coordinates_points", dtype=DataType.JSON, nullable=True),
FieldSchema(name="coordinates_system", dtype=DataType.VARCHAR, max_length=64, nullable=True),
FieldSchema(name="coordinates_layout_width", dtype=DataType.INT32, nullable=True),
FieldSchema(name="coordinates_layout_height", dtype=DataType.INT32, nullable=True),
FieldSchema(name="filetype", dtype=DataType.VARCHAR, max_length=64, nullable=True),
FieldSchema(name="languages", dtype=DataType.ARRAY, element_type=DataType.VARCHAR, max_length=64, max_capacity=10, nullable=True),
FieldSchema(name="page_number", dtype=DataType.INT32, nullable=True),
FieldSchema(name="image_mime_type", dtype=DataType.VARCHAR, max_length=64, nullable=True),
FieldSchema(name="filename", dtype=DataType.VARCHAR, max_length=256, nullable=True),
FieldSchema(name="data_source_url", dtype=DataType.VARCHAR, max_length=1024, nullable=True),
FieldSchema(name="data_source_record_locator_protocol", dtype=DataType.VARCHAR, max_length=64, nullable=True),
FieldSchema(name="data_source_record_locator_remote_file_path", dtype=DataType.VARCHAR, max_length=1024, nullable=True),
FieldSchema(name="entities_items", dtype=DataType.JSON, nullable=True),
FieldSchema(name="entities_relationships", dtype=DataType.JSON, nullable=True)
]
# ...
```
Fields with a `DataType.VARCHAR` data type are limited to a maximum length of 65,535 characters. Attempting to exceed this character count
will cause Unstructured to throw errors when attempting to write to a Milvus collection, and the associated Unstructured job could fail.
For example, `metadata` fields that typically exceed these character counts include `image_base64` and `orig_elements`.
The Milvus connector dependencies:
```bash CLI, Python theme={null}
pip install "unstructured-ingest[milvus]"
```
You might also need to install additional dependencies, depending on your needs. [Learn more](/open-source/ingestion/ingest-dependencies).
The following environment variables:
* `MILVUS_URI` - The Milvus instance's URI, represented by `--uri` (CLI) or `uri` (Python).
* `MILVUS_USER` and `MILVUS_PASSWORD`, or `MILVUS_TOKEN` - The username and password, or token, to access the instance. This is represented by `--user` and `--password`, or `--token` (CLI); or `user` and `password`, or `token` (Python).
* `MILVUS_DB` - The database's name, represented by `--db-name` (CLI) or `db_name` (Python).
* `MILVUS_COLLECTION` - The collection's name, represented by `--collection-name` (CLI) or `collection_name` (Python).
* `MILVUS_FIELDS_TO_INCLUDE` - A list of fields to include a comma-separated list (CLI) or an array of strings (Python), represented by `--field-to-include` (CLI) or `fields_to_include` (Python).
Additional settings include:
* To emit the `metadata` field's child fields directly into the output, include `--flatten-metadata` (CLI) or `flatten_metadata=True` (Python). This is the default if not specified.
* To keep the `metadata` field with its child fields intact in the output, include `--no-flatten-metadata` (CLI) or `flatten_metadata=False` (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 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 \
--chunking-strategy by_title \
--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}" \
milvus \
--uri $MILVUS_URI \
--user $MILVUS_USER \
--password $MILVUS_PASSWORD \
--db-name $MILVUS_DB \
--collection-name $MILVUS_COLLECTION \
--fields-to-include type,element_id,text,embeddings
```
```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.milvus import (
MilvusConnectionConfig,
MilvusAccessConfig,
MilvusUploadStagerConfig,
MilvusUploaderConfig
)
from unstructured_ingest.processes.connectors.local import (
LocalIndexerConfig,
LocalDownloaderConfig,
LocalConnectionConfig
)
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(),
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"),
strategy="hi_res",
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=MilvusConnectionConfig(
access_config=MilvusAccessConfig(
password=os.getenv("MILVUS_PASSWORD")
),
uri=os.getenv("MILVUS_URI"),
user=os.getenv("MILVUS_USER"),
db_name=os.getenv("MILVUS_DB")
),
stager_config=MilvusUploadStagerConfig(fields_to_include=["type", "element_id", "text", "embeddings"]),
uploader_config=MilvusUploaderConfig(collection_name=os.getenv("MILVUS_COLLECTION"))
).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).