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Batch process all your records to store structured outputs in Milvus. The requirements are as follows. The following video shows how to fulfill the minimum set of requirements for Milvus cloud-based instances, demonstrating Milvus on IBM watsonx.data:
  • For Zilliz Cloud, you will need:
    • A Zilliz Cloud account.
    • A Zilliz Cloud cluster.
    • The URI of the cluster, also known as the cluster’s public endpoint, which takes a format such as https://<cluster-id>.<cluster-type>.<cloud-provider>-<region>.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 in the instance.
    • The name of the collection in the database. The collection must have a a defined schema before Unstructured can write to the collection. The minimum viable schema for Unstructured contains only the fields element_id, embeddings, and record_id, as follows:
      Field NameField TypeMax LengthDimension
      element_id (primary key field)VARCHAR200—
      embeddings (vector field)FLOAT_VECTOR—3072
      record_idVARCHAR200—
      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.
  • For Milvus on IBM watsonx.data, you will need:
  • For Milvus local, you will need:
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, and record_id, as follows. Adding a text field is optional but highly recommended.This example code demonstrates the use of the Python SDK for Milvus to create a collection with this schema, targeting Milvus on IBM watsonx.data. For the connections.connect arguments to connect to other types of Milvus deployments, see your Milvus provider’s documentation:
Python
import os
from pymilvus import (
    connections,
    FieldSchema,
    DataType,
    CollectionSchema,
    Collection,
)

connections.connect(
    alias="default",
    host=os.getenv("MILVUS_GRPC_HOST"),
    port=os.getenv("MILVUS_GRPC_PORT"),
    user=os.getenv("MILVUS_USER"),
    password=os.getenv("MILVUS_PASSWORD"),
    secure=True
)

primary_key = FieldSchema(
    name="element_id",
    dtype=DataType.VARCHAR,
    is_primary=True,
    max_length=200
)

vector = FieldSchema(
    name="embeddings",
    dtype=DataType.FLOAT_VECTOR,
    dim=3072
)

record_id = FieldSchema(
    name="record_id",
    dtype=DataType.VARCHAR,
    max_length=200
)

text = FieldSchema(
    name="text",
    dtype=DataType.VARCHAR,
    max_length=65536
)

schema = CollectionSchema(
    fields=[primary_key, vector, record_id, text],
    enable_dynamic_field=True
)

collection = Collection(
    name="my_collection",
    schema=schema,
    using="default"
)

index_params = {
    "metric_type": "L2",
    "index_type": "IVF_FLAT",
    "params": {"nlist": 1024}
}

collection.create_index(
    field_name="embeddings",
    index_params=index_params
)
Other approaches, such as creating collections instantly or setting nullable and default fields, have not been fully evaluated by Unstructured and might produce unexpected results. 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. The Milvus connector dependencies:
CLI, Python
pip install "unstructured-ingest[milvus]"
You might also need to install additional dependencies, depending on your needs. Learn more. 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. 
#!/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
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, which applies to Starter and Team accounts.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. 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.If you do not have an API key, get one now.If you are using an Enterprise 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.