Milvus

• Unstructured Pipelines and Unstructured API support Milvus cloud-based instances (such as Milvus on IBM watsonx.data, or Zilliz Cloud).
• Unstructured Ingest supports both local and cloud-based Milvus instances.

​

IBM watsonx.data

• An IBM Cloud account.
• An IBM watsonx.data Lite plan or Enterprise plan 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.
  • 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://<host>:<port>. To get the instance URI
  1. Sign in to your IBM Cloud account.
     
  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 title bar.
     
  3. Expand Databases, and then click the name of the target watsonx.data plan.
     
  4. Click Open web console.
     
  5. 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.
     
  6. Click the target Milvus service instance.
     
  7. On the Details tab, under Type, click View connect details.
     
  8. Under Service details, expand GRPC, and note the value of GRPC host and GRPC port.
     
• The name of the database in the instance.
• The name of the collection 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_<your-IBMid> instead, where <your-IBMid> 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
    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. 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.
       
    4. Click Create.
       
    5. Enter some Name for the API key.
       
    6. Optionally, enter some Description for the API key.
       
    7. For Leaked action, leave Disable the leaked key selected.
       
    8. For Session management, leave No selected.
       
    9. Click Create.
       
    10. 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).
        

​

Zilliz Cloud

• 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 the cluster public endpoint value
  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. To get the username and password
  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 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. For more information, see How connectors use record IDs, element IDs, and IDs.

​

Milvus local

• A Milvus instance.
• The URI of the instance.
• The name of the database in the instance.
• The name of the collection in the database. Note the collection requirements at the end of this section.
• The username and password, or token to access the instance.

​

Minimal required schema

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)

​

Storing document metadata

• Store the metadata as a single nested JSON field:
  {
      "metadata": {
      "category_depth": 1,
      "data_source": {
          "url": "s3://my-source-bucket/path/chart-and-table.pdf",
      . . .
      }}
  }
  
• Flatten the metadata by writing each metadata field as its own typed, queryable column:
  {
      "category_depth": 1,
      "data_source_url": "s3://my-source-bucket/path/chart-and-table.pdf",
      . . .
  }
  

• Performing dot.notation queries on the stored JSON is sufficient for your needs.
• Document metadata schemas vary across file sources. When flattening document metadata, Unstructured drops fields that do not match existing columns in the schema.
• You want the connector to automatically generate the destination table. This option is not supported when flattening document metadata.

• You want to query individual metadata fields directly using standard SQL, without JSON parsing.
• The business intelligence or analytics tools you are using require columnar data.

​

Considerations when flattening metadata

• The collection must contain a column for each metadata field you want to store. Any metadata field that does not have a corresponding column in the collection is silently dropped, although the event is written to the logs. For more information, see Logging and monitoring.
• Do not declare metadata columns as NOT NULL. Missing metadata values are written as NULL.
• Unstructured passes values through as their JSON-native type: strings, numbers, boolean, and so on. For example, no special formatting is applied to timestamp values.
• Metadata fields that are lists are not further flattened. Lists remain single columns.

​

Metadata flattening example

{
  "metadata": {
    "category_depth": 1,
    "data_source": {
      "url": "s3://my-source-bucket/path/chart-and-table.pdf",
      "version": "864ae40b0719e976e98ba0a7b9fcba92",
      "record_locator": {
        "protocol": "s3",
        "remote_file_path": "s3://my-source-bucket/path/"
      }
    },
    "languages": ["eng"]
  }
}

{
  "category_depth": 1,
  "data_source_url": "s3://my-source-bucket/path/chart-and-table.pdf",
  "data_source_version": "864ae40b0719e976e98ba0a7b9fcba92",
  "data_source_record_locator_protocol": "s3",
  "data_source_record_locator_remote_file_path": "s3://my-source-bucket/path/",
  "languages": ["eng"]
}

pip install "unstructured-ingest[milvus]"

• 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).

• 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).

#!/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

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()

• 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 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. 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, request support.If you do not have an API key, get one now.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 request support.