Build A Weaviate Transformation Agent

In this recipe, we will use a Weaviate TransformationAgent to enhance our data in Weaviate. We will build an agent that has access to a collection containing a bunch or research papers, their abstracts and titles. We will then use the agent to create additional properties for eaach of our objects in the collection.

⚠️ The Weaviate Transformation Agent is designed to modify data in Weaviate in place. While the Agent is in technical preview, do not use it in a production environment. The Agent may not work as expected, and the data in your Weaviate instance may be affected in unexpected ways.

The TransformationAgent is able to access a Weaviate collection of your chosing, and perform operations on the objects within it. However, each operation for the agent can be defined in natural language. The agent will then use an LLM to complete the instructions in the operation.

📚 You can learn more about the new TransformationAgent, you can read our accompanyin "Introducing the Weaviate Transformation Agent" blog

To get started, we've prepared an open datasets, available on Hugging Face. The first step will be walking through how to populate your Weaviate Cloud collections.

• ArxivPapers: A dataset that lists titles and abstracts of research papers.

If you'd like to try out building more agents with different datasets, check out the list of demo datasets we have available on Hugging Face Weaviate agents dataset

Setting Up Weaviate & Importing Data

To use the Weaviate Transformation Agent, first, create a Weaviate Cloud account👇

1. Create Serverless Weaviate Cloud account and setup a free Sandbox
2. Go to 'Embedding' and enable it, by default, this will make it so that we use Snowflake/snowflake-arctic-embed-l-v2.0 as the embedding model
3. Take note of the WEAVIATE_URL and WEAVIATE_API_KEY to connect to your cluster below

Info: We recommend using Weaviate Embeddings so you do not have to provide any extra keys for external embedding providers.

!pip install "weaviate-client[agents]" datasets
!pip install -U weaviate-agents

Python output:

/Users/tuanacelik/miniconda3/envs/agent/lib/python3.13/pty.py:95: DeprecationWarning: This process (pid=93073) is multi-threaded, use of forkpty() may lead to deadlocks in the child.
  pid, fd = os.forkpty()

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import os
from getpass import getpass

if "WEAVIATE_API_KEY" not in os.environ:
  os.environ["WEAVIATE_API_KEY"] = getpass("Weaviate API Key")
if "WEAVIATE_URL" not in os.environ:
  os.environ["WEAVIATE_URL"] = getpass("Weaviate URL")

import weaviate
from weaviate.auth import Auth

client = weaviate.connect_to_weaviate_cloud(
        cluster_url=os.environ.get("WEAVIATE_URL"),
        auth_credentials=Auth.api_key(os.environ.get("WEAVIATE_API_KEY")),
)

Prepare the Collections

In the following code block, we are pulling our demo "papers" datasets from Hugging Face and writing them to a new collection in our Weaviate Serverless cluster.

Important: Please enable 'Embeddings' in the Weavaite Cloud console. This way, you can use the text2vec_weaviate vectorizer, which will create vectors for each object using Snowflake/snowflake-arctic-embed-l-v2.0 by default.

from weaviate.classes.config import Configure

# To re-run cell you may have to delete collections
# client.collections.delete("ArxivPapers")
client.collections.create(
    "ArxivPapers",
    description="A dataset that lists research paper titles and abstracts",
    vectorizer_config=Configure.Vectorizer.text2vec_weaviate()
)

Python output:

<weaviate.collections.collection.sync.Collection at 0x128ae3680>

from datasets import load_dataset

dataset = load_dataset("weaviate/agents", "transformation-agent-papers", split="train", streaming=True)

papers_collection = client.collections.get("ArxivPapers")

with papers_collection.batch.dynamic() as batch:
    for i, item in enumerate(dataset):
      if i < 200:
        batch.add_object(properties=item["properties"])

Inspect the Collection in Explorer

The TransformationAgent will modify the collection as we go along. This is a good time to take a look at the contents of your "ArxivPapers" collection. You can inspect the data in the Explorer tool in the Weaviate Cloud Console. If all goes well, you should be seeing 2 properties listed for each object:

• title: the title of the paper.
• abstract: the abstract of the paper.

As well as the vectors for each object.

Define Transformation Operations

The star of the show for the TransformationAgent are the operations.

We can now define transformation operations which we want to perform on our collection. An operation can be:

• Appending a new property
• Updating an existing property

Currently, the TransformationAgent supports operations that update existing objects in Weaviate.

Append New Properties

To append a new property, we define an operation with:

• instrcution: This is where you can describe, in natural language, what you want this new property to be.
• property_name: The name you want the property to have
• data_type: The specific datatype the property should be. E.g.: DataType.TEXT, DataType.TEXT_ARRAY, DataType.BOOL, DataType.INT etc.
• view_properties: Sometimes, you may want to create properties that are based on information provided in other properties, this is where you can list out which properties the instruction should view.

Create a List of Topics

First, let's append a new property called "topics", which should be a TEXT_ARRAY. Based on the "abstract" and "title", let's ask for the LLM to extract a list of topic tags. We can be specific here. Let's ask for no more than 5

from weaviate.agents.classes import Operations
from weaviate.classes.config import DataType

add_topics = Operations.append_property(
    property_name="topics",
    data_type=DataType.TEXT_ARRAY,
    view_properties=["abstract"],
    instruction="""Create a list of topic tags based on the abstract.
    Topics should be distinct from eachother. Provide a maximum of 5 topics.
    Group similar topics under one topic tag.""",
)

Add a French Translation

Next, let's add a new "french_abstract" property which is simply a translation of the "abstract"

add_french_abstract = Operations.append_property(
      property_name="french_abstract",
      data_type=DataType.TEXT,
      view_properties=["abstract"],
      instruction="Translate the abstract to French",
)

Update the Title

This time, we are updating the title property to include the French translation of itself in parantheses.

update_title = Operations.update_property(
    property_name="title",
    view_properties=["title"],
    instruction="""Update the title to ensure that it contains the French translation of itself in parantheses, after the original title.""",
)

Determine If It's a Survey Paper

Finally, let's ask for a BOOL property which indicates whether the paper is a survey or not. I.e., we'll ask the LLM to determine if the paper presents novel techniques, or whether it's a survey of existing ones.

is_survey_paper = Operations.append_property(
    property_name="is_survey_paper",
    data_type=DataType.BOOL,
    view_properties=["abstract"],
    instruction="""Determine if the paper is a "survey".
    A paper is considered survey it's a surveys existing techniques, and not if it presents novel techniques""",
)

Create & Run the Transformation Agent

Once we have all of our operations defined, we can initialize a TransformationAgent.

When initializing the agent, we have to decide which collection it may have accesss to modify. In this case, we want it to have access to the "ArxivPapers" collection we previously created.

Next, we need to provide a list of operations which the agent should run. Here, we provide all the operations we defined above.

Note: We are working on resolving a known issue which can result in data consistency issues when multiple operations act on the same object at once.

from weaviate.agents.transformation import TransformationAgent

agent = TransformationAgent(
    client=client,
    collection="ArxivPapers",
    operations=[
        add_topics,
        add_french_abstract,
        is_survey_paper,
        update_title,
    ],
)

Running the Transformations

By calling update_all(), we get the agent to spin up individual workflows for each operation. Each operation will then run on each object in our collectoion.

response = agent.update_all()

Inspect the Operation Workflows

To inspect the status of our operations, we can take a look at the workflow_id in the returned TransformationResponse, and get their status with agent.get_status(workflow_id). These operations are asynchronous.

response

Python output:

[TransformationResponse(operation_name='topics', workflow_id='TransformationWorkflow-1766a450c35039c2a44e1fa33dc49dd4'),
 TransformationResponse(operation_name='french_abstract', workflow_id='TransformationWorkflow-67e90d88830347a5581d3ee1aa10b867'),
 TransformationResponse(operation_name='is_survey_paper', workflow_id='TransformationWorkflow-6294dd575fad55c318ee7b0e8a38a8ff'),
 TransformationResponse(operation_name='title', workflow_id='TransformationWorkflow-bba64a5bf204b00c3572310de715d1e2')]

agent.get_status(workflow_id=response.workflow_id)

Python output:

{'workflow_id': 'TransformationWorkflow-1766a450c35039c2a44e1fa33dc49dd4',
 'status': {'batch_count': 1,
  'end_time': '2025-03-11 14:58:57',
  'start_time': '2025-03-11 14:57:55',
  'state': 'completed',
  'total_duration': 62.56732,
  'total_items': 200}}