Skip to main content

Integrating with Mongo Vector Database in Orkes Conductor

To effectively utilize AI and LLM tasks in Orkes Conductor, it's essential to integrate your Orkes Conductor cluster with the necessary Vector Database models.

Atlas Vector Search from MongoDB is a full-featured vector database known for building intelligent applications powered by semantic search and generative AI over any type of data. This integration empowers you to access, query, and manipulate vector data effectively, enhancing the capabilities of Orkes Conductor in various natural language processing and artificial intelligence applications.

This guide will provide the steps for integrating MongoDB’s Atlas Vector Search as a Vector Database with Orkes Conductor.

Before integrating with Atlas Vector Search, you must get specific configuration parameters, such as MongoDB Atlas connection string and database name.

Once you have the parameters, let’s integrate this with Orkes Conductor.

  1. Navigate to Integrations from the left menu on your Orkes Conductor cluster.
  2. Click +New integration button from the top-right corner.
  3. Under the Vector Databases section, choose Mongo Vector Database.
  4. Click +Add and provide the following parameters:

Create MongoDB Integration

ParametersDescription
Integration nameA name for the integration.
MongoDB Atlas connection stringThe MongoDB Atlas connection string. Check out the official MongoDB documentation on how to get the Atlas connection string. It will be of the format: mongodb+srv://username:password@cluster0.mongodb.net/.
Database nameThe database name to store and query vector data.
Embedding dimensionsThe number of dimensions in the embeddings. The embedding dimensions often depend on the AI model used to generate the embeddings.
Distance metricChoose the distance metric, which is a metric to measure the similarity or distance between vectors. Supported values:
  • Cosine Similarity
  • Euclidean Distance
  • Dot Product
Number of nearest neighboursThe number of nearest neighbours to be used during the search.
DescriptionA description of your integration.
  1. You can toggle-on the Active button to activate the integration instantly.
  2. Click Save.

Adding Indexes to MongoDB Atlas Vector Search Integration​

Now that you have integrated your Orkes Conductor cluster with the MongoDB Atlas Vector Search provider, the next step is to integrate with the specific indexes.

To add a new index to the integration:

  1. Navigate to the integrations page and click the '+' button next to the integration you created.

Create Indexes for MongoDB Integration

  1. Click +New Index.
  2. Provide the index name and an optional description.

Create Indexes for MongoDB Integration Model

  1. Toggle-on the Active button to enable immediately.
  2. Click Save.

This ensures the integration model is saved for future use in LLM tasks within Orkes Conductor.

RBAC - Governance on who can use Integrations​

The integration with the required models is now ready. Next, we should determine the access control to these models.

The permission can be granted to applications/groups within the Orkes Conductor cluster.

To provide explicit permission to Groups:

  1. Navigate to Access Control > Groups from the left menu on your Orkes Conductor cluster.
  2. Create a new group or choose an existing group.
  3. Under the Permissions section, click +Add Permission.
  4. Under the Integrations tab, select the required integrations with the required permissions.

Add Permissions for Mongo Vector Database Integration

  1. Click Add Permissions. This ensures that all the group members can access these integration models in their workflows.

Similarly, you can also provide permissions to applications.