LLM Store Embeddings

The LLM Store Embeddings task is used to store the generated embeddings produced by the LLM Generate Embeddings task in a vector database. The stored embeddings serve as a repository of information that can be later accessed by the LLM Get Embeddings task for efficient and quick retrieval of related data.

The LLM Store Embeddings task takes the embeddings generated by the LLM Generate Embeddings task and stores them in a specified vector database. This involves specifying parameters such as the vector database provider, index, namespace, and embedding model details. The task ensures the embeddings are organized and accessible for future retrieval operations.

Prerequisites

• Integrate the required AI model with Orkes Conductor.
• Integrate the required vector database with Orkes Conductor.

Task parameters

Configure these parameters for the LLM Store Embeddings task.

Parameter	Description	Required/ Optional
inputParameters.vectorDB	The vector database to store the data. Note: If you haven’t configured the vector database on your Orkes Conductor cluster, navigate to the Integrations tab and configure your required provider. Refer to the documentation on how to integrate Vector Databases with Orkes console.	Required.
inputParameters.index	The index in your vector database where the text or data will be stored. The terminology of the index field varies depending on the integration: For Weaviate, the index field indicates the collection name. For other integrations, it denotes the index name.	Required.
inputParameters.namespace	Namespaces are separate isolated environments within the database to manage and organize vector data effectively. Choose from the available namespace configured within the chosen vector database. The usage and terminology of the namespace field vary depending on the integration: For Pinecone, the namespace field is applicable. For Weaviate, the namespace field is not applicable. For MongoDB, the namespace field is referred to as “Collection” in MongoDB. For Postgres, the namespace field is referred to as “Table” in Postgres.	Required.
inputParameters.embeddingModelProvider	The LLM provider used for generating the embeddings. Note: If you haven’t configured your AI/LLM provider on your Orkes console, navigate to the Integrations tab and configure your required provider. Refer to the documentation on how to integrate the LLM providers with Orkes Conductor.	Required.
inputParameters.embeddingModel	The embedding model provided by the selected LLM provider to generate the embeddings.	Required.
inputParameters.id	An arbitrary vector ID to identify the vector in the database.	Optional.

Task configuration

This is the task configuration for an LLM Store Embeddings task.

{
  "name": "llm_store_embeddings",
  "taskReferenceName": "llm_store_embeddings_ref",
  "inputParameters": {
    "vectorDB": "pineconedb",
    "index": "test",
    "namespace": "myNewModel",
    "embeddingModelProvider": "azure_openai",
    "embeddingModel": "text-davinci-003",
    "id": "xxxxxx"
  },
  "type": "LLM_STORE_EMBEDDINGS"
}

Task output

There is no output. The LLM Store Embeddings task will store the embeddings in the specified vector database.

Adding an LLM Store Embeddings task in UI

To add an LLM Store Embeddings task:

1. In your workflow, select the (+) icon and add an LLM Store Embeddings task.
2. Choose the Vector database, Index, and Namespace to store the embeddings.
3. Choose the Embedding model provider and Embedding model used to generate the embeddings.
4. In Vector ID, enter an arbitrary ID to identify the vector in the database.