LLM Index Text

The LLM Index Text task is designed to index the provided text into a vector space for efficient search, retrieval, and processing at a later stage.

It takes text input, processes it using a specified language model to generate embeddings, and stores these embeddings in a chosen vector database.

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 Index Text 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.	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.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.embeddingModelProvider	The LLM provider 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.	Required.
inputParameters.embeddingModel	The embedding model provided by the selected LLM provider to generate the embeddings.	Required.
inputParameters.text	The text to be indexed.	Required.
inputParameters.docId	A unique ID to identify the document where the indexed text will be stored.	Optional.
inputParameters.dimensions	The size of the vector, which is the number of elements in the vector.	Optional.

Caching parameters

You can cache the task outputs using the following parameters. Refer to Caching Task Outputs for a full guide.

Parameter	Description	Required/ Optional
cacheConfig.ttlInSecond	The time to live in seconds, which is the duration for the output to be cached.	Required if using cacheConfig.
cacheConfig.key	The cache key is a unique identifier for the cached output and must be constructed exclusively from the task’s input parameters. It can be a string concatenation that contains the task’s input keys, such as `${uri}-${method}` or `re_${uri}_${method}`.	Required if using cacheConfig.

Schema parameters

You can enforce input/output validation for the task using the following parameters. Refer to Schema Validation for a full guide.

Parameter	Description	Required/ Optional
taskDefinition.enforceSchema	Whether to enforce schema validation for task inputs/outputs. Set to true to enable validation.	Optional.
taskDefinition.inputSchema	The name and type of the input schema to be associated with the task.	Required if enforceSchema is set to true.
taskDefinition.outputSchema	The name and type of the output schema to be associated with the task.	Required if enforceSchema is set to true.

Other generic parameters

Here are other parameters for configuring the task behavior.

Parameter	Description	Required/ Optional
optional	Whether the task is optional. The default is false. If set to true, the workflow continues to the next task even if this task is in progress or fails.	Optional.

Task configuration

This is the task configuration for an LLM Index Text task.

{
  "name": "llm_index_text_task",
  "taskReferenceName": "llm_index_text_task_ref",
  "inputParameters": {
    "vectorDB": "pineconedb",
    "namespace": "myNewModel",
    "index": "test",
    "embeddingModelProvider": "azure_openai",
    "embeddingModel": "text-davinci-003",
    "text": "${workflow.input.text}",
    "docId": "XXXX",
    "dimensions": "${workflow.input.dimensions}"
  },
  "type": "LLM_INDEX_TEXT"
}

Task output

There is no output. The LLM Index Text task will store the indexed data in the specified vector database.

Adding an LLM Index Document task in UI

To add an LLM Index Document task:

In your workflow, select the (+) icon and add an LLM Index Document task.
Choose the Vector database, Namespace, Index, Embedding model provider, and Embedding model.
Enter the Text to be indexed.
(Optional) Enter an arbitrary Doc ID to store the indexed text.

LLM Index Text Task - UI

Task parameters​

Caching parameters​

Schema parameters​

Other generic parameters​

Task configuration​

Task output​

Adding an LLM Index Document task in UI​