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