LLM Store Embeddings

A system task responsible for storing the generated embeddings produced by the LLM Generate Embeddings task, into 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.

Definitions

{
     "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"
   }

Input Parameters

Parameter	Description
vectorDB	Choose the vector database to which the data is to be stored. Note: If you haven’t configured the vector database on your Orkes console, navigate to the Integrations tab and configure your required provider. Refer to the documentation on how to integrate Vector Databases with Orkes console..
index	Choose the index in your vector database where the text or data is to be stored. Note: For Weaviate integration, this field refers to the class name, while for other integrations, it denotes the index name.
namespace	Choose from the available namespace configured within the chosen vector database. Namespaces are separate isolated environments within the database to manage and organize vector data effectively. Note: The namespace field has different names and applicability based on the integration: For Pinecone integration, the namespace field is applicable. For Weaviate integration, the namespace field is not applicable. For MongoDB integration, the namespace field is referred to as “Collection” in MongoDB. For Postgres integration, the namespace field is referred to as “Table” in Postgres.
embeddingModelProvider	Choose the required LLM provider for embedding. 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 console.
embeddingModel	Choose from the available language models provided by the selected LLM provider.
Id	Optional field to provide the vector ID.

Examples

UI

1. Add task type LLM Store Embeddings.
2. Choose the vector database for storing the embeddings.
3. Provide the input parameters.

JSON

{
     "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"
   }