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LLM Index Text

A system task designed to index the provided text into a vector space for efficient search, retrieval, and processing at a later stage.

Definitions​

{
"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"
},
"type": "LLM_INDEX_TEXT"
}

Input Parameters​

ParameterDescription
vectorDBChoose the required vector database.

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..
namespaceChoose 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.
indexChoose the index in your vector database where indexed text or data was stored.

Note: For Weaviate integration, this field refers to the class name, while for other integrations, it denotes the index name.
embeddingModelProviderChoose the required LLM provider for embedding.

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.
embeddingModelChoose from the available language models provided by the selected LLM provider.
textProvide the text to be indexed.
docIdAssign a unique ID to identify the document where the indexed text will be stored.

Examples​



  1. Add task type LLM Index Text.
  2. Choose the vector database, & LLM provider for embedding the text.
  3. Provide the input parameters.

LLM Index Text Task