This component is used to create a RAG flow based on your mlindex data and best prompts. The flow will look into your indexed data and give answers based on your own data context. The flow also provides the capability to bulk test with any built-in or custom evaluation flows.

Version: 0.0.83

Preview

View in Studio: https://ml.azure.com/registries/azureml/components/llm_rag_create_promptflow/version/0.0.83

Name	Description	Type	Default	Optional	Enum
best_prompts	JSON file containing prompt options to create variants from. Must either have single key of 'best_prompt' with a value of a list of prompt strings or have best prompts specific to certain metrics.	uri_file		True
mlindex_asset_id	Asset ID for MLIndex file that contains information about index to use for document lookup in promptflow	uri_file
mlindex_name	Name of the MLIndex asset	string		True
mlindex_asset_uri	Folder containing MLIndex to use in the generated flow.	uri_folder
llm_connection_name	Workspace connection full name for completion or chat	string		True
llm_config	JSON describing the llm provider and model details to use for completion generation.	string		True
embedding_connection	Workspace connection full name for embedding.	string		True
embeddings_model	The model to use to embed data. E.g. 'hugging_face://model/sentence-transformers/all-mpnet-base-v2' or 'azure_open_ai://deployment/{deployment_name}/model/{model_name}'	string		True

azureml:llm-rag-embeddings@latest