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components llm_rag_create_promptflow
github-actions[bot] edited this page Dec 12, 2024
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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: 0.0.86
Preview
View in Studio: https://ml.azure.com/registries/azureml/components/llm_rag_create_promptflow/version/0.0.86
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