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models multi index rerank qna
This "Multi-Source Rerank Q&A" demonstrates Q&A application, enabled by reranking data from multiple sources and powered by GPT. It utilizes indexed files and the rerank tool from Azure Machine Learning to provide grounded answers. You can ask a wide range of questions and receive responses based on a wide set of stored data. The process involves taking the query, extracting pre-existing documents across multiple sources, reranking said documents for the most relevant context, and then using GPT to chat with you, given those documents.
Inference type | CLI | VS Code Extension |
---|---|---|
Real time | deploy-promptflow-model-cli-example | deploy-promptflow-model-vscode-extension-example |
Batch | N/A | N/A |
{
"inputs": {
"query": "How to use SDK V2?"
}
}
{
"outputs": {
"answer": "To use the Azure Machine Learning Python SDK v2, you need to have an Azure Machine Learning workspace and the SDK installed. You can either create a compute instance, which automatically installs the SDK and is pre-configured for ML workflows, or use the provided commands to install the SDK. (Source: https://github.com/prakharg-msft/azureml-tutorials/blob/main//how-to-auto-train-image-models.md)"
}
}
Version: 1
View in Studio: https://ml.azure.com/registries/azureml/models/multi-index-rerank-qna/version/1
is-promptflow: True
azureml.promptflow.section: gallery
azureml.promptflow.type: standard
azureml.promptflow.name: Multi-Source Rerank Q&A
azureml.promptflow.description: Use LLM and reranked data from multiple indices to ground question and answering capabilities.
inference-min-sku-spec: 2|0|14|28
inference-recommended-sku: Standard_DS3_v2