models bring your own data qna

bring-your-own-data-qna

Overview

Description: The "Bring your own data QnA" is a pre-trained Q&A model, enhanced by GPT3.5, that leverages your personally indexed data to deliver more concrete and relevant answers. It involves processing the raw query through an embedding procedure, followed by a "Vector Search" to pinpoint the most pertinent context within the user's data. Subsequently, GPT3.5 is employed to generate a comprehensive answer to the question using the sourced documents. ### Inference samples Inference type|Python sample (Notebook)|CLI with YAML |--|--|--| Real time|deploy-promptflow-model-python-example|deploy-promptflow-model-cli-example Batch | N/A | N/A ### Sample inputs and outputs (for real-time inference) #### Sample input json { "inputs": { "question": "How to use SDK V2?" } } #### Sample output json { "outputs": { "output": "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: 4

View in Studio: https://ml.azure.com/registries/azureml/models/bring-your-own-data-qna/version/4

Properties

is-promptflow: True

azureml.promptflow.section: gallery

azureml.promptflow.type: rag

azureml.promptflow.name: Bring Your Own Data QnA

azureml.promptflow.description: Create flows for Q&A with GPT3.5 using data from your own indexed files to make the answer more grounded for entreprise chat scenarios.

inference-min-sku-spec: 2|0|14|28

inference-recommended-sku: Standard_DS3_v2