QnA Maker Documentation | Microsoft Learn
WEBQnA Maker is a cloud-based API service that creates a conversational, question-and-answer layer over your data. QnA Maker enables you to power a question-and-answer service from your semi-structured content like FAQ documents, URLs, and …
What is QnA Maker service? - Azure AI services | Microsoft Learn
WEBAug 28, 2024 · QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your data. It is used to find the most appropriate answer for any input from your …
Data sources and content types - QnA Maker - Azure AI services
WEBAug 28, 2024 · Learn how to import question and answer pairs from data sources and supported content types, which include many standard structured documents such as PDF, DOCX, and TXT - QnA Maker.
Generate Synthetic QnAs from Real-world Data - GitHub
WEBThis hands-on lab aims to alleviate some of that headache by demonstrating how to create/augment a QnA dataset from complex unstructured data, assuming a real-world scenario. The sample aims to be ...
Build a RAG-based QnA application using Llama3 models from …
WEBSep 12, 2024 · The notebook is powered by an ml.t3.medium instance to demonstrate deploying the model as an API endpoint using an SDK through SageMaker JumpStart. You can use these model endpoints to explore, experiment, and optimize for comparing advanced RAG application techniques using LangChain. We also illustrate the integration of the FAISS embeddings ...
QnA Maker
WEBFrom data to bot in minutes. Build, train and publish a sophisticated bot using FAQ pages, support websites, product manuals, SharePoint documents or editorial content through an easy-to-use UI or via REST APIs. Get started.
Improving question-answering models that use data from tables
WEBQuestion-answering (QA) models sometimes need to retrieve information from tables, which use an entirely different set of semantic cues than free-form text. Historically, most work on table-based QA has concentrated on extracting the contents of a single table cell as the answer to a question.
WEBQnAMaker aims to simplify the process of bot creation by extract-ing Question-Answer (QA) pairs from data given by users into a Knowledge Base (KB) and providing a conversational layer over it. KB here refers to one instance of azure search index, where the extracted QA are stored.
RAG Powered Document QnA & Semantic Caching with Gemini Pro
WEBMar 22, 2024 · In this article, we will dive into the world of RAG-powered document QnA using Google’s Gemini AI and Langchain. Alongside this, there has been a lot of discussion around preserving conversational memory while leveraging LLMs for QnA.
[2203.16714] End-to-End Table Question Answering via Retrieval ...
WEBMar 30, 2022 · To alleviate this problem, we introduce T-RAG, an end-to-end Table QA model, where a non-parametric dense vector index is fine-tuned jointly with BART, a parametric sequence-to-sequence model to generate answer tokens.