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Azure-Samples / repository
A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.
name: Chat with your data - Solution accelerator (Python) description: Chat with your data using OpenAI and AI Search with Python. languages:
Welcome to the Chat with your data Solution accelerator repository! The Chat with your data Solution accelerator is a powerful tool that combines the capabilities of Azure AI Search and Large Language Models (LLMs) to create a conversational search experience. This solution accelerator uses an Azure OpenAI GPT model and an Azure AI Search index generated from your data, which is integrated into a web application to provide a natural language interface, including speech-to-text functionality, for search queries. Users can drag and drop files, point to storage, and take care of technical setup to transform documents. Everything can be deployed in your own subscription to accelerate your use of this technology.
This repository provides an end-to-end solution for users who want to query their data with natural language. It includes a well designed ingestion mechanism for multiple file types, an easy deployment, and a support team for maintenance. The accelerator demonstrates both Push or Pull Ingestion; the choice of orchestration (Semantic Kernel, LangChain, OpenAI Functions or Prompt Flow) and should be the minimum components needed to implement a RAG pattern. It is not intended to be put into Production as-is without experimentation or evaluation of your data. It provides the following features:
If you need to customize your scenario beyond what Azure OpenAI on your data offers out-of-the-box, use this repository. By default, this repo comes with one specific set of RAG configurations including but not limited to: chunk size, overlap, retrieval/search type and system prompt. It is important that you evaluate the retrieval/search and the generation of the answers for your data and tune these configurations accordingly before you use this repo in production. For a starting point to understand and perform RAG evaluations, we encourage you to look into the RAG Experiment Accelerator.
The accelerator presented here provides several options, for example:
*Have you seen ChatGPT + Enterprise data with Azure OpenAI and AI Search demo? If you would like to experiment: Play with prompts, understanding RAG pattern different implementation approaches, see how different features interact with the RAG pattern and choose the best options for your RAG deployments, take a look at that repo.
Here is a comparison table with a few features offered by Azure, an available GitHub demo sample and this repo, that can provide guidance when you need to decide which one to use:
| Name | Feature or Sample? | What is it? | When to use? |
|---|---|---|---|
| "Chat with your data" Solution Accelerator - (This repo) | Azure sample | End-to-end baseline RAG pattern sample that uses Azure AI Search as a retriever. | This sample should be used by Developers when the RAG pattern implementations provided by Azure are not able to satisfy business requirements. This sample provides a means to customize the solution. Developers must add their own code to meet requirements, and adapt with best practices according to individual company policies. |
| Azure OpenAI on your data | Azure feature | Azure OpenAI Service offers out-of-the-box, end-to-end RAG implementation that uses a REST API or the web-based interface in the Azure AI Foundry to create a solution that connects to your data to enable an enhanced chat experience with Azure OpenAI ChatGPT models and Azure AI Search. | This should be the first option considered for developers that need an end-to-end solution for Azure OpenAI Service with an Azure AI Search retriever. Simply select supported data sources, that ChatGPT model in Azure OpenAI Service , and any other Azure resources needed to configure your enterprise application needs. |
| Azure Machine Learning prompt flow | Azure feature | RAG in Azure Machine Learning is enabled by integration with Azure OpenAI Service for large language models and vectorization. It includes support for Faiss and Azure AI Search as vector stores, as well as support for open-source offerings, tools, and frameworks such as LangChain for data chunking. Azure Machine Learning prompt flow offers the ability to test data generation, automate prompt creation, visualize prompt evaluation metrics, and integrate RAG workflows into MLOps using pipelines. | When Developers need more control over processes involved in the development cycle of LLM-based AI applications, they should use Azure Machine Learning prompt flow to create executable flows and evaluate performance through large-scale testing. |
| ChatGPT + Enterprise data with Azure OpenAI and AI Search demo | Azure sample | RAG pattern demo that uses Azure AI Search as a retriever. | Developers who would like to use or present an end-to-end demonstration of the RAG pattern should use this sample. This includes the ability to deploy and test different retrieval modes, and prompts to support business use cases. |
Note: The current model allows users to ask questions about unstructured data, such as PDF, text, and docx files. See the supported file types.
Company personnel (employees, executives) looking to research against internal unstructured company data would leverage this accelerator using natural language to find what they need quickly.
This accelerator also works across industry and roles and would be suitable for any employee who would like to get quick answers with a ChatGPT experience against their internal unstructured company data.
Tech administrators can use this accelerator to give their colleagues easy access to internal unstructured company data. Admins can customize the system configurator to tailor responses for the intended audience.
The sample data illustrates how this accelerator could be used for an employee onboarding scenario in across industries.
In this scenario, a newly hired employee is in the process of onboarding to their organization. Leveraging the solution accelerator, she navigates through the extensive offerings of her organization’s health and retirement benefits. With the newly integrated chat history capabilities, they can revisit previous conversations, ensuring continuity and context across multiple days of research. This functionality allows the new employee to efficiently gather and consolidate information, streamlining their onboarding experience. For more details, refer to the README.
The sample data illustrates how this accelerator could be used in the financial services industry (FSI).
In this scenario, a financial advisor is preparing for a meeting with a potential client who has expressed interest in Woodgrove Investments’ Emerging Markets Funds. The advisor prepares for the meeting by refreshing their understanding of the emerging markets fund's overall goals and the associated risks.
Now that the financial advisor is more informed about Woodgrove’s Emerging Markets Funds, they're better equipped to respond to questions about this fund from their client.
Additionally, we have implemented a Legal Review and Summarization Assistant scenario to demonstrate how this accelerator can be utilized in any indu
| RAG Experiment Accelerator | Tool | The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. | RAG Experiment Accelerator is to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI. This tool is useful for researchers, data scientists, and developers who want to, Test the performance of different Search and OpenAI related hyperparameters. |