One of the key advantages of custom skills is that they enable developers to run their own custom code as part of the ingestion pipeline, while still benefiting from the robust features of theAzure AI Search built-in indexers. This includes support for avariety of...
Here'sa quick overview of theAzureML-Metrics SDK implementationto evaluate Multi-turn RAG scenario, offering a glimpse into its capabilities and functionalities. RAG Evaluation (azureml-metrics) for a Multi-turn chat-completion task Lets take a step by step look at how...
You must make various implementation decisions as you design your RAG solution. The following diagram illustrates some of the questions you should ask when you make those decisions. The following list provides a brief description of what you should do during each phase of RAG solution deve...
It focuses on the architectural design and implementation strategies employed. While this post provides a comprehensive overview, it is not intended as a step-by-step tutorial for building the application from scratch. For those interested in hands-on experimentation, the complete source code is ...
Azure OpenAI service gets grounded based on previous interactions. For example, in a chat-like experience such as ChatGPT or Bing Chat, the Orchestrator app maintains or caches the history of the conversational session so that it's considered in the conversation flow by the LLM service ...
reranking process to enhance search result precision. The final chunks are then passed as context to an Azure OpenAI LLM to generate an answer to the user query grounded in the relevant source information, resulting in the final LLM-generated response to the user’s query and related citations...
常见的信息检索方法包括:全文(关键词)搜索、向量搜索(语义搜索)、混合搜索。目前langchain4j以向量搜索为主(例如通过Qdrant等向量数据库构建高效检索系统),后续会扩展支持全文搜索及混合搜索(目前Azure AI Search支持,详细见AzureAiSearchContentRetriever)。 RAG可以分为两步:索引、检索。
可以借助Azure OpenAI的功能调用能力来实现一个能自主使用搜索工具查找所需信息以协助处理用户请求的Agent。
Data Source Retrieves external knowledge in real time Embeds knowledge directly into the model Implementation Time Faster – Relies on external knowledge base setup, which can vary in complexity Slower – Requires dataset preparation and training Cost & Maintenance Potentially Lower – Depends on knowled...
Azure OpenAI on your dataAzure featureAzure 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 Ch...