Build RAG pipelines with Azure AI Search for hybrid retrieval.
npx skills add https://github.com/pascalvanderheiden/my-agent-skills --skill rag-patterns
Please help me install this Agent Skill. Skill: rag-patterns Source: https://github.com/pascalvanderheiden/my-agent-skills/tree/main/skills/rag-patterns Command: npx skills add https://github.com/pascalvanderheiden/my-agent-skills --skill rag-patterns
This skill enables building Retrieval-Augmented Generation (RAG) pipelines using Azure AI Search to ground LLM responses with knowledge bases and structured data.
High-intent search queries and answers about installing and using this skill.
To build a RAG pipeline with Azure AI Search, you ground LLM responses using vector indexes and full-text search patterns. You need Azure OpenAI and azd deployment templates to configure indexes and implement scalable retrieval.
Hybrid search combines BM25 keyword full-text search with semantic vector similarities to improve retrieval-augmented generation. This configuration grounds LLM responses more robustly by matching both exact keywords and semantic meaning across knowledge bases.
Agentic retrieval supports complex multi-step questions through multi-query planning and synthesis across knowledge sources. It enables enterprise chatbots to search product docs, manuals, and policy PDFs, then provide cited answers to user queries.
Semantic ranking works with vector search in Azure AI Search by leveraging integrated semantic configurations. This improves result relevance and citation quality when grounding LLMs with structured data and knowledge bases.
You need Azure OpenAI to implement scalable RAG pipelines with this approach. It requires Azure OpenAI, vector indexing, and azd deployment templates to ground LLMs with Azure AI Search across knowledge bases and full-text patterns.
The best way to combine full-text and vector search for enterprise data is using a hybrid configuration in Azure AI Search. This pairs BM25 keyword search with semantic vector similarities to ground LLM responses with robust answer citations.
henrybravoAutomate Azure deployments with azd, Bicep, and GitHub Actions.
kevinsweetBuild RAG chatbots that ground answers in data using Elasticsearch retrieval.
DoubleslashSETeach and enforce CQRS patterns in .NET applications.
NousResearchManage an open-source vector database for storing embeddings and metadata.
