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RAG & Knowledge Retrieval

AI Business Cloud Core Data Infrastructure

5 Blueprints

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Build a Chat-With-Your-Data System Using Elasticsearch and OpenAI (RAG)

Build a RAG chat-with-your-data flow that retrieves answers from Elasticsearch and generates grounded responses with OpenAI.

Build a RAG Pipeline Ingest Documents and Answer Questions Using OpenAI

Ingest documents into a vector store and answer questions grounded in retrieved content with a Kestra-native RAG pipeline on OpenAI.

Nightly RAG Ingestion Pipeline with Google Gemini

Refresh a RAG knowledge base every night by fetching URLs, chunking content, and indexing Google Gemini embeddings into a vector store.

Nightly RAG Ingestion with Gemini Embeddings

Re-ingest documents nightly, embed them with Gemini, and store the vectors so a RAG app retrieves grounded context.

Turn an Apify web crawl into a fresh RAG vector index

Crawl a site with Apify's Website Content Crawler, embed the cleaned content into a vector store, and answer questions grounded in the freshly scraped data.