initial commit; Claude generated SEO descriptions

qdrant-landing/content/documentation/tutorials-build-essentials/_index.md

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 title: Essential Examples
+short_description: "Hands-on Qdrant integration tutorials: connect AI agents, RAG pipelines, ingestion stacks, and automation tools to a vector database."
+description: "Step-by-step tutorials integrating Qdrant with LangChain, LlamaIndex, n8n, AWS S3, and other AI ecosystem tools to build production retrieval pipelines."
 weight: 1200
 partition: ecosystem
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qdrant-landing/content/documentation/tutorials-build-essentials/agentic-rag-camelai-discord.md

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 title: Discord RAG Bot
+short_description: "Build an agentic RAG Discord bot powered by Qdrant vector search, CAMEL-AI agents, and OpenAI embeddings for context-aware answers."
+description: "Tutorial: build an agentic RAG Discord bot that uses Qdrant for vector retrieval, CAMEL-AI agents for reasoning, and OpenAI embeddings for context-aware replies."
 weight: 20
 #partition: ecosystem
 social_preview_image: /documentation/examples/agentic-rag-camelai-discord/social-preview.png

qdrant-landing/content/documentation/tutorials-build-essentials/agentic-rag-crewai-zoom.md

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 title: Agentic RAG with CrewAI
+short_description: "Combine Qdrant vector search with CrewAI agents and Streamlit to extract insights from meeting transcripts and recordings."
+description: "Step-by-step tutorial: build an agentic RAG system with Qdrant and CrewAI to store, retrieve, and analyze meeting transcripts through coordinated AI agents."
 weight: 5
 partition: ecosystem
 social_preview_image: /documentation/examples/agentic-rag-crewai-zoom/social_preview.png

qdrant-landing/content/documentation/tutorials-build-essentials/agentic-rag-langgraph.md

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 title: Agentic RAG with LangGraph
+short_description: "Build an agentic RAG system with LangGraph and Qdrant that orchestrates multi-step retrieval, web search, and tool selection."
+description: "Tutorial: build an agentic RAG workflow using LangGraph for state management and Qdrant for vector retrieval, with multi-source routing and tool orchestration."
 weight: 15
 partition: ecosystem
 hideInSidebar: true

qdrant-landing/content/documentation/tutorials-build-essentials/data-ingestion-beginners.md

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 title: S3 Ingestion with LangChain
+short_description: "Stream documents from AWS S3 into Qdrant with LangChain to build a vector ingestion pipeline for unstructured data."
+description: "Tutorial: build a data ingestion pipeline that pulls documents from AWS S3, generates embeddings via LangChain, and stores them in Qdrant for semantic search."
 weight: 10
 partition: ecosystem
 hideInSidebar: true

qdrant-landing/content/documentation/tutorials-build-essentials/multimodal-search.md

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 title: Multimodal and Multilingual RAG
+short_description: "Build a multimodal, multilingual RAG application with LlamaIndex and Qdrant that searches across image and text modalities."
+description: "Tutorial: combine LlamaIndex with Qdrant to power multimodal, multilingual RAG over images and text using a shared embedding space and vector search."
 weight: 25
 hideInSidebar: true
 partition: ecosystem

qdrant-landing/content/documentation/tutorials-build-essentials/qdrant-n8n.md

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 title: n8n Workflow Automation
+short_description: "Automate Qdrant workflows in n8n: build no-code pipelines for vector search, recommendations, and unstructured data analysis."
+description: "Tutorial: connect Qdrant to n8n to automate vector search, recommendations, and unstructured data workflows using a low-code visual pipeline builder."
 weight: 35
 #partition: ecosystem
 social_preview_image: /documentation/examples/qdrant-n8n-2/preview/social_preview.png

qdrant-landing/content/documentation/tutorials-build-essentials/rag-deepseek.md

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 title: 5-Minute RAG with DeepSeek
+short_description: "Build a five-minute RAG pipeline pairing Qdrant vector search with the DeepSeek LLM to enrich prompts with retrieved context."
+description: "Step-by-step tutorial: build a RAG pipeline with Qdrant and DeepSeek that stores embeddings in a vector database and grounds LLM answers in retrieved context."
 weight: 30
 partition: ecosystem
 social_preview_image: /documentation/examples/rag-deepseek/social_preview.png

qdrant-landing/content/documentation/tutorials-build-essentials/video-anomaly-edge-part-1.md

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 title: "Video Anomaly Detection Part 1: Architecture, Twelve Labs, and NVIDIA VSS"
+short_description: "Architect a real-time video anomaly detection system on Qdrant Edge with Twelve Labs video intelligence and NVIDIA Metropolis VSS."
+description: "Part 1 tutorial: design a real-time video anomaly detection pipeline using Qdrant Edge vector search, Twelve Labs embeddings, and NVIDIA Metropolis VSS."
 weight: 40
 partition: ecosystem
 aliases:

qdrant-landing/content/documentation/tutorials-build-essentials/video-anomaly-edge-part-2.md

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 title: "Video Anomaly Detection Part 2: Edge-to-Cloud Pipeline"
+short_description: "Build the edge-to-cloud pipeline for video anomaly detection using Qdrant Edge shards, snapshot sync, and offline-capable kNN search."
+description: "Part 2 tutorial: implement the edge-to-cloud pipeline for video anomaly detection with Qdrant Edge shards, snapshot sync, and offline-ready vector search."
 weight: 45
 partition: ecosystem
 aliases:

qdrant-landing/content/documentation/tutorials-build-essentials/video-anomaly-edge-part-3.md

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 title: "Video Anomaly Detection Part 3: Scoring, Governance, and Deployment"
+short_description: "Score, govern, and deploy a video anomaly detection system on Qdrant Edge with incident formation and baseline protection."
+description: "Part 3 tutorial: turn raw kNN scores into incidents, protect the baseline, and deploy a Qdrant Edge video anomaly detection system to production."
 weight: 50
 partition: ecosystem
 aliases: