minor formatting changes

2026-10-02 17:38:31 +02:00 · 2026-03-15 20:27:11 -07:00
parent fdc583b9f6
commit 824c5a27e0
4 changed files with 29 additions and 29 deletions
@@ -10,9 +10,9 @@ partition: build
 | Tutorial | Objective | Stack | Time | Level |
 | :--- | :--- | :--- | :--- | :--- |
 | [5-Minute RAG with DeepSeek](/documentation/tutorials-build-essentials/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | <span class="pill">Python</span> | 5m | <span class="text-green">Beginner</span> |
-| [Discord RAG Bot](/documentation/tutorials-build-essentials/agentic-rag-camelai-discord/) | Develop a functional bot with CAMEL-AI. | <span class="pill">OpenAI</span> | 45m | <span class="text-yellow">Intermediate</span> |
 | [Agentic RAG with CrewAI](/documentation/tutorials-build-essentials/agentic-rag-crewai-zoom/) | Step-by-step multi-agent RAG system. | <span class="pill">CrewAI</span> | 45m | <span class="text-green">Beginner</span> |
 | [n8n Workflow Automation](/documentation/tutorials-build-essentials/qdrant-n8n/) | Combine Qdrant with low-code n8n workflows. | <span class="pill">n8n</span> | 45m | <span class="text-yellow">Intermediate</span> |
+| [Discord RAG Bot](/documentation/tutorials-build-essentials/agentic-rag-camelai-discord/) | Develop a functional bot with CAMEL-AI. | <span class="pill">OpenAI</span> | 45m | <span class="text-yellow">Intermediate</span> |
 | [Video Anomaly Detection Part I](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/) | Architecture, Twelve Labs, and NVIDIA VSS integration. | <span class="pill">Python</span> | 90m | <span class="text-red">Advanced</span> |
 | [Video Anomaly Detection Part II](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/) | Two-shard Qdrant Edge architecture and escalation pipeline. | <span class="pill">Python</span> | 90m | <span class="text-red">Advanced</span> |
 | [Video Anomaly Detection Part III](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/) | Scoring, baseline governance, and deployment on Vultr. | <span class="pill">Python</span> | 90m | <span class="text-red">Advanced</span> |
@@ -1,5 +1,5 @@
 ---
-title: "Video Anomaly Detection Part I | Architecture, Twelve Labs, and NVIDIA VSS"
+title: "Video Anomaly Detection Part 1: Architecture, Twelve Labs, and NVIDIA VSS"
 weight: 9
 partition: build
 social_preview_image: /articles_data/video-anomaly-edge/preview/social_preview.jpg
@@ -13,12 +13,12 @@ aliases:
 | Time: 90 min | Level: Advanced | Output: [GitHub](https://github.com/qdrant/video-anomaly-edge) |
 | --- | ----------- | ----------- |

-*This is Part I of a 3-part series on building real-time video anomaly detection from edge to cloud. We'll go from architecture and integrations to a production-grade detection pipeline.*
+*This is Part 1 of a 3-part series on building real-time video anomaly detection from edge to cloud. We'll go from architecture and integrations to a production-grade detection pipeline.*

 **Series:**
- Part I | Architecture, Twelve Labs, and NVIDIA VSS (here)
- [Part II | Edge-to-Cloud Pipeline](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/)
- [Part III | Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)
+- Part 1 | Architecture, Twelve Labs, and NVIDIA VSS (here)
+- [Part 2 | Edge-to-Cloud Pipeline](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/)
+- [Part 3 | Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)

 ---

@@ -522,13 +522,13 @@ services:

 ## Recap

-In Part I, you set up the project, learned why kNN anomaly detection in Qdrant outperforms traditional classifiers for open-world surveillance, integrated Twelve Labs Marengo and Pegasus for video embeddings and Q&A, and connected NVIDIA VSS for GPU-accelerated ingestion. The architecture is in place. Now we need to build the edge.
+In Part 1, you set up the project, learned why kNN anomaly detection in Qdrant outperforms traditional classifiers for open-world surveillance, integrated Twelve Labs Marengo and Pegasus for video embeddings and Q&A, and connected NVIDIA VSS for GPU-accelerated ingestion. The architecture is in place. Now we need to build the edge.

 ## What's Next

-In **[Part II | Edge-to-Cloud Pipeline](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/)**, we'll implement the two-shard Qdrant Edge architecture, edge triage scoring, escalation flow with ensemble scoring, and offline resilience.
+In **[Part 2 | Edge-to-Cloud Pipeline](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/)**, we'll implement the two-shard Qdrant Edge architecture, edge triage scoring, escalation flow with ensemble scoring, and offline resilience.

-In **[Part III | Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)**, we'll cover incident formation, baseline governance, unified retrieval, results on UCF-Crime, and deployment on Vultr Cloud GPUs.
+In **[Part 3 | Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)**, we'll cover incident formation, baseline governance, unified retrieval, results on UCF-Crime, and deployment on Vultr Cloud GPUs.

 ---

@@ -1,5 +1,5 @@
 ---
-title: "Video Anomaly Detection Part II | Edge-to-Cloud Pipeline"
+title: "Video Anomaly Detection Part 2: Edge-to-Cloud Pipeline"
 weight: 10
 partition: build
 social_preview_image: /articles_data/video-anomaly-edge/preview/social_preview.jpg
@@ -12,16 +12,16 @@ aliases:
 | Time: 90 min | Level: Advanced | Output: [GitHub](https://github.com/qdrant/video-anomaly-edge) |
 | --- | ----------- | ----------- |

-*This is Part II of a 3-part series on building real-time video anomaly detection from edge to cloud.*
+*This is Part 2 of a 3-part series on building real-time video anomaly detection from edge to cloud.*

 **Series:**
- [Part I | Architecture, Twelve Labs, and NVIDIA VSS](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/)
- Part II | Edge-to-Cloud Pipeline (here)
- [Part III | Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)
+- [Part 1 | Architecture, Twelve Labs, and NVIDIA VSS](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/)
+- Part 2 | Edge-to-Cloud Pipeline (here)
+- [Part 3 | Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)

 ---

-In [Part I](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/), we set up the project, covered why kNN anomaly detection in Qdrant outperforms classifiers, integrated Twelve Labs for video embeddings and Q&A, and connected NVIDIA VSS. Now we build the edge.
+In [Part 1](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/), we set up the project, covered why kNN anomaly detection in Qdrant outperforms classifiers, integrated Twelve Labs for video embeddings and Q&A, and connected NVIDIA VSS. Now we build the edge.

 ## Why Qdrant Edge

@@ -517,19 +517,19 @@ This triggers a snapshot download to that specific device, letting you roll out

 ## Recap

-In Part II, you built Qdrant Edge's two-shard architecture (immutable baseline + mutable live context), implemented edge triage that reduces cloud processing by ~6x, wired the escalation pipeline with ensemble scoring and temporal boosting, and added offline resilience. The edge is running. Now we need to turn raw scores into actionable incidents.
+In Part 2, you built Qdrant Edge's two-shard architecture (immutable baseline + mutable live context), implemented edge triage that reduces cloud processing by ~6x, wired the escalation pipeline with ensemble scoring and temporal boosting, and added offline resilience. The edge is running. Now we need to turn raw scores into actionable incidents.

 ## What's Next

-In **[Part III | Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)**, we'll cover incident formation from raw scores, baseline governance to prevent poisoning, unified retrieval across cameras, evaluation results on UCF-Crime, and deployment on Vultr Cloud GPUs.
+In **[Part 3 | Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)**, we'll cover incident formation from raw scores, baseline governance to prevent poisoning, unified retrieval across cameras, evaluation results on UCF-Crime, and deployment on Vultr Cloud GPUs.

 ---

 Additional Resources:

 - **Project Repository**: [qdrant/video-anomaly-edge](https://github.com/qdrant/video-anomaly-edge)
- **Part I**: [Architecture, Twelve Labs, and NVIDIA VSS](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/)
- **Part III**: [Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)
+- **Part 1**: [Architecture, Twelve Labs, and NVIDIA VSS](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/)
+- **Part 3**: [Scoring, Governance, and Deployment](/documentation/tutorials-build-essentials/video-anomaly-edge-part-3/)
 - **Qdrant Edge Documentation**: [qdrant.tech/documentation/edge](/documentation/edge/)
 - **Twelve Labs Documentation**: [docs.twelvelabs.io](https://docs.twelvelabs.io/)
 - **Vultr Cloud GPUs**: [vultr.com/products/cloud-gpu](https://www.vultr.com/products/cloud-gpu/)
@@ -1,5 +1,5 @@
 ---
-title: "Video Anomaly Detection Part III | Scoring, Governance, and Deployment"
+title: "Video Anomaly Detection Part 3: Scoring, Governance, and Deployment"
 weight: 11
 partition: build
 social_preview_image: /articles_data/video-anomaly-edge/preview/social_preview.jpg
@@ -12,27 +12,27 @@ aliases:
 | Time: 90 min | Level: Advanced | Output: [GitHub](https://github.com/qdrant/video-anomaly-edge) |
 | --- | ----------- | ----------- |

-*This is Part III of a 3-part series on building real-time video anomaly detection from edge to cloud.*
+*This is Part 3 of a 3-part series on building real-time video anomaly detection from edge to cloud.*

 **Series:**
- [Part I | Architecture, Twelve Labs, and NVIDIA VSS](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/)
- [Part II | Edge-to-Cloud Pipeline](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/)
- Part III | Scoring, Governance, and Deployment (here)
+- [Part 1 | Architecture, Twelve Labs, and NVIDIA VSS](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/)
+- [Part 2 | Edge-to-Cloud Pipeline](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/)
+- Part 3 | Scoring, Governance, and Deployment (here)

 ---

-In [Part I](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/), we set up the architecture, Twelve Labs integration, and NVIDIA VSS connection. In [Part II](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/), we built Qdrant Edge's two-shard architecture and the escalation pipeline. Now we turn raw scores into incidents, protect the baseline, and deploy.
+In [Part 1](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/), we set up the architecture, Twelve Labs integration, and NVIDIA VSS connection. In [Part 2](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/), we built Qdrant Edge's two-shard architecture and the escalation pipeline. Now we turn raw scores into incidents, protect the baseline, and deploy.

 ## Getting Started

-Before continuing, make sure you have completed Parts I and II and have the following running:
+Before continuing, make sure you have completed Parts 1 and 2 and have the following running:

 - Docker stack up (`docker compose up`)
 - Qdrant Cloud collection populated with baseline embeddings
 - At least one edge device registered and synced
 - Twelve Labs indexes created (Marengo and Pegasus)

-If you're starting fresh, return to [Part I](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/) for setup instructions.
+If you're starting fresh, return to [Part 1](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/) for setup instructions.

 ## Anomaly Scoring and Incident Formation

@@ -292,8 +292,8 @@ The key takeaways:

 Check out the full series and additional resources:

- **Part I**: [Architecture, Twelve Labs, and NVIDIA VSS](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/)
- **Part II**: [Edge-to-Cloud Pipeline](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/)
+- **Part 1**: [Architecture, Twelve Labs, and NVIDIA VSS](/documentation/tutorials-build-essentials/video-anomaly-edge-part-1/)
+- **Part 2**: [Edge-to-Cloud Pipeline](/documentation/tutorials-build-essentials/video-anomaly-edge-part-2/)
 - **Project Repository**: [qdrant/video-anomaly-edge](https://github.com/qdrant/video-anomaly-edge)
 - **Live Demo**: [qdrant-edge-video-anomaly.vercel.app](https://qdrant-edge-video-anomaly.vercel.app/)
 - **NVIDIA VSS Twelve Labs Integration**: [qdrant/twelvelabs-nvidia-vss](https://github.com/qdrant/twelvelabs-nvidia-vss)