Merge pull request #2125 from qdrant/tutorials-standardization

Tutorials Standardization
2026-09-27 23:18:29 +02:00 · 2026-02-05 12:01:38 -08:00
parent 3b89a0c5a3 87180dc77f
commit 7e9ca56437
49 changed files with 144 additions and 203 deletions
@@ -1,16 +1,3 @@
---
-title: Basics
-weight: 17
-is_empty: false
-aliases:
-  - how-to
-  - tutorials
-partition: qdrant
---
-
-### Basic Tutorials
-*Get up and running with Qdrant in minutes.*
-
 | Tutorial | Objective | Stack | Time | Level |
 | :--- | :--- | :--- | :--- | :--- |
 | [Qdrant Local Quickstart](/documentation/quickstart/) | Basic CRUD operations and local deployment. | <span class="pill">Python</span> | 10m | <span class="text-green">Beginner</span> |
@@ -1,14 +1,4 @@
---
-title: Develop & Implement
-weight: 21
-is_empty: false
-partition: qdrant
---
-
-### Develop & Implement Tutorials
-*Core tools and APIs for building with Qdrant.*
-
 | Tutorial | Objective | Stack | Time | Level |
 | :--- | :--- | :--- | :--- | :--- |
 | [Bulk Operations](/documentation/tutorials-develop/bulk-upload/) | High-scale ingestion approaches. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
-| [Async API](/documentation/tutorials-develop/async-api/) | Use Asynchronous programming for efficiency. | <span class="pill">Python</span> | 25m | <span class="text-yellow">Intermediate</span> |
+| [Async API](/documentation/tutorials-develop/async-api/) | Use Asynchronous programming for efficiency. | <span class="pill">Python</span> | 25m | <span class="text-yellow">Intermediate</span> |
@@ -1,20 +1,8 @@
---
-title: Operations & Scale
-weight: 20
-is_empty: false
-aliases:
-  - how-to
-  - tutorials
-partition: qdrant
---
-
-### Operations & Scale Tutorials
-*Production-grade management, monitoring, and high-volume optimization.*
-
 | Tutorial | Objective | Stack | Time | Level |
 | :--- | :--- | :--- | :--- | :--- |
 | [Snapshots](/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | <span class="pill">Python</span> | 20m | <span class="text-green">Beginner</span> |
 | [Data Migration](/documentation/tutorials-operations/migration/) | Move embeddings to Qdrant. | <span class="pill">CLI</span> | 30m | <span class="text-yellow">Intermediate</span> |
+| [Embedding Model Migration](/documentation/tutorials-operations/embedding-model-migration/) | Use your new model with zero downtime. | <span class="pill">None</span> | 40m | <span class="text-yellow">Intermediate</span> |
+| [Large-Scale Search](/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | <span class="pill">None</span> | 48h | <span class="text-red">Advanced</span> |
 | [Qdrant Cloud Prometheus Monitoring](/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Self-Hosted Prometheus Monitoring](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Large-Scale Search](/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | <span class="pill">None</span> | 2d | <span class="text-red">Advanced</span> |
+| [Self-Hosted Prometheus Monitoring](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> |
@@ -1,16 +1,3 @@
---
-title: Search Engineering
-weight: 18
-is_empty: false
-aliases:
-  - how-to
-  - tutorials
-partition: qdrant
---
-
-### Search Engineering Tutorials
-*Master vector search modalities, reranking, and retrieval quality.*
-
 | Tutorial | Objective | Stack | Time | Level |
 | :--- | :--- | :--- | :--- | :--- |
 | [Semantic Search Intro](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | <span class="pill">FastAPI</span> | 30m | <span class="text-green">Beginner</span> |
@@ -1,10 +1,11 @@
 ---
-title: Local Quickstart
+title: Qdrant Quickstart
 weight: 4
 partition: qdrant
 aliases:
  - quick_start
  - quick-start
+  - quickstart
 ---
 # How to Get Started with Qdrant Locally

@@ -1,11 +1,11 @@
 ---
-title: Automate filtering with LLMs
+title: LLM-Powered Filter Automation
 weight: 2
 alias:
    - /documentation/database-tutorials/automate-filtering-with-llms/
 ---

-# Automate filtering with LLMs
+# LLM-Powered Filter Automation with Qdrant

 Our [complete guide to filtering in vector search](/articles/vector-search-filtering/) describes why filtering is
 important, and how to implement it with Qdrant. However, applying filters is easier when you build an application
@@ -1,12 +1,12 @@
 ---
-title: Reranking in Semantic Search
+title: Reranking for Better Search
 weight: 1
 partition: build
 aliases:
  - ../search-precision
 ---

-# Reranking in RAG with Qdrant Vector Database
+# Improve Semantic Search with Reranking using Qdrant

 In Retrieval-Augmented Generation (RAG) systems, irrelevant or missing information can throw off your model's ability to produce accurate, meaningful outputs. One of the best ways to ensure you're feeding your language model the most relevant, context-rich documents is through reranking. It’s a game-changer.

@@ -1,12 +1,12 @@
 ---
-title: How to Setup Seamless Data Streaming with Kafka and Qdrant
+title: Kafka Streaming into Qdrant
 weight: 49
 hideInSidebar: true
 aliases:
  - /examples/data-streaming-kafka-qdrant/
 ---

-# Setup Data Streaming with Kafka via Confluent
+# Stream Real-Time Data into Qdrant with Kafka and Confluent

 **Author:** [M K Pavan Kumar](https://www.linkedin.com/in/kameshwara-pavan-kumar-mantha-91678b21/) , research scholar at [IIITDM, Kurnool](https://iiitk.ac.in). Specialist in hallucination mitigation techniques and RAG methodologies. 
 • [GitHub](https://github.com/pavanjava) • [Medium](https://medium.com/@manthapavankumar11)  
@@ -1,12 +1,12 @@
 ---
-title: Qdrant on Databricks
+title: Databricks Ingestion
 weight: 36
 hideInSidebar: true
 aliases:
  - /documentation/examples/databricks/
 ---

-# Qdrant on Databricks
+# Ingest Databricks Data into Qdrant

 | Time: 30 min | Level: Intermediate | [Complete Notebook](https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/4750876096379825/93425612168199/6949977306828869/latest.html) |
 | ------------ | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
@@ -1,12 +1,12 @@
 ---
-title: Semantic Querying with Airflow and Astronomer
+title: Querying with Airflow
 weight: 36
 hideInSidebar: true
 aliases:
  - /documentation/examples/qdrant-airflow-astronomer/
 ---

-# Semantic Querying with Airflow and Astronomer
+# Qdrant Semantic Querying with Airflow and Astronomer

 | Time: 45 min | Level: Intermediate |     |     |
 | ------------ | ------------------- | --- | --- |
@@ -1,9 +1,13 @@
 ---
-title: Using Cloud Inference to Build Hybrid Search
+title: Cloud Inference Hybrid Search
 hideInSidebar: true
 weight: 35
 ---
-# Using Cloud Inference with Qdrant for Vector Search
+# Hybrid Search Using Qdrant Cloud Inference
+
+| Time: 30 min | Level: Intermediate |
+| --- | ----------- |
+
 In this tutorial, we'll walkthrough building a **hybrid semantic search engine** using Qdrant Cloud's built-in [inference](/documentation/cloud/inference/) capabilities. You'll learn how to:
 - Automatically embed your data using [cloud Inference](/documentation/cloud/inference/) without needing to run local models,
 - Combine dense semantic embeddings with [sparse BM25 keywords](https://qdrant.tech/documentation/advanced-tutorials/reranking-hybrid-search/),  and
@@ -1,9 +1,11 @@
 ---
-title: Monitoring Hybrid/Private Cloud with Prometheus and Grafana
+title: Self-Hosted Prometheus Monitoring
 weight: 37
 ---

 # Monitoring Hybrid/Private Cloud with Prometheus and Grafana
+| Time: 30 min | Level: Intermediate |
+| --- | ----------- |

 This tutorial will guide you through the process of setting up Prometheus and Grafana to monitor Qdrant databases running in a Kubernetes cluster used for Hybrid or Private Cloud.

@@ -1,5 +1,5 @@
 ---
-title: Monitoring Managed Cloud with Prometheus and Grafana
+title: Managed Cloud Prometheus Monitoring
 weight: 36
 ---

@@ -1,19 +1,14 @@
 ---
 title: Basics
-weight: 31
-is_empty: true
-hideInSidebar: true
+weight: 17
+is_empty: false
 aliases:
  - how-to
  - tutorials
 partition: qdrant
 ---

-# Basic Tutorials
+### Basic Tutorials
 *Get up and running with Qdrant in minutes.*

-| Tutorial | Objective | Stack | Time | Level |
-| :--- | :--- | :--- | :--- | :--- |
-| [Local Qdrant Setup](/documentation/quickstart/) | Basic CRUD operations and local deployment. | <span class="pill">Python</span> | 10m | <span class="text-green">Beginner</span> |
-| [5-Minute Semantic Search](/documentation/tutorials-basics/search-beginners/) | Build a search engine for science fiction books. | <span class="pill">Python</span> | 5m | <span class="text-green">Beginner</span> |
-| [5-Minute RAG with DeepSeek](/documentation/tutorials-basics/rag-deepseek/) | Build a RAG pipeline with DeepSeek enrichment. | <span class="pill">Python</span> | 5m | <span class="text-green">Beginner</span> |
+{{% include "content/documentation/headless/content/tutorials/basic.md" %}}
@@ -1,12 +1,13 @@
 ---
-title: Load a HuggingFace Dataset
+title: Hugging Face Dataset Ingestion
+hideInSidebar: true
 aliases:
  - /documentation/tutorials/huggingface-datasets/
  - /documentation/database-tutorials/huggingface-datasets/
 weight: 3
 ---

-# Load and Search Hugging Face Datasets with Qdrant
+# Load Hugging Face Datasets into Qdrant

 [Hugging Face](https://huggingface.co/) provides a platform for sharing and using ML models and
 datasets. [Qdrant](https://huggingface.co/Qdrant) also publishes datasets along with the
@@ -1,13 +1,13 @@
 ---
 title: Semantic Search 101
-weight: 1
+weight: 4
 aliases:
  - /documentation/tutorials/mighty.md/
  - /documentation/tutorials/search-beginners/
  - /documentation/beginner-tutorials/search-beginners/
 ---

-# Build Your First Semantic Search Engine in 5 Minutes
+# Build a Semantic Search Engine in 5 Minutes

 | Time: 5 - 15 min | Level: Beginner |  |   |
 | --- | ----------- | ----------- |----------- |
@@ -3,7 +3,7 @@ title: Essential Examples
 weight: 21
 partition: build 
 ---
-# Essential Examples
+# Integration Examples

 *Step-by-step guides for connecting Qdrant to the broader AI ecosystem and data stacks.*

@@ -1,5 +1,5 @@
 ---
-title: Agentic RAG Discord Bot with CAMEL-AI
+title: Discord RAG Bot
 weight: 4
 #partition: build
 social_preview_image: /documentation/examples/agentic-rag-camelai-discord/social-preview.png
@@ -7,9 +7,9 @@ aliases:
  - /documentation/agentic-rag-camelai-discord/
 ---

-![agentic-rag-camelai-astronaut](/documentation/examples/agentic-rag-camelai-discord/astronaut-main.png)
+<!--  ![agentic-rag-camelai-astronaut](/documentation/examples/agentic-rag-camelai-discord/astronaut-main.png) -->

-# Agentic RAG Discord ChatBot with Qdrant, CAMEL-AI, & OpenAI
+# Qdrant Agentic RAG Discord Bot with CAMEL-AI and OpenAI

 | Time: 45 min | Level: Intermediate | [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Ymqzm6ySoyVOekY7fteQBCFCXYiYyHxw#scrollTo=QQZXwzqmNfaS) |
 | --- | ----------- | ----------- |----------- |
@@ -1,14 +1,14 @@
 ---
-title: Simple Agentic RAG System
+title: Agentic RAG with CrewAI
 weight: 2
 partition: build
 social_preview_image: /documentation/examples/agentic-rag-crewai-zoom/social_preview.png
 aliases:
  - /documentation/agentic-rag-crewai-zoom/
 ---
-![agentic-rag-crewai-zoom](/documentation/examples/agentic-rag-crewai-zoom/agentic-rag-1.png)
+<!-- ![agentic-rag-crewai-zoom](/documentation/examples/agentic-rag-crewai-zoom/agentic-rag-1.png) -->

-# Agentic RAG With CrewAI & Qdrant Vector Database
+# Qdrant Agentic RAG System with CrewAI

 | Time: 45 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/examples/tree/master/agentic_rag_zoom_crewai) |
 | --- | ----------- | ----------- |----------- |
@@ -86,7 +86,7 @@ The system is built on three main components:

 ## Getting Started

-![agentic-rag-crewai-zoom](/documentation/examples/agentic-rag-crewai-zoom/agentic-rag-2.png)
+<!--  ![agentic-rag-crewai-zoom](/documentation/examples/agentic-rag-crewai-zoom/agentic-rag-2.png) -->

 1. **Get API Credentials for Qdrant**:
   - Sign up for an account at [Qdrant Cloud](https://cloud.qdrant.io/signup).
@@ -352,7 +352,7 @@ This combination of features creates an interface that's both powerful and appro
 ---
 ## Conclusion

-![agentic-rag-crewai-zoom](/documentation/examples/agentic-rag-crewai-zoom/agentic-rag-3.png)
+<!-- ![agentic-rag-crewai-zoom](/documentation/examples/agentic-rag-crewai-zoom/agentic-rag-3.png) -->

 This tutorial has demonstrated how to build a sophisticated meeting analysis system that combines vector search with AI agents. Let's recap the key components we've covered:

@@ -384,5 +384,3 @@ This foundation can be extended in many ways, such as:
 - Integrating with other data sources

 The code is available in the [repository](https://github.com/qdrant/examples/tree/master/agentic_rag_zoom_crewai), and we encourage you to experiment with your own modifications and improvements.
-
---
@@ -1,12 +1,15 @@
 ---
-title: Agentic RAG With LangGraph 
+title: Agentic RAG with LangGraph
 weight: 3
 partition: build
 hideInSidebar: true
 aliases:
  - /documentation/agentic-rag-langgraph/
 ---
-# Agentic RAG With LangGraph and Qdrant
+# Agentic RAG with LangGraph and Qdrant
+
+| Time: 45 min | Level: Intermediate |
+| --- | ----------- |

 Traditional Retrieval-Augmented Generation (RAG) systems follow a straightforward path: query → retrieve → generate. Sure, this works well for many scenarios. But let’s face it—this linear approach often struggles when you're dealing with complex queries that demand multiple steps or pulling together diverse types of information.

@@ -1,5 +1,5 @@
 ---
-title: Data Ingestion for Beginners
+title: S3 Ingestion with LangChain
 weight: 2
 partition: build
 hideInSidebar: true
@@ -7,9 +7,9 @@ social_preview_image: /documentation/examples/data-ingestion-beginners/social_pr
 aliases:
  - /documentation/data-ingestion-beginners/
 ---
-![data-ingestion-beginners-7](/documentation/examples/data-ingestion-beginners/data-ingestion-7.png)
+<!-- ![data-ingestion-beginners-7](/documentation/examples/data-ingestion-beginners/data-ingestion-7.png) -->

-# Send S3 Data to Qdrant Vector Store with LangChain
+# S3 Ingestion with LangChain and Qdrant

 | Time: 30 min | Level: Beginner |  |    |
 | --- | ----------- | ----------- |----------- |
@@ -1,5 +1,5 @@
 ---
-title: Multilingual & Multimodal RAG with LlamaIndex
+title: Multimodal and Multilingual RAG
 weight: 5
 hideInSidebar: true
 partition: build
@@ -10,9 +10,9 @@ aliases:
  - /documentation/multimodal-search/
 ---

-# Multilingual & Multimodal Search with LlamaIndex
+# Multimodal and Multilingual RAG with LlamaIndex and Qdrant

-![Snow prints](/documentation/examples/multimodal-search/image-1.png)
+<!-- ![Snow prints](/documentation/examples/multimodal-search/image-1.png) -->

 | Time: 15 min | Level: Beginner |Output: [GitHub](https://github.com/qdrant/examples/blob/master/multimodal-search/Multimodal_Search_with_LlamaIndex.ipynb)|[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/qdrant/examples/blob/master/multimodal-search/Multimodal_Search_with_LlamaIndex.ipynb)   |
 | --- | ----------- | ----------- | ----------- |
@@ -1,5 +1,5 @@
 ---
-title: Automating Processes with Qdrant and n8n
+title: n8n Workflow Automation
 weight: 7
 #partition: build
 social_preview_image: /documentation/examples/qdrant-n8n-2/preview/social_preview.png
@@ -9,9 +9,9 @@ aliases:

 ---

-![n8n-qdrant](/documentation/examples/qdrant-n8n-2/cover.png)
+<!-- ![n8n-qdrant](/documentation/examples/qdrant-n8n-2/cover.png) -->

-# Automating Processes with Qdrant and n8n beyond simple RAG
+# Automate Qdrant Workflows with n8n

 | Time: 45 min | Level: Intermediate |
 | --- | ----------- |
@@ -1,5 +1,5 @@
 ---
-title: 5 Minute RAG with Qdrant and DeepSeek
+title: 5-Minute RAG with DeepSeek
 weight: 6
 partition: build
 social_preview_image: /documentation/examples/rag-deepseek/social_preview.png
@@ -7,9 +7,9 @@ aliases:
    - /documentation/rag-deepseek/
 ---

-![deepseek-rag-qdrant](/documentation/examples/rag-deepseek/deepseek.png)
+<!-- ![deepseek-rag-qdrant](/documentation/examples/rag-deepseek/deepseek.png) -->

-# 5 Minute RAG with Qdrant and DeepSeek
+# RAG in 5 Minutes with DeepSeek and Qdrant

 | Time: 5 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/examples/blob/master/rag-with-qdrant-deepseek/deepseek-qdrant.ipynb) |
 | --- | ----------- | ----------- |----------- |
@@ -1,15 +1,11 @@
 ---
 title: Develop & Implement
-weight: 35
-is_empty: true
-hideInSidebar: true
+weight: 21
+is_empty: false
 partition: qdrant
 ---

-# Develop & Implement Tutorials
+### Develop & Implement Tutorials
 *Core tools and APIs for building with Qdrant.*

-| Tutorial | Objective | Stack | Time | Level |
-| :--- | :--- | :--- | :--- | :--- |
-| [Bulk Data Uploads](/documentation/tutorials-develop/bulk-upload/) | High-scale ingestion tricks for power users. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
-| [Python Async API](/documentation/tutorials-develop/async-api/) | Use Asynchronous programming for efficiency. | <span class="pill">Python</span> | 25m | <span class="text-yellow">Intermediate</span> |
+{{% include "content/documentation/headless/content/tutorials/develop.md" %}}
@@ -1,12 +1,15 @@
 ---
-title: Build With Async API
+title: Async API
 aliases:
  - /documentation/tutorials/async-api/
  - /documentation/database-tutorials/async-api/
 weight: 4
 ---

-# Using Qdrant’s Async API for Efficient Python Applications
+# Build High-Throughput Applications with Qdrant's Async API
+
+| Time: 25 min | Level: Intermediate |
+| --- | ----------- |

 Asynchronous programming is being broadly adopted in the Python ecosystem. Tools such as FastAPI [have embraced this new 
 paradigm](https://fastapi.tiangolo.com/async/), but it is also becoming a standard for ML models served as SaaS. For example, the Cohere SDK
@@ -1,5 +1,5 @@
 ---
-title: Bulk Upload Vectors
+title: Bulk Operations
 aliases:
  - /documentation/tutorials/bulk-upload/
  - /documentation/database-tutorials/bulk-upload/
@@ -8,6 +8,9 @@ weight: 1

 # Bulk Upload Vectors to a Qdrant Collection

+| Time: 20 min | Level: Intermediate |
+| --- | ----------- |
+
 Uploading a large-scale dataset fast might be a challenge, but Qdrant has a few tricks to help you with that.

 The first important detail about data uploading is that the bottleneck is usually located on the client side, not on the server side.
@@ -13,50 +13,28 @@ partition: qdrant
 ### Basic Tutorials
 *Get up and running with Qdrant in minutes.*

-| Tutorial | Objective | Stack | Time | Level |
-| :--- | :--- | :--- | :--- | :--- |
-| [Qdrant Local Quickstart](/documentation/quickstart/) | Basic CRUD operations and local deployment. | <span class="pill">Python</span> | 10m | <span class="text-green">Beginner</span> |
-| [Semantic Search 101](/documentation/tutorials-basics/search-beginners/) | Build a search engine for science fiction books. | <span class="pill">Python</span> | 5m | <span class="text-green">Beginner</span> |
+{{% include "content/documentation/headless/content/tutorials/basic.md" %}}

 ---

 ### Search Engineering Tutorials
 *Master vector search modalities, reranking, and retrieval quality.*

-| Tutorial | Objective | Stack | Time | Level |
-| :--- | :--- | :--- | :--- | :--- |
-| [Semantic Search Intro](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | <span class="pill">FastAPI</span> | 30m | <span class="text-green">Beginner</span> |
-| [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search. | <span class="pill">FastAPI</span> | 20m | <span class="text-green">Beginner</span> |
-| [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
-| [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
-| [Semantic Search for Code](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
-| [Multivectors and Late Interaction](/documentation/advanced-tutorials/using-multivector-representations/) | Effective use of multivector representations. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Static Embeddings](/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the utility of static embeddings. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
+{{% include "content/documentation/headless/content/tutorials/search-engineering.md" %}}

 ---

 ### Operations & Scale
 *Production-grade management, monitoring, and high-volume optimization.*

-| Tutorial | Objective | Stack | Time | Level |
-| :--- | :--- | :--- | :--- | :--- |
-| [Snapshots](/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | <span class="pill">Python</span> | 20m | <span class="text-green">Beginner</span> |
-| [Data Migration](/documentation/tutorials-operations/migration/) | Move embeddings to Qdrant. | <span class="pill">CLI</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Qdrant Cloud Prometheus Monitoring](/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Self-Hosted Prometheus Monitoring](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Large-Scale Search](/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | <span class="pill">None</span> | 2d | <span class="text-red">Advanced</span> |
+{{% include "content/documentation/headless/content/tutorials/operations.md" %}}

 ---

 ### Develop & Implement
 *Core tools and APIs for building with Qdrant.*

-| Tutorial | Objective | Stack | Time | Level |
-| :--- | :--- | :--- | :--- | :--- |
-| [Bulk Operations](/documentation/tutorials-develop/bulk-upload/) | High-scale ingestion approaches. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
-| [Async API](/documentation/tutorials-develop/async-api/) | Use Asynchronous programming for efficiency. | <span class="pill">Python</span> | 25m | <span class="text-yellow">Intermediate</span> |
+{{% include "content/documentation/headless/content/tutorials/develop.md" %}}

 <!-- KEEP BELOW FOR REFERENCE -->
 <!-- 
@@ -1,22 +1,20 @@
 ---
 title: Operations & Scale
-weight: 34
-is_empty: true
-hideInSidebar: true
+weight: 20
+is_empty: false
 aliases:
  - how-to
  - tutorials
 partition: qdrant
 ---

-# Operations & Scale Tutorials
+### Operations & Scale Tutorials
 *Production-grade management, monitoring, and high-volume optimization.*

-| Tutorial | Objective | Stack | Time | Level |
-| :--- | :--- | :--- | :--- | :--- |
-| [Snapshot & Backup](/documentation/tutorials-operations/create-snapshot/) | Create and restore collection snapshots. | <span class="pill">Python</span> | 20m | <span class="text-green">Beginner</span> |
-| [Cloud Inference Search](/documentation/tutorials-and-examples/cloud-inference-hybrid-search/) | Hybrid search using Qdrant's built-in inference. | <span class="pill">Any</span> | 20m | <span class="text-green">Beginner</span> |
-| [Embedding Migration](/documentation/tutorials-operations/migration/) | Move dense and sparse embeddings to Qdrant. | <span class="pill">CLI</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Monitor Managed Cloud](/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Monitor Private Cloud](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Billion-Scale Search](/documentation/tutorials-operations/large-scale-search/) | Cost-efficient search for LAION-400M datasets. | <span class="pill">None</span> | 2 days | <span class="text-red">Advanced</span> |
+{{% include "content/documentation/headless/content/tutorials/operations.md" %}}
+
+
+<!-- KEEP BELOW FOR REFERENCE -->
+
+<!-- | [Qdrant Cloud Prometheus Monitoring](/documentation/tutorials-and-examples/managed-cloud-prometheus/) | Observability with Prometheus and Grafana. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> | -->
+<!-- | [Self-Hosted Prometheus Monitoring](/documentation/tutorials-and-examples/hybrid-cloud-prometheus/) | Observability for hybrid/private cloud setups. | <span class="pill">Prometheus</span> | 30m | <span class="text-yellow">Intermediate</span> | -->
@@ -1,12 +1,12 @@
 ---
-title: Create & Restore Snapshots
+title: Snapshots
 aliases:
  - /documentation/tutorials/create-snapshot/
  - /documentation/database-tutorials/create-snapshot/
-weight: 2
+weight: 10
 ---

-# Backup and Restore Qdrant Collections Using Snapshots
+# Backup & Restore Qdrant with Snapshots

 | Time: 20 min | Level: Beginner |  |    |
 |--------------|-----------------|--|----|
@@ -2,10 +2,13 @@
 title: Migrate to a New Embedding Model
 aliases:
  - /documentation/tutorials/embedding-model-migration/
-weight: 191
+weight: 30
 ---

-# Migrate to a New Embedding Model with Zero Downtime
+# Migrate to a New Embedding Model with Zero Downtime in Qdrant
+
+| Time: 40 min | Level: Intermediate |
+| --- | ----------- |

 When building a semantic search application, you need to [choose an embedding 
 model](/articles/how-to-choose-an-embedding-model/). Over time, you may want to switch to a different model for better 
@@ -1,12 +1,12 @@
 ---
-title: Large Scale Search
+title: Large-Scale Search
 aliases:
    - /documentation/database-tutorials/large-scale-search/
-weight: 2
+weight: 40
 ---


-# Upload and Search Large collections cost-efficiently
+# Large-Scale Search in Qdrant

 | Time: 2 days | Level: Advanced |  |    |
 |--------------|-----------------|--|----|
@@ -1,11 +1,14 @@
 ---
-title: Migration to Qdrant
+title: Data Migration
 aliases:
    - /documentation/database-tutorials/migration/
-weight: 180
+weight: 20
 ---

-# Migration
+# Migrate Your Embeddings to Qdrant
+
+| Time: Varies | Level: Intermediate |
+| --- | ----------- |

 Migrating data between vector databases, especially across regions, platforms, or deployment types, can be a hassle. That’s where the [Qdrant Migration Tool](https://github.com/qdrant/migration) comes in. It supports a wide range of migration needs, including transferring data between Qdrant instances and migrating from other vector database providers to Qdrant.

@@ -1,25 +1,14 @@
 ---
 title: Search Engineering
-weight: 32
-is_empty: true
-hideInSidebar: true
+weight: 18
+is_empty: false
 aliases:
  - how-to
  - tutorials
 partition: qdrant
 ---

-# Search Engineering Tutorials
+### Search Engineering Tutorials
 *Master vector search modalities, reranking, and retrieval quality.*

-| Tutorial | Objective | Stack | Time | Level |
-| :--- | :--- | :--- | :--- | :--- |
-| [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search for startups. | <span class="pill">FastAPI</span> | 20m | <span class="text-green">Beginner</span> |
-| [Neural Search Service](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | <span class="pill">FastAPI</span> | 30m | <span class="text-green">Beginner</span> |
-| [Movie Recommendations](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
-| [Advanced PDF Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Retrieval Quality Benchmarking](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Multivector Reranking](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | <span class="pill">Python</span> | 30m | <span class="text-yellow">Intermediate</span> |
-| [Hybrid Search Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | <span class="pill">Python</span> | 40m | <span class="text-yellow">Intermediate</span> |
-| [Semantic Code Search](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | <span class="pill">Python</span> | 45m | <span class="text-yellow">Intermediate</span> |
-| [Static Embeddings Analysis](/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | <span class="pill">Python</span> | 20m | <span class="text-yellow">Intermediate</span> |
+{{% include "content/documentation/headless/content/tutorials/search-engineering.md" %}}
@@ -1,12 +1,12 @@
 ---
-title: Search Through Your Codebase
+title: Semantic Search for Code
 aliases:
  - /documentation/tutorials/code-search/
  - /documentation/advanced-tutorials/code-search/
 weight: 2
 ---

-# Navigate Your Codebase with Semantic Search and Qdrant
+# Semantic Search for Code with Qdrant

 | Time: 45 min | Level: Intermediate | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qdrant/examples/blob/master/code-search/code-search.ipynb) |    |
 |--------------|---------------------|--|----|
@@ -1,5 +1,5 @@
 ---
-title: Build a Recommendation System with Collaborative Filtering
+title: Collaborative Filtering
 aliases:
  - /documentation/tutorials/collaborative-filtering/
  - /documentation/advanced-tutorials/collaborative-filtering/
@@ -10,7 +10,7 @@ social_preview_image: /blog/collaborative-filtering/social_preview.png
 weight: 3
 ---

-# Use Collaborative Filtering to Build a Movie Recommendation System with Qdrant
+# Build a Recommendation System with Collaborative Filtering using Qdrant

 | Time: 45 min | Level: Intermediate | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://githubtocolab.com/qdrant/examples/blob/master/collaborative-filtering/collaborative-filtering.ipynb) |    |
 |--------------|---------------------|--|----|
@@ -1,12 +1,12 @@
 ---
-title: Setup Hybrid Search with FastEmbed
+title: Hybrid Search with FastEmbed
 aliases:
  - /documentation/tutorials/hybrid-search-fastembed/
  - /documentation/beginner-tutorials/hybrid-search-fastembed/
 weight: 3
 ---

-# Build a Hybrid Search Service with FastEmbed and Qdrant
+# Hybrid Search with Qdrant's FastEmbed

 | Time: 20 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/qdrant_demo/) |
 | --- | ----------- | ----------- |----------- |
@@ -1,12 +1,12 @@
 ---
-title: Build a Neural Search Service
+title: Semantic Search Basics
 aliases:
  - /documentation/tutorials/neural-search/
  - /documentation/beginner-tutorials/neural-search/
 weight: 2
 ---

-# Build a Neural Search Service with Sentence Transformers and Qdrant
+# Semantic Search Basics with Qdrant

 | Time: 30 min | Level: Beginner | Output: [GitHub](https://github.com/qdrant/qdrant_demo/tree/sentense-transformers) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kPktoudAP8Tu8n8l-iVMOQhVmHkWV_L9?usp=sharing)   |
 | --- | ----------- | ----------- |----------- |
@@ -1,5 +1,5 @@
 ---
-title: Scaling PDF Retrieval with Qdrant
+title: Multivector Document Retrieval
 aliases:
  - /documentation/tutorials/pdf-retrieval-at-scale/
  - /documentation/advanced-tutorials/pdf-retrieval-at-scale/
@@ -10,7 +10,7 @@ preview_image: /documentation/tutorials/pdf-retrieval-at-scale/social_preview.pn
 social_preview_image: /documentation/tutorials/pdf-retrieval-at-scale/social_preview.png
 ---

-# Scaling PDF Retrieval with Qdrant
+# Qdrant Multivector Document Retrieval with ColPali/ColQwen

 ![scaling-pdf-retrieval-qdrant](/documentation/tutorials/pdf-retrieval-at-scale/image1.png)

@@ -1,12 +1,15 @@
 ---
-title: Reranking in Hybrid Search
+title: Hybrid Search with Reranking
 weight: 2
 aliases:
  - /documentation/search-precision/reranking-hybrid-search/
  - /documentation/advanced-tutorials/reranking-hybrid-search/
 ---

-# Reranking Hybrid Search Results with Qdrant Vector Database
+# Qdrant Hybrid Search with Reranking
+
+| Time: 40 min | Level: Intermediate |
+| --- | ----------- |

 Hybrid search combines dense and sparse retrieval to deliver precise and comprehensive results. By adding reranking with ColBERT, you can further refine search outputs for maximum relevance. 

@@ -1,12 +1,12 @@
 ---
-title: Measure Search Quality
+title: Retrieval Quality Evaluation
 aliases:
  - /documentation/tutorials/retrieval-quality/
  - /documentation/beginner-tutorials/retrieval-quality/
 weight: 4
 ---

-# Measure and Improve Retrieval Quality in Semantic Search
+# Evaluate Retrieval Quality with Qdrant

 | Time: 30 min | Level: Intermediate |  |    |
 |--------------|---------------------|--|----|
@@ -1,11 +1,15 @@
 ---
-title: Static Embeddings. Should you pay attention?
+title: Static Embeddings
 weight: 181
 aliases:
  - /blog/static-embeddings/
  - /documentation/database-tutorials/static-embeddings/
 ---
-# Static Embeddings: should you pay attention?
+# Static Embeddings in Practice
+
+| Time: 20 min | Level: Intermediate |
+| --- | ----------- |
+
 In the world of resource-constrained computing, a quiet revolution is taking place. While transformers dominate 
 leaderboards with their impressive capabilities, static embeddings are making an unexpected comeback, offering 
 remarkable speed improvements with surprisingly small quality trade-offs. **We evaluated how Qdrant users can benefit
@@ -1,11 +1,15 @@
 ---
-title: How to Use Multivector Representations with Qdrant Effectively
+title: Multivectors and Late Interaction
 weight: 2
 aliases:
  - /documentation/search-precision/multivector-representations-with-Qdrant/
  - /documentation/advanced-tutorials/using-multivector-representations/
 ---
-# How to Effectively Use Multivector Representations in Qdrant for Reranking
+# Multivector Representations for Reranking in Qdrant
+
+| Time: 30 min | Level: Intermediate |
+| --- | ----------- |
+
 Multivector Representations are one of the most powerful features of Qdrant. However, most people don't use them effectively, resulting in massive RAM overhead, slow inserts, and wasted compute. 

 In this tutorial, you'll discover how to effectively use multivector representations in Qdrant. 
@@ -82,7 +82,7 @@ content:
      description: Start with our beginner-friendly exercises on vector embeddings and basic concepts.
      link:
        text: Start Learning
-        url: /documentation/tutorials-lp-basics
+        url: /documentation/tutorials-basics
    - id: 2
      icon:
        src: /icons/outline/hacker-purple.svg
@@ -1,7 +1,7 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /documentation/tutorials-lp-basics
+reference: /documentation/tutorials-basics
 weight: 311
 sitemapExclude: True
 _build:
@@ -1,7 +1,7 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /documentation/tutorials-lp-develop
+reference: /documentation/tutorials-develop
 weight: 315
 sitemapExclude: True
 _build:
@@ -1,7 +1,7 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /documentation/tutorials-lp-operations
+reference: /documentation/tutorials-operations
 weight: 314
 sitemapExclude: True
 _build:
@@ -1,7 +1,7 @@
 ---
 #Delimiter files are used to separate the list of documentation pages into sections.
 type: reference
-reference: /documentation/tutorials-lp-search-engineering
+reference: /documentation/tutorials-search-engineering
 weight: 312
 sitemapExclude: True
 _build:
@@ -0,0 +1 @@
+{{- readFile (.Get 0) | markdownify -}}