Merge remote-tracking branch 'upstream/master' into inference

2026-10-04 02:18:29 +02:00 · 2025-11-13 09:43:23 +01:00
parent fae7d01816 dd115d555e
commit 2541942c09
62 changed files with 199 additions and 48 deletions
@@ -35,7 +35,7 @@ HUGO_PARAMS_onetrustScriptId = "0196246a-3663-7350-9a45-b65f645d6314"
        X-Frame-Options = "DENY"

 [[headers]]
-    for = "/web-ui-banner.json"
+    for = "/web-ui-info.json"
    [headers.values]
        Access-Control-Allow-Origin = "*"

@@ -6,7 +6,7 @@ preview_dir: /articles_data/agentic-builders-guide/preview
 social_preview_image: /articles_data/agentic-builders-guide/preview/social_preview.jpg
 author: Thierry Damiba
 draft: false
-date: 2025-10-22T00:00:00.000Z
+date: 2025-10-26T00:00:00.000Z
 category: rag-and-genai
 ---
 ## Overview
@@ -1,4 +1,7 @@
 ---
+title: "Welcome to Qdrant Academy"
+description: Master vector search and AI-powered applications with Qdrant Academy. Free, self-paced courses guide you from beginner to expert with hands-on projects, code notebooks, and certification.
+weight: 50
 ---

 # Welcome to Qdrant Academy
@@ -1,5 +1,5 @@
 ---
-title: Qdrant Essentials Course
+title: "Qdrant Essentials Course"
 page_title: Qdrant Essentials Course
 description: Learn hybrid search, multivectors, and production deployment in 7 days. Build and ship a docs search engine.
 content:
@@ -1,5 +1,6 @@
 ---
-title: Qdrant Essentials Certification
+title: "Qdrant Essentials Certification"
+description: Get officially certified by Qdrant today!
 weight: 100
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Day 0: Setup and First Steps"
+description: Set up Qdrant and build your first vector search app. Learn how to configure Qdrant Cloud, run a basic search, and complete your first project.
 isLesson: true
 weight: 10
 ---
@@ -1,5 +1,6 @@
 ---
-title: Implementing a Basic Vector Search
+title: "Implementing a Basic Vector Search"
+description: Learn how to build a basic vector search in Qdrant. Create collections, insert vectors, and run your first similarity search step-by-step with Python. 
 weight: 3
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Project: Building Your First Vector Search System"
+description: Apply your Qdrant skills to build a complete vector search system. Create collections, insert data, run similarity and filtered searches, and share your results. 
 weight: 4
 ---

@@ -1,5 +1,6 @@
 ---
-title: Qdrant Cloud Setup
+title: "Qdrant Cloud Setup"
+description: Set up your Qdrant Cloud cluster in minutes. Learn to create collections, manage data, access the Web UI, and connect securely from Python.
 weight: 2
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Day 1: Vector Search Fundamentals"
+description: Learn vector search fundamentals in Qdrant. Explore points, payloads, and distance metrics, then apply them in a hands-on semantic movie search project.
 isLesson: true
 weight: 20
 ---
@@ -1,5 +1,6 @@
 ---
-title: Text Chunking Strategies
+title: "Text Chunking Strategies"
+description: Learn how to split text into meaningful chunks for vector search. Compare six chunking strategies and discover how metadata improves retrieval precision in Qdrant.
 weight: 4
 ---

@@ -1,5 +1,6 @@
 ---
-title: Distance Metrics
+title: "Distance Metrics"
+description: Learn how distance metrics like cosine, Euclidean, Manhattan, and dot product shape vector similarity in Qdrant. Discover which metric fits your data and use case. 
 weight: 3
 ---

@@ -1,5 +1,6 @@
 ---
-title: Points, Vectors and Payloads
+title: "Points, Vectors and Payloads"
+description: Learn Qdrant’s core data model with points, vectors, payloads, and named vectors. Compare dense, sparse, and multivectors, understand dimensionality trade-offs, and master filtering with payload indexes for precise retrieval. 
 weight: 2
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Demo: Semantic Movie Search"
+description: Build a semantic movie search with Qdrant. Compare chunking strategies, embed descriptions, and combine cosine similarity with metadata filters and grouping for accurate, theme-aware recommendations. 
 weight: 5
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Project: Building a Semantic Search Engine"
+description: Build a semantic search engine with Qdrant. Compare chunking strategies, index embeddings, and query by meaning to discover what works best for your domain. 
 weight: 6
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Day 2: Indexing and Performance"
+description: Learn how Qdrant’s HNSW indexing accelerates vector search. Explore index parameters, filtering integration, and performance tuning to balance speed, recall, and precision. 
 isLesson: true
 weight: 30
 ---
@@ -1,5 +1,6 @@
 ---
 title: "Demo: HNSW Performance Tuning"
+description: Tune Qdrant’s HNSW index for speed and precision. Optimize bulk uploads, test filters, and benchmark performance on a real 100K OpenAI embedding dataset. 
 weight: 4
 ---

@@ -1,5 +1,6 @@
 ---
-title: Combining Vector Search and Filtering
+title: "Combining Vector Search and Filtering"
+description: Learn how Qdrant combines HNSW vector search with payload filtering. Understand Filterable HNSW, query planning, and payload indexing for accurate, high-performance retrieval.
 weight: 3
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Project: HNSW Performance Benchmarking"
+description: Optimize vector search with Qdrant. Test multiple HNSW configurations, time uploads and queries, and evaluate filtering with and without payload indexes to find the best settings for your domain.
 weight: 5
 ---

@@ -1,5 +1,6 @@
 ---
-title: HNSW Indexing Fundamentals
+title: "HNSW Indexing Fundamentals"
+description: Learn how HNSW indexing powers fast, scalable vector search in Qdrant. Understand parameters like m, ef_construct, and hnsw_ef to balance recall, speed, and memory efficiency. 
 weight: 2
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Day 3: Hybrid Search"
+description: Learn how to combine dense and sparse vector search in Qdrant. Master hybrid search, score fusion, and keyword indexing to boost retrieval precision and recall. 
 isLesson: true
 weight: 40
 ---
@@ -1,5 +1,6 @@
 ---
 title: "Demo: Implementing a Hybrid Search System"
+description: Step-by-step demo on implementing hybrid search using Qdrant’s Universal Query API. Explore dense vs. sparse search, score fusion algorithms, and real-world evaluation techniques.
 weight: 5
 ---

@@ -1,5 +1,6 @@
 ---
-title: Hybrid Search and the Universal Query API
+title: "Hybrid Search and the Universal Query API"
+description: Master hybrid search in Qdrant using dense and sparse vectors. Explore retrieval, reranking, and Reciprocal Rank Fusion (RRF) to build efficient, adaptive search experiences.
 weight: 4
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Project: Building a Hybrid Search Engine"
+description: Build a hybrid search engine in Qdrant combining dense and sparse vectors with Reciprocal Rank Fusion. Compare performance, optimize retrieval, and understand when hybrid search outperforms single-vector methods.
 weight: 6
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Demo: Keyword Search with Sparse Vectors"
+description: Hands-on sparse retrieval in Qdrant—create BM25 collections, enable IDF, index with FastEmbed, try SPLADE++ expansion, and execute keyword queries via the Universal Query API.
 weight: 3
 ---

@@ -1,5 +1,6 @@
 ---
-title: Sparse Vectors and Inverted Indexes
+title: "Sparse Vectors and Inverted Indexes"
+description: Learn sparse vectors and inverted indexes in Qdrant, create named sparse vectors, store index–value pairs, run exact dot-product search, and prepare for hybrid search with dense vectors.
 weight: 2
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Day 4: Optimization and Scale"
+description: Learn Qdrant performance optimization with vector quantization, accuracy recovery with rescoring, and high-throughput data ingestion. Compare memory usage, recall, and speed to tune large-scale vector search. 
 isLesson: true
 weight: 50
 ---
@@ -1,5 +1,6 @@
 ---
-title: Large-Scale Data Ingestion
+title: "Large-Scale Data Ingestion"
+description: Master large-scale vector ingestion in Qdrant. Explore batching, upload_points, and upload_collection methods, on-disk storage, and parallel streaming for billion-scale AI data pipelines.
 weight: 4
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Project: Quantization Performance Optimization"
+description: Apply vector quantization in Qdrant to boost search speed, reduce memory, and balance accuracy. Test scalar, binary, and 2-bit quantization with oversampling and rescoring optimization. 
 weight: 5
 ---

@@ -1,5 +1,6 @@
 ---
-title: Accuracy Recovery with Rescoring
+title: "Accuracy Recovery with Rescoring"
+description: Learn how oversampling and rescoring restore accuracy in quantized vector search. Improve Qdrant search precision while maintaining high performance using efficient reranking on original vectors. 
 weight: 3
 ---

@@ -1,5 +1,6 @@
 ---
-title: Vector Quantization Methods
+title: "Vector Quantization Methods"
+description: Explore scalar, binary, and product quantization in Qdrant. Learn how compression boosts vector search speed, cuts memory costs, and balances accuracy for large-scale AI retrieval. 
 weight: 2
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Day 5: Advanced APIs"
+description: Learn advanced Qdrant APIs, including multivectors and the Universal Query API, to power hybrid retrieval, late interaction models, and recommendation systems with high accuracy and flexibility. 
 isLesson: true
 weight: 60
 ---
@@ -1,5 +1,6 @@
 ---
-title: Multivectors for Late Interaction Models
+title: "Multivectors for Late Interaction Models"
+description: Learn how Qdrant supports late interaction models like ColBERT and ColPali using multivectors for token-level precision, enabling fine-grained, context-aware text and visual document retrieval.
 weight: 2
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Project: Building a Recommendation System"
+description: Build a hybrid AI recommendation system with Qdrant’s Universal Query API—combining dense, sparse, and multivector retrieval, ColBERT reranking, and RRF fusion in one atomic query. 
 weight: 5
 ---

@@ -1,5 +1,6 @@
 ---
-title: The Universal Query API
+title: "The Universal Query API"
+description: Learn how to run dense, sparse, and ColBERT multivector retrieval with Qdrant’s Universal Query API—fusing, filtering, and reranking results in a single atomic request.
 weight: 3
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Demo: Universal Query for Hybrid Retrieval"
+description: Build a hybrid research discovery system using Qdrant’s Universal Query API—combine dense, sparse, and ColBERT vectors for semantic, keyword, and reranked retrieval in one query. 
 weight: 4
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Day 6: Final Project - Building a Production-Grade Search Engine"
+description: Build a production-grade documentation search engine with Qdrant—combining hybrid retrieval, multivector reranking, and evaluation to showcase real-world, portfolio-ready vector search expertise.
 isLesson: true
 weight: 70
 ---
@@ -1,5 +1,6 @@
 ---
-title: Course Completion and Next Steps
+title: "Course Completion and Next Steps"
+description: Complete your Qdrant course by earning certification and mastering hybrid, multivector, and production-ready vector search techniques—skills to design, evaluate, and deploy real-world AI search systems. 
 weight: 3
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Final Project: Production-Ready Documentation Search Engine"
+description: Create a complete documentation search system with Qdrant, featuring hybrid retrieval, multivector reranking, and performance evaluation for a portfolio-ready, production-quality vector search application.
 weight: 2
 ---

@@ -1,5 +1,6 @@
 ---
 title: "Day 7: Partner Ecosystem Integrations (Bonus)"
+description: Learn how to extend Qdrant with integrations across top AI platforms like Haystack, LlamaIndex, and Unstructured.io for scalable, intelligent, and agentic search pipelines.
 isLesson: true
 weight: 80
 ---
@@ -1,5 +1,6 @@
 ---
-title: Integrating with Camel AI
+title: "Integrating with Camel AI"
+description: Learn how Camel AI and Qdrant enable automated RAG pipelines with multi-agent communication, vector-based memory, and seamless integration into live environments like Discord bots. 
 weight: 8
 ---

@@ -1,5 +1,6 @@
 ---
-title: Integrating with Haystack
+title: "Integrating with Haystack"
+description: Learn how Qdrant and Haystack combine to deliver end-to-end search and recommendation systems with hybrid retrieval, semantic filtering, and agentic AI orchestration. 
 weight: 2
 ---

@@ -1,5 +1,6 @@
 ---
-title: Integrating with Jina AI
+title: "Integrating with Jina AI"
+description: Learn how Jina AI’s Embeddings v4 and Qdrant enable advanced multimodal retrieval, supporting text-to-image, image-to-text, and hybrid search with high-performance vector storage.
 weight: 9
 ---

@@ -1,5 +1,6 @@
 ---
-title: Integrating with LlamaIndex
+title: "Integrating with LlamaIndex"
+description: Learn how LlamaIndex and Qdrant power intelligent RAG pipelines, function-calling agents, and cloud-synced vector search systems with structured workflows and dynamic query handling.
 weight: 6
 ---

@@ -1,5 +1,6 @@
 ---
-title: Integrating with Quotient
+title: "Integrating with Quotient"
+description: Learn how Quotient and Qdrant combine to deliver end-to-end AI monitoring, hallucination detection, and performance analytics for reliable, high-quality retrieval-augmented generation systems.
 weight: 7
 ---

@@ -1,5 +1,6 @@
 ---
-title: Integrating with Superlinked
+title: "Integrating with Superlinked"
+description: Learn how Superlinked’s Mixture of Encoders and Qdrant enable rich, multi-modal embeddings that fuse semantic, numerical, and temporal data for optimized vector retrieval.
 weight: 5
 ---

@@ -1,5 +1,6 @@
 ---
-title: Integrating with Tensorlake
+title: "Integrating with Tensorlake"
+description: Learn how TensorLake and Qdrant combine document parsing, knowledge graphs, and vector search to build scalable, structured data lakes for advanced RAG and research discovery applications.
 weight: 4
 ---

@@ -1,5 +1,6 @@
 ---
-title: Integrating with Unstructured.io
+title: "Integrating with Unstructured.io"
+description: Learn how Unstructured.io and Qdrant transform unstructured enterprise data into structured embeddings through VLM document understanding, smart chunking, and secure, production-ready ETL pipelines. 
 weight: 3
 ---

@@ -1,5 +1,6 @@
 ---
-title: Qdrant Essentials FAQs
+title: "Qdrant Essentials FAQs"
+description: Learn everything about the Qdrant Essentials course — who it’s for, tools required, certification details, setup options, and how to get help while building your vector search expertise.
 weight: 200
 ---

@@ -2,6 +2,8 @@
 title: Vector Search Basics
 aliases:
  - /documentation/tutorials/
+  - how-to
+  - tutorials
 weight: 16
 # If the index.md file is empty, the link to the section will be hidden from the sidebar
 is_empty: false
@@ -213,11 +213,11 @@ For all decay functions, there are these parameters available
 | `x`        | N/A     | The value to decay                                                                                                                                                                                |
 | `target`   | 0.0     | The value at which the decay will be at its peak. For distances it is usually set at 0.0, but can be set to any value.                                                                            |
 | `scale`    | 1.0     | The value at which the decay function will be equal to `midpoint`. This is in terms of `x` units, for example, if `x` is in meters, `scale` of 5000 means 5km. Must be a non-zero positive number |
-| `midpoint` | 0.5     | Output is `midpoint` when `x` equals `scale`. Must be in the range (0.0, 1.0), exclusive                                                                                                          |
+| `midpoint` | 0.5     | Output is `midpoint` when `x` equals `target` ± `scale`. Must be in the range (0.0, 1.0), exclusive                                                                                                          |

-The formulas for each decay function are as follows:
+![Decay functions.](/docs/decay-function.png)

-<iframe src="https://www.desmos.com/calculator/idv5hknwb1?embed" width="600" height="400" style="border: 1px solid #ccc" frameborder=0 class="mx-auto d-block"></iframe>
+The [formulas for each decay function](https://www.desmos.com/calculator/idv5hknwb1) are as follows:

 <br>
    
@@ -12,7 +12,6 @@ partition: build
 | [Airflow](/documentation/data-management/airflow/)           | Platform designed for developing, scheduling, and monitoring batch-oriented workflows.             |
 | [Chonkie](/documentation/data-management/chonkie/)           | No-nonsense, ultra-light and lightning fast RAG pipelines library.                                |
 | [CocoIndex](/documentation/data-management/cocoindex/)           | High performance ETL framework to transform data for AI, with real-time incremental processing     |
-| [Cognee](/documentation/data-management/cognee/)             | AI memory frameworks that allows loading from 30+ data sources to graph and vector stores          |
 | [Connect](/documentation/data-management/redpanda/)          | Declarative data-agnostic streaming service for efficient, stateless processing.                   |
 | [Confluent](/documentation/data-management/confluent/)       | Fully-managed data streaming platform with a cloud-native Apache Kafka engine.                     |
 | [DLT](/documentation/data-management/dlt/)                   | Python library to simplify data loading processes between several sources and destinations.        |
@@ -12,6 +12,7 @@ aliases: ["/documentation/frameworks/memgpt/"]
 | [AutoGen](/documentation/frameworks/autogen/)                 | Framework from Microsoft building LLM applications using multiple conversational agents.                             |
 | [Camel](/documentation/frameworks/camel/)                     | Framework to build and use LLM-based agents for real-world task solving                                              |
 | [Cheshire Cat](/documentation/frameworks/cheshire-cat/)       | Framework to create personalized AI assistants using custom data.                                                    |
+| [Cognee](/documentation/frameworks/cognee/)                   | AI memory frameworks that allows loading from 30+ data sources to graph and vector stores                            |
 | [CrewAI](/documentation/frameworks/crewai/)                   | CrewAI is a framework to build automated workflows using multiple AI agents that perform complex tasks.              |
 | [Dagster](/documentation/frameworks/dagster/)                 | Python framework for data orchestration with integrated lineage, observability.                                      |
 | [DeepEval](/documentation/frameworks/deepeval/)               | Python framework for testing large language model systems.                                                           |
@@ -1,5 +1,6 @@
 ---
-title: Cognee
+title: "Cognee"
+description: Cognee ships a Qdrant adapter and documents Qdrant as a preferred, built-in vector database option. That means you configure one URI and key, and Cognee's pipelines will read/write embeddings directly to Qdrant while building and querying the graph.
 ---

 # Cognee
@@ -8,7 +9,7 @@ Embeddings make it easy to retrieve similar chunks of information — but most a

 ## Why Qdrant For The Memory Layer

-At runtime, Cognee's semantic memory layer requires fast and predictable lookups to surface candidates for graph reasoning, as well as tight control over metadata to ground multi-hop traversals. Qdrant's design aligns with those needs with its:
+At runtime, [Cognee](https://www.cognee.ai/)'s semantic memory layer requires fast and predictable lookups to surface candidates for graph reasoning, as well as tight control over metadata to ground multi-hop traversals. Qdrant's design aligns with those needs with its:

 - Nearest-neighbor search for fast candidate recall.
 - Expressive payload filtering to constrain by factors like timestamp windows, document type, or source tags.
@@ -90,3 +91,4 @@ If you prefer not to run infrastructure, Cognee's hosted option — [cogwit](htt

 - [Cognee Documentation](https://docs.Cognee.ai/getting-started/introduction)
 - [Cognee Source](https://github.com/topoteretes/Cognee)
+- [Cognee Website](https://www.cognee.ai/)
@@ -1088,9 +1088,11 @@ Before responding to the client, the peer handling the request dispatches all op
 - reads are using a partial fan-out strategy to optimize latency and availability
 - writes are executed in parallel on all active sharded replicas

-![Embeddings](/docs/concurrent-operations-replicas.png)
+By default, concurrent updates on one point can result in an inconsistent state. For example, if two clients simultaneously update the same point in a collection with three replicas per shard. On some replicas, the point may reflect the update from one client, while on other replicas, the point may reflect the update from the other client.

-However, in some cases, it is necessary to ensure additional guarantees during possible hardware instabilities, mass concurrent updates of same documents, etc.
+![Two clients updating the same point at the same time.](/docs/concurrent-operations-replicas.png)
+
+In some cases, it is necessary to ensure additional guarantees during possible hardware instabilities, mass concurrent updates of same documents, etc.

 Qdrant provides a few options to control consistency guarantees:

@@ -1,11 +1,11 @@
 ---
 title: Monitoring Hybrid/Private Cloud with Prometheus and Grafana
-weight: 36
+weight: 37
 ---

 # Monitoring Hybrid/Private Cloud with Prometheus and Grafana

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

 ## Prerequisites

@@ -40,7 +40,7 @@ metadata:
 spec:
  endpoints:
  - honorLabels: true
-    interval: 60s
+    interval: 30s
    port: metrics
    scheme: http
    scrapeTimeout: 55s
@@ -63,7 +63,7 @@ metadata:
 spec:
  endpoints:
  - honorLabels: true
-    interval: 60s
+    interval: 30s
    port: metrics
    scheme: http
    scrapeTimeout: 55s
@@ -0,0 +1,94 @@
+---
+title: Monitoring Managed Cloud with Prometheus and Grafana
+weight: 36
+---
+
+# Monitoring Managed Cloud with Prometheus and Grafana
+
+This tutorial will guide you through the process of setting up Prometheus and Grafana to monitor Qdrant databases running in Qdrant Managed Cloud.
+
+## Prerequisites
+
+This tutorial assumes that you already have a Kubernetes cluster running where you want to deploy your monitoring stack, and a Qdrant database created in Qdrant Managed Cloud. You should also have `kubectl` and `helm` configured to interact with your cluster.
+
+## Step 1: Install Prometheus and Grafana
+
+If you haven't installed Prometheus and Grafana yet, you can use the [kube-prometheus-stack](https://artifacthub.io/packages/helm/prometheus-community/kube-prometheus-stack) Helm chart to deploy them in your Kubernetes cluster.
+
+A minimal example of installing the stack:
+  
+```bash
+helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
+
+helm install prometheus prometheus-community/kube-prometheus-stack --namespace monitoring --create-namespace
+```
+
+This command will install Prometheus, Grafana, and all necessary components into a new `monitoring` namespace.
+
+## Step 2: Configure Prometheus to Scrape Qdrant Metrics
+
+To monitor Qdrant, you need to configure Prometheus to scrape metrics from the Qdrant database. You can do this by creating a `ScrapeConfig` resource in your Kubernetes cluster. The API key to authenticate at your Qdrant database should be stored in a Kubernetes Secret. A read-only API key is sufficient for monitoring purposes.
+
+```yaml
+apiVersion: v1
+kind: Secret
+metadata:
+  name: qdrant-cluster-api-key
+  namespace: monitoring
+  labels:
+    app: qdrant-cluster
+stringData:
+  apiKey: "a-read-only-api-key"
+---
+apiVersion: monitoring.coreos.com/v1alpha1
+kind: ScrapeConfig
+metadata:
+  name: qdrant-cluster
+  namespace: monitoring
+  labels:
+    app: qdrant-cluster
+    release: prometheus
+spec:
+  metricsPath: /sys_metrics
+  scrapeInterval: 30s
+  scheme: HTTPS
+  authorization:
+    type: Bearer
+    credentials:
+      name: qdrant-cluster-api-key
+      key: apiKey    
+  staticConfigs:
+    - labels:
+        job: prometheus
+      targets:
+        - your-cluster.europe-west3-0.gcp.cloud.qdrant.io:443
+```
+
+## Step 3: Access Grafana
+
+Once Prometheus is configured to scrape metrics from Qdrant, you can access Grafana to visualize the metrics.
+
+Get the Grafana 'admin' user password by running:
+
+```bash
+kubectl --namespace monitoring get secrets prometheus-grafana -o jsonpath="{.data.admin-password}" | base64 -d ; echo
+```
+
+Access the Grafana dashboard by port-forwarding:
+
+```bash
+export POD_NAME=$(kubectl --namespace monitoring get pod -l "app.kubernetes.io/name=grafana,app.kubernetes.io/instance=prometheus" -oname)
+kubectl --namespace monitoring port-forward $POD_NAME 3000
+```
+
+Now you can open your web browser and go to `http://localhost:3000`. Log in with the username `admin` and the password you retrieved earlier.
+
+## Step 4: Import Qdrant Dashboard
+
+Qdrant Cloud offers an example Grafana Dashboard on the [Qdrant GitHub repository](https://github.com/qdrant/qdrant-cloud-grafana-dashboard). This comes with built in views and graphs to get you started with monitoring your Qdrant Clusters.
+
+To import the dashboard:
+
+1. In Grafana, go to "Dashboards" and click on "New" -> "Import".
+2. Copy and paste the dashboard JSON from the Qdrant GitHub repository.
+3. Click "Load" and then "Import".
@@ -1,6 +1,6 @@
 ---
 stats:
-  githubStars: 26.8k
+  githubStars: 27.0k
  discordMembers: 8.8k
  twitterFollowers: 7.5k
 ---
@@ -1,5 +0,0 @@
-{
-  "message": "Qdrant 1.15 is out! Smarter Quantization & better Text Filtering.",
-  "link": "https://qdrant.tech/blog/qdrant-1.15.x/",
-  "link_text": "Qdrant 1.15 Release Blog"
-}
@@ -0,0 +1,8 @@
+{
+  "banner": {
+    "message": "Qdrant 1.15 is out! Smarter Quantization & better Text Filtering.",
+    "link": "https://qdrant.tech/blog/qdrant-1.15.x/",
+    "link_text": "Qdrant 1.15 Release Blog"
+  },
+  "latest_version": "1.15.5"
+}