From 48d8d761f27bd89c7407c568dfdf14d827ac3d79 Mon Sep 17 00:00:00 2001 From: goodnight Date: Tue, 31 Mar 2026 00:53:46 +0100 Subject: [PATCH] fix(docs): correct typos and improve clarity in monitoring and some relevance docs --- .../documentation/cloud/cluster-monitoring.md | 20 +++++++++---------- .../networking-logging-monitoring.md | 16 +++++++-------- .../documentation/operations/monitoring.md | 6 +++--- .../private-cloud/logging-monitoring.md | 10 +++++----- .../documentation/search/search-relevance.md | 4 ++-- .../hybrid-cloud-prometheus.md | 4 ++-- .../managed-cloud-prometheus.md | 2 +- .../using-relevance-feedback.md | 8 ++++---- 8 files changed, 35 insertions(+), 35 deletions(-) diff --git a/qdrant-landing/content/documentation/cloud/cluster-monitoring.md b/qdrant-landing/content/documentation/cloud/cluster-monitoring.md index 0085b6c14..78498b60f 100644 --- a/qdrant-landing/content/documentation/cloud/cluster-monitoring.md +++ b/qdrant-landing/content/documentation/cloud/cluster-monitoring.md @@ -176,7 +176,7 @@ The account owner will receive automatic alerts via email if your cluster has an **What can I do to resolve this?** - You can upgrade you cluster version by visiting the cluster details page. + You can upgrade your cluster version by visiting the cluster details page. **Where can I learn more about this alert?** @@ -188,11 +188,11 @@ The account owner will receive automatic alerts via email if your cluster has an content: | **Why am I getting this alert?** - A Database Key is expiring at soon. + A Database Key is expiring soon. **What does this mean for me?** - Requests using an expired key won’t be successful, this could lead to fail queries and application failures. + Requests using an expired key won’t be successful, which could lead to failed queries and application failures. **What can I do to resolve this?** @@ -230,7 +230,7 @@ The account owner will receive automatic alerts via email if your cluster has an **Where can I learn more about this alert?** - Learn about how to optimizer Qdrant for performance and how to configure indexing [here](/documentation/concepts/indexing/) and [here](/documentation/guides/optimize/). + Learn how to optimize Qdrant for performance and configure indexing [here](/documentation/concepts/indexing/) and [here](/documentation/guides/optimize/). Learn about optimizers [here](/documentation/concepts/optimizer/). @@ -294,7 +294,7 @@ To scrape metrics from a Qdrant cluster running in Qdrant Cloud, an [API key](/d ### Qdrant Node Metrics -Metrics in a Prometheus compatible format are available at the `/metrics` endpoint of each Qdrant database node. When scraping, you should use the [node specific URLs](/documentation/cloud/cluster-access/#node-specific-endpoints) to ensure that you are scraping metrics from all nodes in each cluster. For more information see [Qdrant monitoring](/documentation/guides/monitoring/). +Metrics in a Prometheus-compatible format are available at the `/metrics` endpoint of each Qdrant database node. When scraping, you should use the [node specific URLs](/documentation/cloud/cluster-access/#node-specific-endpoints) to ensure that you are scraping metrics from all nodes in each cluster. For more information, see [Qdrant monitoring](/documentation/guides/monitoring/). You can also access the `/telemetry` [endpoint](https://api.qdrant.tech/api-reference/service/telemetry) of your database. This endpoint is available on the cluster endpoint and provides information about the current state of the database, including the number of vectors, shards, and other useful information. @@ -304,17 +304,17 @@ For more information, see [Qdrant monitoring](/documentation/guides/monitoring/) Cluster system metrics is a cloud-only endpoint that not only shares all the information about the database from `/metrics` but also provides additional operational data from our infrastructure about your cluster, including information from our load balancers, ingresses, and cluster workloads themselves. -Metrics in a Prometheus-compatible format are available at the `/sys_metrics` cluster endpoint. Database API Keys are used to authenticate access to cluster system metrics. `/sys_metrics` only need to be queried once per cluster on the main load-balanced cluster endpoint. You don't need to scrape each cluster node individually, instead it will always provide metrics about all nodes. +Metrics in a Prometheus-compatible format are available at the `/sys_metrics` cluster endpoint. Database API Keys are used to authenticate access to cluster system metrics. `/sys_metrics` only needs to be queried once per cluster on the main load-balanced cluster endpoint. You don't need to scrape each cluster node individually, instead it will always provide metrics about all nodes. ## Grafana Dashboard -If you scrape your Qdrant Cluster system metrics into your own monitoring system, and your are using Grafana, you can use our [Grafana dashboard](https://github.com/qdrant/qdrant-cloud-grafana-dashboard) to visualize these metrics. +If you scrape your Qdrant cluster system metrics into your own monitoring system, and you are using Grafana, you can use our [Grafana dashboard](https://github.com/qdrant/qdrant-cloud-grafana-dashboard) to visualize these metrics. -![Grafa dashboard](/documentation/cloud/cloud-grafana-dashboard.png) +![Grafana dashboard](/documentation/cloud/cloud-grafana-dashboard.png) -### Cluster System Mtrics `/sys_metrics` +### Cluster System Metrics `/sys_metrics` In Qdrant Cloud, each Qdrant cluster will expose the following metrics. This endpoint is not available when running Qdrant open-source. @@ -325,7 +325,7 @@ In Qdrant Cloud, each Qdrant cluster will expose the following metrics. This end | app_info | gauge | Information about the Qdrant server | | app_status_recovery_mode | gauge | If Qdrant is currently started in recovery mode | | cluster_commit | | | -| cluster_enabled | | Indicates wether multi-node clustering is enabled | +| cluster_enabled | | Indicates whether multi-node clustering is enabled | | cluster_peers_total | counter | Total number of cluster peers | | cluster_pending_operations_total | counter | Total number of pending operations in the cluster | | cluster_term | | | diff --git a/qdrant-landing/content/documentation/hybrid-cloud/networking-logging-monitoring.md b/qdrant-landing/content/documentation/hybrid-cloud/networking-logging-monitoring.md index 86bcfb463..57881776a 100644 --- a/qdrant-landing/content/documentation/hybrid-cloud/networking-logging-monitoring.md +++ b/qdrant-landing/content/documentation/hybrid-cloud/networking-logging-monitoring.md @@ -6,7 +6,7 @@ weight: 4 ## Configure network policies -For security reasons, each database cluster is secured with network policies. By default, database pods only allow egress traffic between each and allow ingress traffic to ports 6333 (rest) and 6334 (grpc) from within the Kubernetes cluster. +For security reasons, each database cluster is secured with network policies. By default, database pods only allow egress traffic between each other and ingress traffic to ports 6333 (REST) and 6334 (gRPC) from within the Kubernetes cluster. You can modify the default network policies in the Hybrid Cloud environment configuration: @@ -54,15 +54,15 @@ You can integrate the logs into any log management system that supports Kubernet The Qdrant Cloud console gives you access to basic metrics about CPU, memory and disk usage of your Qdrant clusters. -If you want to integrate the Qdrant metrics into your own monitoring system, you can instruct it to scrape the following endpoints that provide metrics in a Prometheus/OpenTelemetry compatible format: +If you want to integrate Qdrant metrics into your own monitoring system, configure it to scrape the following endpoints, which provide metrics in a Prometheus/OpenMetrics-compatible format: -* `/metrics` on port 6333 of every Qdrant database Pod, this provides metrics about each the database and its internals itself -* `/metrics` on port 9290 of the Qdrant Operator Pod, this provides metrics about the Operator, as well as the status of Qdrant Clusters and Snapshots -* `/metrics` on port 9090 of the Qdrant Cloud Agent Pod, this provides metrics about the Agent and its connection to the Qdrant Cloud control plane -* `/metrics` on port 8080 of the [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics) Pod, this provides metrics about the state of Kubernetes resources like Pods and PersistentVolumes within the Qdrant Hybrid Cloud namespace (useful, if you are not running kube-state-metrics cluster-wide anyway) +* `/metrics` on port 6333 of every Qdrant database Pod. This provides metrics about each database and its internals. +* `/metrics` on port 9290 of the Qdrant Operator Pod. This provides metrics about the Operator, as well as the status of Qdrant clusters and snapshots. +* `/metrics` on port 9090 of the Qdrant Cloud Agent Pod. This provides metrics about the Agent and its connection to the Qdrant Cloud control plane. +* `/metrics` on port 8080 of the [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics) Pod. This provides metrics about the state of Kubernetes resources like Pods and PersistentVolumes within the Qdrant Hybrid Cloud namespace and is useful if you are not running kube-state-metrics cluster-wide. ### Grafana dashboard -If you scrape the above metrics into your own monitoring system, and your are using Grafana, you can use our [Grafana dashboard](https://github.com/qdrant/qdrant-cloud-grafana-dashboard) to visualize these metrics. +If you scrape the above metrics into your own monitoring system, and you are using Grafana, you can use our [Grafana dashboard](https://github.com/qdrant/qdrant-cloud-grafana-dashboard) to visualize these metrics. -![Grafa dashboard](/documentation/cloud/cloud-grafana-dashboard.png) +![Grafana dashboard](/documentation/cloud/cloud-grafana-dashboard.png) diff --git a/qdrant-landing/content/documentation/operations/monitoring.md b/qdrant-landing/content/documentation/operations/monitoring.md index 2b92efb15..7686701da 100644 --- a/qdrant-landing/content/documentation/operations/monitoring.md +++ b/qdrant-landing/content/documentation/operations/monitoring.md @@ -142,14 +142,14 @@ QDRANT__SERVICE__METRICS_PREFIX="qdrant_" ## Telemetry endpoint -Qdrant also provides a `/telemetry` endpoint, which provides information about the current state of the database, including the number of vectors, shards, and other useful information. You can find a full documentation of this endpoint in the [API reference](https://api.qdrant.tech/api-reference/service/telemetry). +Qdrant also provides a `/telemetry` endpoint, which provides information about the current state of the database, including the number of vectors, shards, and other useful information. You can find the full documentation for this endpoint in the [API reference](https://api.qdrant.tech/api-reference/service/telemetry). ## Cluster-wide telemetry -The `/telemetry` endpoint reports from the point of view of the peer being queried. But Qdrant also provides a `/cluster/telemetry` endpoint, which aggregates telemetries from all peers. +The `/telemetry` endpoint reports from the point of view of the peer being queried. Qdrant also provides a `/cluster/telemetry` endpoint, which aggregates telemetry from all peers. This includes less information than `/telemetry`, but provides information like shard transfer progress more reliably. -You can find a full documentation of this endpoint in the [API reference](https://api.qdrant.tech/api-reference/service/cluster-telemetry). +You can find the full documentation for this endpoint in the [API reference](https://api.qdrant.tech/api-reference/service/cluster-telemetry). ## Kubernetes health endpoints diff --git a/qdrant-landing/content/documentation/private-cloud/logging-monitoring.md b/qdrant-landing/content/documentation/private-cloud/logging-monitoring.md index 39bc4d21f..8383c66cb 100644 --- a/qdrant-landing/content/documentation/private-cloud/logging-monitoring.md +++ b/qdrant-landing/content/documentation/private-cloud/logging-monitoring.md @@ -43,15 +43,15 @@ You can integrate the logs into any log management system that supports Kubernet The Qdrant Cloud console gives you access to basic metrics about CPU, memory and disk usage of your Qdrant clusters. -If you want to integrate the Qdrant metrics into your own monitoring system, you can instruct it to scrape the following endpoints that provide metrics in a Prometheus/OpenTelemetry compatible format: +If you want to integrate Qdrant metrics into your own monitoring system, configure it to scrape the following endpoints, which provide metrics in a Prometheus/OpenMetrics-compatible format: -* `/metrics` on port 6333 of every Qdrant database Pod, this provides metrics about each the database and its internals itself -* `/metrics` on port 9290 of the Qdrant Operator Pod, this provides metrics about the Operator, as well as the status of Qdrant Clusters and Snapshots +* `/metrics` on port 6333 of every Qdrant database Pod. This provides metrics about each database and its internals. +* `/metrics` on port 9290 of the Qdrant Operator Pod. This provides metrics about the Operator, as well as the status of Qdrant clusters and snapshots. * For metrics about the state of Kubernetes resources like Pods and PersistentVolumes within the Qdrant Hybrid Cloud namespace, we recommend using [kube-state-metrics](https://github.com/kubernetes/kube-state-metrics) ### Grafana dashboard -If you scrape the above metrics into your own monitoring system, and your are using Grafana, you can use our [Grafana dashboard](https://github.com/qdrant/qdrant-cloud-grafana-dashboard) to visualize these metrics. +If you scrape the above metrics into your own monitoring system, and you are using Grafana, you can use our [Grafana dashboard](https://github.com/qdrant/qdrant-cloud-grafana-dashboard) to visualize these metrics. -![Grafa dashboard](/documentation/cloud/cloud-grafana-dashboard.png) +![Grafana dashboard](/documentation/cloud/cloud-grafana-dashboard.png) diff --git a/qdrant-landing/content/documentation/search/search-relevance.md b/qdrant-landing/content/documentation/search/search-relevance.md index e7d659463..9a02e9cdc 100644 --- a/qdrant-landing/content/documentation/search/search-relevance.md +++ b/qdrant-landing/content/documentation/search/search-relevance.md @@ -179,7 +179,7 @@ For example, in this set of retrieved results: | 222 | 0.81 | 0.72 | | 333 | 0.77 | 0.61 | -The feedback model considers the second result with ID 222 to be the most relevant, which is a discrepancy with retriever's ranking. Hence, this feedback can potentially help make the next iteration of retrieval better. +The feedback model considers the second result with ID 222 to be the most relevant, which is a discrepancy with the retriever's ranking. Hence, this feedback can potentially help make the next iteration of retrieval better. --- @@ -187,7 +187,7 @@ To leverage the feedback in search across the entire collection, Qdrant provides 1. The original query (`target`), which can be a point ID, an inference object, or a raw vector. 2. A short list of initial retrieval results and their relevance score (`feedback`). Each feedback item consists of: - - `example`, which can be point ID, an inference object, or a raw vector used by the retriever. + - `example`, which can be a point ID, an inference object, or a raw vector used by the retriever. - `score`, the feedback score. 3. A definition of the formula that modifies retrieval based on the feedback (`strategy`). diff --git a/qdrant-landing/content/documentation/tutorials-and-examples/hybrid-cloud-prometheus.md b/qdrant-landing/content/documentation/tutorials-and-examples/hybrid-cloud-prometheus.md index bde8668ef..d1530382d 100644 --- a/qdrant-landing/content/documentation/tutorials-and-examples/hybrid-cloud-prometheus.md +++ b/qdrant-landing/content/documentation/tutorials-and-examples/hybrid-cloud-prometheus.md @@ -78,7 +78,7 @@ spec: app.kubernetes.io/name: operator ``` -The example aboves assumes that your Qdrant database and the cloud platform exporter are deployed in the `qdrant` namespace. Adjust the `namespaceSelector` and `namespace` fields according to your deployment. +The example above assumes that your Qdrant database and the cloud platform exporter are deployed in the `qdrant` namespace. Adjust the `namespaceSelector` and `namespace` fields according to your deployment. ## Step 3: Access Grafana @@ -101,7 +101,7 @@ Now you can open your web browser and go to `http://localhost:3000`. Log in with ## 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. +Qdrant Cloud offers an example Grafana dashboard in the [Qdrant GitHub repository](https://github.com/qdrant/qdrant-cloud-grafana-dashboard). This comes with built-in views and graphs to help you get started monitoring your Qdrant clusters. To import the dashboard: diff --git a/qdrant-landing/content/documentation/tutorials-and-examples/managed-cloud-prometheus.md b/qdrant-landing/content/documentation/tutorials-and-examples/managed-cloud-prometheus.md index e89716f9c..ae58d2c08 100644 --- a/qdrant-landing/content/documentation/tutorials-and-examples/managed-cloud-prometheus.md +++ b/qdrant-landing/content/documentation/tutorials-and-examples/managed-cloud-prometheus.md @@ -85,7 +85,7 @@ Now you can open your web browser and go to `http://localhost:3000`. Log in with ## 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. +Qdrant Cloud offers an example Grafana dashboard in the [Qdrant GitHub repository](https://github.com/qdrant/qdrant-cloud-grafana-dashboard). This comes with built-in views and graphs to help you get started monitoring your Qdrant clusters. To import the dashboard: diff --git a/qdrant-landing/content/documentation/tutorials-search-engineering/using-relevance-feedback.md b/qdrant-landing/content/documentation/tutorials-search-engineering/using-relevance-feedback.md index fd3449201..cc8355ec4 100644 --- a/qdrant-landing/content/documentation/tutorials-search-engineering/using-relevance-feedback.md +++ b/qdrant-landing/content/documentation/tutorials-search-engineering/using-relevance-feedback.md @@ -229,7 +229,7 @@ TRAIN_LIMIT = 25 The bigger the `TRAIN_LIMIT`, the more training data our formula gets, but the more expensive and slow the training becomes. -*For training, we need our feedback model to provide ground truth relevancy scores, so it rescores #queries * TRAIN_LIMIT, here 50 * 25 = 1250 query-document pairs. Adjust based on your training budget.* +*For training, we need our feedback model to provide ground-truth relevance scores, so it rescores #queries * TRAIN_LIMIT, here 50 * 25 = 1250 query-document pairs. Adjust based on your training budget.* #### Training Process @@ -243,13 +243,13 @@ formula_params = relevance_feedback.train( ``` You'll see a "**Building training data**" process running on 50 queries. -Additionally, the framework will provide you with a sensibility check, something like: +Additionally, the framework will provide you with a sanity check, something like: ```bash On 22.00% of training queries the feedback model strongly disagreed with the retriever model. ``` -> If the feedback model agrees with your retriever in all cases (if percentage is 0.00), there's little point in using the chosen setup for relevance feedback-based retrieval, consider changing the setup. +> If the feedback model agrees with your retriever in all cases (if percentage is 0.00), there is little point in using the chosen setup for relevance feedback-based retrieval, consider changing the setup. Then after a blazingly fast training, you'll get your parameters, something like: @@ -319,7 +319,7 @@ Works, but perhaps there's something else in our collection that would answer th Now we get feedback from our `mxbai-embed-large-v1` feedback model on the top 3 results for the query *"recommendations API how to use"*. -The feedback model rescores them according to its own judgement of semantic similarity. We only show it a small number of results (`CONTEXT_LIMIT` = 3) to keep the pipeline fast and cheap. +The feedback model rescores them according to its own judgment of semantic similarity. We only show it a small number of results (`CONTEXT_LIMIT` = 3) to keep the pipeline fast and cheap. ```python feedback_model_scores = feedback.score(query, responses_raw)