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Qdrant Monitoring Debugging

Organization
qdrant
qdrant-monitoring-debugging

Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.

Overview

Publisherqdrant
Repositoryskills
Skill nameqdrant-monitoring-debugging
Stars
262
Forks
31
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by qdrant on GitHub. Read the source before you install it.

Installation

Install the Qdrant Monitoring Debugging AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/qdrant/skills/tree/main/skills/qdrant-monitoring/debugging
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/qdrant/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/qdrant-monitoring/debugging .claude/skills/qdrant-monitoring-debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qdrant Monitoring Debugging in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qdrant Monitoring Debugging on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qdrant Monitoring Debugging is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

How to Debug Qdrant with Metrics

First check optimizer status. Most production issues trace back to active optimizations competing for resources. If optimizer is clean, check memory, then request metrics.

Optimizer Stuck or Too Slow

Use when: optimizer running for hours, not finishing, or showing errors.

  • Use /collections/{collection_name}/optimizations endpoint (v1.17+) to check status Optimization monitoring
  • Query with optional detail flags: ?with=queued,completed,idle_segments
  • Returns: queued optimizations count, active optimizer type, involved segments, progress tracking
  • Web UI has an Optimizations tab with timeline view and per-task duration metrics Web UI
  • If optimizer_status shows an error in collection info, check logs for disk full or corrupted segments
  • Large merges and HNSW rebuilds legitimately take hours on big datasets. Check progress before assuming it's stuck.

Memory Seems Too High

Use when: memory exceeds expectations, node crashes with OOM, or memory keeps growing.

  • Process memory metrics available via /metrics (RSS, allocated bytes, page faults)
  • Qdrant uses two types of RAM: resident memory (data structures, quantized vectors) and OS page cache (cached disk reads). Page cache filling available RAM is normal. Memory article
  • If resident memory (RSSAnon) exceeds 80% of total RAM, investigate
  • Check /telemetry for per-collection breakdown of point counts and vector configurations
  • Estimate expected memory per component (vectors, HNSW index, ID tracker, payload indexes) for the structures kept in RAM, plus about 20% headroom, and compare it with actual usage Capacity planning
  • Common causes of unexpected growth: quantized vectors pinned in RAM (memory: pinned on Qdrant 1.19 or newer, always_ram: true on 1.18 or older), too many payload indexes, large max_segment_size during optimization

Queries Are Slow

Use when: queries slower than expected and you need to identify the cause.

  • Track rest_responses_avg_duration_seconds and rest_responses_max_duration_seconds per endpoint
  • Use histogram metric rest_responses_duration_seconds (v1.8+) for percentile analysis in Grafana
  • Equivalent gRPC metrics with grpc_responses_ prefix
  • Check optimizer status first. Active optimizations compete for CPU and I/O, degrading search latency.
  • Check segment count via collection info. Too many unmerged segments after bulk upload causes slower search.
  • Compare filtered vs unfiltered query times. Large gap means missing payload index. Payload index

What NOT to Do

  • Ignore optimizer status when debugging slow queries (most common root cause)
  • Assume memory leak when page cache fills RAM (normal OS behavior)
  • Make config changes while optimizer is running (causes cascading re-optimizations)
  • Blame Qdrant before checking if bulk upload just finished (unmerged segments)

Frequently asked questions

What does the Qdrant Monitoring Debugging AI skill do?

Diagnoses Qdrant production issues using metrics and observability tools. Use when someone reports 'optimizer stuck', 'indexing too slow', 'memory too high', 'OOM crash', 'queries are slow', 'latency spike', or 'search was fast now it's slow'. Also use when performance degrades without obvious config changes.

Why use Qdrant Monitoring Debugging on TypingMind?

Because you install it once and use it with any model. Qdrant Monitoring Debugging is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qdrant Monitoring Debugging in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/qdrant/skills/tree/main/skills/qdrant-monitoring/debugging. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Qdrant Monitoring Debugging?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qdrant Monitoring Debugging?

As many as you like. As long as a model supports skills, you can use Qdrant Monitoring Debugging with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qdrant Monitoring Debugging AI skill free?

Yes. It is published on GitHub by qdrant under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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