Hire an AI Qdrant model migration and search-test planner

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• Embedding migration plan
• Search relevance checks
• Monitoring and rollback gates

How it does the job well

• Read the existing code, its conventions and its tests before changing anything. Match the project's patterns.
• Make the smallest change that fully solves the request. Keep unrelated refactors out.
• Reproduce a bug before fixing it, and add a test that fails without the fix.
• Treat input from users, files and networks as untrusted. Validate it at the boundary.

Mistakes it avoids

• Claiming code works without running it or its tests.
• Inventing APIs, package versions or settings instead of checking the installed ones.
• Changing behaviour outside the request, such as reformatting whole files or renaming public interfaces.

What a good result looks like

An example, not your task: You are switching the embedding model behind your Qdrant search and need a plan that can be rolled back.

• Embedding migration plan. The strategy you chose with the reason, numbered phases, checks before the swap and the rollback steps.
• Search relevance checks. Likely causes ranked, one test for each, fixes in order and a before and after measure on your own searches.
• Monitoring and rollback gates. Metrics, health checks and alerts to watch after the switch, and the point where you go back.
• Missing facts and next action. Every MISSING line in one list, then the first step to take.

How the work is checked

• The changed files build, and the relevant tests and linters pass. The commands and results are shown.
• Each change is explained in one line with its reason, and risky areas are named.
• Any check that could not run is reported as NOT_RUN with what it needs.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Qdrant embedding migration planner. Share your current and new models. Get a search migration plan with validation and rollback steps. It hands the plan and the new model to the next two steps.
• Qdrant search relevance investigator. Share queries and weak results. Get likely relevance problems and tests to compare proposed fixes. It starts from your own failing searches and model details. From step 1 it takes only the new model's name and vector size.
• Qdrant health monitoring planner. Share your Qdrant setup. Get useful health metrics, alerts and checks for slow or unhealthy search. It starts from your own setup and builds the alerts and rollback gates around the plan and the tests.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id plan-a-qdrant-model-change only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/plan-a-qdrant-model-change/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Your current and new embedding models, with vector sizes
• The number of points in the collection, and whether search must stay online
• Three to ten failing searches, each with the result you expected
• Your Qdrant version and where it runs

Tools and access

• Any good chat AI: It only writes the plan, so no code or login is needed.
• Claude Code: Helpful once you run the plan, since it can write the re-embed script in your project.
• Claude or ChatGPT with web access: They can open the linked Qdrant docs for exact search settings.
• A short test set in a spreadsheet: A list of searches with the right answers lets you measure each fix.
• Prometheus and Grafana: They collect and chart Qdrant metrics, and Qdrant publishes example setups for both.
• Claude or ChatGPT with web access: They can open the Qdrant docs the skill links to.

Functions and deliverables

• Qdrant model migration and search-test planner: Share your current search setup, new model and query examples. Get a migration plan, relevance checks, monitoring and rollback gates.
  • Embedding migration plan
  • Search relevance checks
  • Monitoring and rollback gates
• Qdrant embedding migration planner: Switch your Qdrant search to a new embedding model with no downtime. Get a step-by-step migration plan with checks and a rollback.
  • The safest way to switch, with the reason
  • A numbered plan with a check at each step
  • A validation list filled in for your collection
  • Rollback steps and how long they stay possible
  • A time estimate from your own sample, or a note that it is unknown
  Source: GitHub Copilot community.
• Qdrant search relevance investigator: Find why Qdrant search returns wrong or missing results. Get ranked causes, one test for each and the fixes to try in order.
  • A ranked list of likely causes for your case
  • One test per cause, with what each result would mean
  • Fixes in the order to try them
  • A plan to measure quality before and after each change
  Source: GitHub Copilot community.
• Qdrant health monitoring planner: Get a Qdrant monitoring plan: what to watch, which alerts to set, and how to find why search is slow or memory keeps growing.
  • A list of the metrics to collect and where to find them
  • Health checks for your setup
  • A short list of alerts to set, with the reason for each
  • A log plan, if you need central or audit logs
  • For a live problem, the likely cause, the number that proves it and what not to touch
  Source: GitHub Copilot community.
• AI project context organiser: Share your project and next task. Get a short rules file, a focused file list and a clear handoff for later sessions.
  • A ready rules file for your project
  • A list of which files to load for each task
  • Rules for when to reset or trim the session
  • A checklist for spotting drifting answers
  Source: Addy Osmani.

Tasks you can use it for

• You are switching the embedding model behind your Qdrant search and need a plan that can be rolled back.
• Some searches already return weak results and you want to know whether the new model helps.
• You want alerts and rollback gates ready before you send live traffic to the new model.

Limits to check before starting

• Your Qdrant search is slow, not wrong or changing models: use Qdrant performance reviewer or the Qdrant search speed tuner.
• You only need to pick where to host Qdrant: use the Qdrant hosting option adviser.
• You are upgrading Qdrant itself and keeping your model: use the Qdrant version upgrade planner.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Add the complete skill pack to your AI.
• Share your goal and the files listed above.
• Ask it to follow the steps and mark any missing facts.
• Review the result before approving external actions.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/plan-a-qdrant-model-change for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.