Use when working with Milvus — milvus vector database management, collection schema inspection, index build monitoring, and query node health analysis. Covers partition management, segment compaction, resource group allocation, replica configuration, and consistency level tuning. Read this skill before any Milvus operations.
Scanned 9/8/2026
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---
name: managing-milvus
description: |
Use when working with Milvus — milvus vector database management, collection
schema inspection, index build monitoring, and query node health analysis.
Covers partition management, segment compaction, resource group allocation,
replica configuration, and consistency level tuning. Read this skill before
any Milvus operations.
connection_type: milvus
preload: false
---
# Milvus Management Skill
Monitor, analyze, and optimize Milvus vector database instances safely.
## MANDATORY: Discovery-First Pattern
**Always check cluster health and list collections before any search or insert operations. Never assume collection names, field schemas, or index types.**
### Phase 1: Discovery
```bash
#!/bin/bash
MILVUS_URL="${MILVUS_URL:-http://localhost:9091}"
MILVUS_GRPC="${MILVUS_HOST:-localhost}:${MILVUS_PORT:-19530}"
echo "=== Health Check ==="
curl -s "$MILVUS_URL/healthz" 2>/dev/null || curl -s "$MILVUS_URL/api/v1/health" 2>/dev/null
echo ""
echo "=== Metrics Summary ==="
curl -s "$MILVUS_URL/metrics" 2>/dev/null | grep -E "^milvus_" | grep -E "num_collections|num_partitions|num_loaded|query_node" | head -15
echo ""
echo "=== Collections (via REST v2) ==="
curl -s -X POST "${MILVUS_URL%:9091}:19530/v2/vectordb/collections/list" \
-H "Content-Type: application/json" \
-d '{}' 2>/dev/null | python3 -c "
import sys, json
data = json.load(sys.stdin)
for c in data.get('data', []):
print(f\"Collection: {c}\")
" 2>/dev/null || echo "REST v2 not available"
echo ""
echo "=== Using pymilvus if available ==="
python3 -c "
from pymilvus import connections, utility
connections.connect(host='${MILVUS_HOST:-localhost}', port=${MILVUS_PORT:-19530})
collections = utility.list_collections()
print(f'Collections: {len(collections)}')
for c in collections:
print(f' {c}')
" 2>/dev/null || echo "pymilvus not available"
echo ""
echo "=== Component Health ==="
curl -s "$MILVUS_URL/api/v1/health" 2>/dev/null | python3 -c "
import sys, json
data = json.load(sys.stdin)
print(f\"isHealthy: {data.get('isHealthy','?')}\")
for reason in data.get('reasons', []):
print(f\" {reason}\")
" 2>/dev/null
```
**Phase 1 outputs:** Health status, collection list, component health, key metrics.
### Phase 2: Analysis
```bash
#!/bin/bash
MILVUS_URL="${MILVUS_URL:-http://localhost:9091}"
COLLECTION="${1:-my_collection}"
echo "=== Collection Details ==="
python3 -c "
from pymilvus import connections, Collection, utility
connections.connect(host='${MILVUS_HOST:-localhost}', port=${MILVUS_PORT:-19530})
c = Collection('$COLLECTION')
c.load()
schema = c.schema
print(f'Collection: {c.name}')
print(f'Description: {schema.description}')
print(f'Num entities: {c.num_entities}')
print(f'Fields:')
for f in schema.fields:
print(f' {f.name}: {f.dtype.name} | primary={f.is_primary} | dim={f.params.get(\"dim\",\"-\")}')
print(f'Indexes:')
for idx in c.indexes:
print(f' Field: {idx.field_name} | Type: {idx.params.get(\"index_type\",\"?\")} | Metric: {idx.params.get(\"metric_type\",\"?\")}')
print(f'Partitions: {[p.name for p in c.partitions]}')
" 2>/dev/null || echo "pymilvus required for detailed inspection"
echo ""
echo "=== Compaction Status ==="
curl -s "$MILVUS_URL/metrics" 2>/dev/null | grep -E "compaction" | head -5
echo ""
echo "=== Query Node Stats ==="
curl -s "$MILVUS_URL/metrics" 2>/dev/null | grep -E "query_node" | grep -E "search|query|loaded" | head -10
echo ""
echo "=== Resource Groups ==="
python3 -c "
from pymilvus import connections, utility
connections.connect(host='${MILVUS_HOST:-localhost}', port=${MILVUS_PORT:-19530})
groups = utility.list_resource_groups()
print(f'Resource groups: {groups}')
" 2>/dev/null || echo "Resource group info not available"
echo ""
echo "=== Replica Info ==="
python3 -c "
from pymilvus import connections, Collection
connections.connect(host='${MILVUS_HOST:-localhost}', port=${MILVUS_PORT:-19530})
c = Collection('$COLLECTION')
replicas = c.get_replicas()
for g in replicas.groups:
print(f' Group {g.id}: shards={len(g.shards)} nodes={g.group_nodes}')
" 2>/dev/null || echo "Replica info not available"
```
## Output Format
```
MILVUS ANALYSIS
===============
Health: [healthy/unhealthy] | Collections: [count]
Total Entities: [count] | Query Nodes: [count]
ISSUES FOUND:
- [issue with affected collection/index]
RECOMMENDATIONS:
- [actionable recommendation]
```
## Anti-Hallucination Rules
1. **NEVER assume resource names** — always discover via CLI/API in Phase 1 before referencing in Phase 2.
2. **NEVER fabricate metric names or dimensions** — verify against the service documentation or `--help` output.
3. **NEVER mix CLI commands between service versions** — confirm which version/API you are targeting.
4. **ALWAYS use the discovery → verify → analyze chain** — every resource referenced must have been discovered first.
5. **ALWAYS handle empty results gracefully** — an empty response is valid data, not an error to retry.
## Counter-Rationalizations
| Shortcut | Counter | Why |
|----------|---------|-----|
| "I'll skip discovery and check known resources" | Always run Phase 1 discovery first | Resource names change, new resources appear — assumed names cause errors |
| "The user only asked for a quick check" | Follow the full discovery → analysis flow | Quick checks miss critical issues; structured analysis catches silent failures |
| "Default configuration is probably fine" | Audit configuration explicitly | Defaults often leave logging, security, and optimization features disabled |
| "Metrics aren't needed for this" | Always check relevant metrics when available | API/CLI responses show current state; metrics reveal trends and intermittent issues |
| "I don't have access to that" | Try the command and report the actual error | Assumed permission failures prevent useful investigation; actual errors are informative |
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