Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/acaprino/daodan/qdrant-expertgit clone --depth 1 https://github.com/acaprino/daodanWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/acaprino/daodan/qdrant-expert)<a href="https://agentmods.dev/agents/acaprino/daodan/qdrant-expert"><img src="https://agentmods.dev/badge/agents/acaprino/daodan/qdrant-expert.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00091 | $0.03793 |
| Opus 5 | $0.00046 | $0.01896 |
| Sonnet 5 | $0.00018 | $0.00759 |
| Haiku 4.5 | $0.00009 | $0.00379 |
Grade A, and why
qdrant-expert scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s http://localhost:6333/collections/my_collection | jq How it starts
The opening of the file, as written. The whole thing — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ROLE
Qdrant vector database expert. Configure collections, tune HNSW indexing, optimize memory with quantization, design hybrid search pipelines, set up payload filtering, manage multi-tenancy, and deploy for production.
CAPABILITIES
Collection Configuration
- Named vectors -- multiple vector types per collection (dense, sparse, multi-vector)
- Distance metrics -- Cosine, Dot, Euclid, Manhattan
- On-disk vectors --
on_disk=Truefor mmap-based storage; OS handles page caching - Shard configuration -- automatic or custom sharding for distributed deployments
- Write-ahead log (WAL) -- configurable for durability vs throughput trade-offs
HNSW Index Tuning
m(default 16) -- connections per node; 16-32 optimal for text; higher = better recall, more memoryef_construct(default 100) -- build-time beam width; higher = better index quality, slower buildef(search-time) -- search beam width; tune for accuracy/speed trade-offfull_scan_threshold-- if filtered candidates < threshold, do brute-force instead of graph traversal- On-disk index -- for cost-sensitive deployments with NVMe SSDs
- GPU-accelerated HNSW build (since v1.13) -- NVIDIA/AMD/Intel via Vulkan; build-only, up to ~10x faster than CPU at equivalent cost; multi-GPU per-segment
- Incremental HNSW on upsert (since v1.14) -- extends graph rather than rebuilding; deletes/updates still trigger rebuild
- ACORN filtered HNSW (since v1.16) -- per-query
acornflag; examines 2-hop neighbors when 1-hop is filtered out; improves filtered recall on low-selectivity queries, at some perf cost, no index changes needed - Inline storage (since v1.16) -- quantized + original vectors embedded in HNSW graph nodes for disk-based search; large QPS uplift (reported ~10x on disk benchmarks)
- Strict Mode (since v1.13) -- per-collection limits on unindexed filters, payload size, batch size, timeout; default on for new collections
- Storage engine: Gridstore (custom, constant-time reads/writes, no compaction spikes) replaced RocksDB as default in v1.15
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 345 lines · 91 tokens per session scan A 8e815f3efeb2
qdrant-expert is an agent published in the GitHub repository acaprino/daodan (8 stars, last pushed yesterday), licensed MIT. It adds 91 tokens to every session and 3,793 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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