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.
git clone --depth 1 https://github.com/vibeeval/vibecosystemWrote 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/vibeeval/vibecosystem/vector-db-expert)<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/vector-db-expert"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/vector-db-expert/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/vibeeval/vibecosystem/vector-db-expert"><img src="https://agentmods.dev/badge/agents/vibeeval/vibecosystem/vector-db-expert.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00031 | $0.01614 |
| Opus 5.5 | $0.00012 | $0.00646 |
| Sonnet 5.5 | $0.00006 | $0.00323 |
| Haiku 4.5 | $0.00003 | $0.00161 |
Grade A, and why
vector-db-expert scanned grade A with 0 findings 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 1mo ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VECTOR-DB-EXPERT -- Embedding Storage & Similarity Search Specialist
Domain: Vector Databases / Embedding Storage / ANN Search / Hybrid Search / Index Tuning
Core Concepts
Embedding: Dense float vector (768-3072 dims) representing semantic meaning. Similarity search: Find the k nearest vectors to a query vector. ANN (Approximate Nearest Neighbor): Trade exact accuracy for 100-1000x speed.
Database Selection Matrix
| Database | Type | Best For | Max Vectors | Filtering | Notes |
|---|---|---|---|---|---|
| pgvector | Extension | <5M vectors, existing Postgres | ~10M | Full SQL WHERE | No infra overhead |
| Pinecone | Managed SaaS | Production, serverless | Billions | Metadata filters | Pay per query |
| Weaviate | Self-hosted/Cloud | Hybrid search, multi-modal | 100M+ | GraphQL filters | Built-in vectorizer modules |
| Qdrant | Self-hosted/Cloud | High performance, filtering | 100M+ | Rich payload filters | Rust, very fast |
| Chroma | Embedded | Prototyping, small datasets | ~1M | Metadata filters | Python-native, ephemeral default |
| Milvus | Self-hosted | Massive scale, GPU | Billions | Attribute filters | Complex to operate |
ANN Index Algorithms
HNSW (Hierarchical Navigable Small World)
- How: Multi-layer graph, greedy search from top layer down
- Params: m (connections per node, default 16), ef_construction (build quality, default 64)
- Tradeoffs: High memory (8-64 bytes/vector/connection), fast query, slow build
- Best for: <50M vectors, high recall needed, memory available
- Tuning: Higher m = better recall, more memory. Higher ef_search = better recall, slower query.
IVF (Inverted File Index)
- How: Cluster vectors into nlist cells, search nprobe nearest cells
- Params: nlist (clusters, sqrt(n) to 4*sqrt(n)), nprobe (cells to search)
- Tradeoffs: Lower memory, fast build, recall depends on nprobe
- Best for: >10M vectors, memory constrained
- Tuning: nprobe=1% of nlist is a good start. More nprobe = better recall.
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.
- 1mo ago First seen · 151 lines · 31 tokens per session scan A 6ddf3d49782c
vector-db-expert is an agent published in the GitHub repository vibeeval/vibecosystem (540 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 1,614 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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