Agentdb Vector Search
High-performance vector search for RAG pipelines using AgentDB's HNSW indexing
What You Can Do
You can build production-grade vector search systems that retrieve semantically similar documents in sub-millisecond time using AgentDB's optimized HNSW indexing. The skill enables you to implement RAG pipelines, semantic search engines, and intelligent knowledge bases with configurable embedding dimensions, distance metrics (cosine, Euclidean, dot product), and similarity thresholds—all with built-in quantization and caching for massive performance gains.
Features
sub-millisecond search (<100µs) with 150x-12,500x faster retrieval than traditional databases
cosine similarity, Euclidean distance, and dot product calculations for flexible similarity matching
supports 384, 768, 1536+ dimensional embeddings across OpenAI, sentence-transformers, and custom models
optimized presets for small (<10K), medium (10K-100K), and large (>100K) vector collections
return only results above configurable similarity thresholds to control relevance
structured query results for seamless pipeline integration and automation
switch between fast in-memory databases for testing and persistent storage for production
automatic performance optimization for reduced memory footprint and faster repeated queries
Example Output
Query 1: Top 5 similar documents (cosine similarity)
[
{"id": "doc_001", "similarity": 0.94, "content": "Machine learning fundamentals..."},
{"id": "doc_042", "similarity": 0.89, "content": "Deep learning architectures..."},
{"id": "doc_156", "similarity": 0.84, "content": "Neural network training..."}
]
Query 2: Documents above 0.75 similarity threshold
Found 12 documents matching your query with similarity ≥ 0.75
Top match: doc_001 (0.94) — Retrieved in 0.082ms
Query 3: Euclidean distance for multi-modal embeddings
Query vector dimension: 768 | Distance metric: euclidean
Matched 8 results | Query time: 0.045ms
What's Included
- SKILL.md: Complete AgentDB vector search implementation guide with CLI commands and configuration options
- Vector database initialization templates: Pre-configured setup scripts for small, medium, and large-scale deployments
- Query patterns & examples: Copy-paste ready prompts for similarity search, threshold filtering, and multi-metric queries
- Embedding dimension reference guide: Quick lookup for supported embedding models (OpenAI, sentence-transformers, Hugging Face)
- Performance tuning checklist: Best practices for indexing, quantization, and scaling to 100K+ vectors
Who It's For
- AI/ML engineers — building production RAG pipelines and semantic search systems
- Data scientists — implementing retrieval-augmented generation for LLM applications
- Backend developers — integrating vector databases into knowledge management systems
- AI product managers — deploying scalable semantic search features in applications
- Prompt engineers — optimizing context retrieval for multi-turn AI conversations
Best For
- Retrieval-augmented generation (RAG) pipelines with large document sets
- Semantic search engines and similarity-based recommendations
- Intelligent knowledge base queries with relevance thresholds
- High-volume vector lookups requiring sub-millisecond latency
- Multi-modal embedding search across different model architectures
You might also like
This skill guides you through rigorous experiment design from hypothesis to analysis plan. You'll compute the statistical power needed for your tests, identify confounding variables to control for, design optimal randomization strategies, and generate pre-registered analysis plans that prevent p-hacking and publication bias. Whether you're running a product A/B test or investigating causal effects in observational data, you get structured, validated designs that account for real-world constraints.
by moonwell-fi
This skill transforms high-level feature requests into actionable implementation plans by conducting deep requirement interviews, analyzing existing codebases (or planning greenfield projects), and producing comprehensive handoff documents. It distinguishes between brownfield development (with existing code to integrate) and greenfield projects (starting from scratch), ensuring your technical strategy accounts for your actual constraints and patterns.
by moonwell-fi
This skill helps you systematically diagnose failures in distributed ML training and serving infrastructure. You provide system logs, metrics, and error traces, and Claude performs structured root-cause analysis to identify the underlying issue—whether it's resource exhaustion, distributed system deadlock, data pipeline corruption, or model serving misconfiguration. You get a detailed diagnosis with remediation steps ranked by likelihood and implementation effort.
by moonwell-fi
Systematically evaluate your Claude prompts using quantitative scoring frameworks to identify weaknesses in clarity, task alignment, and output quality. You'll receive detailed optimization recommendations with A/B testing guidance to improve prompt effectiveness and consistency across different use cases and input variations.
by moonwell-fi
You can rapidly compile, organize, and format disparate health economic evidence—from clinical trials to cost-effectiveness analyses—into structured, regulatory-compliant dossiers. This skill maps your evidence to specific payer and HTA requirements, automatically generates evidence hierarchies, and produces submission-ready dossier outlines with formatting that meets regulatory standards for NICE, EUnetHTA, and other major bodies.
by moonwell-fi
You can systematically diagnose why your RAG system isn't returning relevant results and identify specific misalignments between queries and context. This skill walks you through analyzing retrieval quality, scoring context relevance, and optimizing prompt-retrieval alignment to improve your RAG accuracy. You'll get concrete, actionable recommendations for fixing bottlenecks in your pipeline.
by moonwell-fi
You get structured guidance for designing controlled experiments, selecting appropriate metrics, and analyzing results with statistical rigor. Claude helps you identify confounding variables, calculate required sample sizes, interpret statistical significance, and document methodology for reproducible benchmarking that stands up to scrutiny.
by moonwell-fi
You can confidently navigate complex statistical analyses by working through structured workflows that match your research question and data type to the right test. The skill guides you through assumption validation, helps you interpret results accurately without common pitfalls, and walks you through regression diagnostics with full transparency about what your findings actually mean.
by moonwell-fi