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
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