Agentdb Vector Search

What it does

This skill enables AI agents to quickly retrieve relevant information from a database using semantic vector search powered by ruvnet's AgentDB. It leverages high-performance vector database operations, boasting speeds up to 150x-12,500x faster than traditional methods. The system utilizes techniques like HNSW indexing, quantization, and delivers sub-millisecond search times (less than 100µs) for efficient information retrieval.

When to use it

• Semantic Search: When you need to find data based on the meaning of a query rather than just keyword matches.
• Large Datasets: Ideal for searching through large collections of documents or other data where traditional search methods are slow.
• Rapid Prototyping: Quickly test and iterate on vector database configurations with in-memory options.
• Knowledge Base Retrieval: Efficiently retrieve information from a knowledge base to answer user questions or inform decision-making.

Key capabilities

• Vector-based Semantic Search: Finds data based on meaning, not just keywords.
• High Performance: Offers significantly faster search speeds compared to traditional methods.
• HNSW Indexing & Quantization: Optimizes for speed and memory efficiency.
• Multiple Distance Metrics: Supports cosine similarity, Euclidean distance (L2), and dot product calculations.
• Preset Configurations: Includes options for small (<10K vectors), medium (10K-100K vectors), and large (>100K vectors) databases.

Example prompts

• "Find documents related to 'quantum computing breakthroughs'."
• "Retrieve the top 5 most similar entries to [0.1, 0.2, 0.3, ...]."
• "Search for information with a cosine similarity threshold of 0.75."

Tips & gotchas

• Prerequisites: Requires Node.js 18+, AgentDB v1.0.7 or later (via agentic-flow or standalone), and an OpenAI API key (or custom embedding model) to generate embeddings.
• Embedding Dimensions: Ensure the --dimension parameter during initialization matches your chosen embedding model's output size (e.g., 768 for sentence-transformers).
• Quantization: Experiment with different quantization types to balance memory usage and search performance.