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AgentDB Vector Search

Implement semantic vector search with AgentDB for document retrieval and similarity matching.

Updated May 8, 2026
Search Tags:#semantic search#embeddings#rag#agentdb#vector search#similarity search#hnsw
One-click install
npx skills add https://github.com/FrekiManagarm/d-chambaud --skill agentdb-vector-search-frekimanagarm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/FrekiManagarm/d-chambaud/tree/main/.agents/skills/agentdb-vector-search
Command: npx skills add https://github.com/FrekiManagarm/d-chambaud --skill agentdb-vector-search-frekimanagarm

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires agentdb, agentic-flow.

What problem does it solve? Building semantic search and RAG systems requires fast vector storage, embedding management, and similarity retrieval, which is complex to implement from scratch and slow with traditional databases. ## Core Features & Use Cases - Vector Storage & Search: Store documents with embeddings and run similarity queries using HNSW indexing with cosine, euclidean, or dot product metrics. - Hybrid & MMR Search: Combine vector similarity with metadata filters and use Maximal Marginal Relevance for diverse result sets. - Memory-Efficient Quantization: Reduce memory usage 4-32x with binary, scalar, or product quantization. - Use Case: Build a RAG pipeline where user questions are embedded, matched against a knowledge base of documents, and the retrieved context is passed to an LLM for answer generation. ## Quick Start Initialize an AgentDB vector database and run a semantic similarity search over my documents using the agentdb CLI.

Frequently Asked Questions about AgentDB Vector Search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement semantic vector search with AgentDB?▼

Initialize a database with npx agentdb init, store documents with their embeddings via the API or CLI, then run similarity queries using npx agentdb query with a query vector. Choose cosine, euclidean, or dot product as the distance metric.

How do I build a RAG pipeline with vector search?▼

Embed the user question, retrieve the top-k similar documents from AgentDB with a similarity threshold around 0.7, then pass the retrieved context together with the question to an LLM for answer generation.

What embedding dimensions does AgentDB support?▼

AgentDB supports configurable dimensions set at initialization: 1536 for OpenAI ada-002, 768 for sentence-transformers, and 384 for all-MiniLM-L6-v2. Specify the dimension with the --dimension flag during init.

How do I reduce memory usage in a vector database?▼

Enable quantization when creating the AgentDB adapter. Binary quantization reduces memory 32x, scalar 4x, and product quantization 8-16x, trading some precision for significantly lower storage.

Why is my vector search returning poor relevance results?▼

Low relevance usually comes from a loose similarity threshold or redundant matches. Raise the threshold toward 0.8, or enable MMR (Maximal Marginal Relevance) to balance relevance with result diversity.

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