Perform semantic vector search to retrieve relevant documents from AgentDB.
npx skills add https://github.com/bjpl/hablas --skill agentdb-vector-search
Please help me install this Agent Skill. Skill: AgentDB Vector Search Source: https://github.com/bjpl/hablas/tree/main/.claude/skills/agentdb-vector-search Command: npx skills add https://github.com/bjpl/hablas --skill agentdb-vector-search
Implements vector-based semantic search using AgentDB's high-performance vector database to enable fast document retrieval, similarity matching, and context-aware querying for RAG and knowledge bases.
Initialize the AgentDB vector store, index documents with embeddings, and run a query to retrieve relevant results.
High-intent search queries and answers about installing and using this skill.
Semantic vector search retrieves documents based on meaning rather than keyword matching. AgentDB's vector search stores document embeddings and finds top-k similar results using HNSW indexing, enabling sub-millisecond queries across knowledge bases and RAG systems.
Build RAG pipelines by indexing documents with embeddings in AgentDB, then querying for semantically relevant context to feed into language models. The Skill handles vector storage, similarity matching, and metadata filtering for context-aware retrieval.
Yes, hybrid search combines vector similarity scoring with metadata filters to refine results. AgentDB supports filtering on document metadata alongside semantic similarity, letting you narrow results by domain, date, category, or custom attributes.
Yes, semantic vector search requires an embedding model to convert text into vectors. The Skill supports OpenAI's API or custom embedding models; you provide the embeddings, and AgentDB indexes and searches them for retrieval.
AgentDB achieves sub-millisecond search performance using HNSW indexing and optional quantization for memory efficiency. It's designed for fast retrieval across large document collections without sacrificing accuracy.
breakingcircuits1337Implement semantic vector search with AgentDB for document retrieval and similarity matching.
majidraza1228Evaluate RAG pipelines by measuring retrieval and generation quality separately.
jg-chalk-ioBuild RAG pipelines with vector databases, embeddings, and LLM generation.
haxlysCombine vector similarity with keyword matching using RRF or linear fusion.
