agentdb-semantic-vector-search
Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
Best use case
agentdb-semantic-vector-search is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
Teams using agentdb-semantic-vector-search should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agentdb-semantic-vector-search/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How agentdb-semantic-vector-search Compares
| Feature / Agent | agentdb-semantic-vector-search | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AgentDB Semantic Vector Search
## Overview
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Build RAG systems, semantic search engines, and knowledge bases.
## SOP Framework: 5-Phase Semantic Search
### Phase 1: Setup Vector Database (1-2 hours)
- Initialize AgentDB
- Configure embedding model
- Setup database schema
### Phase 2: Embed Documents (1-2 hours)
- Process document corpus
- Generate embeddings
- Store vectors with metadata
### Phase 3: Build Search Index (1-2 hours)
- Create HNSW index
- Optimize search parameters
- Test retrieval accuracy
### Phase 4: Implement Query Interface (1-2 hours)
- Create REST API endpoints
- Add filtering and ranking
- Implement hybrid search
### Phase 5: Refine and Optimize (1-2 hours)
- Improve relevance
- Add re-ranking
- Performance tuning
## Quick Start
```typescript
import { AgentDB, EmbeddingModel } from 'agentdb-vector-search';
// Initialize
const db = new AgentDB({ name: 'semantic-search', dimensions: 1536 });
const embedder = new EmbeddingModel('openai/ada-002');
// Embed documents
for (const doc of documents) {
const embedding = await embedder.embed(doc.text);
await db.insert({
id: doc.id,
vector: embedding,
metadata: { title: doc.title, content: doc.text }
});
}
// Search
const query = 'machine learning tutorials';
const queryEmbedding = await embedder.embed(query);
const results = await db.search({
vector: queryEmbedding,
topK: 10,
filter: { category: 'tech' }
});
```
## Features
- **Semantic Search**: Meaning-based retrieval
- **Hybrid Search**: Vector + keyword search
- **Filtering**: Metadata-based filtering
- **Re-ranking**: Improve result relevance
- **RAG Integration**: Context for LLMs
## Success Metrics
- Retrieval accuracy > 90%
- Query latency < 100ms
- Relevant results in top-10: > 95%
- API uptime > 99.9%
## Additional Resources
- Full docs: SKILL.md
- AgentDB Vector Search: https://agentdb.dev/docs/vector-searchRelated Skills
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