When Building Semantic Search Use Agentdb Vector Search

/============================================================================/ /* AGENTDB SEMANTIC VECTOR SEARCH SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: AgentDB Semantic Vector Search version: 1.0.0 description: | [assert|neutral] AgentDB Semantic Vector Search skill for agentdb workflows [ground:given] [conf:0.95] [state:confirmed] category: agentdb tags:

• general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute AgentDB Semantic Vector Search workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic agentdb processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "AgentDB Semantic Vector Search", category: "agentdb", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["AgentDB Semantic Vector Search", "agentdb", "workflow"], context: "user needs AgentDB Semantic Vector Search capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

AgentDB Semantic Vector Search

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

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

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

• [assert|neutral] Retrieval accuracy > 90% [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Query latency < 100ms [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] Relevant results in top-10: > 95% [ground:acceptance-criteria] [conf:0.90] [state:provisional]
• [assert|neutral] API uptime > 99.9% [ground:acceptance-criteria] [conf:0.90] [state:provisional]

MCP Requirements

This skill operates using AgentDB's npm package and API only. No additional MCP servers required.

All AgentDB operations are performed through:

• npm CLI: npx agentdb@latest
• TypeScript/JavaScript API: import { AgentDB } from 'agentdb-vector-search'

Additional Resources

• Full docs: SKILL.md
• AgentDB Vector Search: https://agentdb.dev/docs/vector-search

Core Principles

AgentDB Semantic Vector Search operates on 3 fundamental principles for building intelligent document retrieval systems:

Principle 1: Meaning Over Keywords

Semantic search retrieves documents based on meaning similarity rather than exact keyword matching, enabling context-aware retrieval.

In practice:

• Generate embeddings for documents using models like OpenAI ada-002 (1536 dimensions) to capture semantic meaning
• Store vectors with rich metadata (title, content, category, tags) to enable hybrid search combining semantic and keyword filters
• Search using query embeddings to find semantically similar documents even when exact keywords differ
• Implement distance metrics (cosine similarity, euclidean) to rank results by semantic relevance rather than keyword frequency

Principle 2: Performance Through Indexing

Build HNSW indexes for 150x faster vector search compared to exhaustive search, essential for production-scale retrieval.

In practice:

• Create HNSW (Hierarchical Navigable Small World) indexes after document ingestion to enable fast approximate nearest neighbor search
• Optimize search parameters (ef_construction, M) based on accuracy vs speed tradeoffs for your use case
• Test retrieval accuracy with evaluation datasets to en

/----------------------------------------------------------------------------/ /* S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S5 MCP INTEGRATION / /----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/----------------------------------------------------------------------------/ /* S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := { pattern: "skills/agentdb/AgentDB Semantic Vector Search/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := { WHO: "AgentDB Semantic Vector Search-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S8 ABSOLUTE RULES / /----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* PROMISE / /----------------------------------------------------------------------------*/

[commit|confident] AGENTDB SEMANTIC VECTOR SEARCH_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]