The Knowledge Base
Your AI Agent Needs

# Semantic search - flat (exact, default)
curl -X POST http://localhost:11023/v1/vector-search \ -H "Content-Type: application/json" \ -d '{ "collection": "kb", "query": "how do I cancel my subscription?", "topK": 5, "threshold": 0.7, "includeContent": true }' # HNSW - fast approximate nearest neighbor
curl -X POST http://localhost:11023/v1/vector-search \ -d '{"collection":"kb", "query":"refund", "topK":5, "algorithm":"hnsw"}' # IVF - clustered search for large collections
curl -X POST http://localhost:11023/v1/vector-search \ -d '{"collection":"kb", "query":"refund", "topK":5, "algorithm":"ivf"}' # PQ - compressed, memory-efficient search
curl -X POST http://localhost:11023/v1/vector-search \ -d '{"collection":"kb", "query":"refund", "topK":5, "algorithm":"pq"}'

# Simple search with BM25 scoring
curl -X POST http://localhost:11023/v1/fts \ -H "Content-Type: application/json" \ -d '{ "collection": "blog", "query": "markdown database tutorial", "limit": 10, "algorithm": "bm25" }' # Boolean search (AND, OR, NOT, +required, -excluded)
curl -X POST http://localhost:11023/v1/fts \ -H "Content-Type: application/json" \ -d '{ "collection": "blog", "query": "rust AND performance NOT garbage", "mode": "boolean" }' # Phrase search — exact word sequence
curl -X POST http://localhost:11023/v1/fts \ -H "Content-Type: application/json" \ -d '{ "collection": "blog", "query": "\"machine learning\"", "mode": "phrase" }' # Proximity search — terms within 5 words
curl -X POST http://localhost:11023/v1/fts \ -H "Content-Type: application/json" \ -d '{ "collection": "blog", "query": "\"database performance\"~5", "mode": "proximity", "distance": 5 }'

curl -X POST http://localhost:11023/v1/search \ -H "Content-Type: application/json" \ -d '{ "collection": "blog", "filterMeta": { "category": ["tutorial"], "status": ["published"] }, "sort": "updatedAt", "limit": 10 }'

from mddb import MDDB # TCP connection
db = MDDB.connect('localhost:11023', 'write').collection('kb') # Unix Domain Socket (MDDB 2.9.11+, zero-network local transport)
# db = MDDB.connect('unix:/tmp/mddb.sock', 'write').collection('kb') # Add document
db.add('faq', 'en_US', {'category': ['billing']}, '# Billing FAQ\n\nCancel in Settings.') # Vector search (RAG pipeline step 1)
results = db.vector_search('how to cancel?', top_k=3, include_content=True) # Full-text search
results = db.fts_search('billing refund', limit=10) # Webhooks
db.register_webhook('https://app.com/hook', ['doc.added'], 'kb') # Import from URL
db.import_url('https://example.com/docs.md', 'en_US') # TTL
db.set_ttl('temp-key', 'en', 3600) # expires in 1h

<?php
require_once 'mddb.php'; // TCP connection
$db = mddb::connect('localhost:11023', 'write'); // Unix Domain Socket (MDDB 2.9.11+)
// $db = mddb::connect('unix:/tmp/mddb.sock', 'write'); // Add document
$db->collection('blog')->add('hello', 'en_US', ['category' => ['tutorial']], '# Hello'); // Vector search
$results = $db->collection('kb')->vectorSearch('cancel subscription', 5, 0.7, true); // Full-text search
$results = $db->collection('blog')->ftsSearch('database tutorial', 10); // Webhooks
$db->registerWebhook('https://app.com/hook', ['doc.added', 'doc.updated']); // Import from URL
$db->collection('docs')->importUrl('https://example.com/post.md', 'en_US'); // TTL
$db->collection('cache')->setTtl('temp', 'en', 3600);

# Docker (Easiest) - for Windsurf / Claude Desktop
# Configure ~/.windsurf/mcp.json:
{ "mcpServers": { "mddb": { "command": "docker", "args": [ "run", "-i", "--rm", "--network", "host", "-e", "MDDB_GRPC_ADDRESS=localhost:11024", "-e", "MDDB_REST_BASE_URL=http://localhost:11023", "-e", "MDDB_MCP_STDIO=true", "tradik/mddb:latest" ] } }
} # MCP provides 67 built-in tools including:
# - add_document, search_documents, semantic_search
# - full_text_search, hybrid_search, cross_search
# - classify_document, find_duplicates
# - geo_search, geo_within, geo_encode, geo_decode, geo_stats
# - automation, webhooks, schemas, backups
# - collection config, revisions, aggregate, and more