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Query MongoDB notes store for memory analysis and statistics.
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Basierend auf der SOC-Berufsklassifikation
| name | mongodb-query |
| description | Query MongoDB notes store for memory analysis and statistics. |
| triggers | ["mongodb","mongo","notes store","memory storage","notes query"] |
Query the MongoDB notes store to investigate memory contents, embeddings, and note statistics.
mongodb://localhost:27017 (or MONGODB_URI env var)qq_memorynotesfrom qq.memory.mongo_store import MongoNotesStore
# Initialize store
store = MongoNotesStore()
# Get a specific note
note = store.get_note("note_id_here")
# Get recent notes
recent = store.get_recent_notes(limit=10)
# Get notes by importance range
important = store.get_by_importance_range(min_importance=0.7, max_importance=1.0)
# Get stale notes (not accessed recently)
stale = store.get_stale_notes(days_threshold=30)
from pymongo import MongoClient
client = MongoClient("mongodb://localhost:27017")
db = client["qq_memory"]
notes = db["notes"]
# Count all notes
total = notes.count_documents({})
# Find all notes
all_notes = list(notes.find({}, {"content": 1, "section": 1, "importance": 1}))
# Count by section
pipeline = [
{"$group": {"_id": "$section", "count": {"$sum": 1}}},
{"$sort": {"count": -1}}
]
by_section = list(notes.aggregate(pipeline))
# Find notes without embeddings
no_embedding = notes.count_documents({"embedding": None})
# Sample notes
sample = list(notes.find().limit(10))
# Use mongosh directly
docker exec -it qq-mongodb-1 mongosh qq_memory --eval "db.notes.countDocuments({})"
# Get collection stats
docker exec -it qq-mongodb-1 mongosh qq_memory --eval "db.notes.stats()"
Each note document contains:
note_id: Unique identifiercontent: Note textembedding: Vector embedding (list of floats)section: Category (e.g., "Key Topics", "Preferences")metadata: Additional key-value pairsimportance: Score 0.0-1.0 (default 0.5)decay_rate: How fast importance decays (default 0.01)access_count: Number of times accessedlast_accessed: Timestamp of last accesscreated_at: Creation timestampupdated_at: Last update timestampBuild and deploy the realtime-api Docker container with full verification that deployed code matches local source.
Speak text aloud using the Magpie TTS container in the realtime-api Docker stack. Zero external dependencies — just curl + aplay.
Run TTS round-trip tests — pure-logic unit tests and optional integration tests against live TTS/STT services.
Unified memory management for notes, knowledge graph, RAG search, and file analysis. Use when working with: (1) Core memory — protected identity, projects, relationships, and system facts that should never be forgotten, (2) Working notes — per-session ephemeral notes organized by section, (3) MongoDB RAG — vector-search-enabled notes with importance scoring, decay, deduplication, and archival, (4) Neo4j knowledge graph — entities, relationships, merge duplicates, reinforce mentions, Cypher queries, (5) File analysis — deep file reading that extracts knowledge into all memory layers, (6) Service initialization — health-check, start/stop MongoDB, Neo4j, TEI embeddings via docker-compose with partial setup support.
Query Neo4j knowledge graph for entities, relationships, and graph analysis.
Calls qq agent from cli.