Knowledge Management
Use Stellar Memory as a personal or team knowledge base with natural language retrieval.
Setup
from stellar_memory import StellarMemory, StellarConfig
config = StellarConfig(db_path="knowledge.db")
memory = StellarMemory(config)
Storing Knowledge
# Store facts with appropriate importance
memory.store("Python 3.12 introduced type parameter syntax (PEP 695)", importance=0.7)
memory.store("Docker multi-stage builds reduce image size by 60-80%", importance=0.6)
memory.store("CRDT (Conflict-free Replicated Data Type) enables offline-first sync", importance=0.8)
Natural Language Search
results = memory.recall("how to reduce docker image size")
for item in results:
print(f"[Zone {item.zone}] {item.content}")
Graph-Based Discovery
Find related knowledge through memory connections:
# Store related items
id1 = memory.store("FastAPI uses Pydantic for validation", importance=0.7).id
id2 = memory.store("Pydantic v2 is 5-50x faster than v1", importance=0.6).id
# Later, discover connections via graph
if memory._analyzer:
neighbors = memory._analyzer.neighbors(id1, depth=2)
Export and Backup
# Export all knowledge
data = memory.export_json()
with open("knowledge_backup.json", "w") as f:
f.write(data)
# Import on another machine
with open("knowledge_backup.json") as f:
count = memory.import_json(f.read())
print(f"Imported {count} memories")
REST API Access
Start the server for team access:
Team members can query via HTTP: