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OmniMem Quickstart Example
Demonstrates basic text memory operations: store conversations and query them.
Prerequisites:
pip install omnimem
export OPENAI_API_KEY=your_key_here
"""
import os
import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
from omni_memory import OmniMemoryOrchestrator, OmniMemoryConfig
def main():
# 1. Create configuration
config = OmniMemoryConfig()
config.embedding.model_name = "all-MiniLM-L6-v2" # Local embedding (no API needed)
config.embedding.embedding_dim = 384
# 2. Initialize orchestrator
orchestrator = OmniMemoryOrchestrator(
config=config,
data_dir="./quickstart_data",
)
# 3. Store conversation turns
conversations = [
{"text": "User mentioned they love hiking in the Rocky Mountains every summer.",
"tags": ["session_id:D1", "timestamp:2024-06-15"]},
{"text": "User discussed their new camera, a Sony A7IV, for landscape photography.",
"tags": ["session_id:D1", "timestamp:2024-06-15"]},
{"text": "User planned a trip to Yellowstone National Park next month.",
"tags": ["session_id:D2", "timestamp:2024-07-01"]},
{"text": "User bought a new telephoto lens (200-600mm) for wildlife photography.",
"tags": ["session_id:D2", "timestamp:2024-07-01"]},
{"text": "User shared photos from their Yellowstone trip — saw grizzly bears and bison.",
"tags": ["session_id:D3", "timestamp:2024-08-10"]},
]
print("Storing conversations...")
for conv in conversations:
orchestrator.add_text(conv["text"], tags=conv["tags"])
print(f"Stored {len(conversations)} conversation turns.\n")
# 4. Query the memory
queries = [
"What camera does the user have?",
"Where did the user go hiking?",
"What animals did the user see?",
"What lens did the user buy?",
]
for query in queries:
print(f"Q: {query}")
result = orchestrator.query(query, top_k=3)
for item in result.items[:2]:
summary = item.get("summary", "")[:100]
print(f" → {summary}")
print()
# 5. Cleanup
orchestrator.close()
print("Done!")
if __name__ == "__main__":
main()
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