56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
"""
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Session Memory Snapshotter
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==========================
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Auto-summarizes conversation and embeds it.
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Called every 15 messages.
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"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from datetime import datetime
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from memory_vector import store_memory
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import requests
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OLLAMA_URL = "http://localhost:11434"
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EMBED_MODEL = "nomic-embed-text"
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def get_embedding(text: str) -> list:
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"""Generate embedding via Ollama."""
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response = requests.post(
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f"{OLLAMA_URL}/api/embeddings",
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json={"model": EMBED_MODEL, "prompt": text[:2000]},
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timeout=30
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)
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response.raise_for_status()
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return response.json()["embedding"]
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def save_snapshot(summary: str, participants: str = "Corey, Alex"):
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"""Save a conversation snapshot with embedding."""
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M")
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# Generate embedding
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embedding = get_embedding(summary)
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# Store in database
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source_path = f"session://{datetime.now().strftime('%Y-%m-%d')}#{timestamp}"
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store_memory(
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source_type="session_snapshot",
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source_path=source_path,
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content=summary,
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embedding=embedding
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)
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return source_path
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if __name__ == "__main__":
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# Called with summary as argument
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if len(sys.argv) < 2:
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print("Usage: python session_snapshotter.py 'summary text'")
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sys.exit(1)
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summary = sys.argv[1]
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path = save_snapshot(summary)
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print(f"[OK] Snapshot saved: {path}")
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