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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
37 lines
1 KiB
Python
37 lines
1 KiB
Python
from supermemory import Supermemory
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client = Supermemory()
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USER_ID = "dhravya"
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conversation = [
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{"role": "assistant", "content": "Hello, how are you doing?"},
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{"role": "user", "content": "Hello! I am Dhravya. I am 20 years old. I love to code!"},
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{"role": "user", "content": "Can I go to the club?"},
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]
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# Get user profile + relevant memories for context
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profile = client.profile(container_tag=USER_ID, q=conversation[-1]["content"])
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static = "\n".join(profile.profile.static)
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dynamic = "\n".join(profile.profile.dynamic)
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memories = "\n".join(r.get("memory", "") for r in profile.search_results.results)
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context = f"""Static profile:
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{static}
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Dynamic profile:
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{dynamic}
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Relevant memories:
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{memories}"""
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# Build messages with memory-enriched context
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messages = [{"role": "system", "content": f"User context:\n{context}"}, *conversation]
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# response = llm.chat(messages=messages)
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# Store conversation for future context
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client.add(
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content="\n".join(f"{m['role']}: {m['content']}" for m in conversation),
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container_tag=USER_ID,
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)
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