Three lines of code. Persistent AI memory.

import asyncio
import os
from hebbrix import MemoryClient

async def main():
    async with MemoryClient(api_key=os.environ["HEBBRIX_API_KEY"]) as memory:
        collection = await memory.collections.create(name="example-preferences")
        receipt = await memory.memories.create(
            collection_id=collection["id"],
            content="The user prefers concise Python examples.",
            wait_for_index=True,
        )
        if receipt.get("searchable") is not True:
            raise RuntimeError("Indexing is not confirmed; do not assume read readiness.")
        evidence = await memory.search(
            query="preferred examples", collection_id=collection["id"]
        )
        print(evidence)

if __name__ == "__main__":
    asyncio.run(main())

import openai

# Change two lines. Keep everything else.
client = openai.OpenAI(
    base_url="https://api.hebbrix.com/v1",
    api_key="your_hebbrix_key"
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{
        "role": "user",
        "content": "What are my preferences?"
    }]
)
# Hebbrix searched memories and injected
# context before forwarding to GPT-4.