wehub-resource-sync
76 lines
2.0 KiB
Python
76 lines
2.0 KiB
Python
from agno.agent import Agent
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from agno.memory.v2.db.sqlite import SqliteMemoryDb
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from agno.memory.v2.memory import Memory
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from agno.models.nebius import Nebius
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from agno.storage.sqlite import SqliteStorage
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from rich.pretty import pprint
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import os
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from dotenv import load_dotenv
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load_dotenv()
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# UserId for the memories
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user_id = "arindam"
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# Database file for memory and storage
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db_file = "tmp/agent.db"
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# Initialize memory.v2
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memory = Memory(
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# Use any model for creating memories
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model=Nebius(
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id="deepseek-ai/DeepSeek-V3-0324", api_key=os.getenv("NEBIUS_API_KEY")
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),
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db=SqliteMemoryDb(table_name="user_memories", db_file=db_file),
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)
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# Initialize storage
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storage = SqliteStorage(table_name="agent_sessions", db_file=db_file)
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# Initialize Agent
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memory_agent = Agent(
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model=Nebius(
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id="deepseek-ai/DeepSeek-V3-0324", api_key=os.getenv("NEBIUS_API_KEY")
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),
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# Store memories in a database
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memory=memory,
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# Give the Agent the ability to update memories
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enable_agentic_memory=True,
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# OR - Run the MemoryManager after each response
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enable_user_memories=True,
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# Store the chat history in the database
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storage=storage,
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# Add the chat history to the messages
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add_history_to_messages=True,
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# Number of history runs
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num_history_runs=3,
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markdown=True,
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)
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memory.clear()
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memory_agent.print_response(
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"My name is Arindam and I support Mohun Bagan.",
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user_id=user_id,
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stream=True,
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stream_intermediate_steps=True,
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)
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print("Memories about Arindam:")
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pprint(memory.get_user_memories(user_id=user_id))
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memory_agent.print_response(
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"I live in Kolkata, where should i move within a 4 hour drive?",
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user_id=user_id,
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stream=True,
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stream_intermediate_steps=True,
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)
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print("Memories about Arindam:")
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pprint(memory.get_user_memories(user_id=user_id))
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memory_agent.print_response(
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"Tell me about Arindam",
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user_id=user_id,
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stream=True,
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stream_intermediate_steps=True,
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)
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print("Memories about Arindam:")
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pprint(memory.get_user_memories(user_id=user_id))
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