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Custom Storage Backend

Chat supports multiple storage backends for persistent memory and session data.

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Available Storage Providers

  • InMemoryStorage: Ephemeral in-memory storage (default)
  • SqliteStorage: SQLite database for local persistence
  • PostgresStorage: PostgreSQL for production
  • MongoStorage: MongoDB for scalable deployments
  • RedisStorage: Redis for high-performance caching
  • JSONStorage: File-based JSON storage
  • Mem0Storage: Mem0 platform for advanced memory management

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Using SQLite Storage

from upsonic import Agent, Chat
from upsonic.storage.providers import SqliteStorage

# SQLite storage (sessions_table_name, profiles_table_name, db_file)
storage = SqliteStorage("sessions", "profiles", "chat.db")

agent = Agent("openai/gpt-4o")

chat = Chat(
    session_id="session1",
    user_id="user1",
    agent=agent,
    storage=storage
)

await chat.invoke("Hello")

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Using PostgreSQL Storage

from upsonic import Agent, Chat
from upsonic.storage.providers import PostgresStorage

# PostgreSQL storage
storage = PostgresStorage(
    sessions_table_name="sessions",
    profiles_table_name="profiles",
    db_url="postgresql://user:pass@localhost:5432/dbname",
    schema="public"
)

agent = Agent("openai/gpt-4o")

chat = Chat(
    session_id="session1",
    user_id="user1",
    agent=agent,
    storage=storage
)

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Using MongoDB Storage

from upsonic import Agent, Chat
from upsonic.storage.providers import MongoStorage

# MongoDB storage
storage = MongoStorage(
    db_url="mongodb://localhost:27017",
    database_name="chat_db",
    sessions_collection_name="sessions",
    profiles_collection_name="profiles"
)

agent = Agent("openai/gpt-4o")

chat = Chat(
    session_id="session1",
    user_id="user1",
    agent=agent,
    storage=storage
)

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Using Redis Storage

from upsonic import Agent, Chat
from upsonic.storage.providers import RedisStorage

# Redis storage with TTL
storage = RedisStorage(
    prefix="chat",
    host="localhost",
    port=6379,
    expire=3600  # 1 hour TTL
)

agent = Agent("openai/gpt-4o")

chat = Chat(
    session_id="session1",
    user_id="user1",
    agent=agent,
    storage=storage
)

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Using JSON Storage

from upsonic import Agent, Chat
from upsonic.storage.providers import JSONStorage

# JSON file-based storage
storage = JSONStorage(
    directory_path="./chat_data",
    pretty_print=True
)

agent = Agent("openai/gpt-4o")

chat = Chat(
    session_id="session1",
    user_id="user1",
    agent=agent,
    storage=storage
)

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Using Mem0 Storage

from upsonic import Agent, Chat
from upsonic.storage.providers import Mem0Storage

# Mem0 Platform storage
storage = Mem0Storage(
    api_key="your-api-key",
    org_id="your-org-id",
    project_id="your-project-id",
    namespace="upsonic"
)

# Or Mem0 Open Source
storage = Mem0Storage(
    local_config={"vector_store": {"provider": "chroma"}},
    namespace="upsonic"
)

agent = Agent("openai/gpt-4o")

chat = Chat(
    session_id="session1",
    user_id="user1",
    agent=agent,
    storage=storage
)

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Using In-Memory Storage

from upsonic import Agent, Chat
from upsonic.storage.providers import InMemoryStorage

# In-memory storage with LRU cache limits
storage = InMemoryStorage(
    max_sessions=1000,
    max_profiles=100
)

agent = Agent("openai/gpt-4o")

chat = Chat(
    session_id="session1",
    user_id="user1",
    agent=agent,
    storage=storage
)

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Storage Connection Management

Chat automatically manages storage connections: 
from upsonic import Agent, Chat
from upsonic.storage.providers import SqliteStorage

storage = SqliteStorage("sessions", "profiles", "chat.db")
agent = Agent("openai/gpt-4o")

chat = Chat(
    session_id="session1",
    user_id="user1",
    agent=agent,
    storage=storage
)

# Storage connection managed automatically
await chat.invoke("Hello")

# Close connection when done
await chat.close()