storage , minimal code change as possible in th... #971
Description
storage , minimal code change as possible in the client side
💡 Proposed PraisonAI Agents Implementation
1. MongoDB Storage
from praisonaiagents.memory import Memory
MongoDB backend for memory
memory = Memory(
provider="mongodb",
config={
"url": "mongodb://localhost:27017/",
"database": "praisonai",
"collection": "agent_memory",
# Optional advanced settings
"indexes": ["user_id", "agent_id", "timestamp"],
"ttl_field": "expires_at" # For automatic expiration
}
)agent = Agent(
name="DataAnalyst",
memory=memory
)Alternative: Direct storage usage
from praisonaiagents.storage import MongoDBStorage
storage = MongoDBStorage(
url="mongodb://localhost:27017/praisonai",
collection="sessions"
)2. PostgreSQL Storage
from praisonaiagents.memory import Memory
PostgreSQL with full SQL capabilities
memory = Memory(
provider="postgresql",
config={
"url": "postgresql://user:password@localhost/dbname",
"schema": "praisonai", # Optional schema
"table_prefix": "agent_",
# Advanced features
"use_jsonb": True, # For flexible data
"connection_pool_size": 10
}
)Or configuration-based
memory = Memory.from_config({
"provider": "postgresql",
"connection_string": "postgresql://localhost/praisonai"
})3. DynamoDB Storage
from praisonaiagents.memory import Memory
AWS DynamoDB for serverless
memory = Memory(
provider="dynamodb",
config={
"table_name": "praisonai-memory",
"region": "us-east-1",
# AWS credentials (optional if using IAM roles)
"aws_access_key_id": "...",
"aws_secret_access_key": "...",
# Performance settings
"read_capacity": 5,
"write_capacity": 5,
"enable_streams": True
}
)With AWS profile
memory = Memory(
provider="dynamodb",
config={
"table_name": "praisonai-memory",
"aws_profile": "production"
}
)4. Redis Caching Layer
from praisonaiagents.memory import Memory
Redis as primary storage
memory = Memory(
provider="redis",
config={
"host": "localhost",
"port": 6379,
"password": "secure_password",
"ssl": True,
"db": 0,
# Caching features
"default_ttl": 3600, # 1 hour
"key_prefix": "praisonai:",
"compression": "gzip" # Optional compression
}
)Or as cache layer over another storage
memory = Memory(
provider="rag", # Primary storage
cache={
"provider": "redis",
"host": "localhost",
"ttl": 300 # 5 minute cache
}
)5. Cloud Storage Integration
from praisonaiagents.memory import Memory
Google Cloud Storage
memory = Memory(
provider="gcs",
config={
"bucket": "praisonai-sessions",
"project": "my-project",
"prefix": "agents/",
"credentials_path": "/path/to/credentials.json"
}
)Amazon S3
memory = Memory(
provider="s3",
config={
"bucket": "praisonai-sessions",
"region": "us-east-1",
"prefix": "agents/",
"storage_class": "STANDARD_IA" # For infrequent access
}
)Azure Blob Storage
memory = Memory(
provider="azure",
config={
"container": "praisonai-sessions",
"connection_string": "DefaultEndpointsProtocol=https;...",
"prefix": "agents/"
}
)📋 Implementation Recommendations
1. Unified Storage Interface
Create a base storage interface that all backends implement:
praisonaiagents/storage/base.py
from abc import ABC, abstractmethod
class BaseStorage(ABC):
@AbstractMethod
async def read(self, key: str) -> dict:
pass@abstractmethod async def write(self, key: str, data: dict) -> None: pass @abstractmethod async def delete(self, key: str) -> None: pass @abstractmethod async def search(self, query: dict) -> list: pass2. Configuration-First Approach
Example configuration file
storage_config = {
"default": "postgresql",
"backends": {
"postgresql": {
"url": "${DATABASE_URL}",
"pool_size": 10
},
"redis": {
"url": "${REDIS_URL}",
"ttl": 3600
},
"mongodb": {
"url": "${MONGODB_URL}",
"database": "praisonai"
}
},
"cache": {
"enabled": True,
"backend": "redis"
}
}3. Multi-Storage Support
from praisonaiagents import Agent, Memory
Use different storage for different purposes
agent = Agent(
name="MultiStorageAgent",
# Long-term memory in PostgreSQL
memory=Memory(provider="postgresql"),
# Fast cache in Redis
cache=Memory(provider="redis", config={"ttl": 300}),
# Documents in MongoDB
knowledge=Memory(provider="mongodb")
)4. Storage Migration Tools
from praisonaiagents.storage import migrate_storage
Migrate from SQLite to PostgreSQL
migrate_storage(
source={"provider": "sqlite", "path": ".praison/memory.db"},
destination={"provider": "postgresql", "url": "postgresql://..."},
batch_size=1000
)5. Connection Pooling & Performance
Automatic connection pooling
memory = Memory(
provider="postgresql",
config={
"url": "postgresql://...",
"pool": {
"min_size": 2,
"max_size": 10,
"timeout": 30,
"retry_attempts": 3
}
}
)6. Backward Compatibility
Ensure new storage options work with existing code:
Old way (still works)
agent = Agent(name="Assistant", memory=True)
New way with storage selection
agent = Agent(
name="Assistant",
memory=Memory(provider="mongodb")
)Auto-detection based on config
agent = Agent(name="Assistant", memory="auto") # Uses default from config