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Memory Configuration

This page provides comprehensive documentation for configuring the PraisonAI memory system, including graph database integration, quality scoring mechanisms, and embedder options.

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Memory System Overview

The memory system in PraisonAI supports multiple storage backends and sophisticated retrieval mechanisms:
  • Short-term Memory: Recent interactions and context
  • Long-term Memory: Persistent knowledge storage
  • Entity Memory: Relationship and entity tracking
  • Graph Memory: Complex relationship networks
  • User Memory: User-specific preferences and history

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Core Configuration

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Basic Memory Setup

from praisonaiagents import Agent

agent = Agent(
    name="MemoryAgent",
    memory=True,  # Enable memory
    memory_config={
        "provider": "rag",
        "use_embedding": True,
        "embedding_model": "text-embedding-3-small",
        "quality_threshold": 0.7,
        "max_results": 10
    }
)

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Provider Configuration

# RAG Provider (Default)
rag_config = {
    "provider": "rag",
    "rag_db_path": "./memory/rag_db",
    "chunk_size": 1000,
    "chunk_overlap": 200,
    "use_semantic_chunking": True
}

# Mem0 Provider
mem0_config = {
    "provider": "mem0",
    "api_key": "your-mem0-key",
    "user_id": "user-123",
    "organization_id": "org-456"
}

# Custom Provider
custom_config = {
    "provider": "custom",
    "class": "myapp.CustomMemoryProvider",
    "connection_string": "custom://localhost:8080"
}

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Graph Store Configuration

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Graph Database Setup

# Neo4j Configuration
neo4j_config = {
    "graph_enabled": True,
    "graph_provider": "neo4j",
    "graph_uri": "bolt://localhost:7687",
    "graph_user": "neo4j",
    "graph_password": "password",
    "graph_database": "memories",
    "graph_config": {
        "max_connection_lifetime": 3600,
        "max_connection_pool_size": 50,
        "connection_acquisition_timeout": 60,
        "encrypted": True
    }
}

# Memgraph Configuration
memgraph_config = {
    "graph_enabled": True,
    "graph_provider": "memgraph",
    "graph_uri": "bolt://localhost:7687",
    "graph_user": "memgraph",
    "graph_password": "password",
    "graph_config": {
        "lazy": True,
        "ssl": False
    }
}

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Graph Schema Definition

graph_schema = {
    "nodes": {
        "Entity": {
            "properties": ["name", "type", "description", "created_at"],
            "indexes": ["name", "type"]
        },
        "Concept": {
            "properties": ["name", "definition", "category"],
            "indexes": ["name", "category"]
        },
        "Event": {
            "properties": ["name", "timestamp", "description", "participants"],
            "indexes": ["timestamp"]
        }
    },
    "relationships": {
        "RELATES_TO": {
            "properties": ["strength", "context", "timestamp"]
        },
        "CAUSES": {
            "properties": ["probability", "mechanism"]
        },
        "PARTICIPATES_IN": {
            "properties": ["role", "duration"]
        }
    }
}

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Advanced Graph Queries

# Complex graph query configuration
graph_query_config = {
    "retrieval_queries": {
        "find_related_entities": """
            MATCH (e1:Entity {name: $entity_name})-[r:RELATES_TO*1..3]-(e2:Entity)
            WHERE r.strength > $min_strength
            RETURN e2, r
            ORDER BY r.strength DESC
            LIMIT $limit
        """,
        "trace_causality": """
            MATCH path = (cause:Event)-[:CAUSES*1..5]->(effect:Event)
            WHERE cause.name = $event_name
            RETURN path
        """
    },
    "update_patterns": {
        "strengthen_relationship": """
            MATCH (e1:Entity {name: $entity1})-[r:RELATES_TO]-(e2:Entity {name: $entity2})
            SET r.strength = r.strength * $multiplier
            SET r.last_accessed = timestamp()
        """
    }
}

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Quality Score Configuration

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Quality Scoring System

quality_config = {
    "quality_threshold": 0.7,  # Minimum score for retrieval
    "scoring_weights": {
        "relevance": 0.4,
        "recency": 0.2,
        "frequency": 0.2,
        "confidence": 0.2
    },
    "scoring_functions": {
        "relevance": "cosine_similarity",
        "recency": "exponential_decay",
        "frequency": "log_normalization",
        "confidence": "source_reliability"
    }
}

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Quality Score Components

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Relevance Scoring

relevance_config = {
    "method": "hybrid",
    "components": {
        "semantic": {
            "weight": 0.6,
            "model": "text-embedding-3-small",
            "distance_metric": "cosine"
        },
        "keyword": {
            "weight": 0.3,
            "algorithm": "bm25",
            "parameters": {"k1": 1.2, "b": 0.75}
        },
        "structural": {
            "weight": 0.1,
            "factors": ["title_match", "section_match"]
        }
    }
}

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Recency Scoring

recency_config = {
    "decay_function": "exponential",
    "half_life_days": 30,
    "boost_recent": {
        "last_hour": 2.0,
        "last_day": 1.5,
        "last_week": 1.2
    }
}

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Confidence Scoring

confidence_config = {
    "source_weights": {
        "verified": 1.0,
        "agent_generated": 0.8,
        "external": 0.6,
        "unverified": 0.4
    },
    "consensus_threshold": 3,  # Number of sources for high confidence
    "contradiction_penalty": 0.5
}

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Embedder Configuration

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Embedder Options

# OpenAI Embedder
openai_embedder = {
    "provider": "openai",
    "model": "text-embedding-3-large",
    "dimensions": 3072,
    "api_key": "your-api-key",
    "batch_size": 100,
    "retry_config": {
        "max_retries": 3,
        "retry_delay": 1.0
    }
}

# Local Embedder
local_embedder = {
    "provider": "sentence-transformers",
    "model": "all-MiniLM-L6-v2",
    "device": "cuda",  # or "cpu"
    "normalize_embeddings": True,
    "batch_size": 32
}

# Custom Embedder
custom_embedder = {
    "provider": "custom",
    "class": "myapp.embedders.DomainSpecificEmbedder",
    "model_path": "/models/domain-embedder",
    "preprocessing": {
        "lowercase": True,
        "remove_punctuation": True,
        "custom_tokenizer": "medical"
    }
}

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Multi-Modal Embedding

multimodal_config = {
    "embedders": {
        "text": {
            "provider": "openai",
            "model": "text-embedding-3-small",
            "weight": 0.6
        },
        "image": {
            "provider": "clip",
            "model": "openai/clip-vit-base-patch32",
            "weight": 0.4
        }
    },
    "fusion_method": "weighted_average",
    "normalize_before_fusion": True
}

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Storage Configuration

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Database Paths and Structure

storage_config = {
    # SQLite databases
    "short_db": "./memory/short_term.db",
    "long_db": "./memory/long_term.db",
    "entity_db": "./memory/entities.db",
    "user_db": "./memory/users.db",
    
    # Vector storage
    "vector_db": {
        "provider": "chromadb",
        "path": "./memory/vectors",
        "collection_name": "memories"
    },
    
    # Backup configuration
    "backup": {
        "enabled": True,
        "interval": "daily",
        "retention_days": 30,
        "path": "./memory/backups"
    }
}

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Memory Persistence

persistence_config = {
    "auto_save": True,
    "save_interval": 300,  # seconds
    "compression": "gzip",
    "encryption": {
        "enabled": True,
        "algorithm": "AES-256",
        "key_derivation": "PBKDF2"
    },
    "migration": {
        "auto_migrate": True,
        "backup_before_migration": True
    }
}

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Performance Optimization

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Caching Configuration

cache_config = {
    "embedding_cache": {
        "enabled": True,
        "size": 10000,
        "ttl": 3600,  # seconds
        "eviction": "lru"
    },
    "query_cache": {
        "enabled": True,
        "size": 1000,
        "ttl": 1800,
        "cache_similarity_threshold": 0.95
    },
    "graph_cache": {
        "enabled": True,
        "cached_patterns": ["neighbors", "paths", "subgraphs"],
        "ttl": 900
    }
}

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Indexing Strategy

indexing_config = {
    "vector_index": {
        "type": "hnsw",
        "parameters": {
            "m": 16,
            "ef_construction": 200,
            "ef_search": 100
        }
    },
    "text_index": {
        "type": "inverted",
        "analyzer": "standard",
        "features": ["positions", "frequencies"]
    },
    "rebuild_schedule": "weekly"
}

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Memory Cleanup and Maintenance

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Cleanup Configuration

cleanup_config = {
    "ttl": {
        "short_term": 86400,  # 1 day in seconds
        "long_term": 2592000,  # 30 days
        "entity": None,  # No expiry
        "user": 31536000  # 1 year
    },
    "cleanup_strategy": {
        "method": "quality_based",
        "min_quality_score": 0.3,
        "max_memory_size": "10GB",
        "cleanup_interval": "daily"
    },
    "compression": {
        "enabled": True,
        "threshold_days": 7,
        "algorithm": "zstd"
    }
}

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Complete Configuration Example

from praisonaiagents import Agent

# Comprehensive memory configuration
memory_agent = Agent(
    name="AdvancedMemoryAgent",
    memory=True,
    memory_config={
        # Provider settings
        "provider": "rag",
        "use_embedding": True,
        
        # Storage paths
        "rag_db_path": "./memory/rag_db",
        "short_db": "./memory/short_term.db",
        "long_db": "./memory/long_term.db",
        
        # Graph configuration
        "graph_enabled": True,
        "graph_provider": "neo4j",
        "graph_uri": "bolt://localhost:7687",
        "graph_user": "neo4j",
        "graph_password": "password",
        
        # Quality scoring
        "quality_threshold": 0.7,
        "scoring_weights": {
            "relevance": 0.4,
            "recency": 0.2,
            "frequency": 0.2,
            "confidence": 0.2
        },
        
        # Embedder configuration
        "embedder_config": {
            "provider": "openai",
            "model": "text-embedding-3-small",
            "batch_size": 100
        },
        
        # Performance settings
        "cache_enabled": True,
        "max_results": 20,
        "parallel_retrieval": True,
        
        # Maintenance
        "cleanup_interval": "daily",
        "ttl": {
            "short_term": 86400,
            "long_term": 2592000
        }
    }
)

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Environment Variables

# Memory provider
export PRAISONAI_MEMORY_PROVIDER="rag"
export PRAISONAI_MEMORY_PATH="./memory"

# Graph database
export PRAISONAI_GRAPH_ENABLED="true"
export PRAISONAI_GRAPH_URI="bolt://localhost:7687"
export PRAISONAI_GRAPH_USER="neo4j"
export PRAISONAI_GRAPH_PASSWORD="password"

# Quality settings
export PRAISONAI_MEMORY_QUALITY_THRESHOLD="0.7"
export PRAISONAI_MEMORY_MAX_RESULTS="10"

# Embedder
export PRAISONAI_EMBEDDER_PROVIDER="openai"
export PRAISONAI_EMBEDDER_MODEL="text-embedding-3-small"

# Performance
export PRAISONAI_MEMORY_CACHE_ENABLED="true"
export PRAISONAI_MEMORY_CACHE_SIZE="10000"

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Troubleshooting

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Common Issues

  1. Graph connection failures 
    # Add connection retry logic
"graph_config": {
    "retry_attempts": 5,
    "retry_delay": 2,
    "health_check_interval": 60
}
  2. Memory retrieval too slow 
    # Enable parallel retrieval and caching
"parallel_retrieval": True,
"cache_enabled": True,
"index_type": "hnsw"
  3. Quality scores too low 
    # Adjust scoring weights and thresholds
"quality_threshold": 0.5,  # Lower threshold
"scoring_weights": {
    "relevance": 0.6,  # Increase relevance weight
    "recency": 0.2,
    "frequency": 0.1,
    "confidence": 0.1
}

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See Also