PraisonAI/examples/python/mongodb

MongoDB Integration Examples for PraisonAI Agents

This directory contains comprehensive examples demonstrating how to integrate MongoDB with PraisonAI agents for memory, knowledge, and data operations.

Features

MongoDB integration with PraisonAI provides:

• Memory Provider: Use MongoDB as a persistent memory store for agents
• Knowledge Store: Store and retrieve documents with vector search capabilities
• Tools Integration: Perform MongoDB operations directly from agents
• Vector Search: Leverage MongoDB Atlas Vector Search for semantic similarity
• Scalability: Handle large datasets with MongoDB's scalable architecture
• Flexibility: Use MongoDB as both key-value store and vector database

Prerequisites

Installation

# Install PraisonAI with MongoDB support
pip install 'praisonaiagents[mongodb]'

Dependencies

• MongoDB: Local MongoDB instance or MongoDB Atlas
• OpenAI API Key: For embeddings and LLM operations
• Python 3.10+: Required for PraisonAI

Environment Setup

# Set your OpenAI API key
export OPENAI_API_KEY="your-openai-api-key"

# Optional: Set MongoDB connection string
export MONGODB_CONNECTION_STRING="mongodb://localhost:27017/"

Examples Overview

1. mongodb_memory_example.py

Demonstrates using MongoDB as a memory provider for PraisonAI agents.

Features:

• MongoDB as persistent memory storage
• Quality scoring and filtering
• Vector search for memory retrieval
• Multi-session memory persistence
• Memory search and context building

Usage:

python mongodb_memory_example.py

2. mongodb_knowledge_example.py

Shows how to use MongoDB as a knowledge store with vector search capabilities.

Features:

• MongoDB as knowledge vector store
• Document processing and storage
• Vector search for knowledge retrieval
• Knowledge-based agent interactions
• File processing with MongoDB storage

Usage:

python mongodb_knowledge_example.py

3. mongodb_tools_example.py

Demonstrates MongoDB tools integration for database operations.

Features:

• MongoDB CRUD operations
• Vector search with embeddings
• Data analysis and aggregation
• Collection management
• Index creation and optimization

Usage:

python mongodb_tools_example.py

4. mongodb_comprehensive_example.py

Complete business scenario simulation using all MongoDB features.

Features:

• Full MongoDB integration (memory + knowledge + tools)
• Multi-agent business workflow
• Real-world e-commerce simulation
• Business intelligence analytics
• Customer service automation

Usage:

python mongodb_comprehensive_example.py

Configuration

MongoDB Memory Configuration

mongodb_memory_config = {
    "provider": "mongodb",
    "config": {
        "connection_string": "mongodb://localhost:27017/",
        "database": "praisonai_memory",
        "use_vector_search": True,  # Enable Atlas Vector Search
        "max_pool_size": 50,
        "min_pool_size": 10,
        "server_selection_timeout": 5000
    }
}

MongoDB Knowledge Configuration

mongodb_knowledge_config = {
    "vector_store": {
        "provider": "mongodb",
        "config": {
            "connection_string": "mongodb://localhost:27017/",
            "database": "praisonai_knowledge",
            "collection": "knowledge_base",
            "use_vector_search": True
        }
    },
    "embedder": {
        "provider": "openai",
        "config": {
            "model": "text-embedding-3-small"
        }
    }
}

MongoDB Atlas Vector Search

For production use with advanced vector search capabilities:

Setup

1. Create MongoDB Atlas Cluster

   • Sign up for MongoDB Atlas
   • Create a new cluster
   • Get your connection string

2. Enable Vector Search

   • Create vector search indexes
   • Configure embedding dimensions (1536 for OpenAI)
   • Set similarity metric (cosine, euclidean, dotProduct)

3. Update Connection String

   "connection_string": "mongodb+srv://username:[email protected]/"
   

Vector Search Index Creation

# Vector search index definition
{
    "mappings": {
        "dynamic": True,
        "fields": {
            "embedding": {
                "type": "knnVector",
                "dimensions": 1536,
                "similarity": "cosine"
            }
        }
    }
}

Best Practices

1. Connection Management

# Use connection pooling
mongodb_config = {
    "connection_string": "mongodb://localhost:27017/",
    "max_pool_size": 50,
    "min_pool_size": 10,
    "maxIdleTimeMS": 30000,
    "serverSelectionTimeoutMS": 5000
}

2. Indexing Strategy

# Create appropriate indexes
collection.create_index([("content", "text")])  # Text search
collection.create_index([("created_at", -1)])   # Time-based queries
collection.create_index([("metadata.category", 1)])  # Category filtering

3. Error Handling

try:
    # MongoDB operations
    result = collection.insert_one(document)
except PyMongoError as e:
    logger.error(f"MongoDB error: {e}")
    # Implement fallback strategy

4. Data Validation

# Validate data before storage
def validate_document(doc):
    required_fields = ["content", "metadata", "created_at"]
    return all(field in doc for field in required_fields)

Performance Considerations

1. Indexing

• Create indexes on frequently queried fields
• Use compound indexes for complex queries
• Monitor index usage with MongoDB profiler

2. Vector Search Optimization

• Use appropriate vector dimensions
• Optimize numCandidates parameter
• Consider filtering to reduce search space

3. Connection Pooling

• Configure appropriate pool sizes
• Use connection pooling for high-concurrency scenarios
• Monitor connection metrics

Troubleshooting

Common Issues

1. Connection Errors

   • Check MongoDB server is running
   • Verify connection string format
   • Ensure network connectivity

2. Vector Search Issues

   • Verify Atlas Vector Search indexes exist
   • Check embedding dimensions match
   • Ensure proper index configuration

3. Memory Issues

   • Monitor memory usage with large datasets
   • Use appropriate batch sizes
   • Consider data pagination

Debugging

# Enable detailed logging
import logging
logging.basicConfig(level=logging.DEBUG)

# Monitor MongoDB operations
from pymongo import monitoring
monitoring.register(CommandLogger())

Production Deployment

1. Security

# Use authentication
connection_string = "mongodb://username:password@host:port/database"

# Enable SSL/TLS
client = MongoClient(connection_string, tls=True)

2. Monitoring

• Use MongoDB Atlas monitoring
• Implement application-level metrics
• Set up alerting for critical issues

3. Backup and Recovery

• Configure automated backups
• Test restore procedures
• Implement disaster recovery plans

Support

For additional support:

• PraisonAI Documentation
• MongoDB Documentation
• MongoDB Atlas Vector Search

License

These examples are provided under the same license as PraisonAI.