Project 3: Multi-Modal GenAI Application

A scalable application that processes multiple data types using a distributed vector database architecture for efficient retrieval and generation.

Weaviate Redis Cache LangChain Agents ECS Fargate FastAPI Terraform Prometheus/Grafana

Project Overview

The Multi-Modal GenAI Application is a scalable system designed to process and generate responses from multiple data types (text, images, structured data) using a distributed vector database architecture. The system leverages advanced sharding and replication techniques to ensure high availability and efficient query handling at scale.

Problem Statement

Multi-modal AI applications face several challenges:

  • Efficient storage and retrieval of different data types
  • Scaling to handle large vector databases with billions of embeddings
  • Query performance degradation at scale
  • High availability requirements for production systems
  • Complex processing pipelines for different data modalities

Solution

This application addresses these challenges by:

  • Implementing a distributed vector database with Weaviate
  • Using sharding and replication for scalability and availability
  • Implementing Redis caching for query optimization
  • Designing specialized processing pipelines for different data types
  • Providing a unified API for multi-modal queries
  • Enabling horizontal scaling through containerized deployment
Key Features
  • Distributed Weaviate deployment with sharding
  • Replication for high availability
  • Redis caching for query optimization
  • Hybrid search combining vector and keyword search
  • Multi-modal processing pipelines
  • LangChain agents for task routing
  • Horizontal scaling architecture
  • Comprehensive monitoring with Prometheus/Grafana

Architecture

Project 3 Architecture

Architecture Components

  • Weaviate Cluster: Distributed vector database with sharding
  • Redis Cache: Query caching for performance
  • FastAPI Service: API endpoints and orchestration
  • LangChain Agents: Task routing and specialized processing
  • ECS Fargate: Containerized deployment
  • Application Load Balancer: Traffic distribution
  • S3: Object storage for media files
  • CloudFront: CDN for media delivery
  • Prometheus/Grafana: Monitoring and visualization
  • Terraform: Infrastructure as Code

Key Components

Distributed Vector Database

The core of the system is implemented in distributed_vector_database.py, which provides a comprehensive interface to the distributed Weaviate cluster:


# Initialize vector database
db = DistributedVectorDatabase(config)

# Add text object
text_id = db.add_text_object(text_data, "Document", text_vector)

# Vector search
results = db.vector_search(query_vector, "Document", limit=5)

# Hybrid search
hybrid_results = db.hybrid_search(
    query="vector databases",
    vector=query_vector,
    class_name="Document",
    limit=5,
    alpha=0.7
)

The DistributedVectorDatabase class handles:

  • Management of multiple Weaviate instances
  • Sharding data across nodes based on data type
  • Replication for data redundancy and fault tolerance
  • Redis caching for query performance optimization
  • Hybrid search combining vector and keyword search
  • Comprehensive monitoring and status reporting

Multi-Modal Processing Pipelines

The application includes specialized processing pipelines for different data types:

Text Processing
  • Text chunking and preprocessing
  • Embedding generation with AWS Bedrock
  • Metadata extraction
  • Language detection and translation
  • Named entity recognition
Image Processing
  • Image preprocessing and resizing
  • Feature extraction with CLIP
  • Object detection and classification
  • Caption generation
  • Visual question answering
Structured Data Processing
  • Schema detection and validation
  • Feature engineering
  • Embedding generation for tabular data
  • Relationship extraction
  • Time-series analysis

Key Features

Sharding and Replication

Data is distributed across multiple Weaviate instances based on data type, with replication for high availability and fault tolerance.

Query Optimization

Redis caching improves query performance for frequent searches, with intelligent cache invalidation and time-to-live settings.

Hybrid Search

Combines vector similarity search with keyword search for more accurate and relevant results, with adjustable weighting between the two.

Horizontal Scaling

The architecture allows for adding more Weaviate shards and application instances to handle increased load and data volume.

LangChain Agents

Specialized agents handle different query types and data modalities, with intelligent routing based on query content.

Comprehensive Monitoring

Prometheus and Grafana provide detailed metrics and visualizations for system performance, query patterns, and resource utilization.

Implementation Details

Deployment Architecture

The system is deployed using:

  1. Weaviate Cluster:
    • Multiple Weaviate instances for different data types
    • Configured with sharding and replication
    • Deployed on ECS Fargate for scalability
    • Persistent storage with EBS volumes
  2. Redis Cache:
    • ElastiCache Redis cluster for query caching
    • Configured with appropriate TTL settings
    • Memory allocation and eviction policies
    • Cluster mode for scalability
  3. Application Layer:
    • FastAPI service for API endpoints
    • LangChain for orchestration
    • Deployed on ECS with auto-scaling
    • Authentication and rate limiting
  4. Load Balancing:
    • Application Load Balancer for traffic distribution
    • Health checks and automatic failover
    • SSL termination and HTTPS support
    • WAF integration for security

Scaling Strategies

  • Horizontal Scaling:
    • Add more Weaviate shards for increased capacity
    • Scale application instances based on load
    • Auto-scaling groups for dynamic adjustment
    • Distributed processing across nodes
  • Query Optimization:
    • Caching for frequent queries
    • Hybrid search with appropriate weights
    • Optimized vector dimensions and indexing
    • Query batching and prioritization
  • Load Distribution:
    • Route queries to appropriate shards
    • Balance read operations across replicas
    • Write-through caching for updates
    • Asynchronous processing for non-critical operations

High Availability and Disaster Recovery

  • Replication Strategy:
    • Multiple replicas of each shard
    • Automatic failover to replicas
    • Cross-AZ deployment for resilience
    • Read replicas for query distribution
  • Backup and Recovery:
    • Regular snapshots of Weaviate data
    • Point-in-time recovery capabilities
    • Cross-region backup replication
    • Automated recovery procedures
  • Monitoring and Alerting:
    • Prometheus metrics for system health
    • Grafana dashboards for visualization
    • Alerting for critical issues
    • Automated remediation for common problems

Relevance to Job Requirements

Vector Database Expertise

This project demonstrates experience with distributed vector databases:

  • Weaviate implementation with sharding and replication
  • Efficient query handling with caching and optimization
  • Scaling strategies for large vector collections
  • Hybrid search combining vector and keyword search
  • Multi-modal data storage and retrieval
Efficient Query Handling

The project showcases optimization techniques for vector search:

  • Redis caching for frequent queries
  • Query routing to appropriate shards
  • Load balancing across replicas
  • Hybrid search with adjustable weights
  • Performance monitoring and optimization
Scaling Mechanisms

The project implements horizontal scaling strategies:

  • Sharding based on data characteristics
  • Replication for high availability
  • Auto-scaling for application layer
  • Distributed processing across nodes
  • Containerized deployment with ECS Fargate
LangChain Agents

The project utilizes LangChain for orchestration:

  • Specialized agents for different data types
  • Task routing based on query content
  • Multi-modal processing pipelines
  • Integration with vector search
  • Comprehensive response generation

Next Steps

Future enhancements to the Multi-Modal GenAI Application could include:

Advanced Vector Compression

Implement techniques like Product Quantization (PQ) and Scalar Quantization (SQ) to reduce vector storage requirements while maintaining search quality.

Federated Learning Integration

Add support for federated learning to improve embeddings and models without centralizing sensitive data.

Multi-Region Deployment

Extend the architecture to support multi-region deployment with cross-region replication for global availability and reduced latency.

Advanced Retrieval Techniques

Implement techniques like Retrieval-Augmented Generation (RAG) with multi-hop reasoning and knowledge graph integration for more complex queries.

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