Catalyst: Production-Ready RAG Pipelines

The End-to-End RAG Library providing reliability, scalability, and monitoring features required to confidently deploy contextual GenAI systems in production. 

pip install catalyst-rag

The Production Promise

Engineered for enterprise-grade deployments with zero compromises.

Reliability

Built for mission-critical uptime with automated caching, exponential backoff retries, and graceful degradation strategies that ensure your RAG pipeline never fails silently.

  • •Automated Caching
  • •Intelligent Retries
  • •Graceful Degradation

Scalability

Handle enterprise-scale queries with concurrent processing, intelligent load balancing, and optimized batching that reduces LLM costs while maximizing throughput.

  • •Concurrent Processing
  • •Dynamic Load Balancing
  • •Optimized Batching

Observability

Gain complete visibility into production performance with built-in hooks for tracking retrieval latency, monitoring LLM costs, and seamless integration with enterprise tracing tools.

  • •Production Hooks
  • •Latency & Cost Tracking
  • •Traceability Integration

Go Beyond Demos.
Deploy with Confidence.

Catalyst eliminates the dangerous gap between proof-of-concept and secure enterprise deployment. We enforce production requirements, security standards, and operational excellence from the first line of code.

  • ✓ Secure credential management for vector stores and LLMs
  • ✓ Configurable rate limiting and quota management
  • ✓ Data encryption at rest and in transit
  • ✓ Role-based access control (RBAC) for pipeline operations
from catalyst_rag import MonitoredRAGPipeline, VDB, LLMFactory

# Production-ready from the start
pipeline = MonitoredRAGPipeline(
    vector_store=VDB.connect(
        'prod_index',
        credentials_env='VDB_PROD_CREDS'
    ),
    llm=LLMFactory.from_env(
        provider='azure-openai',
        model='gpt-4-turbo'
    ),
    monitoring_endpoint='http://metrics.internal-corp.com/api',
    cache_enabled=True,
    retry_policy={
        'max_attempts': 3,
        'backoff': 'exponential'
    },
    batch_size=100,
    encryption=True,
    rate_limit=1000  # req/min
)
                
# Run with full observability
result = pipeline.query(
    "What are Q3 compliance updates?",
    user_id="usr_12345",
    trace_id="trace_abc789"
)

Built For Mission-Critical Applications

Trusted by enterprise teams to power RAG systems where failure is not an option.

Internal Knowledge Search

Empower employees with instant, accurate answers from vast document repositories while maintaining strict access controls.

Customer Support Automation

Deflect tickets and resolve issues faster with RAG-powered chatbots that pull from up-to-date product documentation and policies.

Regulatory Compliance Analysis

Analyze thousands of legal documents to ensure business practices align with constantly evolving global regulations.

Codebase Q&A

Accelerate developer onboarding and productivity with a RAG system that understands your entire codebase and architecture.