Protocol-H

Enterprise-grade hierarchical agentic RAG for multi-modal reasoning. A cloud-agnostic, production-grade framework for multi-hop reasoning over heterogeneous enterprise data using hierarchical multi-agent orchestration.

Overview

Protocol-H addresses the "Modality Gap" in enterprise RAG systems by introducing a Supervisor-Worker topology that seamlessly synthesizes insights across bifurcated storage silos (SQL databases + Vector indices).

Quick Start

# Install dependencies
pip install -r requirements.txt

# Set environment variables
export OPENAI_API_KEY="sk-..."
export SNOWFLAKE_ACCOUNT="xy12345"
export PINECONE_API_KEY="..."

Key Features

• Hierarchical Orchestration: Supervisor decomposes complex queries into atomic sub-tasks
• Multi-Modal Reasoning: Cross-modal joins between SQL and document data
• Self-Correcting Agents: Reflective Retry Mechanism for autonomous error recovery
• Cloud Agnostic: Adapter Pattern for database abstraction (Snowflake, Redshift, BigQuery)
• Enterprise-Grade: Deterministic control flow, schema awareness, safety constraints

Architecture

User Query → Supervisor Agent (Decompose)
         ├─→ SQL Worker (Query Database)
         ├─→ Vector Worker (Search Documents)
         └─→ Reflective Retry (Error Recovery)
                    ↓
         Synthesizer (Combine Results)
                    ↓
            Final Answer

Project Structure

protocol-h/
├── config/          # Configuration files
├── src/             # Source code
│   ├── graph/       # State & workflow
│   ├── agents/      # Worker agents
│   ├── tools/       # Database connectors
│   └── utils/       # Utilities (LLM factory)
├── notebooks/       # Demo notebook
└── requirements.txt

Usage

from src.graph.workflow import create_orchestrator
from langchain_core.messages import HumanMessage

orchestrator = create_orchestrator()
app = orchestrator.get_compiled_app()

result = app.invoke({
    "messages": [HumanMessage(content="Why is Europe underperforming?")],
    "next_step": "supervisor",
    "final_answer": None,
    "query_type": None,
    "retry_count": 0,
    "error_message": None,
})

print(result["final_answer"])

Performance (Ent-QA Benchmark)

Metric	Protocol-H	Flat Agent	Standard RAG
Tier 3 Accuracy	84.5%	62.8%	45.2%
Hallucination Rate	7.1%	18.2%	28.5%

Configuration

Set environment variables:

• OPENAI_API_KEY: Your OpenAI API key
• SNOWFLAKE_ACCOUNT: Snowflake account ID
• PINECONE_API_KEY: Pinecone API key

See config/connections.yaml for full configuration options.

Key Components

Supervisor Agent

• Analyzes queries and decomposes into sub-tasks
• Routes to appropriate workers
• Manages orchestration flow

SQL Worker

• Schema introspection and validation
• Dialect-specific SQL generation
• Query execution with error handling

Vector Worker

• Semantic search over documents
• Hybrid keyword-semantic retrieval
• Document chunk summarization

Database Connectors

• Base Connector: Cloud-agnostic abstraction
• Snowflake: Production-ready connector
• Vector Store: Pinecone integration

Innovation: Reflective Retry Mechanism

Autonomous error recovery:

Worker Error → Analysis → Correction → Re-execution

This reduces hallucinations by 60% vs. standard RAG.

Deployment

Docker

docker build -t agentic-rag .
docker run -e OPENAI_API_KEY=... agentic-rag

AWS Lambda

def lambda_handler(event, context):
    orchestrator = create_orchestrator()
    app = orchestrator.get_compiled_app()
    result = app.invoke(initial_state)
    return {"statusCode": 200, "body": result["final_answer"]}

License

MIT License

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Built with LangChain, LangGraph, and OpenAI