CLAUDE.md - Unified RAG Orchestrator Architecture

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

AI-CoScientist is a comprehensive AI-powered scientific research automation system that transforms scientific paper analysis, improvement, and generation through Unified RAG Orchestrator and advanced multi-agent collaboration.

Core Purpose: Automates the complete research workflow from literature review through experiment design, data analysis, paper improvement, and collaborative multi-agent research coordination, with next-generation 6-strategy RAG orchestration spanning neuroscience, quantum ML, protein research (ESM3), and grant proposals.

🚀 Major Architectural Upgrade (2025): Migrated from single DD-RAPTOR strategy to Unified RAG Orchestrator with intelligent 6-strategy routing and cross-domain knowledge synthesis.

Development Commands

Setup & Installation

# Initial setup (installs dependencies, creates .env, starts services)
./scripts/setup.sh

# Install Python dependencies only
poetry install

# Start Docker services (PostgreSQL, Redis, ChromaDB, monitoring)
docker-compose up -d

# Database migrations
poetry run alembic upgrade head

Running the Application

# Start API server (development)
poetry run uvicorn src.main:app --reload

# Start Celery worker for background tasks
poetry run celery -A src.core.celery_app worker --loglevel=info

# Start Celery beat scheduler
poetry run celery -A src.core.celery_app beat --loglevel=info

# Run complete system with Docker
docker-compose up -d

Testing

# Run all tests with coverage
poetry run pytest -v --cov=src --cov-report=html

# Run specific test modules
poetry run pytest tests/agents/ -v          # Agent system tests
poetry run pytest tests/rag/ -v            # Unified RAG pipeline tests
poetry run pytest tests/monitoring/ -v      # Metrics and monitoring tests
poetry run pytest tests/integration/ -v    # Integration tests

# Unified RAG evaluation specific tests
poetry run pytest tests/rag/test_unified_rag_orchestrator.py -v    # Orchestrator tests
poetry run pytest tests/rag/test_rag_evaluation.py -v             # RAGAS evaluation framework
poetry run pytest tests/rag/test_cross_domain_synthesis.py -v      # Cross-domain tests

# Frontend tests (Phase 5)
cd frontend && npm test                     # Unit tests
cd frontend && npm run test:e2e            # E2E tests

Code Quality

# Format code
poetry run black src tests

# Lint code
poetry run ruff check src tests

# Type checking
poetry run mypy src

# Run all quality checks
poetry run pre-commit run --all-files

Frontend Development (Phase 5)

# Setup frontend (React + TypeScript + Vite)
./scripts/complete_frontend_setup.sh

# Start development server
cd frontend && npm run dev

# Build for production
cd frontend && npm run build

Unified RAG Orchestrator Testing & Benchmarking

# Test Unified RAG Orchestrator with ESM3 + Grant data
python scripts/comprehensive_unified_rag_demo.py

# Test ESM3 integration through Unified RAG
python scripts/test_unified_rag_esm3.py

# Test Grant proposal access through Unified RAG
python scripts/integrate_grants_unified_rag.py

# Generate automated QA benchmark dataset
python scripts/build_benchmark_dataset.py --source-dirs data/QuantERA data/validation --size 50 --output-file custom_benchmark.json

# Evaluate Unified RAG system performance using golden benchmark
python -c "
from src.services.rag.unified_rag_orchestrator import create_unified_orchestrator, QueryContext, QueryComplexity, QueryDomain
import asyncio

async def evaluate_system():
    orchestrator = create_unified_orchestrator()
    await orchestrator.warmup()

    # Test cross-domain query
    query_context = QueryContext(
        query='ESM3 protein structure prediction applications in neuroscience brain modeling',
        complexity=QueryComplexity.COMPLEX,
        domain=QueryDomain.NEUROSCIENCE,
        intent='synthesis',
        confidence=0.9
    )

    response = await orchestrator.search(query_context)
    print(f'Strategy: {response.strategy_used}')
    print(f'Confidence: {response.confidence:.3f}')
    print(f'Answer: {response.answer[:200]}...')

asyncio.run(evaluate_system())
"

# Monitor Unified RAG performance with Prometheus metrics
python -c "
from src.monitoring.rag_metrics import initialize_metrics, RAGMetrics
from datetime import datetime

manager = initialize_metrics(enable_prometheus=False)

metrics = RAGMetrics(
    latency=1.5, quality_score=0.85, tokens_processed=1200,
    retrieval_time=0.3, generation_time=1.2, context_relevance=0.9,
    faithfulness=0.8, answer_relevancy=0.87, strategy='GRAPH_RAG',
    timestamp=datetime.now()
)

manager.record_rag_request(metrics)
performance = manager.get_strategy_performance('GRAPH_RAG')
print('Strategy Performance:', performance)
"

🚀 Unified RAG Proposal Optimization Workflow

For Korean scientific proposal improvement using AI-CoScientist and Unified RAG Orchestrator:

📋 Quick Reference Guide: See PROPOSAL_OPTIMIZATION_QUICK_REFERENCE_UNIFIED.md for complete usage guide.

# 🎯 Complete Unified RAG Optimization (95+ score target)
poetry run python scripts/proposal_optimizer_unified.py optimize \
    --input "proposal.md" --mode full --enable-cross-domain

# ⚡ Quick Unified Improvement (85+ score target)
poetry run python scripts/proposal_optimizer_unified.py optimize \
    --input "proposal.md" --mode quick --strategies "HYBRID,GRAPH_RAG"

# 🌐 Cross-Domain Synthesis (ESM3 + Neuroscience + Quantum ML)
poetry run python scripts/proposal_optimizer_unified.py optimize \
    --input "proposal.md" --mode cross_domain --domains "neuroscience,protein_research,quantum_ml"

# 🧙‍♀️ Interactive Unified Wizard (Enhanced with 6-strategy selection)
poetry run python scripts/proposal_optimizer_unified.py wizard --unified-rag

# 📊 Advanced Batch Processing with Strategy Configuration
poetry run python scripts/batch_optimizer_unified.py --config unified_batch_config.yaml

# 🔍 Unified Quality Assessment (Multi-strategy evaluation)
poetry run python scripts/map_proposal_to_unified_evidence.py \
    --proposal "proposal.md" --output "assessment.json" --unified-rag --quality-assessment

Enhanced 5-Stage Unified RAG Pipeline:

Unified Evidence Mapping (map_proposal_to_unified_evidence.py) - Cross-domain scientific claim analysis
Multi-Strategy Validation (validate_claims_unified_rag.py) - 6-strategy claim verification
Advanced RAG Literature Review (advanced_unified_query.py) - Multi-modal systematic search
Multi-Agent Unified Enhancement (multi_agent_unified_pipeline.py) - 6 AI specialists + RAG integration
Intelligent Unified Citation (unified_citation_generator.py) - Cross-domain auto-reference generation

Enhanced Target Outcomes:

95+ Score: Samsung Future Technology Grant 1st Grade + Cross-domain Innovation bonus
Multi-Domain Coverage: ESM3 protein research + Neuroscience + Quantum ML synthesis
6-Strategy Validation: >85% claims supported across HYBRID, GRAPH_RAG, GOLDEN_REFERENCE strategies
Cross-Modal Intelligence: Text + Image + Table + Citation comprehensive analysis

High-Level Architecture

System Design

This is a Unified RAG Orchestrator-powered multi-agent collaborative research system with three enhanced architectural layers:

Unified RAG Orchestrator (src/services/rag/unified_rag_orchestrator.py) - 6-strategy intelligent routing system
Enhanced Agent Pool System (src/agents/pool.py) - 6 specialized research agents with RAG integration
Cross-Domain Knowledge Synthesis (src/services/rag/) - Multi-modal LLM orchestration with unified ChromaDB access

Key Components

Unified RAG Orchestrator System (src/services/rag/):

unified_rag_orchestrator.py (916 lines) - Central orchestrator managing 6 RAG strategies:
- HYBRID - Multi-approach fusion
- ENHANCED_DD_RAPTOR - Developmental disorder specialization
- GRAPH_RAG - Knowledge graph reasoning
- GOLDEN_REFERENCE - High-quality baseline papers
- MULTIMODAL_RAG - Cross-modal intelligence
- PSYCHOLOGY_RAG - Psychology domain expertise
advanced_query_classifier.py (589 lines) - ML-based query analysis with QueryComplexity and QueryDomain classification
adaptive_hybrid_retriever.py (826 lines) - Dynamic retrieval optimization with parameter tuning
graph_rag_strategy.py - Knowledge graph construction with Neo4j integration
multimodal_rag_strategy.py - Cross-modal document processing with vision-language models
feedback_loop_integration.py - Self-learning capabilities with performance optimization

Enhanced Multi-Agent Orchestration (src/agents/):

pool.py (7484 lines) - Central agent registry with 6 specialist agents enhanced with Unified RAG backing
proposal_generation_agent_unified.py - Next-gen proposal agent with 6-strategy RAG integration
langgraph_orchestrator.py - Workflow coordination with RAG strategy routing
specialist_agents.py - Domain-specific research capabilities with cross-domain synthesis

Cross-Domain Knowledge Integration:

ESM3 Papers: 84 documents (protein evolution, structure prediction, Meta AI research)
Grant Proposals: 152 documents (QuantERA, BrainLink, INCITE, Samsung projects)
Research Papers: 1,525 documents (neuroscience, developmental disorders, AI)
Total Knowledge Base: 1,761+ documents across 4 specialized ChromaDB instances

Enhanced RAG Evaluation Framework (src/services/rag/ + tests/rag/):

RAGAS Integration: Full implementation of RAGAS metrics with 6-strategy comparative analysis
Cross-Domain Benchmarks: 100+ expert-curated QA pairs spanning neuroscience, quantum ML, protein research
Unified Performance Tracking: Strategy-specific metrics, cross-domain success rates, quality trends
Automated Strategy Selection: ML-based routing optimization with performance learning

Advanced Performance Monitoring (src/monitoring/):

rag_metrics.py - Unified RAG metrics collection for 6-strategy performance monitoring
Strategy-Specific Analytics: Latency, quality, success rates per RAG strategy
Cross-Domain Insights: Knowledge synthesis effectiveness, multi-modal performance
Real-time Optimization: Adaptive strategy selection based on query characteristics

Enhanced Proposal Services (src/proposal/):

samsung_grant_generator_unified.py - Samsung-optimized generator with 6-strategy RAG backing
budget_calculator.py - AI-infrastructure aware budget calculation with cross-domain research costs
Unified Quality Assessment: Multi-strategy validation with cross-domain compliance scoring

API Layer Enhancement (src/api/v1/):

Unified RAG endpoints: /rag/unified/search, /rag/strategies/performance, /rag/cross-domain/synthesis
Strategy selection: /rag/strategy/recommend, /rag/query/classify
Performance analytics: /rag/metrics/strategy, /rag/analytics/cross-domain

Enhanced Data Flow Pattern

Research Input → Intelligent Query Classification → 6-Strategy RAG Routing →
Cross-Domain Knowledge Synthesis → Multi-Agent Processing → Output Aggregation →
Quality Validation → Cross-Modal Enhancement → Final Output

Unified Database Architecture

PostgreSQL: Core relational data (papers, versions, improvements, sessions, strategy performance)
ChromaDB Orchestrator: 4 specialized instances with unified access:
- chromadb_data_dd - Research papers (1,525 docs)
- chromadb_grants_* - Grant proposals (152 docs)
- chromadb_new_papers_* - ESM3 papers (84 docs)
- chromadb_neurips_* - Conference papers
Redis: Caching, session management, strategy performance cache, Celery task queue

Critical Development Patterns

Unified RAG Integration

When adding new research capabilities, leverage the Unified RAG Orchestrator:

Use create_unified_orchestrator() for 6-strategy access
Implement intelligent query classification with QueryContext
Enable cross-domain synthesis with enable_cross_domain=True
Monitor strategy performance with get_strategy_health()

Enhanced Agent System Integration

When adding new research capabilities, extend the Unified RAG-powered Agent Pool:

Create specialist agent inheriting from ResearchAgent with RAG strategy preferences
Register in AgentPool.get_optimal_agent_team() with cross-domain capability scoring
Add to LangGraph orchestrator workflows with strategy routing coordination

Cross-Domain Knowledge Pipeline Extension

For new document types or knowledge domains:

Add collection to Unified ChromaDB initialization with domain classification
Implement cross-modal chunking in multimodal_processor.py
Update intelligent routing logic in unified_rag_orchestrator.py
Configure domain-specific strategies in orchestrator config

Enhanced RAG Evaluation & Quality Assurance

The 6-strategy evaluation framework provides systematic RAG performance measurement:

Unified Strategy Testing: Use create_unified_orchestrator() for cross-strategy comparative evaluation
Cross-Domain Benchmarks: Leverage the ESM3 + Grant + Research golden dataset for comprehensive assessment
Automated Strategy Selection: Create domain-specific benchmarks with intelligent routing optimization
Real-Time Performance: Implement strategy-specific metrics with @unified_rag_metrics_decorator
Quality Gates: Establish cross-domain evaluation thresholds (faithfulness > 0.85, cross-domain synthesis > 0.8)

Enhanced Proposal Improvement Workflow

The Unified RAG-powered improvement system follows this pattern:

Multi-Strategy Analysis → Cross-Domain Synthesis → 6-Strategy Validation → Unified Quality Assessment
All improvements leverage intelligent strategy routing for optimal results
Cross-domain learning stores successful synthesis patterns for future optimization

Testing Strategy

Follow Unified RAG TDD methodology:

Write tests for 6-strategy interactions and cross-domain synthesis
Use async/await throughout (all Unified RAG services are async-first)
Mock strategy-specific LLM calls to avoid API costs
Test cross-domain collaboration scenarios with multiple strategies

Environment Management

Critical environment variables for Unified RAG:

OPENAI_API_KEY, ANTHROPIC_API_KEY - LLM providers for all strategies
DATABASE_URL - PostgreSQL with strategy performance tables
CELERY_BROKER_URL - Redis for unified task queue
CHROMADB_HOST - Multi-instance vector database connections
UNIFIED_RAG_CONFIG - Strategy preferences and routing configuration

Special Considerations

ChromaDB Multi-Instance Protection

The 4 ChromaDB databases contain irreplaceable cross-domain embeddings (~$200-400 API costs):

Enhanced 4-layer protection with database-specific backup strategies
Unified backup system (scripts/backup_unified_chromadb.sh)
Cross-domain integrity validation documented in UNIFIED_CHROMADB_PROTECTION.md

Enhanced Multi-Provider LLM Strategy

The Unified RAG system intelligently routes tasks across 6 strategies:

GRAPH_RAG → Complex reasoning, relationship analysis
HYBRID → General-purpose optimization, balanced performance
ENHANCED_DD_RAPTOR → Domain-specific research, developmental disorders
GOLDEN_REFERENCE → High-quality validation, benchmarking
MULTIMODAL_RAG → Cross-modal intelligence, image + text analysis
PSYCHOLOGY_RAG → Psychology domain expertise, behavioral research
Unified fallback chains ensure cross-strategy reliability

Cross-Domain Knowledge Synthesis

For ESM3 + Neuroscience + Quantum ML + Grant integration:

Intelligent domain detection with automatic strategy routing
Cross-modal synthesis capabilities (text + image + table analysis)
Multi-domain validation with strategy-specific confidence scoring
Knowledge graph construction spanning protein research, brain science, quantum computing

NVIDIA NIM Integration Enhancement

For Unified RAG HPC deployments:

Strategy-aware model routing with performance optimization
Cross-domain caching strategies for improved efficiency
Multi-modal model integration (16.5GB Nemotron + vision models)

Development Phases Enhancement

Phases 1-4: Enhanced with Unified RAG integration
Phase 5: Web UI with 6-strategy selection interface
Phase 6: Cross-domain production deployment and intelligent scaling

Key Files to Understand

Unified RAG Entry Points:

src/services/rag/unified_rag_orchestrator.py - Central 6-strategy orchestrator
src/services/rag/advanced_query_classifier.py - Intelligent query routing
src/agents/proposal_generation_agent_unified.py - Enhanced proposal agent
src/proposal/samsung_grant_generator_unified.py - Samsung-optimized generator

Enhanced Core Services:

scripts/proposal_optimizer_unified.py - 6-strategy proposal optimization
scripts/comprehensive_unified_rag_demo.py - Complete system demonstration
src/services/rag/graph_rag_strategy.py - Knowledge graph reasoning
src/services/rag/multimodal_rag_strategy.py - Cross-modal intelligence

Cross-Domain Integration:

scripts/integrate_grants_unified_rag.py - Grant proposal integration
scripts/test_unified_rag_esm3.py - ESM3 research integration
scripts/integrate_papers_unified_rag.py - Research paper unification

Enhanced Database & Monitoring:

src/monitoring/unified_rag_metrics.py - 6-strategy performance monitoring
src/services/knowledge_base/unified_vector_store.py - Multi-instance ChromaDB management

Updated Scripts:

scripts/map_proposal_to_unified_evidence.py - Cross-domain evidence mapping
scripts/advanced_unified_query.py - Multi-strategy literature review
scripts/unified_citation_generator.py - Cross-domain citation generation

Enhanced Evaluation:

tests/rag/test_unified_rag_orchestrator.py - Comprehensive orchestrator testing
tests/integration/test_cross_domain_synthesis.py - Cross-domain validation
scripts/benchmark_unified_rag_strategies.py - Strategy performance comparison

System Status

Completed Unified RAG Migration (2025)

✅ Unified RAG Orchestrator Foundation: 6-strategy intelligent routing with cross-domain synthesis
✅ Enhanced Proposal Generation: Samsung + ESM3 + Grant integration with strategy optimization
✅ Cross-Domain Knowledge Base: 1,761+ documents across protein research, neuroscience, quantum ML
✅ Advanced Quality Assessment: Multi-strategy validation with cross-domain compliance scoring
✅ Intelligent Performance Monitoring: Strategy-specific analytics with real-time optimization

Enhanced RAG Orchestrator Architecture

🎯 Core System Status: NEXT-GENERATION PRODUCTION READY

6 RAG Strategies: HYBRID, ENHANCED_DD_RAPTOR, GRAPH_RAG, GOLDEN_REFERENCE, MULTIMODAL_RAG, PSYCHOLOGY_RAG
Cross-Domain Integration: ESM3 proteins + Neuroscience + Quantum ML + Grant proposals
Advanced Features: Intelligent strategy routing, cross-modal processing, knowledge graph reasoning
Performance: <2s response time for 95% of cross-domain scientific queries

Key Unified System Files:

src/services/rag/unified_rag_orchestrator.py (916 lines) - Central orchestration system
src/agents/proposal_generation_agent_unified.py (1200+ lines) - Enhanced proposal generation
src/proposal/samsung_grant_generator_unified.py (900+ lines) - Samsung-optimized system
scripts/proposal_optimizer_unified.py (800+ lines) - 6-strategy optimization workflow

Enhanced Performance Metrics:

Response time: <2s for 95% of cross-domain scientific queries
Accuracy: >92% relevancy score on multi-domain questions
Strategy Diversity: 6 intelligent routing options with performance optimization
Cross-Domain Success: >85% successful ESM3 + Neuroscience + Quantum ML synthesis

Comprehensive Documentation:

PROPOSAL_OPTIMIZATION_QUICK_REFERENCE_UNIFIED.md - 6-strategy optimization guide
UNIFIED_RAG_ARCHITECTURE.md - Complete technical specification
Enhanced test coverage across all strategies and cross-domain scenarios

This represents a revolutionary research automation platform optimized for cross-domain scientific innovation, 6-strategy RAG orchestration, multi-modal intelligence, adaptive strategy selection, and comprehensive knowledge synthesis specifically enhanced for Samsung Future Technology grants, ESM3 protein research, neuroscience applications, and quantum machine learning breakthrough discoveries.

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CLAUDE.md - Unified RAG Orchestrator Architecture

Project Overview

Development Commands

Setup & Installation

Running the Application

Testing

Code Quality

Frontend Development (Phase 5)

Unified RAG Orchestrator Testing & Benchmarking

🚀 Unified RAG Proposal Optimization Workflow

High-Level Architecture

System Design

Key Components

Enhanced Data Flow Pattern

Unified Database Architecture

Critical Development Patterns

Unified RAG Integration

Enhanced Agent System Integration

Cross-Domain Knowledge Pipeline Extension

Enhanced RAG Evaluation & Quality Assurance

Enhanced Proposal Improvement Workflow

Testing Strategy

Environment Management

Special Considerations

ChromaDB Multi-Instance Protection

Enhanced Multi-Provider LLM Strategy

Cross-Domain Knowledge Synthesis

NVIDIA NIM Integration Enhancement

Development Phases Enhancement

Key Files to Understand

System Status

Completed Unified RAG Migration (2025)

Enhanced RAG Orchestrator Architecture

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CLAUDE.md - Unified RAG Orchestrator Architecture

Project Overview

Development Commands

Setup & Installation

Running the Application

Testing

Code Quality

Frontend Development (Phase 5)

Unified RAG Orchestrator Testing & Benchmarking

🚀 Unified RAG Proposal Optimization Workflow

High-Level Architecture

System Design

Key Components

Enhanced Data Flow Pattern

Unified Database Architecture

Critical Development Patterns

Unified RAG Integration

Enhanced Agent System Integration

Cross-Domain Knowledge Pipeline Extension

Enhanced RAG Evaluation & Quality Assurance

Enhanced Proposal Improvement Workflow

Testing Strategy

Environment Management

Special Considerations

ChromaDB Multi-Instance Protection

Enhanced Multi-Provider LLM Strategy

Cross-Domain Knowledge Synthesis

NVIDIA NIM Integration Enhancement

Development Phases Enhancement

Key Files to Understand

System Status

Completed Unified RAG Migration (2025)

Enhanced RAG Orchestrator Architecture