# Multi-Hop Reasoning RAG Workflow

## Overview
Demonstrates advanced reasoning capabilities that traverse multiple knowledge sources and make logical connections across disparate information. This workflow performs complex multi-step reasoning requiring integration of information from multiple documents and knowledge bases.

## Use Case
- Complex research questions requiring multiple sources
- Scientific literature synthesis across disciplines
- Legal case analysis with precedent research
- Technical troubleshooting with multi-system dependencies
- Historical analysis requiring chronological reasoning

## Agent Specification

### Core Functionality
- **Input**: Complex questions requiring multi-step reasoning and source traversal
- **Processing**: Iterative information gathering with logical reasoning chains
- **Output**: Comprehensive answers with reasoning paths and source citations
- **Memory**: Reasoning chains, source relationships, and knowledge graph connections

### Workflow Architecture
```python
class MultiHopPlannerSignature(dspy.Signature):
    """Plan multi-step reasoning strategy for complex questions."""
    complex_question: str = dspy.InputField(desc="Question requiring multi-hop reasoning")
    available_sources: str = dspy.InputField(desc="Available knowledge sources and databases")
    reasoning_constraints: str = dspy.InputField(desc="Logical constraints and requirements")
    depth_requirements: str = dspy.InputField(desc="Required reasoning depth and thoroughness")

    reasoning_strategy: str = dspy.OutputField(desc="Multi-step reasoning approach")
    source_traversal_plan: str = dspy.OutputField(desc="Source exploration and connection strategy")
    logical_dependencies: str = dspy.OutputField(desc="Reasoning step dependencies and order")
    evidence_requirements: str = dspy.OutputField(desc="Required evidence and validation criteria")

class KnowledgeHopperSignature(dspy.Signature):
    """Execute individual reasoning steps with source traversal."""
    reasoning_step: str = dspy.InputField(desc="Current reasoning step to execute")
    current_knowledge: str = dspy.InputField(desc="Knowledge accumulated from previous steps")
    target_sources: str = dspy.InputField(desc="Sources to explore for this step")
    logical_context: str = dspy.InputField(desc="Logical context and constraints")

    step_findings: str = dspy.OutputField(desc="Findings from this reasoning step")
    source_connections: str = dspy.OutputField(desc="Connections discovered between sources")
    next_reasoning_directions: str = dspy.OutputField(desc="Potential next reasoning steps")
    confidence_assessment: float = dspy.OutputField(desc="Confidence in step findings")

class ReasoningIntegratorSignature(dspy.Signature):
    """Integrate multi-hop findings into coherent conclusions."""
    reasoning_chain: str = dspy.InputField(desc="Complete reasoning chain and findings")
    source_evidence: str = dspy.InputField(desc="Evidence gathered from all sources")
    logical_constraints: str = dspy.InputField(desc="Logical consistency requirements")
    original_question: str = dspy.InputField(desc="Original complex question")

    integrated_conclusion: str = dspy.OutputField(desc="Comprehensive reasoned conclusion")
    reasoning_path: str = dspy.OutputField(desc="Clear explanation of reasoning steps")
    evidence_synthesis: str = dspy.OutputField(desc="Synthesis of supporting evidence")
    confidence_score: float = dspy.OutputField(desc="Overall confidence in conclusion")
```

## Expected Execution Flow

### Phase 1: Question Analysis and Planning (0-2s)
```
[00:00:000] Complex question parsed and analyzed
[00:00:500] Reasoning requirements and constraints identified
[00:01:000] Multi-hop strategy developed
[00:01:500] Source traversal plan created
[00:02:000] Initial reasoning chain planned
```

### Phase 2: Multi-Hop Knowledge Traversal (2s-15s)
```
[00:02:000] First reasoning hop initiated
[00:03:000] Initial source exploration and evidence gathering
[00:04:000] Second reasoning hop based on initial findings
[00:05:000] Cross-source connection identification
[00:06:000] Third reasoning hop with deeper analysis
[00:07:000] Pattern recognition across multiple sources
[00:08:000] Fourth reasoning hop for validation
[00:09:000] Contradiction detection and resolution
[00:10:000] Fifth reasoning hop for completeness
[00:11:000] Evidence integration and consistency checking
[00:12:000] Additional hops as needed for thoroughness
[00:13:000] Reasoning chain validation and optimization
[00:14:000] Final evidence gathering and verification
[00:15:000] Multi-hop traversal completed
```

### Phase 3: Integration and Synthesis (15s-18s)
```
[00:15:000] Reasoning chain integration initiated
[00:15:500] Evidence synthesis across all sources
[00:16:000] Logical consistency validation
[00:16:500] Conclusion development with supporting reasoning
[00:17:000] Confidence assessment and uncertainty quantification
[00:17:500] Final answer compilation with reasoning path
[00:18:000] Multi-hop reasoning workflow completed
```

## Technical Requirements

### Dependencies
```python
# Core Kailash SDK
from kailash.workflow.builder import WorkflowBuilder
from kailash.runtime.local import LocalRuntime
from kailash.nodes.llm_agent import LLMAgentNode
from kailash.nodes.vector_search import VectorSearchNode
from kailash.nodes.loop import LoopNode

# Multi-hop reasoning components
import dspy
import networkx as nx
from typing import List, Dict, Optional, Tuple, Set
import json
from dataclasses import dataclass
import logging
```

### Configuration
```yaml
reasoning_config:
  max_hops: 10
  hop_timeout: 30
  confidence_threshold: 0.7
  evidence_validation: true
  reasoning_depth: "comprehensive"

knowledge_sources:
  vector_databases: ["scientific_papers", "technical_docs", "historical_records"]
  graph_databases: ["knowledge_graph", "entity_relationships"]
  structured_data: ["databases", "apis", "knowledge_bases"]

reasoning_strategy:
  approach: "breadth_first_with_pruning"
  evidence_weighting: true
  contradiction_handling: "explicit_resolution"
  uncertainty_propagation: true

llm_config:
  planner_model: "gpt-4"
  hopper_model: "gpt-4"
  integrator_model: "gpt-4"
  temperature: 0.2
  max_tokens: 1500
```

### Memory Requirements
- **Reasoning Engine**: ~1.5GB (complex reasoning state management)
- **Knowledge Cache**: ~2GB (multi-source information caching)
- **Graph Processing**: ~1GB (relationship and connection analysis)
- **Evidence Storage**: ~800MB (accumulated evidence and citations)

## Architecture Overview

### Reasoning Pattern
```
Complex Question → Reasoning Planner → Multi-Hop Explorer
        ↑                                    ↓
Integrated Answer ← Evidence Synthesizer ← Knowledge Connections
```

### Data Flow
1. **Question Decomposition**: Break complex questions into reasoning steps
2. **Hop Planning**: Determine optimal source traversal strategy
3. **Iterative Exploration**: Execute reasoning hops with evidence gathering
4. **Connection Discovery**: Identify relationships between information sources
5. **Evidence Integration**: Synthesize findings across all reasoning steps
6. **Conclusion Formation**: Generate comprehensive answers with reasoning paths

### Reasoning Graph Structure
```python
class ReasoningGraph:
    def __init__(self):
        self.graph = nx.DiGraph()
        self.evidence_nodes = {}
        self.reasoning_edges = {}

    def add_reasoning_step(self, step_id, findings, sources):
        self.graph.add_node(step_id,
                          findings=findings,
                          sources=sources,
                          confidence=self.calculate_confidence(findings))

    def connect_reasoning_steps(self, from_step, to_step, relationship):
        self.graph.add_edge(from_step, to_step,
                          relationship=relationship,
                          logical_strength=self.assess_logical_strength(relationship))

    def find_reasoning_paths(self, start_question, target_conclusion):
        # Find all valid reasoning paths
        return nx.all_simple_paths(self.graph, start_question, target_conclusion)
```

## Success Criteria

### Reasoning Quality
- ✅ Multi-hop reasoning accuracy >85%
- ✅ Source integration effectiveness >80%
- ✅ Logical consistency score >90%
- ✅ Evidence support strength >75%

### Efficiency Metrics
- ✅ Average reasoning completion time <20 seconds
- ✅ Optimal hop count (minimal unnecessary traversals)
- ✅ Source utilization efficiency >70%
- ✅ Memory usage optimization <3GB total

### Answer Completeness
- ✅ Question coverage completeness >90%
- ✅ Evidence citation accuracy >95%
- ✅ Reasoning path clarity >85%
- ✅ Uncertainty quantification accuracy >80%

## Enterprise Considerations

### Knowledge Management
- Integration with enterprise knowledge graphs
- Proprietary source access and security
- Knowledge versioning and temporal reasoning
- Expert validation and human-in-the-loop feedback

### Scalability
- Distributed reasoning across multiple compute nodes
- Parallel hop execution for independent reasoning branches
- Caching strategies for repeated reasoning patterns
- Resource optimization for complex reasoning workloads

### Quality Assurance
- Reasoning path auditing and validation
- Source credibility and reliability scoring
- Bias detection in multi-source integration
- Continuous improvement from reasoning outcomes

## Error Scenarios

### Reasoning Chain Breaks
```python
# Response when logical reasoning chain is interrupted
{
  "reasoning_status": "CHAIN_BREAK_DETECTED",
  "break_point": "hop_4_source_unavailable",
  "partial_reasoning": "Steps 1-3 completed successfully",
  "recovery_strategy": "ALTERNATIVE_SOURCE_ROUTING",
  "confidence_impact": -0.2,
  "completion_estimate": "Extended by 30 seconds"
}
```

### Contradictory Evidence
```python
# Handling conflicting information across sources
{
  "evidence_status": "CONTRADICTION_DETECTED",
  "conflicting_sources": ["academic_paper_A", "technical_manual_B"],
  "contradiction_type": "factual_disagreement",
  "resolution_approach": "SOURCE_CREDIBILITY_WEIGHTING",
  "uncertainty_increase": 0.15,
  "additional_validation": "Expert review recommended"
}
```

### Reasoning Timeout
```python
# Response when reasoning exceeds time limits
{
  "reasoning_status": "TIMEOUT_EXCEEDED",
  "completed_hops": 7,
  "planned_hops": 10,
  "partial_conclusion": "Best available answer with current evidence",
  "confidence_adjustment": -0.3,
  "recommendation": "Continue with extended timeout or accept partial result"
}
```

## Testing Strategy

### Reasoning Validation
- Ground truth comparison for known complex questions
- Logical consistency verification across reasoning chains
- Source integration accuracy assessment
- Expert validation of reasoning quality

### Performance Testing
- Complex reasoning scalability testing
- Memory usage optimization under load
- Concurrent multi-hop reasoning capability
- Response time optimization for various complexity levels

### Quality Assurance
- Reasoning path auditing and validation
- Evidence quality and relevance assessment
- Bias detection in multi-source synthesis
- Uncertainty calibration accuracy verification

## Implementation Details

### Key Components
1. **Reasoning Planner**: Develops multi-step reasoning strategies
2. **Knowledge Hopper**: Executes individual reasoning steps with source traversal
3. **Connection Analyzer**: Identifies relationships between information sources
4. **Evidence Integrator**: Synthesizes findings across reasoning steps
5. **Consistency Validator**: Ensures logical consistency across reasoning chain
6. **Confidence Assessor**: Quantifies uncertainty and confidence levels

### Reasoning Algorithms
- **Breadth-First Exploration**: Systematic exploration of reasoning space
- **Depth-First Validation**: Deep validation of promising reasoning paths
- **Constraint Propagation**: Logical constraint enforcement across reasoning steps
- **Evidence Weighting**: Source credibility and relevance scoring
- **Uncertainty Propagation**: Uncertainty quantification through reasoning chain